Hoshin Vijai Gupta

Interests

Teaching

Hoshin created and teaches a progression of 4 graduate level courses in the Development and Application of Systems Methodology to Modeling in the Earth Sciences (HWRS 528, HWRS 642, HWRS 655, HWRS 696F). He also teaches an undergraduate class (150+ students) for non-science majors entitled ‘Earth: Our Watery Home’ (HWRS/NATS 170A1), and periodically teaches the Hydrology & Water Resources Colloquim (HWRS 695A). In addition he regularly teaches week-long international training workshops on the ‘Systems Approach to Model Identification’ and ‘Learning from Data’ (past venues include Denmark, Brazil, Germany & Spain).

Research

Hoshin's areas of research interest include: Surface water hydrology, rainfall-runoff models, land-atmosphere transfer scheme models, flood-forecasting, hydrology of semi-arid regions, prediction in ungaged basins, theory of evaluation, constraining lumped and distributed hydrologic models with observations, multi-criteria analysis, sensitivity analysis, blending expert knowledge into automated procedures, Bayesian estimation, recursive methods, uncertainty analysis, information content of data, data assimilation, diagnostic model correction, model structure estimation, spatial regularization, application of remotely sensed data in hydrology, estimation of precipitation from remotely sensed data, relationship of scale to hydrologic process dominance, development and applications of artificial neural networks, theory and practice of model building, multi-objective stochastic recursive global optimization, interactive computer modeling, multi-resolution multi-disciplinary integrated modeling, decision analysis and decision support systems, merging hydrologic and economic models in support of decision making and policy analysis, applications of information theory to modeling and hydrology, bridging natural and social sciences.

Courses

Scholarly Contributions

Books

• Poupeau, F., Gupta, H. V., Serrat-Capdevilla, A., Harris, S. W., Sans-Fuentes, M. A., & Hayde, L. (2016). Water Bankruptcy In The Land Of Plenty: Steps towards a transatlantic and transdisciplinary assessment of water scarcity in Southern Arizona. CRC Press.
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  Water Bankruptcy In The Land Of Plenty: Steps towards a transatlantic and transdisciplinary assessment of water scarcity in Southern Arizona, Edited by Poupeau F, Gupta HV, A Serrat-Capdevilla, S Harris, MA Sans-Fuentes & L Hayde (2016), CRC Press
• Poupeau, F., Gupta, H. V., Serrat-capdevila, A., Serrat-capdevila, A. -., Sans-fuentes, S., Harris, S., Harris, S., Hayde, L., & Gupta, H. V. (2016). Water Bankruptcy in the Land of Plenty: Steps towards a transatlantic andtransdisciplinary assessment of water scarcity in Southern Arizona. UNESCO-IHE, Delft.
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  As the American Southwest faces its deepest drought in history, this book explores the provocative notion of “water bankruptcy” with a view towards emphasizing the diversity and complexity of water issues in this region. It bridges between the narratives of growth and the strategies or policies adopted to pursue competing agendas and circumvent the inevitable. A window of opportunity provided by this current long-term drought may be used to induce change by dealing with threats that derive from imbalances between growth patterns and available resources, the primary cause of scarcity. A first of its kind, this book was developed through close collaboration of a broad range of natural scientists, social scientists, and resource managers from Europe and United States. It constitutes a collective elaboration of a transdisciplinary approach to unveiling the inner workings of how water was fought for, allocated and used in the American Southwest, with a focus on Arizona. Specifically, it offers an innovative scientific perspective that produces a critical diagnostic evaluation of water management, with a particular view to identifying risks for the Tucson region that is facing continuous urban sprawl and economic growth. The book offers a diversity of complementary perspectives, including a statement of natural resources, biodiversity and their management, an analysis of water policy and its history, and a statement of ecosystem services in the context of both local biodiversity and also the economic activities that sustain economic growth. Finally, it presents a concerted effort to explore the interplay between a variety of related scientific disciplines and frameworks including climatology, hydrology, water management, ecosystem services, societal metabolism, political economy and social science.
• Bloeschl, G., & Gupta, H. V. (2013). Runoff Prediction in Ungaged Basins – A Benchmark Assessment. IAHS Publications.
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  Runoff Prediction in Ungaged Basins – A Benchmark Assessment, Bloeschl G, and several authors/editors including HV Gupta (2013), IAHS Publications
• Brookshire, D., Gupta, H. V., & Matthews, O. P. (2012). Water Policy in New Mexico: Addressing the Challenge of an Uncertain Future. RFF Press.
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  Water Policy in New Mexico: Addressing the Challenge of an Uncertain Future, Brookshire D, HV Gupta, and OP Matthews (Editors) (2012), RFF Press, Resources for the Future Book Series: Issues in Water Resources Policy Series. ISBN 978-1-933115-99-3
• Yilmaz, K. K., Yucel, I., Gupta, H. V., Wagener, T., Yang, D., Savenije, H., Neal, C., Kuntzman, H., & Pomeroy, J. (2009). New Approaches to Hydrological Prediction in Data Sparse Regions. IAHS Redbook Publication.
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  New Approaches to Hydrological Prediction in Data Sparse Regions, Yilmaz KK, I Yucel, HV Gupta, T Wagener, D Yang, H Savenije, C Neale, H Kunstmann and J Pomeroy (Editors) (2009), Proceedings of Symposium HS.2 at the Joint IAHS & IAH Convention, Hyderabad, India, September 2009, IAHS Redbook Publication 333
• Boegh, E. K., Kuntzman, H., Wagener, T., Hall, A., Bastidas, L., Franks, S., Gupta, H. V., Rosbjerg, D., & Schaake, J. (2007). Quantification and reduction of predictive uncertainty for sustainable water resources management. IAHS Redbook Publication.
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  Quantification and reduction of predictive uncertainty for sustainable water resources management, Boegh E, H Kunstmann, T Wagener, A Hall, L Bastidas, S Franks, HV Gupta, D Rosbjerg and J Schaake (Editors) (2007), IAHS Redbook Publ. no. 313, 507pp. ISBN 978-1-901502-09-1.
• Franks, S., Wagener, T., Gupta, H. V., Boegh, E. K., Bastidas, L., Nobre, C., & de Olivera Galvao, C. (2005). Regional hydrologic impacts of climate variability and change with an emphasis on less developed countries – Volume II – Modeling of climate change. IAHS Redbook Publication.
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  Regional hydrologic impacts of climate variability and change with an emphasis on less developed countries – Volume II – Modeling of climate change, Franks S, T Wagener, HV Gupta, E Bøgh, LA Bastidas, C Nobre, and C de Oliveira Galvão (Editors) (2005), IAHS Redbook Publications
• Wagener, T., Gupta, H. V., Boegh, E. K., Franks, S., Bastidas, L., Nobre, C., & de Olivera Galvao, C. (2005). Regional hydrologic impacts of climate variability and change with an emphasis on less developed countries – Volume I – Impacts of climate change. IAHS Redbook Publication.
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  Regional hydrologic impacts of climate variability and change with an emphasis on less developed countries – Volume I – Impacts of climate change, Wagener T, HV Gupta, E Bøgh, S Franks, LA Bastidas, C Nobre, and C de Oliveira Galvão (Editors) (2005), IAHS Publications 295, pp 356.
• Wagener, T., Wheater, H., & Gupta, H. V. (2004). Rainfall-Runoff Modeling in Gauged and Un-gauged Catchments. Imperial College Press, London, UK.
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  Rainfall-Runoff Modeling in Gauged and Un-gauged Catchments, Wagener T, HS Wheater and HV Gupta, (Authors) (2004), Imperial College Press, London, UK, 300 pp.
• Duan, Q., Gupta, H. V., Sorooshian, S., Rousseau, A. N., & Turcotte, R. (2003). Calibration of watershed models. American Geophysical Union. doi:10.1029/WS006
• Duan, Q., Gupta, H. V., Sorooshian, S., Rousseau, A., & Turcotte, R. (2003). Advances in Calibration of Watershed Models. AGU Monograph, Water Science and Application Series, American Geophysical Union, Washington, D.C.
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  Advances in Calibration of Watershed Models, Duan Q, HV Gupta, S Sorooshian, A Rousseau and R.Turcotte (Editors) (2003), AGU Monograph, Water Science and Application Series, American Geophysical Union, Washington, D.C
• Sorooshian, S., Gupta, H. V., & Rodda, J. C. (1997). Land Surface Processes in Hydrology. Springer Berlin Heidelberg. doi:10.1007/978-3-642-60567-3
• Sorooshian, S., Gupta, H. V., Rodda, J. C., & Gupta, H. V. (1997). Land surface processes in hydrology : trials and tribulations of modeling and measuring. Springer-Verlag.
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  General circulation models (GCMs) predict certain changes in the amounts and distribution of precipitation, but the conversion of these predictions presents novel problems in hydrologic modelling, particularly with regard to the scale of the processes involved. Remote sensing technologies provide the necessary spatially distributed data; however, there are many attendant problems with the translation of remotely sensed signals into hydrologically relevant information. This book demonstrates how to improve the representation of land surface hydrologic processes in GCMs and in regional and global scale climate studies. The book is divided into five sections: models and data; precipitation; soil moisture; evapotranspiration; and runoff.
• Sorooshian, S., Gupta, H. V., & Rodda, J. (1993). Land Surface Processes in Hydrology: Trials and Tribulations of Modeling and Measuring. NATO ASI Series I 46, XVII, 497 p. Springer-Verlag Berlin Heidelberg New York.
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  Land Surface Processes in Hydrology: Trials and Tribulations of Modeling and Measuring, Sorooshian S, HV Gupta and J Rodda (Editors) (1996), Proceedings of the NATO Advanced Research Workshop Global Environmental Change and Land Surface Processes in Hydrology, Tucson, Arizona, May 17-21, 1993, NATO ASI Series I 46, XVII, 497 p. Springer-Verlag Berlin Heidelberg New York

Chapters

• Oliviera, P. T., Almagro, A., Colman, C., Kobayashi, A. N., Meira Neto, A., Rodrigues, D. B., & Gupta, H. V. (2019). Nexus of Water-Food-Energy-Ecosystem Services in the Brazilian Cerrado. In Water & Climate: Modelling in Large Basins 4.
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  Oliveira PTS, A Almagro, C Colman, ANA Kobayashi, AA Meira Neto, DBB Rodrigues and HV Gupta (2019), Nexus of Water-Food-Energy-Ecosystem Services in the Brazilian Cerrado, Chapter in: Water & Climate: Modelling in Large Basins, 4, Brazilian Water Resources Association, Edited by RCV Da Silva, CEM Tucci and CA Scott, ISBN 978-85-88686-41-0
• Gupta, H. V., & Razavi, S. (2017). Challenges and Future Outlook of Sensitivity Analysis. In Sensitivity Analysis In Earth Observation Modelling. Elsevier Inc. doi:10.1016/B978-0-12-803011-0.00020-3
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  As Earth and Environmental System models have rapidly become more complex and computationally intensive, growing in parameter dimensionality as they reflect our growing understanding about the nature and functioning of the world, the need for robust, informative, and computationally efficient sensitivity analysis techniques and tools has become ever more pressing. To date, a variety of different approaches to sensitivity analysis have been proposed in the literature, each of which has its strengths and weaknesses. Furthermore, their application is typically inhibited by computational expense and their usefulness is limited by the inability to extract useful diagnostic information from the model response. This chapter reviews and contrasts some of the major strategies for sensitivity analysis that have been proposed and discusses several challenges that need critical attention. Perhaps the most important of these is to establish a clear definition of how to “compactly” characterize the sensitivity of a model response to perturbations in its causal factors in such a manner that the “diagnostic” information provided by the analysis is maximized.
• Yang, Z., Dominguez, F., Gupta, H. V., Zeng, X., & Norman, L. (2017). Potential impacts of the continuing urbanization on regional climate: The developing Phoenix-Tucson "Sun Corridor".. In Water Bankruptcy In The Land Of Plenty: Steps towards a transatlantic and transdisciplinary assessment of water scarcity in Southern Arizona. CRC Press.
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  Yang, Z., F. Dominguez, H. Gupta, X. Zeng, and L. Norman, 2017: Potential impacts of the continuing urbanization on regional climate: The developing Phoenix-Tucson "Sun Corridor". Chapter 11 (p. 179-193) of the book "Water Bankruptcy In The Land Of Plenty: Steps towards a transatlantic and transdisciplinary assessment of water scarcity in Southern Arizona" edited by F. Poupeau, H. Gupta, A. Serrat-Capdevilla, M.A. Sans-Fuentes, S. Harris, and L. G. Hayde, CRC Press, Boca Raton, FL, p. 437.
• Boyanova, K., Niraula, R., Dominguez, F., Gupta, H. V., & Nedkov, S. (2016). Quantification of water-related ecosystem services in the Upper Santa Cruz watershed. In Water Bankruptcy In The Land Of Plenty: Steps towards a transatlantic and transdisciplinary assessment of water scarcity in Southern Arizona. CRC Press.
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  Boyanova K, R Niraula, F Dominguez, H Gupta and S Nedkov (2016 Invited), Quantification of water-related ecosystem services in the Upper Santa Cruz watershed; Chapter 12 in: Water Bankruptcy In The Land Of Plenty: Steps towards a transatlantic and transdisciplinary assessment of water scarcity in Southern Arizona, Eds: Gupta HV, F Poupeau, MA Sans-Fuentes & A Serrat-Capdevilla, CRC Press.
• Gupta, H. V., & Razavi, S. (2016). Challenges and Future Outlook of Sensitivity Analysis. In Sensitivity Analysis In Earth Observation Modelling. Elsevier.
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  Gupta HV and S Razavi (2016 Invited), Challenges and Future Outlook of Sensitivity Analysis, In: Sensitivity Analysis In Earth Observation Modelling, Edited by PK Srivastava & GP Petropoulos, Elsevier.
• Gupta, H. V., & Sorooshian, S. (2016). Calibration and Evaluation of Watershed Models. In Handbook of Applied Hydrology (New Edition).
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  Gupta HV and S Sorooshian (2016 Invited), Calibration and Evaluation of Watershed Models, In: Handbook of Applied Hydrology (New Edition), McGraw Hill, ISBN-13: 978-0071835091, ISBN-10: 0071835091
• Poupeau, F., Gupta, H. V., Sans-Fuentes, M. A., & Serrat-Capdevilla, A. (2016). Next Steps: Collaborative research and training towards transdisciplinarity. In Water Bankruptcy In The Land Of Plenty: Steps towards a transatlantic and transdisciplinary assessment of water scarcity in Southern Arizona. CRC Press.
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  F Poupeau, Gupta HV, MA Sans-Fuentes & A Serrat-Capdevilla (2016 Invited), Next Steps: Collaborative research and training towards transdisciplinarity; Chapter 22 in: Water Bankruptcy In The Land Of Plenty: Steps towards a transatlantic and transdisciplinary assessment of water scarcity in Southern Arizona, Eds: Gupta HV, F Poupeau, MA Sans-Fuentes & A Serrat-Capdevilla, CRC Press.
• Poupeau, F., Gupta, H. V., Sans-Fuentes, M. A., & Serrat-Capdevilla, A. (2016). Organization of the book and mind map. In Water Bankruptcy In The Land Of Plenty: Steps towards a transatlantic and transdisciplinary assessment of water scarcity in Southern Arizona. CRC Press.
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  F Poupeau, Gupta HV, MA Sans-Fuentes & A Serrat-Capdevilla (2016 Invited), Organization of the book and mind map; Chapter 2 In: Water Bankruptcy In The Land Of Plenty: Steps towards a transatlantic and transdisciplinary assessment of water scarcity in Southern Arizona, Eds: Gupta HV, F Poupeau, MA Sans-Fuentes & A Serrat-Capdevilla, CRC Press.
• Poupeau, F., Gupta, H. V., Sans-Fuentes, M. A., & Serrat-Capdevilla, A. (2016). Preface to the Book. In Water Bankruptcy In The Land Of Plenty: Steps towards a transatlantic and transdisciplinary assessment of water scarcity in Southern Arizona. CRC Press.
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  [BC-45] F Poupeau, Gupta HV, MA Sans-Fuentes & A Serrat-Capdevilla (2016 Invited), Preface to Water Bankruptcy In The Land Of Plenty; Preface to: Water Bankruptcy In The Land Of Plenty: Steps towards a transatlantic and transdisciplinary assessment of water scarcity in Southern Arizona, Eds: Gupta HV, F Poupeau, MA Sans-Fuentes & A Serrat-Capdevilla, CRC Press.
• Serrat-Capdevilla, A., Poupeau, F., Caballo, V., Gupta, H. V., Boyanoba, B., Sans-Fuentes, M. A., Poupeau, K., Serrat-Capdevilla, A., Hernandez-Mora, N., Gupta, H. V., & Sans-Fuentes, M. A. (2016). The idea of a transatlantic dialog. In Water Bankruptcy In The Land Of Plenty: Steps towards a transatlantic and transdisciplinary assessment of water scarcity in Southern Arizona. CRC Press.
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  F Poupeau, Gupta HV, MA Sans-Fuentes & A Serrat-Capdevilla (2016 Invited), The idea of a transatlantic dialog; Chapter 1 In: Water Bankruptcy In The Land Of Plenty: Steps towards a transatlantic and transdisciplinary assessment of water scarcity in Southern Arizona, Eds: Gupta HV, F Poupeau, MA Sans-Fuentes & A Serrat-Capdevilla, CRC Press.
• Yang, Z., Dominguez, F., Gupta, H. V., Zeng, X., & Norman, L. (2016). Potential Impacts of the developing Phoenix-Tucson ‘Sun’ Corridor on Regional Climate. In Water Bankruptcy In The Land Of Plenty: Steps towards a transatlantic and transdisciplinary assessment of water scarcity in Southern Arizona. CRC Press.
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  Yang Z, F Dominguez, H Gupta, X Zeng and L Norman (2016 Invited) Potential Impacts of the developing Phoenix-Tucson ‘Sun’ Corridor on Regional Climate; Chapter 11 in: Water Bankruptcy In The Land Of Plenty: Steps towards a transatlantic and transdisciplinary assessment of water scarcity in Southern Arizona, Eds: Gupta HV, F Poupeau, MA Sans-Fuentes & A Serrat-Capdevilla, CRC Press.
• Serrat-capdevila, A. -., Valdes, J. B., Gupta, H. V., & Schneier-madanes, G. (2014). Water Governance Tools: The Role of Science and Decision Support Systems in Participatory Management. In Globalized Water. Springer, Dordrecht. doi:10.1007/978-94-007-7323-3_17
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  Participatory water resources management and planning have become a main feature of water governance processes. A review of the evolution of decision support systems for water resources planning and management through today demonstrates that stakeholder participation through science-fed collaborative planning processes is an essential factor for integrative science to be perceived as credible, relevant, transparent, and thus acceptable in the public eye to inform and guide decision making. Two case studies from the American Southwest—the Rio Grande in New Mexico and the San Pedro in Arizona—illustrate how a strong scientific contribution that includes an integrated modeling approach can form the foundation for participatory planning processes and the collaborative development of decision support tools. Based on conflict resolution concepts, this approach will not only lead to agreed-upon management solutions, but also to a well informed and educated stakeholder community in the basin, ensuring a sustainable and resilient water governance system.
• Bastidas, L. A., Gupta, H. V., & Sorooshian, S. (2013). Bounding the Parameters of Land‐Surface Schemes Using Observational Data. In Advances in Calibration of Watershed Models. American Geophysical Union (AGU). doi:10.1029/WS003P0065
• Bastidas, L. A., Gupta, H. V., Hsu, K., & Sorooshian, S. (2013). Parameter, Structure, and Model Performance Evaluation for Land‐Surface Schemes. In Advances in Calibration of Watershed Models. American Geophysical Union. doi:10.1029/WS006P0229
• Gupta, H. V., & Wagener, T. (2013). Synthesis. In Runoff Prediction in Ungauged Basins: Synthesis across Processes, Places and Scales. Cambridge University Press.
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  Gupta HV and T Wagener (2013), Synthesis, Chapter 10 in in Bloschl G, M Sivapalan, T Wagener, A Viglione and H Savenije (editors) (2013), Runoff Prediction in Ungauged Basins: Synthesis across Processes, Places and Scales, Cambridge University Press, ISBN-13: 9781107028180
• Gupta, H. V., Bastidas, L. A., Vrugt, J. A., & Sorooshian, S. (2013). Multiple Criteria Global Optimization for Watershed Model Calibration. In Advances in Calibration of Watershed Models. American Geophysical Union. doi:10.1029/WS006P0125
• Gupta, H. V., Bloschl, G., Mcdonnell, J. J., Savenije, H. H., Sivapalan, M., Viglione, A., & Wagener, T. (2013). Runoff Prediction in Ungauged Basins: Outcomes of synthesis. In Advances in Calibration of Watershed Models. doi:10.1017/CBO9781139235761.015
• Gupta, H. V., Sorooshian, S., Hogue, T. S., & Boyle, D. P. (2013). Advances in Automatic Calibration of Watershed Models. In Advances in Automatic Calibration of Watershed Models. American Geophysical Union (AGU). doi:10.1029/WS006P0009
• Hogue, T. S., Gupta, H. V., Sorooshian, S., & Tomkins, C. D. (2013). A Multi‐Step Automatic Calibration Scheme for Watershed Models. In Adb\vances in Calibration of Watershed Models. American Geophysical Union. doi:10.1029/WS006P0165
• Meixner, T., Gupta, H. V., Bastidas, L. A., & Bales, R. C. (2013). Estimating Parameters and Structure of a Hydrochemical Model Using Multiple Criteria. In Advances in Calibration of Watershed Models. American Geophysical Union (AGU). doi:10.1029/WS006P0213
• Misirli, F., Gupta, H. V., Sorooshian, S., & Thiemann, M. (2013). Bayesian Recursive Estimation of Parameter and Output Uncertainty for Watershed Models. In Advances in Calibration of Watershed Models. American Geophysical Union. doi:10.1029/WS006P0113
• Serrat-Capdevila, A. -., Valdes, J. B., Gupta, H. V., Schneier-Madanes, G., Serrat-Capdevila, A. -., Valdes, J. B., Gupta, H. V., & Schneier-Madanes, G. (2013). Water Governance Tools" The Role of Science, Decision Support Systems in Participatory Management. In Water Governance(pp 241-259). Springer.
• Serrat-Capdevila, A. -., Valdes, J. B., Gupta, H. V., Schneier-Madanes, G., Serrat-Capdevila, A. -., Valdes, J. B., Gupta, H. V., Schneier-Madanes, G., Serrat-Capdevila, A. -., Valdes, J. B., Gupta, H. V., & Schneier-Madanes, G. (2013). Towards a Comprehensive Assessment of Climate Change Projections in the Tarim Basin region. How can uncertain predictions inform adaptive police?. In WATARID 3 Uses and Policy of Water Resources in Arid and Sem-Arid Regions(pp 129-137). Editions Hermann.
• Serrat-Capdevilla, A., Valdes, J. B., Gupta, H. V., & Schneier-Madanes, G. (2013). Towards a comprehensive assessment of climate change projections in the Tarim basin region: How can uncertain predictions inform adaptive policy. In WATARID 3 Usages et politiques de l'eau en zones arides et semi-arides.
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  Serrat-Capdevila A, JB Valdes, H Gupta and G Schneier-Madanes (2013), Towards a comprehensive assessment of climate change projections in the Tarim basin region: How can uncertain predictions inform adaptive policy, In WATARID 3 Usages et politiques de l'eau en zones arides et semi-arides, Book resulting from WATARID-3 International Conference On Water, Ecosystems And Sustainable Development, France, Editors: Marie-Françoise Courel, Tiyip Tashpolat, Mahmoud Taleghani, Paris, Editions Hermann, 2013 (French and English)
• Serrat-Capdevilla, A., Valdes, J. B., Gupta, H. V., & Schneier-Madanes, G. (2013). Water Governance Tools: The Role of Science and Decision Support Systems in Participatory Management. In Globalized Water. Springer.
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  Serrat-Capdevila A, JB Valdes, H Gupta and G Schneier-Madanes (2013), Water Governance Tools: The Role of Science and Decision Support Systems in Participatory Management, Chapter 15 in G Schneier-Madanes (editor), “Globalized Water”, Springer Publications
• Vrugt, J. A., Gupta, H. V., Bouten, W., & Sorooshian, S. (2013). A Shuffled Complex Evolution Metropolis algorithm for estimating posterior distribution of watershed model parameters.. In Advances in Calibration of Watershed Models. American Geophysical Union. doi:10.1029/WS006P0105
• Wagener, T., Bloschl, G., Goodrich, D. C., Gupta, H. V., Sivapalan, M., Tachikawa, Y., Troch, P., & Weiler, M. (2013). Runoff Prediction in Ungauged Basins: A synthesis framework for runoff prediction in ungauged basins. In Advances in Calibration of Watershed Models. doi:10.1017/CBO9781139235761.005
• Wagener, T., Weiler, M., Freer, J., & Gupta, H. V. (2013). Assessment Strategy. In Runoff Prediction in Ungauged Basins: Synthesis across Processes, Places and Scales. Cambridge University Press.
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  Wagener T, M Weiler, J Freer and HV Gupta (2013), Assessment Strategy, Chapter 2 in Bloschl G, M Sivapalan, T Wagener, A Viglione and H Savenije (editors) (2013), Runoff Prediction in Ungauged Basins: Synthesis across Processes, Places and Scales, Cambridge University Press, ISBN-13: 9781107028180
• Wagener, T., Wheater, H., & Gupta, H. V. (2013). Identification and Evaluation of Watershed Models. In Advances in Calibration of Watershed Models. American Geophysical Union. doi:10.1029/WS006P0029
• Serrat-capdevila, A. -., Valdes, J. B., & Gupta, H. V. (2011). Decision Support Systems in Water Resources Planning and Management: Stakeholder Participation and the Sustainable Path to Science-Based Decision Making. In NA. IntechOpen. doi:10.5772/16897
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  This chapter will focus on decision support systems (DSS) as they relate to water resources management and planning. Water is a resource that touches and is interwoven with numerous human activities as well as the environment we live in. Its availability and beneficial use depend on the timing and manner of its arrival (rainfall intensity, rain or snow, duration, frequency), the physical setting of the region (climate and weather, topography, geology), the engineering structures in place, the environmental constraints (existing ecosystems), the legal regulatory context and institutional policies. In most contexts, cultural values and preferences are also very important. To make good decisions, it is clear that a detailed understanding of how the system works and behaves is necessary. It is equally important to understand the implications of these decisions what consequences are likely to ripple through the interwoven system, and what parties will be affected as a result of a particular set of actions? Understanding the coupled human and physical system is essential. In addition to looking at the evolution of decision support tools and methods for water resources management (Section 2), this chapter focuses on how integrative science and multi-resolution models provide the basis for a decision support system (Section 3), on the overall setting of the decision making process and ways in which a DSS for water resources should be developed (Section 4). We make the argument that for a DSS to be successful and informative, the process by which it is developed will be as important, or even more so, than the finished decision support tool itself. A description of successful participatory planning approaches and collaborative modeling methods is presented, as well as a comparison of several case studies. Section 5 presents an overview on how to deal with uncertainty. We present our vision to merge adaptive management, integrative modeling and stakeholder participation to face the water management challenges of the arriving future. A synthesis and future challenges are presented in the last section.
• Pokhrel, P., & Gupta, H. V. (2009). Regularized calibration of a distributed hydrological model using available information about watershed properties and signature measures. In IAHS-AISH Publication(pp 20-25).
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  Abstract: Physically-based distributed models are increasingly being used to predict the behaviour of hydrological processes in data-sparse regions. However, a model is a simplified representation of the real system and some form of calibration cannot be avoided. Because distributed models have large numbers of parameters to be specified, the resulting parameter estimation problem becomes ill conditioned. In this study we investigate a calibration approach teat uses: (a) a simple form of spatial regularization (using scalar multipliers) to reduce the dimension of the calibration problem, and (b) signature measures targeting specific behavioural response of a watershed system to guide the parameter search towards a more "hydrologically consistent" set of parameters. Signature measures are applied as "regularization constraints", in an approach that is functionally similar to "Tikhonov regularization", and which results in a better-conditioned optimization problem compared to the benchmark case. The approach is demonstrated for the Blue River Basin in Oklahoma, USA. © 2009 IAHS Press.
• Yilmaz, K. K., Vrugt, J. A., Gupta, H. V., & Sorooshian, S. (2009). Model Calibration in Watershed Hydrology. In Advances in Calibration of Watershed Models(pp 53-105). World Scientific Publishing Co. doi:10.1142/9789814307987_0003
• Liu, Y., Durcik, M., Gupta, H. V., & Wagener, T. (2008). Developing distributed conceptual hydrological models from geospatial databases. In IAHS-AISH Publication(pp 94-102).
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  Abstract: Interest in the development and use of spatially-distributed hydrological models has increased considerably in recent years. However, the application of a model in distributed fashion significantly increases the number of model parameters that need to be estimated, and presents a serious challenge for model identification. In this paper we propose a general framework for developing a distributed conceptual hydrological model from geospatial databases based on hydrological similarity and landscape classification. The framework is applied to spatially-distributed modelling of the upper Rio Grande River basin in the southwestern USA. Preliminary results are encouraging and indicate that the proposed framework holds promise for making improved predictions in ungauged basins, while being computationally inexpensive. Copyright © 2008 IAHS Press.
• Liu, Y., Mahmoud, M., Hartmann, H., Stewart, S., Wagener, T., Semmens, D., Stewart, R., Gupta, H., Dominguez, D., Hulse, D., Letcher, R., Rashleigh, B., Smith, C., Street, R., Ticehurst, J., Twery, M., Delden, H. v., & White, D. (2008). Chapter Nine Formal Scenario Development for Environmental Impact Assessment Studies. In Developments in Integrated Environmental Assessment(pp 145-162).
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  Abstract: Scenario analysis is a process of evaluating possible future events through the consideration of alternative plausible, though not equally likely, states (scenarios). The analysis is designed to enable improved decision making and assessment through a more rigorous evaluation of possible outcomes and their implications. For environmental impact and integrated assessment studies, the process of scenario development typically involves making explicit and/or implicit assumptions about potential future conditions, such as climate change, land cover and land use changes, population growth, economic development and technological changes. Realistic assessment of scenario impacts often requires complex integrated modelling frameworks that represent environmental and socioeconomic systems to the best of our knowledge, including assumptions about plausible future conditions. In addition, scenarios have to be developed in a context relevant to the stakeholders involved, and include estimation and communication of uncertainties, to establish transparency, credibility and relevance of scenario results among the stakeholders. This paper reviews the state of the art of scenario development and analysis, proposes a formal approach to scenario development in environmental studies and discusses existing issues. Major recommendations for future research in this area include proper consideration of uncertainty involved in scenario studies, construction of scenarios of a more variable nature, and sharing of information and resources among the scenario development research community. © 2008 Elsevier B.V. All rights reserved.
• Boegh, E., Kunstmann, H., Wagener, T., Hall, A., Bastidas, L., Franks, S., Gupta, H., Rosbjerg, D., & Schaake, J. (2007). IAHS-AISH Publication: Preface. In IAHS-AISH Publication(pp v-vii).
• Stewart, S., Mahmoud, M., Liu, Y., Hartmann, H., Wagener, T., & Gupta, H. (2007). Scenario development for water resources planning and management in semi-arid regions. In IAHS-AISH Publication(pp 192-198).
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  Abstract: We report progress on a novel effort to develop a unified framework for constructing scenarios for water resource management. The framework comprises five iterative phases: scenario definition, scenario construction, scenario analysis, scenario assessment, and risk management. While the scenario framework can be applied to most water resource applications, we place particular emphasis on semiarid environments and forces not typicallyconsidered in the traditional water management process such as unforeseen changes in government institutions, or second-order effects of climate change on environmental systemsThemain objective of scenario development for water resources is to inform policy-makersabout the implications of various water management strategies. In addition, scenarioscanconsider the possible effects of external drivers, such as changes in political institutions, or large-scale environmental change that may be especially important in developingcountries. Copyright © 2007 IAHS Press.
• Wagener, T., Gupta, H., Yatheendradas, S., Goodrich, D., Unkrich, C., & Schaffner, M. (2007). Understanding sources of uncertainty in flash-flood forecasting for semi-arid regions. In IAHS-AISH Publication(pp 204-212).
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  Abstract: About one-third of the Earth's land surface is located in arid or semi-arid regions, often in areas suffering severely from the negative impacts of desertification and population pressure. Reliable hydrological forecasts across spatial and temporal scales are crucial in order to achieve water security protection from excess and lack of water for people and ecosystems in these areas. At short temporal scales, flash floods are extremely dangerous hazards accounting, for example, for more than 80% of all flood-related deaths in the USA. Forecasting of these floods requires a connected spatially-distributed hydro-meteorological modelling system which accounts for the specific meteorological and hydrological characteristics of semi-arid watersheds, e.g. summertime convective rainfall and channel transmission losses. The spatially highly heterogeneous nature of the precipitation and the nonlinear response behaviour of the system demand the explicit accounting and propagation of uncertainties into the model predictions. This short paper presents the results of a multi-year study in which such a system was developed for flash-flood forecasting in the semi-arid southwestern USA. In particular, we discuss our effort to understand and estimate underlying uncertainties in such a modelling system. To achieve this we use the GLUE approach to uncertainty analysis, in combination with a variance-based global sensitivity analysis technique. In general, the level of uncertainty found was very high and largely dominated by uncertainty in the radar rainfall estimates. Regarding the model parameters, uncertainties in the hillslope model parameter values had a greater impact on the predictions than the uncertainties in the channel parameters, at least for relatively small basins. Copyright © 2007 IAHS Press.
• Chahinian, N., Andréassian, V., Duan, Q., Fortin, V., Gupta, H., Hogue, T., Mathevet, T., Montanari, A., Moretti, G., Moussa, R., Perrin, C., Schaake, J., Wagener, T., & Xie, Z. (2006). Compilation of the MOPEX 2004 results. In IAHS-AISH Publication(pp 313-338).
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  Abstract: As part of the MOPEX 2004 workshop, the participants were asked to submit simulations using the common database provided for the workshop (see Chahinian et al., this issue). The simulations were then analysed and the evaluation criteria computed to compare the models' performance for the gauged and ungauged modes using six criteria describing the model's performance in both high and low flow conditions. The comparisons were undertaken for all three participation levels (i.e. 3, 12 and 40 catchments). The results indicate that on the 3-catchment level model ranking may vary according to the tested criterion and catchment. Hence a larger number of catchments are necessary to evaluate the models' performance. Among the 10 models tested on the 12 and 40 catchment samples in gauged mode, GR5H, Mordor and SAC-SMA rank as the top three models. When analysing model results in the ungauged mode, SAC-SMA ranks as the best of the four tested models. The analysis of the submitted files highlights the need for continuing efforts to develop model parameterization strategies for ungagued catchments in order to improve prediction in ungauged basins (PUB).
• Hogue, T., Yilmaz, K., Wagener, T., & Gupta, H. (2006). Modelling ungauged basins with the Sacramento model. In IAHS-AISH Publication(pp 159-168).
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  Abstract: This paper evaluates two contrasting approaches to parameter estimation for ungauged basins using the US National Weather Service's SACramento Soil Moisture Accounting (SAC-SMA) model. An automatic calibration scheme (Multi-Step Automatic Calibration Scheme, MACS) provides deterministic parameter estimates using a three-step, multiple objective approach. The MACS estimates are then transferred to similar or "sister" watersheds for basins in the French MOPEX data set. Physically-based parameter estimates are also developed for the same basins based on the a priori approach of Koren et al. (2000). In general, the two methods, the transfer and the a priori approaches, show similar overall performance. Parameter estimates appear more consistent between basins using the a priori approach, but statistically the regionalized MACS parameters and the a priori parameters show very similar model performance for the three basins investigated in this study. Model simulated hydrographs are also very similar between the two methods, with both methods tending to underpredict most events (peak and volume) but matching the shape and pattern of flow well. However, both methods have worse performance than a calibrated model for the same basin, indicating the possibility for further refinement and adjustment of the techniques presented here.
• Wagener, T., Freer, J., Zehe, E., Beven, K., Gupta, H. V., & Bardossy, A. (2006). Towards an uncertainty framework for predictions in ungauged basins: The Uncertainty Working Group. In IAHS-AISH Publication(pp 454-462).
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  Abstract: The reduction of predictive uncertainty is the main objective and the main criterion of success for the Predictions in Ungauged Basins (PUB) initiative of IAHS. Achieving this goal requires that an uncertainty framework is created in which models, data and methods can be evaluated with respect to their impact on predictive uncertainty. Here we provide a first overview of the uncertainty working group, including its main objectives and how we intend to achieve them.
• Wagener, T., Hogue, T., Schaake, J., Duan, Q., Gupta, H., Andreassian, V., Hall, A., & Leavesley, G. (2006). The Model Parameter Estimation Experiment (MOPEX): Its structure, connection to other international initiatives and future directions. In IAHS-AISH Publication(pp 339-346).
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  Abstract: The Model Parameter Estimation Experiment (MOPEX) is an international project aimed at developing enhanced techniques for the a priori estimation of parameters in hydrological models and in land surface parameterization schemes connected to atmospheric models. The MOPEX science strategy involves: database creation, a priori parameter estimation methodology development, parameter refinement or calibration, and the demonstration of parameter transferability. A comprehensive MOPEX database has been developed that contains historical hydrometeorological data and land surface characteristics data for many hydrological basins in the United States (US) and in other countries. This database is being continuously expanded to include basins from various hydroclimatic regimes throughout the world. MOPEX research has largely been driven by a series of international workshops that have brought interested hydrologists and land surface modellers together to exchange knowledge and experience in developing and applying parameter estimation techniques. With its focus on parameter estimation, MOPEX plays an important role in the international context of other initiatives such as GEWEX, HEPEX, PUB and PILPS. This paper outlines the MOPEX initiative, discusses its role in the scientific community, and briefly states future directions.
• Yadav, M., Wagener, T., & Gupta, H. (2006). Regionalization of dynamic watershed response behaviour. In IAHS-AISH Publication(pp 220-229).
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  Abstract: Approaches to ungauged basin modelling typically use observable physical characteristics of watersheds (e.g. soil data) to directly infer hydrological model parameters, or they use regionalization methods based on parsimonious hydrological models. A different approach to streamflow prediction in ungauged basins is presented here where, instead of model parameters, the model independent hydrological response behaviour is estimated in the form of streamflow indices, and then regionalized with respect to physical characteristics of watersheds. Therefore, the approach uses a data driven regionalization method (under uncertainty) rather than the common hydrological model driven regionalization method. Ensemble predictions in ungauged basins can then be constrained by limits on acceptable hydrological model behaviour. This study utilizes data from 30 watersheds in the UK. Initial results show that the predictive uncertainty of the model can be reduced considerably through this new approach.
• Franks, S., Wagener, T., Bøgh, E., Gupta, H. V., Bastidas, L., Nobre, C., & Oliveira, C. (2005). Earth and Physical Sciences: Preface. In IAHS-AISH Publication(pp v).
• Gupta, H. V., Beven, K. J., & Wagener, T. (2005). Model Calibration and Uncertainty Estimation. In NA. John Wiley & Sons, Ltd. doi:10.1002/0470848944.hsa138
• Wagener, T., Franks, S., Gupta, H. V., Bøgh, E., Bastidas, L., Nobre, C., & Oliveira, C. (2005). Preface: Earth and Physical Sciences. In IAHS-AISH Publication(pp v-vi).
• Wagener, T., Liu, Y., Gupta, H. V., Springer, E., & Brookshire, D. (2005). Multi-resolution integrated assessment and modelling of climate change impacts on water resources in arid and semiarid regions. In IAHS-AISH Publication(pp 265-272).
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  Abstract: Approximately one-third of the Earth's land surface is considered to be arid or semiarid. The availability of water in such regions is particularly sensitive to climate variability while the demand for water is experiencing an explosive increase as populations continue to grow. The competition for available freshwater is exerting considerable pressure on the management of available water resources. If basin-scale water sustainability is to be achieved, managers must somehow attain a balance between supply and demand among the various users throughout the basin, not just for the basin as a whole. The complexity of the interactions between the natural hydrological system and the human environment leaves modelling as the best mechanism for integrating new knowledge into the decision-making process. To this end, the NSF Center for Sustainability of semi-Arid Hydrology and Riparian Areas (SAHRA) is in the process of developing a multi-resolution integrated modelling framework for the Rio Grande basin in the southwest USA. This paper presents a blueprint of the modelling framework in the context of integrated assessment, describes achievements so far and discusses the science questions which the framework will address.
• Yatheendradas, S., Wagener, T., Gupta, H., Unkrich, C., Schaffner, M., & Goodrich, D. (2005). A distributed real-time semiarid flash-flood forecasting model utilizing radar data. In IAHS-AISH Publication(pp 108-117).
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  Abstract: One-third of the Earth's surface can currently be classified as arid or semiarid. This fraction may increase in the future for example due to global warming effects. Many arid and semiarid regions are particularly affected by flash floods, caused mainly by convective storm systems, and often resulting in significant damages to property and even loss of life. The short duration and the small geographic extent of these events make predicting the subsequent floods extremely difficult. To improve our predictive capability, we are currently developing a semiarid specific model based on the well-established event-based rainfall-runoff model KINEROS2, capable of continuously simulating the response of a specific basin and driven by high-resolution precipitation measurements. This spatially distributed kinematic wave model represents the basin as a cascade of planes and channels. The dynamic infiltration algorithm is particularly well suited for simulation of semiarid hydrological processes. Adjustments to the original model include restructuring the code in a modular fashion, adding long-term soil moisture storage and evapotranspiration algorithms, and including optimization tools for parameter estimation. The project aims towards more accurate, reliable and probabilistic flood warnings, for semiarid flash-flood forecasting, risk assessment and decision making. This paper outlines the model and some associated data processing tools, and represents some initial results of applying the model to a small semiarid basin in the southwestern USA.
• Yilmaz, K. K., Gupta, H., Hogue, T. S., Hsu, K., Wagener, T., & Sorooshian, S. (2005). Evaluating the utility of satellite-based precipitation estimates for runoff prediction in ungauged basins. In IAHS-AISH Publication(pp 273-282).
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  Abstract: Increased availability of global satellite-based precipitation estimates makes them potentially suitable for hydrological applications in remote regions where ground-based measurement networks are missing or sparse. This potential can be evaluated by testing the satellite estimates at locations where relatively dense ground-based networks are available. This study was conducted for two sites in the southeastern United States where precipitation estimates from a raingauge network, weather radar and satellite observations exist. The satellite-based estimates were provided by the PERSIANN system, which combines infrared and microwave information from geostationary and polar orbiting satellites. The precipitation estimates are first compared and then used to drive the Sacramento Soil Moisture Accounting hydrological model for runoff forecasting. The results provide insight into the potential utility of satellite-based precipitation estimates for water resources management of ungauged basins.
• Bastidas, L. A., Gupta, H. V., Sorooshian, S., Singh, V. P., Frevert, D. K., & Gupta, H. V. (2002). Emerging paradigms in the calibration of hydrologic models.. In NA. Water Resources Publications.
• Hsu, K., Gupta, H. V., Gao, X., & Sorooshian, S. (2000). Rainfall Estimation from Satellite Imagery. In NA. Springer, Dordrecht. doi:10.1007/978-94-015-9341-0_12
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  The precipitation process is arguably the most important component of the global hydro-meteorological cycle. The latent heat released by precipitation over the tropical oceans is a primary source of the energy modulating the dynamics of the atmosphere. Over the land, precipitation contributes directly to lakes, rivers and sub-surface moisture storage, which are the primary sources of water supply for plants and animals. Therefore, a better understanding of the temporal and spatial distribution of precipitation is critical to long-term water resources and agricultural system planning.
• Gupta, H. V., & Sorooshian, S. (1997). The Challenges We Face: Panel Discussion on Data and Models. In NA. Springer, Berlin, Heidelberg. doi:10.1007/978-3-642-60567-3_4
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  This lively and engaging panel discussion began with introductory remarks by the moderators Keith Beven and Robert Dickinson. Based on this preamble, an extensive discussion ensued. The following is a summary of the major points of discussion, based primarily on notes prepared by the moderators Keith Beven and Robert Dickinson, and by additional written contributions from participants Jim Washburne, Xiaogang Gao, Paul Houser and Patrice Yapo. Their assistance in documenting the discussions is greatly appreciated.
• Gupta, H. V., & Sorooshian, S. (1997). The Challenges We Face: Panel Discussion on Evapotranspiration. In NA. Springer, Berlin, Heidelberg. doi:10.1007/978-3-642-60567-3_20
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  This panel discussion, moderated by Bernard Seguin and Susan Moran, provided interesting perspectives on the trials and tribulations (as well as the dreams and realities!) of the measurement and modeling of evapotranspiration (ET). The following is a summary of the major points of discussion, based primarily on notes prepared by participants Christa Peters, Helene Unland, and Jim Washburne. Their assistance in documenting the discussions is greatly appreciated.
• Gupta, H. V., & Sorooshian, S. (1997). The Challenges We Face: Panel Discussion on Precipitation. In NA. Springer, Berlin, Heidelberg. doi:10.1007/978-3-642-60567-3_9
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  This panel discussion began with introductory remarks by the moderators David Legates and David Goodrich, which set the tone for a very interesting and enjoyable discussion. The discussion covered a lot of ground and continually reiterated the fact that there needs to be a much better dialogue between the data collectors and the modelers. The following is a summary of the major points of discussion, based primarily on notes prepared by the moderators David Legates and David Goodrich, and by additional written contributions from participants Jean Morrill, Xu Liang and Jim Washburne. Their assistance in documenting the discussions is greatly appreciated.
• Gupta, H. V., & Sorooshian, S. (1997). The Challenges We Face: Panel Discussion on Snow. In NA. Springer Berlin Heidelberg. doi:10.1007/978-3-642-60567-3_11
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  This panel discussion was moderated in two phases; the first half was led by Barry Goodison and the second half by Roni Avissar. The following is a summary of the major points of discussion, based primarily on notes prepared by participants Mark Lynch-Steiglitz, Remigio Galarraga-Sanchez, and Jim Washburne. Their assistance in documenting the discussions is greatly appreciated.
• Gupta, H. V., & Sorooshian, S. (1997). The Challenges We Face: Panel Discussion on Soil Moisture. In NA. Springer, Berlin, Heidelberg. doi:10.1007/978-3-642-60567-3_16
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  This panel discussion was moderated by Jens Refsgaard and Dara Entekhabi. The following is a summary of the major points of discussion, based primarily on notes prepared by participants Maria Costa-Cabral, Paul Houser, Joseph Mas-Pia, and Jim Washburne. Their assistance in documenting the discussions is greatly appreciated. The moderators focused the discussion mainly on (a) the interaction between vegetation and soil parameters, (b) problems related to scale and heterogeneity, and (c) the calibration and validation of hydrologic models.

Journals/Publications

• Ehsani, M. R., Zarei, A., Gupta, H. V., Barnard, K., & Behrangi, A. (2022). Nowcasting-Nets: Deep Neural Network Structures for Precipitation Nowcasting Using IMERG. IEEE Transactions on Geoscience and Remote Sensing..
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  Accurate and timely estimation of precipitation is critical for issuing hazard warnings (e.g., for flash floods or landslides). Current remotely sensed precipitation products have a few hours of latency, associated with the acquisition and processing of satellite data. By applying a robust nowcasting system to these products, it is (in principle) possible to reduce this latency and improve their applicability, value, and impact. However, the development of such a system is complicated by the chaotic nature of the atmosphere, and the consequent rapid changes that can occur in the structures of precipitation systems In this work, we develop two approaches (hereafter referred to as Nowcasting-Nets) that use Recurrent and Convolutional deep neural network structures to address the challenge of precipitation nowcasting. A total of five models are trained using Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation data over the Eastern Contiguous United States (CONUS) and then tested against independent data for the Eastern and Western CONUS. The models were designed to provide forecasts with a lead time of up to 1.5 hours and, by using a feedback loop approach, the ability of the models to extend the forecast time to 4.5 hours was also investigated. Model performance was compared against the Random Forest (RF) and Linear Regression (LR) machine learning methods, and also against a persistence benchmark (BM) that used the most recent observation as the forecast. Independent IMERG observations were used as a reference, and experiments were conducted to examine both overall statistics and case studies involving specific precipitation events. Overall, the forecasts provided by the Nowcasting-Net models are superior, with the Convolutional Nowcasting Network with Residual Head (CNC-R) achieving 25%, 28%, and 46% improvement in the test ... [Journal_ref: ]
• Meles, M. B., Goodrich, D. C., Unkrich, C. L., Gupta, H. V., Burns, I. S., Hirpa, F. A., Razavi, S., & Guertin, D. P. (2024). Rainfall distributional properties control hydrologic model parameter importance.. Journal of Hydrology: Regional Studies, 51.
• Scholbeck, C. A., Moosbauer, J., Casalicchio, G., Gupta, H., Bischl, B., & Heumann, C. (2024). Position Paper: Bridging the Gap Between Machine Learning and Sensitivity Analysis. ArXiv.
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  We argue that interpretations of machine learning (ML) models or the model-building process can bee seen as a form of sensitivity analysis (SA), a general methodology used to explain complex systems in many fields such as environmental modeling, engineering, or economics. We address both researchers and practitioners, calling attention to the benefits of a unified SA-based view of explanations in ML and the necessity to fully credit related work. We bridge the gap between both fields by formally describing how (a) the ML process is a system suitable for SA, (b) how existing ML interpretation methods relate to this perspective, and (c) how other SA techniques could be applied to ML. [Journal_ref: ]
• Thyer, M., Gupta, H., Westra, S., McInerney, D., Maier, H., Kavetski, D., Jakeman, A., Croke, B., Simmons, C., Partington, D., Shanafield, M., & Tague, C. (2024). Virtual Hydrological Laboratories: Developing the Next Generation of Conceptual Models to Support Decision Making Under Change. Water Resources Research, 60(4). doi:10.1029/2022WR034234
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  As hydrological systems are pushed outside the envelope of historical experience, the ability of current hydrological models to serve as a basis for credible prediction and decision making is increasingly challenged. Conceptual models are the most common type of surface water hydrological model used for decision support due to reasonable performance in the absence of change, ease of use and computational speed that facilitate scenario, sensitivity and uncertainty analysis. Hence, conceptual models in effect represent the current “shopfront” of hydrological science as seen by practitioners. However, these models have notable limitations in their ability to resolve internal catchment processes and subsequently capture hydrological change. New thinking is needed to confront the challenges faced by the current generation of conceptual models in dealing with a changing environment. We argue the next generation of conceptual models should combine the parsimony of conceptual models with our best available scientific understanding. We propose a strategy to develop such models using multiple hydrological lines of evidence. This strategy includes using appropriately selected physically resolved models as “Virtual Hydrological Laboratories” to test and refine the simpler models' ability to predict future hydrological changes. This approach moves beyond the sole focus on “predictive skill” measured using metrics of historical performance, facilitating the development of the next generation of conceptual models with hydrological fidelity (i.e., models that “get the right answers for the right reasons”). This quest is more than a scientific curiosity; it is expected by policy makers who need to know what to plan for.
• Venegas-Qui??ones, H., Vald??s-Pineda, R., Garc??a-Chevesich, P., Vald??s, J., Gupta, H. V., Whitaker, M., & Ferr??, T. (2024). Development of Groundwater Levels Dataset for Chile since 1970. Scientific Data, 11(1).
• Wang, Y., & Gupta, H. V. (2024). A Mass‐Conserving‐Perceptron for Machine‐Learning‐Based Modeling of Geoscientific Systems. Water Resources Research, 60(4). doi:10.1029/2023wr036461
• Wang, Y., & Gupta, H. V. (2024). Towards Interpretable Physical-Conceptual Catchment-Scale Hydrological Modeling using the Mass-Conserving-Perceptron. ArXiv.
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  We investigate the applicability of machine learning technologies to the development of parsimonious, interpretable, catchment-scale hydrologic models using directed-graph architectures based on the mass-conserving perceptron (MCP) as the fundamental computational unit. Here, we focus on architectural complexity (depth) at a single location, rather than universal applicability (breadth) across large samples of catchments. The goal is to discover a minimal representation (numbers of cell-states and flow paths) that represents the dominant processes that can explain the input-state-output behaviors of a given catchment, with particular emphasis given to simulating the full range (high, medium, and low) of flow dynamics. We find that a HyMod-like architecture with three cell-states and two major flow pathways achieves such a representation at our study location, but that the additional incorporation of an input-bypass mechanism significantly improves the timing and shape of the hydrograph, while the inclusion of bi-directional groundwater mass exchanges significantly enhances the simulation of baseflow. Overall, our results demonstrate the importance of using multiple diagnostic metrics for model evaluation, while highlighting the need for designing training metrics that are better suited to extracting information across the full range of flow dynamics. Further, they set the stage for interpretable regional-scale MCP-based hydrological modeling (using large sample data) by using neural architecture search to determine appropriate minimal representations for catchments in different hydroclimatic regimes. [Journal_ref: ]
• Condon, L. E., De la Fuente, L., & Gupta, H. V. (2023).

  Towards a Multi-Representational Approach to Prediction, Understanding, and Discovery in Hydrology

  . Water Resources Research. doi:10.1002/essoar.10508656.1
• De la Fuente, L. A., Gupta, H. V., & Condon, L. (2023). Towards a Multi-Representational Approach to Prediction and Understanding, in support of Discovery in Hydrology. Water Resources Research.
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  De la Fuente LA, HV Gupta and Laura Condon (2022), Towards a Multi-Representational Approach to Prediction, Understanding and Discovery in Hydrology, Water Resources Research, https://doi.org/10.1029/2021WR031548.
• Frame, J. M., Kratzert, F., Gupta, H. V., Ullrich, P., & Nearing, G. S. (2023). On Strictly Enforced Mass Conservation Constraints for Modeling the Rainfall Runoff Process. HESS.
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  Frame JM, F Kratzert, HV Gupta, P Ullrich and GS Nearing (2023), On Strictly Enforced Mass Conservation Constraints for Modeling the Rainfall Runoff Process, HESS, http://dx.doi.org/10.1002/hyp.14847
• Frame, J. M., Kratzert, F., Gupta, H. V., Ullrich, P., & Nearing, G. S. (2023). On strictly enforced mass conservation constraints for modelling the Rainfall-Runoff process. Hydrological Processes, 37(3).
• Gauch, M., Kratzert, F., Gilon, O., Gupta, H. V., Mai, J., Nearing, G. S., Tolson, B., Hochreiter, S., & Klotz, D. (2023). In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance. Water Resources Research.
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  Gauch M, F Kratzert, O Gilon, H Gupta, J Mai, G Nearing, B Tolson, S Hochreiter, D Klotz (2022), In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance, Water Resources Research.
• Gupta, H. V., De la Fuente, L. A., & Condon, L. E. (2023). Toward a Multi‐Representational Approach to Prediction and Understanding, in Support of Discovery in Hydrology. Water Resources Research, 59(1). doi:10.1029/2021wr031548
• Gupta, H., Frame, J., Kratzert, F., Ullrich, P., & Nearing, G. (2023). On strictly enforced mass conservation constraints for modeling the rainfall-runoff process. HESS. doi:10.22541/au.164268242.21913137/v1
• Maier, H. R., Zheng, F., Gupta, H., Chen, J., Mai, J., Savic, D., Loritz, R., Wu, W., Guo, D., Bennett, A., Jakeman, A., Razavi, S., & Zhao, J. (2023). On how data are partitioned in model development and evaluation: Confronting the elephant in the room to enhance model generalization. Environmental Modelling and Software, 167.
• Mathevet, T., Le Moine, N., Andr??assian, V., Gupta, H., & Oudin, L. (2023). Multi-objective assessment of hydrological model performances using Nash-Sutcliffe and Kling-Gupta efficiencies on a worldwide large sample of watersheds. Comptes Rendus - Geoscience, 355.
• Mathevet, T., Le Moine, N., Perrin, C., Gupta, H. V., Andréassian, V., & Odin, L. (2023). Multi-objective assessment of hydrological model performances using Nash-Sutcliffe and Kling-Gupta efficiencies on a worldwide large sample of watersheds. Comptes Rendus–Geoscience.
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  Mathevet T, N Le Moine, C Perrin, H Gupta, V Andréassian and L Oudin (2022), Multi-objective assessment of hydrological model performances using Nash-Sutcliffe and Kling-Gupta efficiencies on a worldwide large sample of watersheds, special issue of Comptes Rendus–Geoscience.
• Meles, M. B., Goodrich, D. C., Unkrich, C. L., Gupta, H. V., Burns, I. S., Hirpa, F. A., Razavi, S., & Guertin, D. P. (2020). Understanding the dynamics of parameter importance for a spatially-distributed parameterization scheme when modeling semi-arid watersheds. TBD.
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  Meles MB, DC Goodrich, CL Unkrich, HV Gupta, IS Burns, FA Hirpa, S Razavi, DP Guertin (in prep), Understanding the dynamics of parameter importance for a spatially-distributed parameterization scheme when modeling semi-arid watersheds, for submission to TBD.
• Naderi, M., & Gupta, H. V. (2023). On the Requirements for Inferring Aquifer-Scale T & S in Heterogeneous Confined Aquifers. Water Resources Research.
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  Naderi M and HV Gupta (2023), On the Requirements for Inferring Aquifer-Scale T & S in Heterogeneous Confined Aquifers, Water Resources Research
• Naderi, M., & Gupta, H. V. (2023). On the Requirements for Inferring Aquifer-Scale T and S in Heterogeneous Confined Aquifers. Water Resources Research, 59(5).
• Nearing, G. S., Pellesier, C. S., Kratzert, F., Klotz, D., Gupta, H. V., Sampson, A. K., & Frame, J. M. (2020). Physically Informed Machine Learning for Hydrological Modeling under Climate Nonstationarity. Science and Technology Infusion Climate Bulletin, NOAA’s National Weather Service.
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  Nearing GS, CS Pelissier, F Kratzert, D Klotz, HV Gupta, AK Sampson, JM Frame (in prep), Physically Informed Machine Learning for Hydrological Modeling under Climate Nonstationarity, Extended abstract in Science and Technology Infusion Climate Bulletin, NOAA’s National Weather Service, based on presentation at 44th NOAA Annual Climate Diagnostics and Prediction Workshop, 22-24 October 2019 Durham, NC.
• Ruddell, B., Clark, M., Driscoll, J. M., Gochis, D., Gupta, H. V., Harvey, J., Huntzinger, D., Kirchner, J. W., Larsen, L., Loescher, H. W., Luo, Y., Maxwell, R., Moges, E., Xu, Z., Donatelli, M., & Hoffman, F. (2023). Calling for a National Model Benchmarking Facility. Environmental Research letters.
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  Ruddell BL, M Clark, JM Driscoll, D Gochis, H Gupta, J Harvey, D Huntzinger, JW Kirchner, L Larsen, HW Loescher, Y Luo, R Maxwell, E Moges, Z Xu, M Donatelli, F Hoffman (2022), Calling for a National Model Benchmarking Facility, Environmental Research letters.
• Shen, C., Appling, A. P., Gentine, P., Bandai, T., Gupta, H., Tartakovsky, A., Baity-Jesi, M. .., Fenicia, F., Kifer, D., Li, L., Liu, X., Ren, W., Zheng, Y., Harman, C. J., Clark, M., Farthing, M., Feng, D., Kumar, P., Aboelyazeed, D., , Rahmani, F., et al. (2023). Differentiable modelling to unify machine learning and physical models for geosciences. Nature Reviews Earth and Environment, 4(8), 552-567.
• Shen, C., Appling, A. P., Gentine, P., Bandai, T., Gupta, H., Tartakovsky, A., Baity-Jesi, M., Fenicia, F., Kifer, D., Li, L., Liu, X., Ren, W., Zheng, Y., Harman, C. J., Clark, M., Farthing, M., Feng, D., Kumar, P., Aboelyazeed, D., , Rahmani, F., et al. (2023). Differentiable modeling to unify machine learning and physical models and advance Geosciences. Nat Rev Earth Environ, 552-567.
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  Process-Based Modeling (PBM) and Machine Learning (ML) are often perceived asdistinct paradigms in the geosciences. Here we present differentiablegeoscientific modeling as a powerful pathway toward dissolving the perceivedbarrier between them and ushering in a paradigm shift. For decades, PBM offeredbenefits in interpretability and physical consistency but struggled toefficiently leverage large datasets. ML methods, especially deep networks,presented strong predictive skills yet lacked the ability to answer specificscientific questions. While various methods have been proposed for ML-physicsintegration, an important underlying theme -- differentiable modeling -- is notsufficiently recognized. Here we outline the concepts, applicability, andsignificance of differentiable geoscientific modeling (DG). "Differentiable"refers to accurately and efficiently calculating gradients with respect tomodel variables, critically enabling the learning of high-dimensional unknownrelationships. DG refers to a range of methods connecting varying amounts ofprior knowledge to neural networks and training them together, capturing adifferent scope than physics-guided machine learning and emphasizing firstprinciples. Preliminary evidence suggests DG offers better interpretability andcausality than ML, improved generalizability and extrapolation capability, andstrong potential for knowledge discovery, while approaching the performance ofpurely data-driven ML. DG models require less training data while scalingfavorably in performance and efficiency with increasing amounts of data. WithDG, geoscientists may be better able to frame and investigate questions, testhypotheses, and discover unrecognized linkages.[Journal_ref: Nat Rev Earth Environ 4, 552-567 (2023)]
• Shen, C., Appling, A. P., Genuine, P., Bandai, T., & Gupta, H. V. (2023). Differentiable modeling in Geosciences to unify machine learning and physical models. Nature Reviews Earth & Environment.
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  Shen C, AP Appling, P Gentine, T Bandai, H Gupta, A Tartakovsky, M Baity-Jesi, F Fenicia, D Kifer, L Li, X Liu, W Ren, Y Zheng, CJ Harman, M Clark, M Farthing, D Feng, P Kumar, D Aboelyazeed, F Rahmani, HE Beck, T Bindas, D Dwivedi, K Fang, M Höge, C Rackauckas, T Roy, C Xu, K Lawson (2022), Differentiable modeling in Geosciences to unify machine learning and physical models (ref: NATWATER-22-0626A), Nature Reviews Earth & Environment
• Wang, Y., & Gupta, H. V. (2023). A Mass-Conserving-Perceptron for Machine Learning-Based Modeling of Geoscientific Systems. ArXiv.
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  Although decades of effort have been devoted to building Physical-Conceptual (PC) models for predicting the time-series evolution of geoscientific systems, recent work shows that Machine Learning (ML) based Gated Recurrent Neural Network technology can be used to develop models that are much more accurate. However, the difficulty of extracting physical understanding from ML-based models complicates their utility for enhancing scientific knowledge regarding system structure and function. Here, we propose a physically-interpretable Mass Conserving Perceptron (MCP) as a way to bridge the gap between PC-based and ML-based modeling approaches. The MCP exploits the inherent isomorphism between the directed graph structures underlying both PC models and GRNNs to explicitly represent the mass-conserving nature of physical processes while enabling the functional nature of such processes to be directly learned (in an interpretable manner) from available data using off-the-shelf ML technology. As a proof of concept, we investigate the functional expressivity (capacity) of the MCP, explore its ability to parsimoniously represent the rainfall-runoff (RR) dynamics of the Leaf River Basin, and demonstrate its utility for scientific hypothesis testing. To conclude, we discuss extensions of the concept to enable ML-based physical-conceptual representation of the coupled nature of mass-energy-information flows through geoscientific systems. [Journal_ref: ]
• Zheng, F., Chen, J., Ma, Y., Chen, Q., Maier, H. R., & Gupta, H. (2023). A Robust Strategy to Account for Data Sampling Variability in the Development of Hydrological Models. Water Resources Research, 59(3).
• Zheng, F., Chen, J., Ma, Y., Chen, Q., Maier, H. R., & Gupta, H. V. (2023). A Robust Strategy to Account for Data Sampling Variability in the Development of Hydrological Models. Water Resources Research.
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  Zheng F, J Chen, Y Ma Y, Q Chen, HR Maier and H Gupta (2023), A Robust Strategy to Account for Data Sampling Variability in the Development of Hydrological Models, Water Resources Research, doi: 10.1029/2022WR033703
• Zheng, F., Yin, H., Ma, Y., Duan, H., Gupta, H. V., Savic, D., & Kapelan, Z. (2023). Towards Improved Real-Time Rainfall Intensity Estimation Using Video Surveillance Cameras. Water Resources Research.
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  Zheng F,  H Yin H, Y Ma, H-F Duan, H Gupta, D Savic and Z Kapelan (2023), Towards Improved Real-Time Rainfall Intensity Estimation Using Video Surveillance Cameras, Water Resources Research.
• Zheng, F., Yin, H., Ma, Y., Duan, H., Gupta, H., Savic, D., & Kapelan, Z. (2023). Toward Improved Real-Time Rainfall Intensity Estimation Using Video Surveillance Cameras. Water Resources Research, 59(8).
• Alemayehu, T., Gupta, H. V., van Griensven, A., & Bauwens, W. (2022). On calibration of spatially distributed hydrologic models for poorly gauged basins: Exploiting information from streamflow signatures and satellite-remotely-sensed evapotranspiration data. Water Resources Research.
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  Alemayehu T, HV Gupta, A van Griensven and W Bauwens (2022), On Calibration of Spatially Distributed Hydrologic Models for Poorly Gauged Basins: Exploiting Information from Streamflow Signatures and Satellite-Remotely-Sensed Evapotranspiration Data, Water 2022, 14(8), 1252; https://doi.org/10.3390/w14081252
• Behrangi, A., Gupta, H. V., Zarei, A., Ehsani, M. R., Barnard, K., & Lyons, E. (2022). NowCasting-Nets: Representation Learning to Mitigate Latency Gap of Satellite Precipitation Products Using Convolutional and Recurrent Neural Networks. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-21. doi:10.1109/tgrs.2022.3158888
• Chen, J., Zheng, F., Gupta, H. V., Guo, D., May, R., & Maier, H. R. (2022). Improved Data Splitting Methods for Hydrological Model Development: A Large Catchment Sample Study. Water Resources Research.
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  Chen J, F Zheng, R May, D Guo, H Gupta and HR Maier (2022), Improved Data Splitting Methods for Data-driven Hydrological Model Development Based on A Large Number of Catchment Samples, Journal of Hydrology
• Ehsani, M. R., Zarei, A., Gupta, H. V., Barnard, K., & Behrangi, A. (2022). NowCasting-Nets: Representation Learning to Mitigate Latency Gap of Satellite Precipitation Products using Convolutional and Recurrent Neural Networks. IEEE Transactions on Geoscience and Remote Sensing. doi:http://arxiv.org/abs/2108.06868
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  Ehsani MR, A Zarei, HV Gupta, K Barnard and A Behrangi (2022), NowCasting-Nets: Representation Learning to Mitigate Latency Gap of Satellite Precipitation Products using Convolutional and Recurrent Neural Networks, (http://arxiv.org/abs/2108.06868) IEEE Transactions on Geoscience and Remote Sensing.
• Frame, J. M., Kratzert, F., Klotz, D., Gauch, M., Shalev, G., Gilon, O., Qualls, L. M., Gupta, H. V., & Nearing, G. S. (2022). Deep learning rainfall-runoff predictions of extreme events. Hydrology and Earth System Sciences. doi:https://doi.org/10.5194/hess-2021-423
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  Frame JM, F Kratzert, D Klotz, M Gauch, G Shalev, O Gilon, LM Qualls, HV Gupta and GS Nearing (2022), Deep learning rainfall-runoff predictions of extreme events, Hydrology and Earth System Sciences, 26, 3377–3392, https://doi.org/10.5194/hess-26-3377-2022.
• Gupta, H. V., Wang, Y., Zeng, X., & Niu, G. (2022). Exploring the Potential of Long Short‐Term Memory Networks for Improving Understanding of Continental‐ and Regional‐Scale Snowpack Dynamics. Water Resources Research, 58(3). doi:10.1029/2021wr031033
• Gupta, H., Partington, D., Thyer, M., Shanafield, M., McInerney, D., Westra, S., Maier, H., Simmons, C., Croke, B., Jakeman, A. J., & Kavetski, D. (2022). Predicting wildfire induced changes to runoff: A review and synthesis of modeling approaches. WIREs Water, 9(5). doi:10.1002/wat2.1599
• Partington, D., Partington, D., Thyer, M., Thyer, M., Shanafield, M., Shanafield, M., McInerney, D., McInerney, D., Westra, S., Westra, S., Maier, H., Maier, H., Simmons, C., Simmons, C., Croke, B., Croke, B., Jakeman, A., Jakeman, A., Gupta, H. V., , Gupta, H. V., et al. (2022). Predicting wildfire induced changes to runoff. Wiley Interdisciplinary Reviews (WIREs).
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  Partington D, M Thyer, M Shanafield, D McInerney, S Westra, H Maier, C Simmons, B Croke, A Jakeman, H Gupta, D Kavetski (2022), Predicting wildfire induced changes to runoff, Wiley Interdisciplinary Reviews (WIREs), http://dx.doi.org/10.1002/wat2.1599.
• Puy, A., Sheikholeslami, R., Gupta, H. V., Hall, J. W., Lo Piano, S., Meier, J., Pappenberger, F., Porporato, F., Vico, G., & Saltelli, A. (2022). The delusive accuracy of global irrigation water withdrawal estimates. Nature Communications.
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  Puy A, R Sheikholeslami, HV Gupta, JW Hall, S Lo Piano, J Meier F Pappenberger, A Porporato, G Vico, and A Saltelli (2022), The Delusive Accuracy of Global Irrigation Water Withdrawal Estimates, perspective Nature Communications.
• Wang, Y. H., Gupta, H. V., Zeng, X., & Niu, G. (2022). Exploring the Potential of Long Short-Term Memory Networks (LSTMs) for Improving Understanding of Continental- and Regional-Scale Snow Dynamics. Water Resources Research.
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  Wang YH, HV Gupta, X Zeng and GY Niu (2022) Exploring the Potential of Long Short-Term Memory Networks (LSTMs) for Improving Understanding of Continental- and Regional-Scale Snow Dynamics, Water Resources Research, 58, e2021WR031033, doi. org/10.1029/2021WR031033.
• Zeng, X., Gupta, H. V., Wang, Y., & Niu, G. (2022). Exploring the Potential of Long Short-Term Memory Networks for Improving Understanding of Continental- and Regional-Scale Snowpack Dynamics. Water Resources Research. doi:10.1002/essoar.10507743.1
• Zheng, F., Chen, J., Maier, H. R., & Gupta, H. V. (2022). Achieving Robust and Transferable Performance for Conservation-Based Models of Dynamical Physical Systems. Water Resources Research.
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  Zheng F, J Chen J, HR Maier and H Gupta (2022), Achieving Robust and Transferable Performance for Conservation-Based Models of Dynamical Physical Systems, Water Resources Research, 58, e2021WR031818. https://doi.org/10.1029/2021WR031818
• Dou, Y., Dou, Y., Ye, L., Ye, L., Gupta, H. V., Gupta, H. V., Zhang, H., Zhang, H., Behrangi, A., Behrangi, A., Zhou, H., & Zhou, H. (2021). Improved Flood Forecasting in Basins with No Precipitation Stations: Constrained Runoff Correction Using Multiple Satellite Precipitation Products. Water Resources Research. doi:doi: 10.1029/2021WR029682
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  Dou Y, L Ye, HV Gupta, H Zhang, A Behrangi, H Zhou (2021), Improved Flood Forecasting in Basins with No Precipitation Stations: Constrained Runoff Correction Using Multiple Satellite Precipitation Products, submitted to Water Resources Research, doi: 10.1029/2021WR029682
• Gharari, S., Gupta, H. V., Clark, M. P., Matgen, P., Hrachowitz, M., Fenecia, F., & Savenije, H. H. (2021). Understanding the information content in the hierarchy of model development decisions. Water Resources Research. doi:doi.org/10.1029/2020WR027948
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  Gharari S, HV Gupta, MP Clark, P Matgen, M Hrachowitz, F Fenicia, and HHG. Savenije (2021), Understanding the information content in the hierarchy of model development decisions, Water Resources Research, doi.org/10.1029/2020WR027948
• Gupta, H. V., Ehsani, M. R., Roy, T., Sans-Fuentes, M. A., Ehret, U., & Behrangi, A. (2021). Computing Accurate Probabilistic Estimates of One-D Entropy from Equiprobable Random Samples. Entropy.
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  We develop a simple Quantile Spacing (QS) method for accurate probabilistic estimation of one-dimensional entropy from equiprobable random samples, and compare it with the popular Bin-Counting (BC) method. In contrast to BC, which uses equal-width bins with varying probability mass, the QS method uses estimates of the quantiles that divide the support of the data generating probability density function (pdf) into equal-probability-mass intervals. Whereas BC requires optimal tuning of a bin-width hyper-parameter whose value varies with sample size and shape of the pdf, QS requires specification of the number of quantiles to be used. Results indicate, for the class of distributions tested, that the optimal number of quantile-spacings is a fixed fraction of the sample size (empirically determined to be ~0.25-0.35), and that this value is relatively insensitive to distributional form or sample size, providing a clear advantage over BC since hyperparameter tuning is not required. Bootstrapping is used to approximate the sampling variability distribution of the resulting entropy estimate, and is shown to accurately reflect the true uncertainty. For the four distributional forms studied (Gaussian, Log-Normal, Exponential and Bimodal Gaussian Mixture), expected estimation bias is less than 1% and uncertainty is relatively low even for very small sample sizes. We speculate that estimating quantile locations, rather than bin-probabilities, results in more efficient use of the information in the data to approximate the underlying shape of an unknown data generating pdf. [Journal_ref: ]
• Gupta, H. V., Ehsani, R. M., Roy, T., Sans-Fuentes, M. A., Ehret, U., & Behrangi, A. (2021). Computing Accurate Probabilistic Estimates of One-D Entropy from Equiprobable Random Samples. arXiv.
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  Gupta HV, RM Ehsani, T Roy, MA Sans-Fuentes, U Ehret and A Behrangi (2021), Computing Accurate Probabilistic Estimates of One-D Entropy from Equiprobable Random Samples, posted (02/24/21) to Arxiv; http://arxiv.org/abs/2102.12675
• Gupta, H. V., Gupta, H. V., Gupta, H. V., Ehsani, R. M., Ehsani, R. M., Gupta, H. V., Roy, T., Roy, T., Ehsani, R. M., Sans-Fuentes, M. A., Sans-Fuentes, M. A., Ehsani, R. M., Ehret, U., Ehret, U., Roy, T., Roy, T., Behrangi, A., Behrangi, A., Sans-Fuentes, M. A., , Sans-Fuentes, M. A., et al. (2021). Computing Accurate Probabilistic Estimates of One-D Entropy from Equiprobable Random Samples. Entropy.
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  Gupta HV, RM Ehsani, T Roy, MA Sans-Fuentes, U Ehret and A Behrangi (2021), Computing Accurate Probabilistic Estimates of One-D Entropy from Equiprobable Random Samples, Section on Information Theory, Probability and Statistics, Entropy, 23(6), 740, doi.org/10.3390/e23060740
• Gupta, H., Dwivedi, D., Nearing, G., Sampson, A., Condon, L., Ruddell, B., Klotz, D., Ehret, U., Read, L., Kumar, P., Ferre, T., & Steefel, C. (2021). Knowledge-Guided Machine Learning (KGML) Platform to Predict Integrated Water Cycle and Associated extremes. AI4ESP White Paper, DOE BER Earth and Environmental Systems Science Division. doi:10.2172/1769733
• Ji, L., Gupta, H., McGuire, L. A., Liu, T., Wei, H., Rengers, F. K., & Goodrich, D. C. (2021). The timing and magnitude of changes to Hortonian overland flow at the watershed scale during the post‐fire recovery process. Hydrological Processes, 35(5). doi:10.1002/hyp.14208
• Lahmers, T. M., Hazenberg, P., Gupta, H., Castro, C., Gochis, D., Dugger, A., Yates, D., Read, L., Karsten, L., & Wang, Y. (2021). Evaluation of NOAA National Water Model Parameter Calibration in Semi-Arid Environments Prone to Channel Infiltration. Journal of Hydrometeorology. doi:10.1175/jhm-d-20-0198.1
• Lahmers, T., Hazenberg, P., Gupta, H. V., Castro, C. L., Gochis, D., Dugger, A., Yates, D., Read, L., Karsten, L., & Wang, Y. H. (2021). Evaluation of NOAA National Water Model Parameter Calibration in Semi-Arid Environments Prone to Channel Infiltration. Journal of Hydrometeorology. doi:doi.org/10.1175/JHM-D-20-0198.1
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  Lahmers TM, P Hazenberg, H Gupta, C Castro, D Gochis, A Dugger, D Yates, L Read, L Karsten and YH Wang (2021), Evaluation of NOAA National Water Model Parameter Calibration in Semi-Arid Environments Prone to Channel Infiltration, Journal of Hydrometeorology, doi.org/10.1175/JHM-D-20-0198.1
• Liu, T., McGuire, L., Wei, H., Rengers, F. K., Gupta, H. V., Ji, L., & Goodrich, D. (2021). The Timing And Magnitude of Changes to Hortonian Overland Flow at the Watershed Scale During the Post-Fire Recovery Process. Hydrological Processes. doi:doi.org/10.1002/hyp.14208
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  Liu T, L McGuire, H Wei, FK Rengers, H Gupta, Lin Ji, D Goodrich (2021), The Timing And Magnitude of Changes to Hortonian Overland Flow at the Watershed Scale During the Post-Fire Recovery Process, Hydrological Processes, doi.org/10.1002/hyp.14208
• Meles, M. B., Goodrich, D. C., Gupta, H. V., Burns, I. S., Unkrich, C. L., Razavi, S., & Guertin, D. P. (2021). Multi-Criteria, Time Dependent Sensitivity Analysis of an Event-Oriented, Physically-Based, Distributed Sediment and Runoff Model. Journal of Hydrology. doi:https://doi.org/10.1016/j.jhydrol.2021.126268
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  Meles MB, DC Goodrich, HV Gupta, IS Burns, CL Unkrich, S Razavi and P Guertin (2021), Multi-Criteria, Time Dependent Sensitivity Analysis of an Event-Oriented, Physically-Based, Distributed Sediment and Runoff Model, Journal of Hydrology, https://doi.org/10.1016/j.jhydrol.2021.126268
• Meles, M. B., Goodrich, D. C., Unkrich, C. L., Gupta, H. V., Burns, I. S., Harp, F. A., Razavi, S., & Guertin, D. P. (2021). Understanding the dynamics of parameter importance for a spatially-distributed parameterization scheme when modeling semi-arid watersheds. Journal of Hydrology.
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  Meles MB, DC Goodrich, CL Unkrich, HV Gupta, IS Burns, FA Hirpa, S Razavi, DP Guertin (2021), Understanding the dynamics of parameter importance for a spatially-distributed parameterization scheme when modeling semi-arid watersheds, Journal of Hydrology.
• Meles, M. B., Goodrich, D., Gupta, H. V., Burns, I. S., Unkrich, C. L., Razavi, S., & Guertin, P. (2021). Multi-Criteria, Time Dependent Sensitivity Analysis of an Event-Oriented, Physically-Based, Distributed Sediment and Runoff Model. Journal of Hydrology.
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  Meles MB, DC Goodrich, HV Gupta, IS Burns, CL Unkrich, S Razavi and P Guertin (in review), Multi-Criteria, Time Dependent Sensitivity Analysis of an Event-Oriented, Physically-Based, Distributed Sediment and Runoff Model, Journal of Hydrology.
• Moghaddam, M., Moghaddam, M., Moghaddam, M., Moghaddam, M., Ferre, P. A., Ferre, P. A., Ferre, P. A., Ferre, P. A., Klakovich, J., Klakovich, J., Klakovich, J., Klakovich, J., Gupta, H. V., Gupta, H. V., Gupta, H. V., Gupta, H. V., Ehsani, M. R., Ehsani, M. R., Ehsani, M. R., & Ehsani, M. R. (2021). Can Deep Learning Extract Useful Information about Energy Dissipation and Effective Hydraulic Conductivity from Gridded Conductivity Fields?. Water. doi:doi:10.3390/w13121668
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  [37] Moghaddam M, Ferre T, Klakovich J, Gupta HV and Ehsani MR (2021), Can Deep Learning Extract Useful Information about Energy Dissipation and Effective Hydraulic Conductivity from Gridded Conductivity Fields? Water, 13, 1668, doi:10.3390/w13121668
• Razavi, S., Jakeman, A., Saltelli, A., Prieur, C., Looks, B., Bergonovo, E., Plishke, E., Lo Piano, S., Becker, W., Tarantola, S., Guillaume, J., Jakeman, J., Gupta, H. V., Iwanaga, T., Melillo, N., Rabitti, G., Chabridon, V., Duan, Q., Sun, X., , Sheikholeslami, R., et al. (2021). The Future of Sensitivity Analysis: An Essential Discipline for Systems Modelling and Policy Making. Environmental Modelling and Software.
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  Razavi S, A Jakeman, A Saltelli, C Prieur, B Iooss, E Borgonovo, E Plischke, S Lo Piano, W Becker, S Tarantola, J Guillaume, J Jakeman, H Gupta, T Iwanaga, N Melillo, G Rabitti, V Chabridon, Q Duan, X Sun, R Sheikholeslami, M Asadzadeh, S Kucherenko (2020), The Future of Sensitivity Analysis: An Essential Discipline for Systems Modelling and Policy Making, Environmental Modelling and Software.
• Razavi, S., Razavi, S., Jakeman, A., Jakeman, A., Saltelli, A., Saltelli, A., Several authors, ., Several authors, ., Gupta, H. V., & Gupta, H. V. (2021). The Future of Sensitivity Analysis: An Essential Discipline for Systems Modelling and Policy Making. Environmental Modelling and Software. doi:doi.org/10.1016/j.envsoft.2020.104954
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  Razavi S, A Jakeman, A Saltelli, C Prieur, B Iooss, E Borgonovo, E Plischke, S Lo Piano, W Becker, S Tarantola, J Guillaume, J Jakeman, H Gupta, T Iwanaga, N Melillo, G Rabitti, V Chabridon, Q Duan, X Sun, R Sheikholeslami, M Asadzadeh, S Kucherenko (2021), The Future of Sensitivity Analysis: An Essential Discipline for Systems Modelling and Policy Making, Environmental Modelling and Software, doi.org/10.1016/j.envsoft.2020.104954
• Wang, Y., Karsten, L., Read, L., Yates, D., Dugger, A., Gochis, D., Gupta, H. V., Hazenberg, P., Lahmers, T. M., & Castro, C. L. (2021). Evaluation of NOAA National Water Model Parameter Calibration in Semiarid Environments. Journal of Hydrometeorlogy, 2939-2969.
• Zhang, X., Dong, Z., Gupta, H. V., Wu, G., & Li, D. (2021). Updated Stochastic Dynamic Programming for Improving Monthly Reservoir Operation under Forecast Uncertainty. Stochastic Environmental Research and Risk Assessment.
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  Zhang X, Z Dong, H Gupta, G Wu and D Li (in review), Updated Stochastic Dynamic Programming for Improving Monthly Reservoir Operation under Forecast Uncertainty, Stochastic Environmental Research and Risk Assessment.
• Behrangi, A., Ehret, U., Sans-Fuentes, M. A., Roy, T., Ehsani, R. M., & Gupta, H. V. (2020). Computing Accurate Probabilistic Estimates of One-D Entropy from Equiprobable Random Samples. Entropy.
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  HV Gupta, MR Ehsani, T Roy, MA Sans-Fuentes, U Ehret, A BehrangiEntropy 23 (6), 740
• Castro, C. L., Lahmers, T. M., Hazenberg, P., Gupta, H. V., Gochis, D., Dugger, A., Yates, D., Read, L., Karsten, L., Wang, Y., & Zamora, R. (2020). Evaluation of NOAA National Water Model Parameter Calibration in Semi-Arid Environments Prone to Channel Infiltration. Journal of Hydrometeorology.
• Chang, L. L., Yuan, R., Gupta, H. V., Winter, C. L., & Niu, G. (2020). Why is the Terrestrial Water Storage in Dryland Regions Declining? A Perspective based on GRACE Satellite Observations and Noah-MP Model Simulations. Water Resources Research.
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  Chang LL, R Yuan, HV Gupta, CL Winter and GY Niu (in review), Why is the Terrestrial Water Storage in Dryland Regions Declining? A Perspective based on GRACE Satellite Observations and Noah-MP Model Simulations, Water Resources Research
• Fleming, S. W., & Gupta, H. V. (2020). The Physics of River Prediction. Physics Today.
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  Fleming SW and HV Gupta (2020), The Physics of River Prediction, Physics Today.
• Guo, D., Zheng, F., Gupta, H. V., & Maier, H. R. (2020). On the Robustness of Conceptual Rainfall-Runoff Models to Calibration and Evaluation Dataset Splits Selection: A Large Sample Investigation. Water Resources Research.
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  Guo D, F Zheng, HV Gupta and H Maier (2020), On the Robustness of Conceptual Rainfall-Runoff Models to Calibration and Evaluation Dataset Splits Selection: A Large Sample Investigation, Water Resources Research, doi.org/10.1029/2019WR026752
• Gupta, H. V., & Kumar, P. (2020). Debates—Does Information Theory Provide a New Paradigm for Earth Science?. Water Resources Research, 56(2). doi:10.1029/2019wr026398
• Gupta, H. V., Nearing, G. S., Kratzert, F., Sampson, A. K., Pelissier, C. S., Klotz, D., Frame, J. M., & Prieto, C. (2020). What Role Does Hydrological Science Play in the Age of Machine Learning?. Water Resources Research, 57(3). doi:10.1029/2020wr028091
• Gupta, H., Foufoula-Georgiou, E., Guilloteau, C., Nguyen, P., Aghakouchak, A., Hsu, K., Busalacchi, A., Turk, F. J., Peters-Lidard, C., Oki, T., Duan, Q., Krajewski, W., Uijlenhoet, R., Barros, A., Kirstetter, P., Logan, W., Hogue, T., & Levizzani, V. (2020). Advancing Precipitation Estimation, Prediction, and Impact Studies. Bulletin of the American Meteorological Society, 101(9), E1584-E1592. doi:10.1175/bams-d-20-0014.1
• Guse, B., Pfannerstill, M., Fohrer, N., & Gupta, H. V. (2020). Improving information extraction from model data using sensitivity-weighted performance criteria. Water Resources Research.
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  Guse B, M Pfannerstill, N Fohrer and H Gupta (2020), Improving information extraction from model data using sensitivity-weighted performance criteria, Water Resources Research, doi:10.109/019WR025605.
• Huo, X., Gupta, H. V., Niu, G., Gong, W., & Duan, Q. (2019). Parameter Sensitivity Analysis for Computationally-Intensive Spatially-Distributed Dynamical Environmental Systems Models. Journal of Advances in Modelling Earth Systems.
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  Huo X, H Gupta, GY Niu, W Gong and Q Duan (in prep), Parameter Sensitivity Analysis for Computationally-Intensive Spatially-Distributed Dynamical Environmental Systems Models, Journal of Advances in Modelling Earth Systems.
• Kumar, P., & Gupta, H. V. (2020). Does Information Theory Provide a New Paradigm for Earth Science?. Water Resources Research.
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  Kumar P and HV Gupta, (2020), Does Information Theory Provide a New Paradigm for Earth Science? Water Resources Research “Debate on Information Theory”, doi.org/10.1029/2019WR026398
• Mathevet, T., Gupta, H. V., Garavaglia, F., Le Moine, N., Coron, L., Andreassian, V., Perrin, C., & Brigode, P. (2020). Assessing the Performance, Reliability and Robustness of Two Conceptual Rainfall-Runoff models on a Worldwide Sample of Watersheds. Journal of Hydrology.
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  Mathevet T, HV Gupta, C Perrin, V Andréassian, N Le Moine (2020), Assessing the Performance, Reliability and Robustness of Two Conceptual Rainfall-Runoff models on a Worldwide Sample of Watersheds, Journal of Hydrology, doi.org/10.1016/j.jhydrol.2020.124698
• Moghaddam, M. A., Ferre, T., KLAKOVICH, J., Gupta, H. V., & Ehsani, M. R. (2020). Can Machine Learning Extract Useful Information about Energy Dissipation and Effective Hydraulic Conductivity from Gridded Conductivity Fields?. Water. doi:10.1002/essoar.10505220.1
• Naderi, M., & Gupta, H. V. (2020). On the Reliability of Time-Variable Pumping Test Results: Sensitivity to Information Content of the Recorded Data. Water Resources Research.
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  Naderi M and HV Gupta (2020), On the Reliability of Variable-Rate Pumping Test Results: Sensitivity to Information Content of the Recorded Data, Water Resources Research, doi.org/10.1029/2019WR026961
• Nearing, G. S., Kratzert, F., Sampson, A. K., Pelissier, C. S., Frame, J. M., Klotz, D., & Gupta, H. V. (2020). What Role Does Hydrological Science Have in the Age of Machine Learning?. Water Resources Research.
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  Nearing GS, F Kratzert, AK Sampson, CS Pelissier, JM Frame, D Klotz, HV Gupta (2020), What Role Does Hydrological Science Have in the Age of Machine Learning? Water Resources Research (posted to Earth and Space Science Open Archive (ESSOAr)), doi.org/10.1029/2020WR028091
• Nearing, G., Ruddell, B., Bennett, A. R., Prieto, C., & Gupta, H. V. (2020). Does Information Theory Provide a New Paradigm for Earth Science? Hypothesis Testing. Water Resources Research.
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  Nearing G, B Ruddell, AR Bennett, C Prieto and HV Gupta, (2020), Does Information Theory Provide a New Paradigm for Earth Science? Hypothesis Testing, Water Resources Research “Debate on Information Theory”, doi.org/10.1029/2019WR024918
• Niu, G., Winter, C. L., Gupta, H. V., Chang, L., & Yuan, R. (2020). Why Is the Terrestrial Water Storage in Dryland Regions Declining? A Perspective Based on Gravity Recovery and Climate Experiment Satellite Observations and Noah Land Surface Model With Multiparameterization Schemes Model Simulations. Water Resources Research, 56(11). doi:10.1029/2020wr027102
• Niu, G., Winter, C. L., Gupta, H. V., Yuan, R., & Chang, L. L. (2020). Why Is the Terrestrial Water Storage in Dryland Regions Declining? A Perspective Based on Gravity Recovery and Climate Experiment Satellite Observations and Noah Land Surface Model With Multiparameterization Schemes Model Simulations. Water Resources Research.
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  Why Is the Terrestrial Water Storage in Dryland Regions Declining? A Perspective Based on Gravity Recovery and Climate Experiment Satellite Observations and Noah Land Surface Model With Multiparameterization Schemes Model Simulations
• Roy, T., & Gupta, H. V. (2020). How certain are our uncertainty bounds? Accounting for sample size in Monte Carlo-based uncertainty estimates. Environmental Modeling and Software.
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  Roy T and HV Gupta (2020), How certain are our uncertainty bounds? Accounting for sample size in Monte Carlo-based uncertainty estimates, Environmental Modeling & Software, doi.org/10.1016/j.envsoft.2020.104931.
• Roy, T., Roy, T., Valdes, J., Valdes, J., Serrat-Capdevila, A. -., Serrat-Capdevila, A. -., Durcik, M., Durcik, M., Demaria, E. M., Demaria, E. M., Valdes, R., Valdes, R., Gupta, H. V., & Gupta, H. V. (2020). Detailed Overview of the Multimodel Multiproduct Streamflow Forecasting Platform. Journal of Applied Water Engineering and Research, Online. doi:10.1080/23249676.2020.1799442
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  Roy T, J Valdés, A Serrat-Capdevila, M Durcik, E Demaria, R Valdés-Pineda and H Gupta (2020), Detailed Overview of the Multimodel Multiproduct Streamflow Forecasting Platform, Journal of Applied Water Engineering and Research, doi:10.1080/23249676.2020.1799442
• Roy, T., Valdes-pineda, R., Valdes, J. B., Serrat-capdevila, A., Gupta, H. V., Durcik, M., & Demaria, E. M. (2020). Detailed overview of the multimodel multiproduct streamflow forecasting platform. Journal of Applied Water Engineering and Research, 8(4), 277-289. doi:10.1080/23249676.2020.1799442
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  We present a detailed overview of the Multi-model Multi-product Streamflow Forecasting (MMSF) Platform, which has been developed recently at the University of Arizona under the NASA SERVIR Program,...
• V Gupta, H., Dou, Y., Ye, L., Zhang, H., & Zhou, H. (2020). Improved Flood Forecasting in Ungauged Basins: Constrained Runoff Correction Using Multiple Satellite Products. Water Resources Research. doi:10.1002/essoar.10504273.1
• Wang, Y., Chu, C., You, G. J., Gupta, H. V., & Chiu, P. (2020). Evaluating Uncertainty in Fluvial Geomorphic Response to Dam Removal. Journal of Hydrologic Engineering, 25(6). doi:10.1061/(asce)he.1943-5584.0001917
• You, G. J., Wang, Y. H., Chu, C. C., & Gupta, H. V. (2020). Evaluating uncertainty in Fluvial Geomorphic Responses to Dam Removal. Journal of Hydrologic Engineering.
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  YH Wang, CC Chu, You GJY, HV Gupta (2020), Evaluating uncertainty in Fluvial Geomorphic Responses to Dam Removal, Journal of Environmental Management.
• Zhang, J., Gao, G., Fu, B., & Gupta, H. V. (2020). Identification of climate variables dominating streamflow generation and quantification of streamflow decline in the Loess Plateau. Science of the Total Environment.
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  Zhang J, G Gao, B Fu, and HV Gupta (2020), Identification of climate variables dominating streamflow generation and quantification of streamflow decline in the Loess Plateau, Science of the Total Environment, doi.org/10.1016/j.scitotenv.2020.137935
• Zhang, J., Zhang, J., Gao, G., Gao, G., Fu, B., Fu, B., Gupta, H. V., & Gupta, H. V. (2020). Investigation of the relationship between precipitation extremes and sediment discharge production under extensive land cover change in the Chinese Loess Plateau. Geomorphology Journal.
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  Zhang J, G Gao, B Fu, and HV Gupta (2020), Investigation of the relationship between precipitation extremes and sediment discharge production under extensive land cover change in the Chinese Loess Plateau, Geomorphology, doi.org/10.1016/j.geomorph.2020.107176
• Zhang, J., Zhang, J., Gupta, H. V., Gupta, H. V., Gao, G., Gao, G., Fu, B., Fu, B., Zhang, X., Zhang, X., Li, R., & Li, R. (2020). A Universal Multi-fractal Approach to Assessment of Spatiotemporal Extreme Precipitation over the Loess Plateau of China. Hydrology and Earth System Sciences.
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  Zhang J, HV Gupta, G Gao, B Fu, X Zhang and R Li (2020), A Universal Multifractal Approach to Assessment of Spatiotemporal Extreme Precipitation over the Loess Plateau of China, Hydrology and Earth System Sciences, 10.5194/hess-24-809-2020
• Clutter, M., Clutter, M., Clutter, M., Clutter, M., Ferre, P. A., Ferre, P. A., Ferre, P. A., Ferre, P. A., Zhang, F., Zhang, F., Zhang, F., Zhang, F., Gupta, H. V., Gupta, H. V., Gupta, H. V., & Gupta, H. V. (2019). Robust predictive design of field measurements for evapotranspiration barriers using universal multiple linear regression. Water Resources Research. doi:10.1029/2019WR026194
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  Clutter M, PA Ferre, F Zhang and HV Gupta (2019), Robust predictive design of field measurements for evapotranspiration barriers using universal multiple linear regression, Water Resources Research, https://doi.org/10.1029/2019WR026194.
• Gesualdo, G. G., Oliviera, P. T., Rodrigues, D. B., & Gupta, H. V. (2019). Assessing Water Security In The Sao Paulo Metropolitan Region Under Projected Climate Change. Hydrology and Earth System Sciences (HESS). doi:10.5194/hess-2019-134
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  Gesualdo GC, PTS Oliveira, DBB Rodrigues and HV Gupta (2019), Assessing Water Security In The Sao Paulo Metropolitan Region Under Projected Climate Change, HESS, doi.org/10.5194/hess-2019-134
• Gupta, H. V., Zhang, J., Gao, G., & Fu, B. (2019). Formulating an Elasticity Approach to Quantify the Effects of Climate Variability and Ecological Restoration on Sediment Discharge Change in the Loess Plateau, China. Water Resources Research, 55(11), 9604-9622. doi:10.1029/2019wr025840
• Hazenberg, P., Castro, C. L., Gupta, H., Lahmers, T. M., Gochis, D. J., Yates, D., Dugger, A., & Goodrich, D. (2019). Enhancing the Structure of the WRF-Hydro Hydrologic Model for Semiarid Environments. Journal of Hydrometeorology, 20(4), 691-714. doi:10.1175/jhm-d-18-0064.1
• Huo, X., Gupta, H. V., Niu, G., Gong, W., & Duan, Q. (2019). Parameter Sensitivity Analysis for Computationally-Intensive Spatially-Distributed Dynamical Environmental Systems Models. Water Resources Research. doi:10.1029/2018MS001573
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  Huo X, H Gupta, GY Niu, W Gong and Q Duan (2019), Parameter Sensitivity Analysis for Computationally-Intensive Spatially-Distributed Dynamical Environmental Systems Models, Journal of Advances in Modelling Earth Systems; https://doi.org/10.1029/2018MS001573.
• Jiang, X., Gupta, H. V., Liang, Z., & Li, B. (2019). Towards Improved Probabilistic Predictions for Flood Forecasts Generated using Deterministic Models. Water Resources Research.
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  Jiang X, HV Gupta, Z Liang and B Li (2019), Towards Improved Probabilistic Predictions for Flood Forecasts Generated using Deterministic Models, Water Resources Research, 55, doi.org/10.1029/2019WR025477
• Lahmers, T., Gupta, H. V., Castro, C. L., Gochis, D., Yates, D., Dugger, A., Goodrich, D., & Hazenberg, P. (2019). Enhancing the Structure of the WRF-Hydro Hydrologic Model for Semi-arid Environments. Journal of Hydrometeorology. doi:10.1175/JHM-D-18- 0064.s1
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  Lahmers T, HV Gupta, CL Castro, DJ Gochis, D Yates, A Dugger, D Goodrich and P Hazenberg (2019), Enhancing the Structure of the WRF-Hydro Hydrologic Model for Semi-arid Environments, Journal of Hydrometeorology, https://doi.org/10.1175/JHM-D-18- 0064.s1.
• Loritz, R., Loritz, R., Kleidon, A., Kleidon, A., Jackisch, C., Jackisch, C., Westhoff, M., Westhoff, M., Ehret, U., Ehret, U., Gupta, H. V., Gupta, H. V., Zehe, E., & Zehe, E. (2019). Dissipation per unit length (Dune): A topographic index explaining hydrological similarity by accounting for the joint controls of runoff formation. Hydrology and Earth Systems Sciences. doi:10.5194/hess-23-3807-2019
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  Loritz R, A Kleidon, C Jackisch, M Westhoff, U Ehret, H Gupta and E Zehe (2019), Dissipation per unit length (Dune): A topographic index explaining hydrological similarity by accounting for the joint controls of runoff formation, Hydrology and Earth Systems Sciences, 23, 3807–3821, https://doi.org/10.5194/hess-23-3807-2019
• Mizukami, N., Rakovec, O., Newman, A., Clark, M., Wood, A., Gupta, H. V., & Kumar, R. (2019). On the choice of calibration metrics for “high flow” estimation using hydrologic models. Hydrology and Earth Systems Sciences.
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  Mizukami N, O Rakovec, A Newman, M Clark, A Wood, HV Gupta and R Kumar (2019), On the choice of calibration metrics for “high flow” estimation using hydrologic models, Hydrology and Earth System Sciences
• Mousavi, R. S., Marofi, S., Gupta, H. V., & Amadizadeh, M. (2019). Statistical analysis of discharge fluctuations in a semi-arid basin using effective atmospheric teleconnections: Case study of Dez River basin in Iran. ASCE Journal of Hydrologic Engineering.
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  Mousavi RS, S Marofi, H Gupta and M Amadizadeh (2019), Statistical analysis of discharge fluctuations in a semi-arid basin using effective atmospheric teleconnections: Case study of Dez River basin in Iran, ASCE Journal of Hydrologic Engineering, Vol 24, Issue 7.
• Naeini, M. R., Analui, B., Gupta, H. V., Duan, Q., & Sorooshian, S. (2019). Three Decades of Shuffled Complex Evolution Optimization: Review and Application. Scientia Iranica. doi:10.24200/SCI.2019.21500
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  Naeini MR, B Analui, HV Gupta, Q Duan and S Sorooshian (2019), Three Decades of Shuffled Complex Evolution Optimization: Review and Application, Special Issue of Scientia Iranica Journal Dedicated to Professor Abolhassan Vafai (http://scientiairanica.sharif.edu/), Scientia Iranica A (2019) 26(4), 2015{2031, doi: 10.24200/SCI.2019.21500.
• Niu, G. Y., Yuan, R. Q., Gupta, H. V., & Chang, L. L. (2019). Climatic forcing for recent significant terrestrial drying and wetting. Advances in Water Resources, 133, 103425. doi:10.1016/j.advwatres.2019.103425
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  Abstract Terrestrial water storage (TWS) experienced a substantial change in the past few decades as detected by the Gravity Recovery and Climate Experiment (GRACE). However, the major causes of this change remain unclear, and none of the current state-of-the-art process-based hydrological models are able to reproduce the significantly drying/wetting trends in GRACE TWS. Here we investigate 12 terrestrial regions that show a significantly drying/wetting trend, using partial least-square regression (PLSR) to relate TWS anomalies to various climatic variables and leaf area index (LAI). Through PLSR modeling, we find changes in LAI, downward longwave radiation (DLW) and precipitation are most strongly associated with a wetting trend. Increases in precipitation appear to be responsible for the wetting trend in tropical/subtropical monsoon regions, while decreases in vegetation transpiration and atmospheric demand appear to be responsible for the wetting trend in extratropical regions. Enhanced atmospheric demand caused by increases in air temperature and the resulting enhanced DLW dominates the significant drying trend in the mid-latitude subtropical drylands, and in the cold and alpine regions. The PLSR modeling also suggests that, over global continents, the climatic forcing factors show a dominant impact on TWS over all the wetting regions, while only four out of the seven drying regions show climate-dominated drying, implying an additional impact of anthropogenic responses to the water stress on drying.
• Razavi, S., & Gupta, H. V. (2019). A Multi-Method Generalized Global Sensitivity Matrix Approach to Accounting for the Dynamical Nature of Earth and Environmental Systems Models. Environmental Modeling & Software. doi:10.1016/j.envsoft.2018.12.002
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  Razavi S and HV Gupta (2019), A Multi-Method Generalized Global Sensitivity Matrix Approach to Accounting for the Dynamical Nature of Earth and Environmental Systems Models, Environmental Modelling & Software, Volume 114, Pages 1-11, https://doi.org/10.1016/j.envsoft.2018.12.002.
• Sheikholeslami, R., Razavi, S., Haghnegahdar, A., & Gupta, H. V. (2019). VARS-TOOL: A toolbox for comprehensive, efficient, and robust sensitivity and uncertainty analysis. Environmental Modelling and Software, 112, 95-107. doi:10.1016/j.envsoft.2018.10.005
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  Abstract VARS-TOOL is a software toolbox for sensitivity and uncertainty analysis. Developed primarily around the “Variogram Analysis of Response Surfaces” framework, VARS-TOOL adopts a multi-method approach that enables simultaneous generation of a range of sensitivity indices, including ones based on derivative, variance, and variogram concepts, from a single sample. Other special features of VARS-TOOL include (1) novel tools for time-varying and time-aggregate sensitivity analysis of dynamical systems models, (2) highly efficient sampling techniques, such as Progressive Latin Hypercube Sampling (PLHS), that maximize robustness and rapid convergence to stable sensitivity estimates, (3) factor grouping for dealing with high-dimensional problems, (4) visualization for monitoring stability and convergence, (5) model emulation for handling model crashes, and (6) an interface that allows working with any model in any programming language and operating system. As a test bed for training and research, VARS-TOOL provides a set of mathematical test functions and the (dynamical) HBV-SASK hydrologic model.
• Si, W., Gupta, H. V., Jiang, P., Bao, W., & Ni, P. (2019). Improved Dynamic System Response Curve Method for Real-Time Flood Forecast Updating. Water Resources Research. doi:10.1029/2019WR025520
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  Si W, HV Gupta, P Jiang, W Bao, and P Ni (2019), Improved Dynamic System Response Curve Method for Real-Time Flood Forecast Updating, Water Resources Research, https://doi.org/10.1029/2019WR025520.
• Yuan, R., Chang, L. L., Gupta, H., & Niu, G. (2019). Climate Forcing for Recent Significant Terrestrial Water Storage Changes. Advances in Water Resources. doi:10.1016/j.advwatres.2019.103425
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  Yuan R, LL Chang LL, HV Gupta and GY Niu (2019), Climate Forcing for Recent Significant Terrestrial Water Storage Changes, Advances in Water Resources, Volume 133, 103425, doi.org/10.1016/j.advwatres.2019.103425
• Zhang, J., Gao, G., Fu, B., & Gupta, H. V. (2019). Formulating an Elasticity Approach to Quantifying the Effects of Climate Variability and Catchment Management on Sediment Discharge in the Loess Plateau, China. Water Resources Research. doi:10.1029/2019WR025840
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  Zhang J, G Gao, B Fu and HV Gupta (2019), Formulating an Elasticity Approach to Quantifying the Effects of Climate Variability and Catchment Management on Sediment Discharge in the Loess Plateau, China, 55, Water Resources Research, doi.org/10.1029/2019WR025840
• Durcik, M., Valdes-Pineda, R., Gupta, H. V., Serrat-Capdevila, A., Demaria, E. M., Lyon, B., Valdes, J. B., & Roy, T. (2018). Assessing hydrological impacts of short-term climate change in the Mara River basin of East Africa. Journal of Hydrology, 566, 818-829. doi:10.1016/J.JHYDROL.2018.08.051
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  We assess the impacts of a range of short-term climate change scenarios (2020–2050) on the hydrology of the Mara River Basin in East Africa using a new high-resolution (0.25°) daily climate dataset. The scenarios combine natural climate variability, as captured by a vector autoregressive (VAR) model, with a range of climate trends calculated from 31 models in the Coupled Model Intercomparison Project Phase 5 (CMIP5). The methodology translates these climate scenarios into plausible daily sequences of climate variables utilizing the Agricultural Modern-Era Retrospective Analysis for Research and Applications (AgMERRA) dataset. The new dataset (VARAG) has several advantages over traditional general circulation model outputs, such as, the statistical representation of short-term natural climate variability, availability at a daily time scale and high spatial resolution, not requiring additional downscaling, and the use of the AgMERRA data which is bias-corrected extensively. To assess the associated impacts on basin hydrology, the semi-distributed Variable Infiltration Capacity (VIC) land-surface model is forced with the climate scenarios, after being calibrated for the study area using the fine-resolution (0.05°) merged satellite and in-situ observation-based dataset, Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). The climate data are further bias-corrected by applying a non-parametric quantile mapping scheme, where the cumulative distribution functions are approximated using kernel densities. Three different wetness scenarios (dry, average, and wet) are analyzed to see the potential short-term changes in the basin. We find that the precipitation bias correction is more in effect in the mountainous sub-basins, one of which also shows the maximum difference between the wet and dry scenario streamflows. Precipitation, evapotranspiration, and soil moisture show increasing trends mostly during the primary rainy season, while no trend is found in the corresponding streamflows. The annual values of these variables also do not change much in the coming three decades. The methodology implemented in this study provides a reliable range of possibilities which can greatly benefit risk analysis and infrastructure designing, and shows potential to be applied to other basins.
• Gupta, H. V., & Razavi, S. (2018). Revisiting the Basis of Sensitivity Analysis for Dynamical Environmental Systems Models. Water Resources Research. doi:doi.org/10.1029/2019EO116217]
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  Gupta HV and S Razavi (2018), Revisiting the Basis of Sensitivity Analysis for Dynamical Environmental Systems Models, Water Resources Research, 54. https://doi.org/10.1029/2018WR022668 [Selected as a Research Spotlight article by Eos.org, 2/21/19; https://doi.org/10.1029/2019EO116217]
• Gupta, H. V., Sapriza, G., Jodar-Bermudez, J., & Carrera-Ramirez, J. (2018). Circulation Pattern-Based Assessment of Projected Climate Change for a Catchment in Spain. Journal of Hydrology. doi:https://doi.org/10.1016/j.jhydrol.2016.06.032
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  Gupta HV, G Sapriza, J Jodar-Bermudez, J Carrera-Ramirez (2018), Circulation Pattern-Based Assessment of Projected Climate Change for a Catchment in Spain, Journal of Hydrology, https://doi.org/10.1016/j.jhydrol.2016.06.032
• Gupta, H., Pechlivanidis, I., & Bosshard, T. (2018). An Information Theory Approach to Identifying a Representative Subset of Hydro‐Climatic Simulations for Impact Modeling Studies. Water Resources Research, 54(8), 5422-5435. doi:10.1029/2017wr022035
• Loritz, R., Gupta, H. V., Jackisch, C., Westhoff, M., Kleidon, A., Ehret, U., & Zehe, E. (2018). On the dynamic nature of Hydrological similarity. Hydrology and Earth Systems Sciences.
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  Loritz R, H Gupta, C Jackisch, M Westhoff, A Kleidon, U Ehret and E Zehe (in review), On the dynamic nature of Hydrological similarity, Hydrology and Earth Systems Sciences.
• Nearing, G. S., & Gupta, H. V. (2018). Information vs. Uncertainty as a Foundation for Science. ARVIX.
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  Nearing GS and HV Gupta (2018), Information vs. Uncertainty as a Foundation for Science, ARVIX 1704.07512.
• Nearing, G., & Gupta, H. (2018). Information vs. Uncertainty as the Foundation for a Science of Environmental Modeling. ArXiv.
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  Information accounting provides a better foundation for hypothesis testing than does uncertainty quantification. A quantitative account of science is derived under this perspective that alleviates the need for epistemic bridge principles, solves the problem of ad hoc falsification criteria, and deals with verisimilitude by facilitating a general approach to process-level diagnostics. Our argument is that the well-known inconsistencies of both Bayesian and classical statistical hypothesis tests are due to the fact that probability theory is an insufficient logic of science. Information theory, as an extension of probability theory, is required to provide a complete logic on which to base quantitative theories of empirical learning. The organizing question in this case becomes not whether our theories or models are more or less true, or about how much uncertainty is associated with a particular model, but instead whether there is any information available from experimental data that might allow us to improve the model. This becomes a formal hypothesis test, provides a theory of model diagnostics, and suggests a new approach to building dynamical systems models. [Journal_ref: ]
• Nearing, G., & Gupta, H. V. (2018). Ensembles vs. Information Theory: Supporting Science under Uncertainty. Frontiers of Earth Science.
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  Nearing GS and HV Gupta (in review), Ensembles vs. Information Theory: Supporting Science under Uncertainty, submitted to Special issue on "Uncertainty in Water Resources" of the Frontiers of Earth Science (FESCI) journal.
• Pechlivanidis, I., Gupta, H. V., & Bosshard, T. (2018). How to identify a representative subset of hydro-climatic simulations for impact modeling studies. Water Resources Research.
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  Pechlivanidis IG, H Gupta and T. Bosshard (in review), How to identify a representative subset of hydro-climatic simulations for impact modeling studies, submitted to Water Resources Research.
• Razavi, S., Gupta, H. V., Sheikholeslami, R., & Haghnegahdar, A. (2018). VARS-TOOL: A Toolbox for Comprehensive, Efficient, and Robust Global Sensitivity Analysis. Water Resources Research.
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  Razavi S, R Sheikholeslami, H Gupta, and A Haghnegahdar (2018), VARS-TOOL: A Toolbox for Comprehensive, Efficient, and Robust Global Sensitivity Analysis, Environmental Modelling and Software, https://doi.org/10.1016/j.envsoft.2018.10.005
• Sadegh, M., Moftakhari, H., AghaKouchak, A., Gupta, H. V., Ragno, E., Mazdiyasni, O., Sanders, B., & Matthew, R. (2018). Multi-Hazard scenarios for analysis of compound extreme events. Geophysical Research Letters.
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  Sadegh M, H Moftakhari, A AghaKouchak, HV Gupta, E Ragno, O Mazdiyasni, B Sanders & R Matthew (in review), Multi-Hazard scenarios for analysis of compound extreme events, Geophysical Research Letters.
• Sanders, B. F., Sadegh, M., Ragno, E., Moftakhari, H., Mazdiyasni, O., Matthew, R. A., Gupta, H. V., & Aghakouchak, A. (2018). Multihazard Scenarios for Analysis of Compound Extreme Events. Geophysical Research Letters, 45(11), 5470-5480. doi:10.1029/2018gl077317
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  California Energy Commission [500-15-005]; National Science Foundation Hazards-SEES Program [DMS 1331611]; National Oceanic and Atmospheric Administration Ecological Effects of Sea Level Rise Program [NA16NOS4780206]
• Sheikholeslami, R., Razavi, S., Gupta, H. V., Becker, W., & Haghnegahdar, A. (2018). Global Sensitivity Analysis for High-Dimensional Problems: How to Objectively Group Factors and Measure Robustness and Convergence while Reducing Computational Cost. Environmental Modeling & Software. doi:10.1016/j.envsoft.2018.09.002
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  Sheikholeslami R, S Razavi, H Gupta, W Becker and A Haghnegahdar (2018), Global Sensitivity Analysis for High-Dimensional Problems: How to Objectively Group Factors and Measure Robustness and Convergence while Reducing Computational Cost, Environmental Modeling & Software, https://doi.org/10.1016/j.envsoft.2018.09.002.
• Zheng, F., Maier, H. R., Wu, W., Dandy, G. C., Gupta, H. V., & Zhang, T. (2018). On Lack Of Robustness In Hydrological Model Development Due To Absence Of Guidelines For Selecting Calibration & Evaluation Data. Water Resources Research. doi:DOI: 10.1002/2017WR021470
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  Zheng F, HR Maier, W Wu, GC Dandy, HV Gupta and Tuqiao Zhang (in press, accepted Jan 17, 2018), On Lack Of Robustness In Hydrological Model Development Due To Absence Of Guidelines For Selecting Calibration & Evaluation Data, Water Resources Research, DOI: 10.1002/2017WR021470
• Zhong, P., Yang, S., Wan, X., Hua, L., & Gupta, H. V. (2018). Evaluating the Impacts of a Large-Scale Multi-Reservoir System on Flooding: Case of the Huai River in China. Water Resources Management, 32(3), 1013-1033. doi:10.1007/s11269-017-1852-x
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  The extensive constructions of reservoirs change the hydrologic characteristics of the associated watersheds, which increases the complexity of watershed flood control decisions. By evaluating the impacts of the multi-reservoir system on the flood hydrograph, it becomes possible to improve the effectiveness of the flood control decisions. This study compares the non-reservoir flood hydrograph with the actual observed flood hydrograph using the Lutaizi upstream of Huai River in East China as a representative case, where 20 large-scale/large-sized reservoirs have been built. Based on the total impact of the multi-reservoir system, a novel strategy is presented to evaluate the contribution of each reservoir to the total impact. According their contributions, the “highly effective” reservoirs for watershed flood control are identified via hierarchical clustering. Moreover, the degree of impact of the reservoir operation rules on the flood hydrograph are estimated. We find the multi-reservoir system of Huai River has a significant impact on flooding at the Lutaizi section, on average reducing the flood volume and peak discharge by 13.92 × 108 m3 and 18.7% respectively. Under the current operation rules, the volume and peak discharge of flooding at the Lutaizi section are reduced by 13.69 × 108 m3 and 1429 m3/s respectively. Each reservoir has a different impact on the flood hydrograph at the Lutaizi section. In particular, the Meishan, Xianghongdian, Suyahu, Nanwan, Nianyushan and Foziling reservoirs exert a strong influence on the flood hydrograph, and are therefore important for flood control on the Huai River.
• Bashir, F., Zeng, X., Gupta, H. V., & Hazenberg, P. (2017). A Hydro-meteorological Perspective on the Karakoram Anomaly using Unique Valley-based Synoptic Weather Observations. Geophysical Research Letters.
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  Bashir F, X Zeng, H Gupta and P Hazenberg (2017), A Hydro-meteorological Perspective on the Karakoram Anomaly using Unique Valley-based Synoptic Weather Observations, Geophysical Research Letters, Article ID: GRL56536, DOI: 10.1002/2017GL075284
• Dominguez, F., Gupta, H., Hu, H., Yang, Z., Yang, B., & Zeng, X. (2017). Impact of Irrigation over the California Central Valley on Regional Climate. Journal of Hydrometeorology, 18(5), 1341-1357. doi:10.1175/jhm-d-16-0158.1
• Durcik, M., Gupta, H., Roy, T., Serrat-Capdevila, A., & Valdes, J. (2017). Design and implementation of an operational multimodel multiproduct real-time probabilistic streamflow forecasting platform. Journal of Hydroinformatics, 19(6), 911-919. doi:10.2166/hydro.2017.111
• Fard, A. F., Goodrich, D. C., Gupta, H. V., Niazi, F., & Nourani, V. (2017). Hydrological model parameterization using NDVI values to account for the effects of land cover change on the rainfall–runoff response. Hydrology Research, 48(6), 1455-1473. doi:10.2166/nh.2017.249
• Gupta, H. V. (2016). Using Satellite-Based Evapotranspiration Estimates To Improve The Structure of A Simple Conceptual Rainfall-Runoff Model. Hydrology and Earth System Sciences.
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  Roy T, HV Gupta, A Serrat-Capdevilla and J Valdes (In review), Using Satellite-Based Evapotranspiration Estimates To Improve The Structure of A Simple Conceptual Rainfall-Runoff Model, Hydrology and Earth System Sciences
• Hazenberg, P., Gupta, H., Zeng, X., & Bashir, F. (2017). A Hydrometeorological Perspective on the Karakoram Anomaly Using Unique Valley‐Based Synoptic Weather Observations. Geophysical Research Letters, 44(20). doi:10.1002/2017gl075284
• Nourani, V., Fard, A. F., Gupta, H. V., Goodrich, D., & Niyazi, F. (2017). Hydrological model parameterization using NDVI values to account for the effects of land-cover change on the rainfall-runoff response. Hydrology Research.
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  Nourani V, AF Fard, HV Gupta, D Goodrich and F Niazi, (accepted Jan 2017), Hydrological model parameterization using NDVI values to account for the effects of land-cover change on the rainfall-runoff response, Hydrology Research
• Nourani, V., Fard, A. F., Gupta, H. V., Goodrich, D., & Niyazi, F. (2017). Hydrological model parameterization using NDVI values to account for the effects of land-cover change on the rainfall-runoff response. Hydrology Research. doi:DOI: 10.2166/nh.2017.249
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  Nourani V, AF Fard, HV Gupta, D Goodrich and F Niazi, (2017), Hydrological model parameterization using NDVI values to account for the effects of land-cover change on the rainfall-runoff response, Hydrology Research, Available Online 13 March 2017, nh2017249; DOI: 10.2166/nh.2017.249
• Roy, T., Aleix, S., Valdes, J. B., Matej, D., & Gupta, H. V. (2016). Design and implementation of an operational multi-model multi-product real-time probabilistic streamflow forecasting platform. Environmental Modelling and Software.
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  Roy T, A Serrat-Capdevila, J Valdes, M Durcik and H Gupta (in review), Design and implementation of an operational multi-model multi-product real-time probabilistic streamflow forecasting platform, Environmental Modelling and Software
• Roy, T., Aleix, S., Valdes, J. B., Matej, D., & Gupta, H. V. (2017). Design and implementation of an operational multi-model multi-product real-time probabilistic streamflow forecasting platform. Journal of Hydroinformatics. doi:doi: 10.2166/hydro.2017.111
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  Roy T, A Serrat-Capdevila, J Valdes, M Durcik and H Gupta (2017), Design and implementation of an operational multi-model multi-product real-time probabilistic streamflow forecasting platform, Journal of Hydroinformatics, doi: 10.2166/hydro.2017.111
• Roy, T., Gupta, H. V., Serrat-Capdevila, A., & Valdes, J. B. (2017). Using Satellite-based Evapotranspiration to Improve the Structure of a Simple Conceptual Rainfall-Runoff Model. Hydrology and Earth System Sciences (HESS). doi:10.5194/hess-21-879-2017
• Roy, T., Serrat-Capdevila, A. -., Valdes, J. B., Durcik, M., & Gupta, H. V. (2017). Design and implementation of an operational multi-model multi-product real-time probabilistic streamflow forecasting platform. Journal of Hydroinformatics, 19(6), 911-919. doi:10.2166/hydro.2017.111
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  The task of real-time streamflow monitoring and forecasting is particularly challenging for ungauged or sparsely gauged river basins, and largely relies upon satellite-based estimates of precipitation. We present the design and implementation of a state-of-the-art real-time streamflow monitoring and forecasting platform that integrates information provided by cutting-edge satellite precipitation products (SPPs), numerical precipitation forecasts, and multiple hydrologic models, to generate probabilistic streamflow forecasts that have an effective lead time of 9 days. The modular design of the platform enables adding/removing any model/product as may be appropriate. The SPPs are bias-corrected in real-time, and the model-generated streamflow forecasts are further bias-corrected and merged, to produce probabilistic forecasts that are computed via several model averaging techniques. The platform is currently operational in multiple river basins in Africa, and can also be adapted to any new basin by incorporating some basin-specific changes and recalibration of the hydrologic models.
• Roy, T., Serrat-Capdevila, A., Gupta, H. V., & Valdes, J. B. (2017). A Platform for Probabilistic Multi-model and Multi-product Streamflow Forecasting, Water Resources Research. Water Resources Research. doi:doi:10.1002/ 2016WR019752
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  Roy T, A Serrat-Capdevila, H Gupta and J Valdes (2017), A Platform for Probabilistic Multi-model and Multi-product Streamflow Forecasting, Water Resources Research, 53, doi:10.1002/ 2016WR019752.
• Roy, T., Serrat-Capdevilla, A., Gupta, H. V., & Valdes, J. (2017). A Platform for Probabilistic Multi-model and Multi-product Streamflow Forecasting. Water Resources Research.
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  Roy T, A Serrat-Capdevila, H Gupta and J Valdes (2017), A Platform for Probabilistic Multi-model and Multi-product Streamflow Forecasting, Water Resources Research
• Valdes, J., Gupta, H., Serrat-Capdevila, A., & Roy, T. (2017). A platform for probabilistic Multimodel and Multiproduct Streamflow Forecasting: MULTIMODEL AND MULTIPRODUCT STREAMFLOW FORECASTING. Water Resources Research, 53(1), 376-399. doi:10.1002/2016wr019752
• Wan, X., Mei, J., Yang, S., Gupta, H. V., & Zhong, P. (2017). Evaluating the Impacts of a Large-Scale Multi-Reservoir System on Flooding: Case of the Huai River in China. Journal of Water Resources Management. doi:32:1013-1033, DOI 10.1007/s11269-017-1852-x
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  Wan X, J Mei, S Yang, HV Gupta and P Zhong (2018), Evaluating the Impacts of a Large-Scale Multi-Reservoir System on Flooding: Case of the Huai River in China Journal of Water Resources Management, 32:1013-1033, DOI 10.1007/s11269-017-1852-x
• Yang, Z., Dominguez, F., Zeng, X., Hu, H., Gupta, H. V., & Yang, B. (2016). Impact Of Irrigation Over The California Central Valley On Regional Climate. Journal of Hydrometeorology.
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  Yang Z, F Dominguez, X Zeng, H Hu, H Gupta and B Yang (In review), Impact Of Irrigation Over The California Central Valley On Regional Climate, Journal of Hydrometeorology
• Yang, Z., Dominguez, F., Zeng, X., Hu, H., Gupta, H. V., & Yang, B. (2017). Impact Of Irrigation Over The California Central Valley On Regional Climate. Journal of Hydrometeorology.
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  Yang Z, F Dominguez, X Zeng, H Hu, H Gupta and B Yang (In review), Impact Of Irrigation Over The California Central Valley On Regional Climate, Journal of Hydrometeorology
• Gupta, H. V., Sapriza, G., Jodar Bermudez, J., & Carrera Ramirez, J. (2016). Circulation Pattern-Based Assessment of Projected Climate Change for a Catchment in Spain. Journal of Hydrology.
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  Gupta HV, G Sapriza, J Jodar-Bermudez, J Carrera-Ramirez (in press, accepted June 2016), Circulation Pattern-Based Assessment of Projected Climate Change for a Catchment in Spain, Journal of Hydrology
• Gupta, H. V., Ye, L., Zhou, J., Zhang, H., Zeng, X., & Chen, L. (2016). Efficient estimation of flood forecast prediction intervals via single‐ and multi‐objective versions of the LUBE method. Hydrological Processes, 30(15), 2703-2716. doi:10.1002/hyp.10799
• Guse, B., Pfannerstill, M., Gafurov, A., Fohrer, N., & Gupta, H. V. (2016). Demasking the integrated information represented by discharge - Advancing sensitivity analyses to consider different hydrological components and their rates of change. Water Resources Research.
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  Guse B, M Pfannerstill, A Gafurov, N Fohrer and H Gupta (2016), Demasking the integrated information represented by discharge - Advancing sensitivity analyses to consider different hydrological components and their rates of change, Water Resources Research, doi:10.1002/2016WR018894
• Guse, B., Pfannerstill, M., Strauch, M., Reusser, D., Lüdtke, S., Volk, M., Gupta, H. V., & Fohrer, N. (2016). On characterizing the temporal dominance patterns of model parameters and processes. Hydrological Processes. doi:DOI: 10.1002/hyp.10764
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  Guse B, M Pfannerstill, M Strauch, DReusser, S Lüdtke, MVolk, H Gupta, and N Fohrer (2016), On characterizing the temporal dominance patterns of model parameters and processes, Hydrological Processes, 30, pp 2255-2270, DOI: 10.1002/hyp.10764
• Lei, Y., Zhou, J., Gupta, H. V., Zhang, H., & Liu, Y. (2016). Efficient Estimation of Flood Forecast Prediction Intervals: A Multiobjective Lower-Upper Bounds Method. Journal of Hydrologic Engineering.
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  Lei Y, J Zhou, H Gupta, H Zhang, Y Liu (2016), Efficient Estimation of Flood Forecast Prediction Intervals: A Multiobjective Lower-Upper Bounds Method, Hydrological Processes, 30(15), 10.1002/hyp.10799
• Lei, Y., Zhou, J., Gupta, H. V., Zhang, H., Zeng, X., & Chen, L. (2016). Efficient Estimation of Flood Forecast Prediction Intervals via Single- and Multi-objective Versions of the LUBE Method. Hydrological Processes.
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  Lei Y, J Zhou, H Gupta, H Zhang, X Zeng, L Chen (2016), Efficient Estimation of Flood Forecast Prediction Intervals via Single- and Multi-objective Versions of the LUBE Method, Hydrological Processes, 30(15), pp. 2703-2716, DOI: 10.1002/hyp.10799
• Moreno, H. A., White, D. D., Gupta, H. V., Sampson, D. A., & Vivoni, E. R. (2016). Modeling the Distributed Effects of Forest Thinning on the Long-Term Water Balance and Stream Flow Extremes for a Semi-Arid Basin in the Southwestern U.S.. Hydrology and Earth System Sciences.
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  Moreno HA, DD White, HV Gupta, DA Sampson AND ER Vivoni (2016), Modeling the Distributed Effects of Forest Thinning on the Long-Term Water Balance and Stream Flow Extremes for a Semi-Arid Basin in the Southwestern U.S., Hydrology and Earth Systems Science, 20, 1–27, doi:10.5194/hess-20-1-2016
• Nearing, G. S., Tian, Y., Gupta, H. V., Harrison, K. W., Clark, M., & Weijs, S. (2016). A philosophical basis for hydrological uncertainty. Hydrologic Sciences Journal.
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  Invited PaperNearing G, Y Tian, H Gupta, K Harrison, M Clark & S Weijs (2016), A philosophical basis for hydrological uncertainty, Hydrologic Sciences Journal, 61(9), pp. 1666-1678, DOI: 10.1080/02626667.2016.1183009
• Pechlivanidis, I. G., Mcmillan, H., Jackson, B., & Gupta, H. V. (2016). Robust informational entropy-based descriptors of flow in catchment hydrology. Hydrological Sciences Journal-journal Des Sciences Hydrologiques, 61(1), 1-18. doi:10.1080/02626667.2014.983516
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  AbstractThis paper explores the use of entropy-based measures in catchment hydrology, and provides an importance-weighted numerical descriptor of the flow–duration curve. Although entropy theory is being applied in a wide spectrum of areas (including environmental and water resources), artefacts arising from the discrete, under-sampled and uncertain nature of hydrological data are rarely acknowledged, and have not been adequately explored. Here, we examine challenges to extracting hydrologically meaningful entropy measures from a flow signal; the effect of binning resolution on calculation of entropy is investigated, along with artefacts caused by (1) emphasis of information theoretic measures towards flow ranges having more data (statistically dominant information), and (2) effects of discharge measurement truncation errors. We introduce an importance-weighted entropy-based measure to counter the tendency of common binning approaches to over-emphasise information contained in the low flows which dominate...
• Razavi, S., & Gupta, H. V. (2016). A New Framework for Comprehensive, Robust, and Efficient Global Sensitivity Analysis: Part I - Theory. Water Resources Research. doi:doi:10.1002/ 2015WR017558
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  Razavi S and HV Gupta (2016), A New Framework for Comprehensive, Robust, and Efficient Global Sensitivity Analysis: Part I - Theory, Water Resources Research, 52, 423–439, doi:10.1002/ 2015WR017558
• Razavi, S., & Gupta, H. V. (2016). A New Framework for Comprehensive, Robust, and Efficient Global Sensitivity Analysis: Part II - Applications. Water Resources Research. doi:doi:10.1002/ 2015WR017559
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  Razavi S and HV Gupta (2016), A New Framework for Comprehensive, Robust, and Efficient Global Sensitivity Analysis: Part II - Applications, Water Resources Research, 52, 440–455, doi:10.1002/ 2015WR017559
• Sadegh, M., Vrugt, J. A., & Gupta, H. V. (2016). The soil water characteristic as new class of closed-form parametric expressions for the flow duration curve. Journal of Hydrology.
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  Nearing G, Y Tian, H Gupta, K Harrison, M Clark & S Weijs (2016), A philosophical basis for hydrological uncertainty, Hydrologic Sciences Journal, 61(9), pp. 1666-1678, DOI: 10.1080/02626667.2016.1183009
• Yang, Z., Dominguez, F., Gupta, H. V., Zeng, X., & Norman, L. (2016). Urban Effects on Regional climate: A Case Study in the Phoenix and Tucson ‘Sun’ Corridor. Earth Interactions, 21. doi:doi:10.1175/EI-D-15-0027.1
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  Yang, Z., F. Dominguez, H. Gupta, X. Zeng, and L. Norman, 2016: Urban Effects on Regional Climate: A Case Study in the Phoenix and Tucson 'Sun' Corridor. Earth Interactions, 21, doi: 10.1175/EI-D-15-0027.1
• Yang, Z., Dominguez, F., Gupta, H. V., Zeng, X., & Norman, L. (2016). Urban Effects on Regional climate: A Case Study in the Phoenix and Tucson ‘Sun’ Corridor. Earth Interactions. doi:doi:10.1175/EI-D-15-0027.1
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  Yang Z, F Dominguez, HV Gupta, X Zeng, L Norman, (2016), Urban Effects on Regional climate: A Case Study in the Phoenix and Tucson ‘Sun’ Corridor, Special Issue on “Biogeophysical Climatic Impacts of LULCC” of Earth Interactions, doi:10.1175/EI-D-15-0027.1
• Zhang, X., Dong, Z., Gupta, H. V., Wu, G., & Li, D. (2016). Impact of the Three Gorges Dam on the hydrology and ecology of the Yangtze River. Water. doi:doi:10.3390/w8120590
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  Zhang X, Z Dong, HV Gupta, G Wu and D Li (2016), Impact of the Three Gorges Dam on the hydrology and ecology of the Yangtze River, Water, 8, 590; doi:10.3390/w8120590
• Gupta, H. V., Rodrigues, D. B., Mendiondo, E. M., & Oliveira, P. T. (2015). Assessing uncertainties in surface water security: An empirical multimodel approach. Water Resources Research, 51(11), 9013-9028. doi:10.1002/2014wr016691
• Gupta, H. V., Sapriza-Azuri, G., Jódar, J., Navarro, V., Slooten, L. J., & Carrera, J. (2015). Impacts of rainfall spatial variability on hydrogeological response. Water Resources Research, 51(2), 1300-1314. doi:10.1002/2014wr016168
• He, Z., Tian, F., Gupta, H. V., Hu, H. C., & Hu, H. P. (2015). Diagnostic calibration of a hydrological model in an alpine area by hydrograph partitioning. Hydrology and Earth System Sciences. doi:doi: 10.5194/hess-19-1807-2015
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  [144] He Z, F Tian, HV Gupta, HC Hu, HP Hu (2015), Diagnostic calibration of a hydrological model in an alpine area by hydrograph partitioning, Hydrology and Earth Systems Science, 19, 1807-1826, www.hydrol-earth-syst-sci.net/19/1807/2015/, doi: 10.5194/hess-19-1807-2015
• Lahmers, T. M., Gupta, H. V., Gochis, D., Elsaadani, M., & Castro, C. L. (2015). Optimization of precipitation and streamflow forecasts in the southwest Contiguous US for warm season convection. BAMS.
• Mathevet, T., Perrin, C., Moine, N. L., Gupta, H. V., & Andreassian, V. (2015). A ‘ Large Catchment Sample’ Investigation of the Performance, Reliability and Robustness of Two Conceptual Rainfall-Runoff Models.. HESS.
• Mendoza, P. A., Clark, M. P., Barlage, M., Rajagopalan, B., Samaniego, L., Abramowitz, G., & Gupta, H. V. (2015). Are we unnecessarily constraining the agility of complex process-based models?. Water Resources Research.
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  [141] Mendoza PA, MP Clark, M Barlage, B Rajagopalan, L Samaniego, G Abramowitz and H Gupta (2015), Are we unnecessarily constraining the agility of complex process-based models? Water Resources Research
• Nearing, G. S., & Gupta, H. V. (2015). The Quantity and Quality of Information in Hydrologic Models. Water Resources Research. doi:doi:10.1002/2014WR015895
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  Nearing GS and HV Gupta (2015), The Quantity and Quality of Information in Hydrologic Models, Water Resources Research, 51, 524–538, doi:10.1002/2014WR015895
• Nourani, V., Fard, A. F., Niazi, F., Gupta, H. V., Goodrich, D. C., & Kamran, K. V. (2015). Implication of remotely sensed data to incorporate land cover effect into a linear-reservoir-based rainfall-runoff model. Journal of Hydrology. doi:http://dx.doi.org/10.1016/j.jhydrol.2015.07.020
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  Nourani V, AF Fard, F Niazi, HV Gupta, DC Goodrich, KV Kamran (2015), Implication of remotely sensed data to incorporate land cover effect into a linear-reservoir-based rainfall-runoff model, Journal of Hydrology, 529, 94–105, http://dx.doi.org/10.1016/j.jhydrol.2015.07.020
• Oliveira, P. T., Maddock, T., Serrat-capdevila, A., Rodrigues, D. B., Oliveira, P. S., Mendiondo, E. M., Mahmoud, M., Maddock, T., & Gupta, H. V. (2015). Contrasting American and Brazilian systems for water allocation and transfers. Journal of Water Resources Planning and Management, 141(7), 04014087. doi:10.1061/(asce)wr.1943-5452.0000483
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  AbstractThe United States and Brazil both deal with water-related problems associated with being large territorial areas having uneven distribution of water resources and population. Water transfer projects have been widely considered to be feasible solutions to the mitigation of local water shortages. This paper contrasts American and Brazilian water allocation systems and water transfer projects, located in the Colorado and Piracicaba River basins, seeking potential exchanges between these two water management systems and analyzing their adaptability to trends in water demand and climate. This evaluation indicates that the American system could potentially benefit from some of the principles present in Brazilian framework, including (1) participatory approach involving government, users, and citizens; (2) recognition of the economic value of water; and (3) prioritization of drinking water supply during shortage times. In turn, the Brazilian system could benefit from certain characteristics of American w...
• Pechlivanidis, I. G., Pechlivanidis, I. G., Jackson, B., Jackson, B., McMillan, H., McMillan, H., Gupta, H. V., & Gupta, H. V. (2015). Robust informational entropy-based descriptors of flow in catchment hydrology. Hydrologic Sciences Journal.
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  Pechlivanidis IG, B Jackson, H McMillan and HV Gupta (2015), Robust informational entropy-based descriptors of flow in catchment hydrology, Hydrologic Sciences Journal, doi: 10.1080/02626667.2014.983516
• Razavi, S., & Gupta, H. V. (2015). What Do We Mean by Sensitivity Analysis? The Need for a Comprehensive Characterisation of ‘Global’ Sensitivity in Earth and Environmental Systems Models. Water Resources Research. doi:doi: 10.1002/2014WR016527
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  Razavi S and HV Gupta (2015), What Do We Mean by Sensitivity Analysis? The Need for a Comprehensive Characterisation of ‘Global’ Sensitivity in Earth and Environmental Systems Models, Water Resources Research, doi: 10.1002/2014WR016527
• Razavi, S., Gupta, H. V., & Gupta, H. V. (2015). Variogram Analysis of Response surfaces (VARS): A New Framework for Global Sensitivity Analysis of Earth and Environmental Systems Models. Water Resources Research.
• Rodrigues, D. B., Gupta, H. V., & Mendiondo, E. M. (2015). Assessing uncertainties in surface water security: An empirical multi-model resampling approach. Water Resources Research.
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  [7] Rodrigues DBB, HV Gupta, EM Mendiondo and PTS Oliveira (2015), Assessing uncertainties in surface water security: An empirical multi-model resampling approach, Water Resources Research, 51, 9013–9028,doi:10.1002/2014WR016691
• Sapriza, G., Jodar-Bermudez, J., Carrera-Ramirez, J., & Gupta, H. V. (2015). Toward a Comprehensive Assessment of the Combined Impacts of Climate Change and Groundwater Pumping on Catchment Dynamics. Journal of Hydrology. doi:http://dx.doi.org/10.1016/j.jhydrol.2015.08.015
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  Sapriza G, J Jodar-Bermudez, J Carrera-Ramirez and HV Gupta (2015), Toward a Comprehensive Assessment of the Combined Impacts of Climate Change and Groundwater Pumping on Catchment Dynamics, Journal of Hydrology, 529, 1701–1712, http://dx.doi.org/10.1016/j.jhydrol.2015.08.015
• Sapriza-Azuri, G., Jodar-Bermudez, J., Navarro, V., Slooten, L. J., Carrera-Ramirez, J., & Gupta, H. V. (2015). Impacts of Rainfall Spatial Variability on Hydrogeological Response. Water Resources Research. doi:doi:10.1002/2014WR016168
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  Sapriza-Azuri G, J Jodar-Bermudez, V Navarro, L Jan Slooten, J Carrera-Ramirez, and HV Gupta (2015), Impacts of Rainfall Spatial Variability on Hydrogeological Response, Water Resources Research, 51, 1300–1314, doi:10.1002/2014WR016168
• Seibert, S. P., Perrin, C., Paturel, J. E., Gupta, H. V., Grimaldi, S., Ehret, U., Crochemore, L., & Andreassian, V. (2015). Comparing expert judgement and numerical criteria for hydrograph evaluation. Hydrological Sciences Journal-journal Des Sciences Hydrologiques, 60(3), 402-423. doi:10.1080/02626667.2014.903331
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  AbstractThis paper investigates the relationship between expert judgement and numerical criteria when evaluating hydrological model performance by comparing simulated and observed hydrographs. Using a web-based survey, we collected the visual evaluations of 150 experts on a set of high- and low-flow hydrographs. We then compared these answers with results from 60 numerical criteria. Agreement between experts was found to be more frequent in absolute terms (when rating models) than in relative terms (when comparing models), and better for high flows than for low flows. When comparing the set of 150 expert judgements with numerical criteria, we found that most expert judgements were loosely correlated with a numerical criterion, and that the criterion that best reflects expert judgement varies from expert to expert. Overall, we identified two groups of 10 criteria yielding an equivalent match with the expertise of the 150 participants in low and high flows, respectively. A single criterion common to both gr...
• Si, W., & Gupta, H. V. (2015). Updating Real-Time Flood Forecasts Via The Dynamic System Response Curve Method. Water Resources Research. doi:doi: 10.1002/2015WR017234
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  Si W, B Weimin and HV Gupta (2015), Updating Real-Time Flood Forecasts Via The Dynamic System Response Curve Method, Water Resources Research, 51, doi: 10.1002/2015WR017234
• Yilmaz, K., Gupta, H. V., & Wagener, T. (2015). A Multi Criteria Penalty Function Approach for Evaluating A Priori Model Parameter Estimates. Journal of Hydrology. doi:http://dx.doi.org/10.1016/j.jhydrol.2015.03.012
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  [143] Yilmaz K, HV Gupta, and T Wagener (2015), A Multi Criteria Penalty Function Approach for Evaluating A Priori Model Parameter Estimates, Journal of Hydrology, 525, 165–177, http://dx.doi.org/10.1016/j.jhydrol.2015.03.012
• Castro, C. L., Troch, P. A., Gupta, H. V., Rajagopal, S., & Dominguez, F. (2014). Physical Mechanisms Related to Climate-Induced Drying of Two Semiarid Watersheds in the Southwestern United States. Journal of Hydrometeorology, 15(4), 1404-1418. doi:10.1175/jhm-d-13-0106.1
• Crochemore, L., Perrin, C., Andréassian, V., Ehret, U., Seibert, S., Grimaldi, S., Gupta, H. V., & Paturel, J. E. (2014). Comparing expert judgement and numerical criteria for hydrograph evaluation. Hydrologic Sciences Journal.
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  DOI: 10.1080/02626667.2014.903331
• Ehret, U., Gupta, H. V., & Sivapalan, M. (2014). Advancing Hydrology to deal with Predictions Under Change. Hydrology and Earth Systems Science, 18, 649–671.
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  doi:10.5194/hess-18-649-2014
• Gharari, S., Shafiei, M., Hrachowitz, M., Kumar, R., Fenicia, F., Gupta, H. V., & Savenije, H. H. (2014). A “Constraint-based” Strategy for Parameter Specification of Environmental Models. Hydrology and Earth System Sciences, 18, 4861–4870.
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  doi:10.5194/hess-18-4861-2014
• Gong, W., Gupta, H. V., & Nearing, G. S. (2014). Estimating Information Entropy for Hydrological Data: One Dimensional Case. Water Resources Research, 50.
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  doi: 10.1002/2014WR015874
• Gupta, H. V., & Gupta, H. V. (2014). Uncertainty Quantification and Learning in Geophysical Modeling: How Information is Coded into Dynamical Models. NA.
• Gupta, H. V., & Nearing, G. (2014). Fewer equations in hydrology can lead to more meaningful results. EOS Transactions.
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  Gupta HV and G Nearing (2014), Fewer equations in hydrology can lead to more meaningful results, Research Spotlight, EOS Transactions, v95(34), p312, August 26th.
• Gupta, H. V., & Nearing, G. S. (2014). Debates—The future of hydrological sciences: A (common) path forward? Using models and data to learn: A systems theoretic perspective on the future of hydrological science. Water Resources Research, 50.
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  doi: 10.1002/2013WR015096Invited Paper
• Gupta, H. V., Gong, W., Yang, D., & Nearing, G. (2014). Estimating information entropy for hydrological data: One-dimensional case. Water Resources Research, 50(6), 5003-5018. doi:10.1002/2014wr015874
• Gupta, H. V., Perrin, C., Blöschl, G., Montanari, A., Kumar, R., Clark, M., & Andréassian, V. (2014). Large-sample hydrology: A need to balance depth with breadth. Hydrology and Earth System Sciences, 18(2), 463-477.
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  Abstract: A holy grail of hydrology is to understand catchment processes well enough that models can provide detailed simulations across a variety of hydrologic settings at multiple spatiotemporal scales, and under changing environmental conditions. Clearly, this cannot be achieved only through intensive place-based investigation at a small number of heavily instrumented catchments, or by empirical methods that do not fully exploit our understanding of hydrology. In this opinion paper, we discuss the need to actively promote and pursue the use of a "large catchment sample" approach to modeling the rainfall-runoff process, thereby balancing depth with breadth. We examine the history of such investigations, discuss the benefits (improved process understanding resulting in robustness of prediction at ungauged locations and under change), examine some practical challenges to implementation and, finally, provide perspectives on issues that need to be taken into account as we move forward. Ultimately, our objective is to provoke further discussion and participation, and to promote a potentially important theme for the upcoming Scientific Decade of the International Association of Hydrological Sciences entitled Panta Rhei. © Author(s) 2014.
• Gupta, H. V., Rodrigues, D. B., & Mendiondo, E. M. (2014). A blue/green water-based accounting framework for assessment of water security. Water Resources Research, 50(9), 7187-7205. doi:10.1002/2013wr014274
• Oliveira, P. T., Nearing, M. A., Moran, M. S., Goodrich, D. C., Wendland, E., & Gupta, H. V. (2014). Trends in water balance components across the Brazilian Cerrado. Water Resources Research, 50.
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  doi:10.1002/2013WR014274
• Pechlivanidis, I. G., Jackson, B., McMillan, H., & Gupta, H. V. (2014). Use of an entropy-based metric in multi-objective calibration to improve model performance. Water Resources Research, 50.
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  doi:10.1002/2013WR014537
• Rajagopal, S., Dominguez, F., Gupta, H. V., Troch, P. A., & Castro, C. L. (2014). Physical mechanisms related to climate-induced drying of two semi-arid watersheds in the southwest US. Journal of Hydrometeorology, 15.
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  doi:10.1175/JHM-D-13-0106.1
• Razavi, S., Gupta, H. V., & Gupta, H. V. (2014). What Do We Mean By Sensitivity Analysis? The Need For A Comprehensive Characterization Of Sensitivity In Earth System Models. Globalized Water.
• Rodrigues, D. B., Gupta, H. V., Serrat Capdevilla, A., Oliveira, P. T., Mendiondo, E. M., & Maddock III, T. (2014). Contrasting Brazilian and American Systems for Water Allocation and Transfers. ASCE Journal of Water Resources Planning and Management.
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  doi:10.1061/(ASCE)WR.1943-5452.0000483, 04014087
• Rodrigues, D. B., Gupta, H. V., Serrat-Capdevilla, A., & Oliveira, P. T. (2014). Assessing uncertainties in surface water security: An empirical multi-model resampling approach. ASCE Journal of Water Resources Planning and Management. doi:10.1061/(ASCE)WR.1943-5452.0000483, 04014087
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  Rodrigues DBB, HV Gupta, A Serrat-Capdevila, PTS Oliveira, EM Mendiondo and T Maddock III (2014), Contrasting Brazilian and American Systems for Water Allocation and Transfers, ASCE Journal of Water Resources Planning and Management, 10.1061/(ASCE)WR.1943-5452.0000483, 04014087
• Tian, F., Hu, H., Hu, H. P., Hu, H. C., He, Z., & Gupta, H. V. (2014). Diagnostic calibration of a hydrological model in an alpine area. Hydrology and Earth System Sciences Discussions, 11(1), 1253-1300. doi:10.5194/hessd-11-1253-2014
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  Hydrological modeling depends on single- or multiple-objective strategies for parameter calibration using long time sequences of observed streamflow. Here, we demonstrate a diagnostic approach to the calibration of a hydrological model of an alpine area in which we partition the hydrograph based on the dominant runoff generation mechanism (groundwater baseflow, glacier melt, snowmelt, and direct runoff). The partitioning reflects the spatiotemporal variability in snowpack, glaciers, and temperature. Model parameters are grouped by runoff generation mechanism, and each group is calibrated separately via a stepwise approach. This strategy helps to reduce the problem of equifinality and, hence, model uncertainty. We demonstrate the method for the Tailan River basin (1324 km 2 ) in the Tianshan Mountains of China with the help of a semi-distributed hydrological model (THREW).
• Azuri, G. S., Jódar, J., Carrera, J., & Gupta, H. V. (2013). Stochastic Simulation of Nonstationary Rainfall Fields, Accounting for Seasonality and Atmospheric Circulation Pattern Evolution. Mathematical Geosciences, 45(5), 621-645.
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  Abstract: A model for generating daily spatial correlated rainfall fields suitable for evaluating the impacts of climate change on water resources is presented. The model, termed Stochastic Rainfall Generating Process, is designed to incorporate two major nonstationarities: changes in the frequencies of different precipitation generating mechanisms (frontal and convective), and spatial nonstationarities caused by interactions of mesoscale atmospheric patterns with topography (orographic effects). These nonstationarities are approximated as discrete sets of the time-stationary Stochastic Rainfall Generating Process, each of which represents the different spatial patterns of rainfall (including its variation with topography) associated with different atmospheric circulation patterns and times of the year (seasons). Each discrete Stochastic Rainfall Generating Process generates daily correlated rainfall fields as the product of two random fields. First, the amount of rainfall is generated by a transformed Gaussian process applying sequential Gaussian simulation. Second, the delimitation of rain and no-rain areas (intermittence process) is defined by a binary random function simulated by sequential indicator simulations. To explore its applicability, the model is tested in the Upper Guadiana Basin in Spain. The result suggests that the model provides accurate reproduction of the major spatiotemporal features of rainfall needed for hydrological modeling and water resource evaluations. The results were significantly improved by incorporating spatial drift related to orographic precipitation into the model. © 2013 International Association for Mathematical Geosciences.
• Gong, W., Gupta, H. V., Yang, D., Sricharan, K., & O., A. (2013). Estimating epistemic and aleatory uncertainties during hydrologic modeling: An information theoretic approach. Water Resources Research, 49(4), 2253-2273.
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  Abstract: With growing interest in understanding the magnitudes and sources of uncertainty in hydrological modeling, the difficult problem of characterizing model structure adequacy is now attracting considerable attention. Here, we examine this problem via a model-structure-independent approach based in information theory. In particular, we (a) discuss how to assess and compute the information content in multivariate hydrological data, (b) present practical methods for quantifying the uncertainty and shared information in data while accounting for heteroscedasticity, (c) show how these tools can be used to estimate the best achievable predictive performance of a model (for a system given the available data), and (d) show how model adequacy can be characterized in terms of the magnitude and nature of its aleatory uncertainty that cannot be diminished (and is resolvable only up to specification of its density), and its epistemic uncertainty that can, in principle, be suitably resolved by improving the model. An illustrative modeling example is provided using catchment-scale data from three river basins, the Leaf and Chunky River basins in the United States and the Chuzhou basin in China. Our analysis shows that the aleatory uncertainty associated with making catchment simulations using this data set is significant (∼50%). Further, estimated epistemic uncertainties of the HyMod, SAC-SMA, and Xinanjiang model hypotheses indicate that considerable room for model structural improvements remain. © 2013. American Geophysical Union. All Rights Reserved.
• Gupta, H. V., Gong, W., Yang, D., Sricharan, K., & Hero, A. O. (2013). Estimating epistemic and aleatory uncertainties during hydrologic modeling: An information theoretic approach: ESTIMATING EPISTEMIC AND ALEATORY UNCERTAINTIES. Water Resources Research, 49(4), 2253-2273. doi:10.1002/wrcr.20161
• Hrachowitz, M., Savenije, H. H., Blöschl, G., McDonnell, J. J., Sivapalan, M., Pomeroy, J. W., Arheimer, B., Blume, T., Clark, M. P., Ehret, U., Fenicia, F., Freer, J. E., Gelfan, A., Gupta, H. V., Hughes, D. A., Hut, R. W., Montanari, A., Pande, S., Tetzlaff, D., , Troch, P. A., et al. (2013). A decade of Predictions in Ungauged Basins (PUB)-a review. Hydrological Sciences Journal, 58(6), 1198-1255.
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  Abstract: The Prediction in Ungauged Basins (PUB) initiative of the International Association of Hydrological Sciences (IAHS), launched in 2003 and concluded by the PUB Symposium 2012 held in Delft (23-25 October 2012), set out to shift the scientific culture of hydrology towards improved scientific understanding of hydrological processes, as well as associated uncertainties and the development of models with increasing realism and predictive power. This paper reviews the work that has been done under the six science themes of the PUB Decade and outlines the challenges ahead for the hydrological sciences community.Editor D. KoutsoyiannisCitation Hrachowitz, M., Savenije, H.H.G., Blöschl, G., McDonnell, J.J., Sivapalan, M., Pomeroy, J.W., Arheimer, B., Blume, T., Clark, M.P., Ehret, U., Fenicia, F., Freer, J.E., Gelfan, A., Gupta, H.V., Hughes, D.A., Hut, R.W., Montanari, A., Pande, S., Tetzlaff, D., Troch, P.A., Uhlenbrook, S., Wagener, T., Winsemius, H.C., Woods, R.A., Zehe, E., and Cudennec, C., 2013. A decade of Predictions in Ungauged Basins (PUB)-a review. Hydrological Sciences Journal, 58 (6), 1198-1255. © 2013 IAHS Press.
• Lopez-Burgos, V., Gupta, H. V., & Clark, M. (2013). Reducing cloud obscuration of MODIS snow cover area products by combining spatio-temporal techniques with a probability of snow approach. Hydrology and Earth System Sciences, 17(5), 1809-1823.
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  Abstract: Satellite remote sensing can be used to investigate spatially distributed hydrological states for use in modeling, assessment, and management. However, in the visual wavelengths, cloud cover can often obscure significant portions of the images. This study develops a rule-based, multistep method for removing clouds from MODIS snow cover area (SCA) images. The methods used include combining images from more than one satellite, time interpolation, spatial interpolation, and estimation of the probability of snow occurrence based on topographic information. Applied over the upper Salt River basin in Arizona, the method reduced the degree of cloud obscuration by 93.8 %, while maintaining a similar degree of image accuracy to that of the original images. © Author(s) 2013.
• Mishra, P. K., Vessilinov, V., & Gupta, H. (2013). On Simulation and Analysis of Variable-Rate Pumping Tests. GroundWater, 51(3), 469-473.
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  PMID: 22775800;Abstract: Analytical solutions for constant-rate pumping tests are widely used to infer aquifer properties. In this note, we implement a methodology that approximates the time-varying pumping record as a series of segments with linearly varying pumping rates. We validate our approach using an analytical solution for a sinusoidally varying pumping test. We also apply our methodology to analyze synthetic test data and compare the results with those from a commonly used method where rate variations are represented by a series of constant-rate steps. © 2012, National Ground Water Association.
• Montanari, A., Young, G., Savenije, H. H., Hughes, D., Wagener, T., Ren, L. L., Koutsoyiannis, D., Cudennec, C., Toth, E., Grimaldi, S., Blöschl, G., Sivapalan, M., Beven, K., Gupta, H., Hipsey, M., Schaefli, B., Arheimer, B., Boegh, E., Schymanski, S. J., , Baldassarre, G. D., et al. (2013). "Panta Rhei-Everything Flows": Change in hydrology and society-The IAHS Scientific Decade 2013-2022. Hydrological Sciences Journal, 58(6), 1256-1275.
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  Abstract: The new Scientific Decade 2013-2022 of IAHS, entitled "Panta Rhei-Everything Flows", is dedicated to research activities on change in hydrology and society. The purpose of Panta Rhei is to reach an improved interpretation of the processes governing the water cycle by focusing on their changing dynamics in connection with rapidly changing human systems. The practical aim is to improve our capability to make predictions of water resources dynamics to support sustainable societal development in a changing environment. The concept implies a focus on hydrological systems as a changing interface between environment and society, whose dynamics are essential to determine water security, human safety and development, and to set priorities for environmental management. The Scientific Decade 2013-2022 will devise innovative theoretical blueprints for the representation of processes including change and will focus on advanced monitoring and data analysis techniques. Interdisciplinarity will be sought by increased efforts to connect with the socio-economic sciences and geosciences in general. This paper presents a summary of the Science Plan of Panta Rhei, its targets, research questions and expected outcomes.Editor Z.W. KundzewiczCitation Montanari, A., Young, G., Savenije, H.H.G., Hughes, D., Wagener, T., Ren, L.L., Koutsoyiannis, D., Cudennec, C., Toth, E., Grimaldi, S., Blöschl, G., Sivapalan, M., Beven, K., Gupta, H., Hipsey, M., Schaefli, B., Arheimer, B., Boegh, E., Schymanski, S.J., Di Baldassarre, G., Yu, B., Hubert, P., Huang, Y., Schumann, A., Post, D., Srinivasan, V., Harman, C., Thompson, S., Rogger, M., Viglione, A., McMillan, H., Characklis, G., Pang, Z., and Belyaev, V., 2013. "Panta Rhei-Everything Flows": Change in hydrology and society-The IAHS Scientific Decade 2013-2022. Hydrological Sciences Journal. 58 (6) 1256-1275. © 2013 IAHS Press.
• Nearing, G. S., Gupta, H. V., & Crow, W. T. (2013). Information loss in approximately Bayesian estimation techniques: A comparison of generative and discriminative approaches to estimating agricultural productivity. Journal of Hydrology, 507, 163-173.
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  Abstract: Data assimilation and regression are two commonly used methods for combining models and remote sensing observations to estimate agricultural productivity. Data assimilation is a generative approach because it requires explicit approximations of a Bayesian prior and likelihood to compute a probability density function of biomass conditional on observations, and regression is discriminative because it models the conditional biomass density function directly. Both of these methods typically approximate Bayes' law and therefore cannot be expected to be perfectly efficient at extracting information from remote sensing observations. In this paper we measure information in observations using Shannon's theory and define missing information, used information, and bad information as partial divergences from the true Bayesian posterior (biomass conditional on observations). These concepts were applied to directly measure the amount and quality of information about end-of-season biomass extracted from observations by the ensemble Kalman filter (EnKF) and Gaussian process regression (GPR). Results suggest that the simpler discriminative approach can be as efficient as the more complex generative approach in terms of extracting high quality information from observations, and may therefore be better suited to dealing with the practical problems associated with remote sensed data (e.g., sub-footprint scale heterogeneity). Our method for analyzing information use has many potential applications: approximations of Bayes' law are used regularly in predictive models of environmental systems of all kinds, and the efficiency of such approximations has heretofore not been directly measured. © 2013 Elsevier B.V.
• Nearing, G. S., Gupta, H. V., Crow, W. T., & Gong, W. (2013). An approach to quantifying the efficiency of a Bayesian filter. Water Resources Research, 49(4), 2164-2173.
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  Abstract: Data assimilation is the Bayesian conditioning of uncertain model simulations on observations to reduce uncertainty about model states. In practice, it is common to make simplifying assumptions about the prior and posterior state distributions, and to employ approximations of the likelihood function, which can reduce the efficiency of the filter. We propose metrics that quantify how much of the uncertainty in a Bayesian posterior state distribution is due to (i) the observation operator, (ii) observation error, and (iii) approximations of Bayes' Law. Our approach uses discrete Shannon entropy to quantify uncertainty, and we define the utility of an observation (for reducing uncertainty about a model state) as the ratio of the mutual information between the state and observation to the entropy of the state prior. These metrics make it possible to analyze the efficiency of a proposed observation system and data assimilation strategy, and provide a way to examine the propagation of information through the dynamic system model. We demonstrate the procedure on the problem of estimating profile soil moisture from observations at the surface (top 5 cm). The results show that when synthetic observations of 5 cm soil moisture are assimilated into a three-layer model of soil hydrology, the ensemble Kalman filter does not use all of the information available in observations. © 2013. American Geophysical Union. All Rights Reserved.
• Nearing, G. S., Gupta, H. V., Crow, W. T., & Gong, W. (2013). An approach to quantifying the efficiency of a Bayesian filter. Water Resources Research, 49(4), 2164-2173. doi:10.1002/wrcr.20177
• Rosolem, R., Rosolem, R., Gupta, H. V., Gupta, H. V., Shuttleworth, W. J., Shuttleworth, W. J., Gustavo, L., Gustavo, L., Zeng, X., Zeng, X., Rosolem, R., Gupta, H. V., Shuttleworth, W. J., Gustavo, L., & Zeng, X. (2013). Towards a comprehensive approach to parameter estimation in land surface parameterization schemes. Hydrological Processes, 27(14), 2075-2097.
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  Abstract: In climate models, the land-atmosphere interactions are described numerically by land surface parameterization (LSP) schemes. The continuing improvement in realism in these schemes comes at the expense of the need to specify a large number of parameters that are either directly measured or estimated. Also, an emerging problem is whether the relationships used in LSPs are universal and globally applicable. One plausible approach to evaluate this is to first minimize uncertainty in model parameters by calibration. In this paper, we conduct a comprehensive analysis of some model diagnostics using a slightly modified version of the Simple Biosphere 3 model for a variety of biomes located mainly in the Amazon. First, the degree of influence of each individual parameter in simulating surface fluxes is identified. Next, we estimate parameters using a multi-operator genetic algorithm applied in a multi-objective context and evaluate simulations of energy and carbon fluxes against observations. Compared with the default parameter sets, these parameter estimates improve the partitioning of energy fluxes in forest and cropland sites and provide better simulations of daytime increases in assimilation of net carbon during the dry season at forest sites. Finally, a detailed assessment of the parameter estimation problem was performed by accounting for the decomposition of the mean squared error to the total model uncertainty. Analysis of the total prediction uncertainty reveals that the parameter adjustments significantly improve reproduction of the mean and variability of the flux time series at all sites and generally remove seasonality of the errors but do not improve dynamical properties. Our results demonstrate that error decomposition provides a meaningful and intuitive way to understand differences in model performance. To make further advancements in the knowledge of these models, we encourage the LSP community to adopt similar approaches in the future. © 2012 John Wiley & Sons, Ltd.
• Shafiei, M., Savenije, H. H., Hrachowitz, M., Gupta, H. V., Gharari, S., & Fenicia, F. (2013). A strategy for “constraint-based” parameter specification for environmental models. Hydrology and Earth System Sciences Discussions, 10(12), 14857-14871. doi:10.5194/hessd-10-14857-2013
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  Many environmental systems models, such as conceptual rainfall-runoff models, rely on model calibration for parameter identification. For this, an observed output time series (such as runoff) is needed, but frequently not available. Here, we explore another way to constrain the parameter values of semi-distributed conceptual models, based on two types of restrictions derived from prior (or expert) knowledge. The first, called “parameter constraints”, restrict the solution space based on realistic relationships that must hold between the different parameters of the model while the second, called “process constraints” require that additional realism relationships between the fluxes and state variables must be satisfied. Specifically, we propose a strategy for finding parameter sets that simultaneously satisfy all such constraints, based on stepwise sampling of the parameter space. Such parameter sets have the desirable property of being consistent with the modeler’s intuition of how the catchment functions, and can (if necessary) serve as prior information for further investigations by reducing the prior uncertainties associated with both calibration and prediction.
• Canon-Barriga, C., Valdes, J. B., & Gupta, H. V. (2012). Modeling the Effect of Irrigation Practices in Flash Floods: A Case Study for the US Southwest. Journal of Water Resource and Protection.
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  Canon-Barriga C, J Valdes and H Gupta (2012), Modeling the Effect of Irrigation Practices in Flash Floods: A Case Study for the US Southwest, Journal of Water Resource and Protection, 4, 415-422, doi: 10.4236/jwarp.2012.47048, Published Online July 2012 (www.SciRP.org/journal/jwarp)
• Gupta, H. V., Clark, M. P., Vrugt, J. A., Abramowitz, G., & Ming, Y. e. (2012). Towards a comprehensive assessment of model structural adequacy. Water Resources Research, 48(8).
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  Abstract: The past decade has seen significant progress in characterizing uncertainty in environmental systems models, through statistical treatment of incomplete knowledge regarding parameters, model structure, and observational data. Attention has now turned to the issue of model structural adequacy (MSA, a term we prefer over model structure "error"). In reviewing philosophical perspectives from the groundwater, unsaturated zone, terrestrial hydrometeorology, and surface water communities about how to model the terrestrial hydrosphere, we identify several areas where different subcommunities can learn from each other. In this paper, we (a) propose a consistent and systematic "unifying conceptual framework" consisting of five formal steps for comprehensive assessment of MSA; (b) discuss the need for a pluralistic definition of adequacy; (c) investigate how MSA has been addressed in the literature; and (d) identify four important issues that require detailed attentionstructured model evaluation, diagnosis of epistemic cause, attention to appropriate model complexity, and a multihypothesis approach to inference. We believe that there exists tremendous scope to collectively improve the scientific fidelity of our models and that the proposed framework can help to overcome barriers to communication. By doing so, we can make better progress toward addressing the question "How can we use data to detect, characterize, and resolve model structural inadequacies?". © 2012. American Geophysical Union. All Rights Reserved.
• Lopez-burgos, V., Gupta, H. V., & Clark, M. P. (2012). A probability of snow approach to removing cloud cover from MODIS Snow Cover Area products. Hydrology and Earth System Sciences Discussions, 9(12), 13693-13728. doi:10.5194/hessd-9-13693-2012
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  Abstract. Satellite remote sensing can be used to investigate spatially distributed hydrological states and fluxes for use in modeling, assessment and management. However, in the visual wavelengths, cloud cover can often obscure significant portions of the images. This study develops a rule-based, multi-step method for removing clouds from MODIS Snow Cover Area (SCA) images. The methods used include a combining images from more than one satellite, time interpolation, spatial interpolation, and estimation of the probability of snow occurrence based on topographic information. Applied over the Upper Salt River Basin in Arizona, the method reduced the degree of cloud obscuration by 93.8% while maintaining a similar degree of image accuracy to that of the original images.
• Nearing, G. S., Crow, W. T., Thorp, K. R., Moran, M. S., Reichle, R. H., & Gupta, H. V. (2012). Assimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: An observing system simulation experiment. Water Resources Research, 48(5).
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  Abstract: Observing system simulation experiments were used to investigate ensemble Bayesian state-updating data assimilation of observations of leaf area index (LAI) and soil moisture () for the purpose of improving single-season wheat yield estimates with the Decision Support System for Agrotechnology Transfer (DSSAT) CropSim-Ceres model. Assimilation was conducted in an energy-limited environment and a water-limited environment. Modeling uncertainty was prescribed to weather inputs, soil parameters and initial conditions, and cultivar parameters and through perturbations to model state transition equations. The ensemble Kalman filter and the sequential importance resampling filter were tested for the ability to attenuate effects of these types of uncertainty on yield estimates. LAI and observations were synthesized according to characteristics of existing remote sensing data, and effects of observation error were tested. Results indicate that the potential for assimilation to improve end-of-season yield estimates is low. Limitations are due to a lack of root zone soil moisture information, error in LAI observations, and a lack of correlation between leaf and grain growth. © 2012. American Geophysical Union.
• Pechlivanidis, I. G., Jackson, B. M., McMillan, H. K., & Gupta, H. V. (2012). Using an informational entropy-based metric as a diagnostic of flow duration to drive model parameter identification. Global Nest Journal, 14(3), 325-334.
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  Abstract: Calibration of rainfall-runoff models is made complicated by uncertainties in data, and by the arbitrary emphasis placed on various magnitudes of the model residuals by most traditional measures of fit. Current research highlights the importance of driving model identification by assimilating information from the data. In this paper, we evaluate the potential use of an entropy based measure as an objective function or as a model diagnostic in hydrological modelling, with particular interest in providing an appropriate quantitative measure of fit to the flow duration curve (FDC). The proposed Conditioned Entropy Difference (CED) metric is capable of characterising the information in the flow frequency distribution and thereby constrain the model calibration to respect this distributional information. Four years of hourly data from the 46.6 km 2 Mahurangi catchment, NZ, are used to calibrate the 6-parameter Probability Distributed Moisture model. Results are analysed using three measures: the proposed entropy-based measure, the Nash-Sutcliffe (NSE), and the recently proposed Kling-Gupta efficiency (KGE). We also examine a conditioned entropy metric that trades-off and reweights different segments of the FDC to drive model calibration in a way that is based on modelling objectives. Overall, the entropy-based measure results in good performance in terms of NSE but poor performance in terms of KGE. This entropy measure is strongly sensitive to the shape of the flow distribution and is, from some viewpoints, the single best descriptor of the FDC. By conditioning entropy to respect multiple segments of the FDC, we can reweight entropy to respect those parts of the flow distribution of most interest to the modelling application. This approach constrains the behavioural parameter space so as to better identify parameters that represent both the "fast" and "slow" runoff processes. Use of this importance-weighted, conditioned entropy metric can constrain high flow predictions equally well as the NSE and KGE, while simultaneously providing wellconstrained low flow predictions that the NSE or KGE are unable to achieve. © 2012 Global NEST Printed in Greece. All rights reserved.
• Pokhrel, P., Yilmaz, K. K., & Gupta, H. V. (2012). Multiple-criteria calibration of a distributed watershed model using spatial regularization and response signatures. Journal of Hydrology, 418-419, 49-60.
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  Abstract: This paper explores the use of a semi-automated multiple-criteria calibration approach for estimating the parameters of the spatially distributed HL-DHM model to the Blue River basin, Oklahoma. The study was performed in the context of Phase 2 of the DMIP project organized by the Hydrology Lab of the NWS. To deal with the problem of ill conditioning, we employ a regularization approach that constrains the search space using information contained in a priori estimates of the spatially distributed parameter fields developed from soils and other geo-spatial datasets. Unlike the commonly used spatial-multiplier method, our more general approach allows the parameters to depart non-uniformly (to some degree) from the a priori spatial pattern. The approach reduces the number of unknowns to be estimated using historical input-output data from 860 to 35. Two commonly used summary statistics of the model residuals, MSE and MSEL, are used to optimize fitting of the model to both the peaks and the recession periods of the time series data. A signature measure approach is used to select parameter sets that are close to Pareto-optimal in terms of MSE and MSEL, but which provide more consistent representation of the hydrologic behavior of the watershed as summarized by measures derived from the flow duration curve. While the results support the methods used in this analysis and show considerable improvement over the a priori parameter estimates, we find that the basin has some peculiar behaviors (including time non-stationarity) that the HL-DHM model as implemented is not set up to reproduce. © 2009 Elsevier B.V.
• Rosolem, R., Gupta, H. V., Shuttleworth, W. J., Zeng, X., & Gustavo, L. (2012). A fully multiple-criteria implementation of the Sobol' method for parameter sensitivity analysis. Journal of Geophysical Research D: Atmospheres, 117(7).
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  Abstract: We present a novel rank-based fully multiple-criteria implementation of the Sobol variance-based sensitivity analysis approach that implements an objective strategy to evaluate parameter sensitivity when model evaluation involves several metrics of performance. The method is superior to single-criterion approaches while avoiding the subjectivity observed in "pseudo" multiple-criteria methods. Further, it contributes to our understanding of structural characteristics of a model and simplifies parameter estimation by identifying insensitive parameters that can be fixed to default values during model calibration studies. We illustrate the approach by applying it to the problem of identifying the most influential parameters in the Simple Biosphere 3 (SiB3) model using a network of flux towers in Brazil. We find 27-31 (out of 42) parameters to be influential, most (∼78%) of which are primarily associated with physiology, soil, and carbon properties, and that uncertainties in the physiological properties of the model contribute most to total model uncertainty in regard to energy and carbon fluxes. We also find that the second most important model component contributing to the total output uncertainty varies according to the flux analyzed; whereas morphological properties play an important role in sensible heat flux, soil properties are important for latent heat flux, and carbon properties (mainly associated with the soil respiration submodel) are important for carbon flux (as expected). These distinct sensitivities emphasize the need to account for the multioutput nature of land surface models during sensitivity analysis and parameter estimation. Applied to other similar models, our approach can help to establish which soil-plant-atmosphere processes matter most in land surface models of Amazonia and thereby aid in the design of field campaigns to characterize and measure the associated parameters. The approach can also be used with other sensitivity analysis procedures that compute at least two model performance metrics.
• Smith, M. B., & Gupta, H. V. (2012). The Distributed Model Intercomparison Project (DMIP) - Phase 2 experiments in the Oklahoma Region, USA. Journal of Hydrology, 418-419, 1-2.
• Smith, M., & Gupta, H. V. (2012). Preface to The Distributed Model Intercomparison Project (DMIP) - Phase 2 Experiments in the Oklahoma Region, USA,. Journal of Hydrology.
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  Smith M and HV Gupta (2012), Preface to The Distributed Model Intercomparison Project (DMIP) - Phase 2 Experiments in the Oklahoma Region, USA, Special DMIP2 Oklahoma Region Issue of Journal of Hydrology 418-419, pp 1-2, doi: 10.1016/j.jhydrol.2011.09.036
• Stewart, A. M., Callegary, J. B., Smith, C. F., Gupta, H. V., Leenhouts, J. M., & Fritzinger, R. A. (2012). Use of the continuous slope-area method to estimate runoff in a network of ephemeral channels, southeast Arizona, USA. Journal of Hydrology, 472-473, 148-158.
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  Abstract: The continuous slope-area (CSA) method is an innovative gaging method for indirect computation of complete-event discharge hydrographs that can be applied when direct measurement methods are unsafe, impractical, or impossible to apply. This paper reports on use of the method to produce event-specific discharge hydrographs in a network of sand-bedded ephemeral stream channels in southeast Arizona, USA, for water year 2008. The method provided satisfactory discharge estimates for flows that span channel banks, and for moderate to large flows, with about 10-16% uncertainty, respectively for total flow volume and peak flow, as compared to results obtained with an alternate method. Our results also suggest that the CSA method may be useful for estimating runoff of small flows, and during recessions, but with increased uncertainty. © 2012.
• Stewart, R. D., Hut, R., Rupp, D. E., Gupta, H., & Selker, J. S. (2012). A resonating rainfall and evaporation recorder. Water Resources Research, 48(8).
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  Abstract: We propose a novel, accurate quantification of precipitation and evaporation, as needed to understand fundamental hydrologic processes. Our system uses a collection vessel placed on top of a slender rod that is securely fixed at its base. As the vessel is deflected, either by manual perturbation or ambient forcing (for example, wind), its oscillatory response is measured, here by a miniature accelerometer. This response can be modeled as a damped mass-spring system. As the mass of water within the collection vessel changes, either through the addition of precipitation or by evaporative loss, the resonant frequency experiences an inverse shift. This shift can be measured and used to estimate the change in the mass of water. We tested this concept by creating a simple prototype which was used in field conditions for a period of 1 month. The instrument was able to detect changes in mass due to precipitation with an accuracy of approximately 1 mm. © 2012. American Geophysical Union. All Rights Reserved.
• Zeng, X., Shuttleworth, W. J., Gupta, H. V., Rosolem, R., & de Gonçalves, L. G. (2012). Towards a comprehensive approach to parameter estimation in land surface parameterization schemes: COMPREHENSIVE APPROACH TO PARAMETER ESTIMATION IN LAND SURFACE MODELS. Hydrological Processes, 27(14), 2075-2097. doi:10.1002/hyp.9362
• Bulygina, N., & Gupta, H. (2011). Correcting the mathematical structure of a hydrological model via Bayesian data assimilation. Water Resources Research, 47(5).
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  Abstract: The goal of model identification is to improve our understanding of the structure and behavior of a system so the model can be used to make inferences about its input-state-output response. It is conventional to preselect some model form and evaluate its "suitability" against historical data. If deemed unsuitable, ways must be found to "correct" the model through some intuitive process. Here, we discuss a Bayesian data assimilation process by which historical observations can be used to diagnose what might be wrong with the presumed mathematical structure of the model and to provide guidance toward fixing the problem. In previous work we showed how, given a suitable conceptual model for the system, the Bayesian estimation of structure (BESt) method can estimate the stochastic form for structural equations of a model that are consistent with historical observations at the spatiotemporal scale of the data while explicitly estimating model structural contributions to prediction uncertainty. However, a prior assumption regarding the form of the equations (an existing model) is often available. Here, we extend BESt to show how the mathematical form of the prior model equations can be corrected/improved to be more consistent with available data while remaining consistent with the presumed physics of the system. Conditions under which convergence will occur are stated. The potential of the extended BESt approach is demonstrated in the context of basin-scale hydrological modeling by correcting the equations of the HyMod model applied to the Leaf River catchment and thereby improving its representation of system input-state-output response. Copyright 2011 by the American Geophysical Union.
• Gupta, H. V., & Kling, H. (2011). On typical range, sensitivity, and normalization of Mean Squared Error and Nash-Sutcliffe Efficiency type metrics. Water Resources Research, 47(10).
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  Abstract: We show that Mean Squared Error (MSE) and Nash-Sutcliffe Efficiency (NSE) type metrics typically vary on bounded ranges under optimization and that negative values of NSE imply severe mass balance errors in the data. Further, by constraining simulated mean and variability to match those of the observations (diagnostic approach), the sensitivity of both metrics is improved, and NSE becomes linearly related to the cross-correlation coefficient. Our results have important implications for analysis of the information content of data and hence about inferences regarding achievable parameter precision. © 2011 by the American Geophysical Union.
• Mahmoud, M. I., Gupta, H. V., & Rajagopal, S. (2011). Scenario development for water resources planning and watershed management: Methodology and semi-arid region case study. Environmental Modelling and Software, 26(7), 873-885.
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  Abstract: Utilizing the scenario development framework from Mahmoud et al. (2009), a set of scenarios were developed for and applied in the Verde River Watershed in Arizona, USA. Through a scenario definition exercise, three dimensions of future change with respective axis extremes were identified: climate change (periodic droughts vs. sustained drought), demographics (water-conservative population vs. water-consumptive population), and the economy (booming economy vs. poor economy). In addition to the various combinations of dimension extremes, each scenario was given a unique event or theme that was characteristic of the combination of dimension extremes it possessed. The scenarios were then fleshed out into narrative forms that expanded on the details of each scenario's internal temporal evolution. The scenarios were analyzed by a water supply and demand model that was specifically constructed for their simulation. Following the analysis of scenario results, assessment narratives were provided to outline the impact of each scenario on the Verde River Watershed and management operations in that basin. © 2011 Elsevier Ltd.
• Martinez, G. F., & Gupta, H. V. (2011). Hydrologic consistency as a basis for assessing complexity of monthly water balance models for the continental United States. Water Resources Research, 47(12).
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  Abstract: Methods to select parsimonious and hydrologically consistent model structures are useful for evaluating dominance of hydrologic processes and representativeness of data. While information criteria (appropriately constrained to obey underlying statistical assumptions) can provide a basis for evaluating appropriate model complexity, it is not sufficient to rely upon the principle of maximum likelihood (ML) alone. We suggest that one must also call upon a "principle of hydrologic consistency," meaning that selected ML structures and parameter estimates must be constrained (as well as possible) to reproduce desired hydrological characteristics of the processes under investigation. This argument is demonstrated in the context of evaluating the suitability of candidate model structures for lumped water balance modeling across the continental United States, using data from 307 snow-free catchments. The models are constrained to satisfy several tests of hydrologic consistency, a flow space transformation is used to ensure better consistency with underlying statistical assumptions, and information criteria are used to evaluate model complexity relative to the data. The results clearly demonstrate that the principle of consistency provides a sensible basis for guiding selection of model structures and indicate strong spatial persistence of certain model structures across the continental United States. Further work to untangle reasons for model structure predominance can help to relate conceptual model structures to physical characteristics of the catchments, facilitating the task of prediction in ungaged basins. Copyright 2011 by the American Geophysical Union.
• Neal, A. L., Gupta, H. V., Kurc, S. A., & Brooks, P. D. (2011). Modeling moisture fluxes using artificial neural networks: Can information extraction overcome data loss?. Hydrology and Earth System Sciences, 15(1), 359-368.
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  Abstract: Eddy covariance sites can experience data losses as high as 30 to 45% on an annual basis. Artificial neural networks (ANNs) have been identified as powerful tools for gap filling, but their performance depends on the representativeness of data used to train the model. In this paper, we develop a normalization method, which has similar performance compared to conventional training approaches, but exhibits differences in the timing of fluxes, indicating different and previously unused information in the data record. Specifically, the differences between half-hourly model fluxes, especially during summer months, indicate that the structure of the information content in the data changes seasonally, diurnally and with the rate of data loss. Extracting more information from data may not improve model performance and indicates the need for improved data and models to address flux behavior at critical times. We advise several approaches to address these concerns, including use of separate models for day and nighttime processes and the use of alternate data streams at dawn, when eddy covariance may be particularly ineffective due to the timing of the onset of turbulent mixing. © Author(s) 2011.
• Neal, A. L., Gupta, H. V., Kurc, S. A., & Brooks, P. D. (2011). Modeling moisture fluxes using artificial neural networks: can information extraction overcome data loss. Hydrology and Earth System Sciences, 15, 359-368.
• Pokhrel, P., & Gupta, H. V. (2011). On the ability to infer spatial catchment variability using streamflow hydrographs. Water Resources Research, 47(8).
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  Abstract: Spatially distributed models can potentially provide improved hydrologic predictions because of their ability to exploit spatially distributed data while providing estimates of hydrologic variables at interior catchment locations. However, attempts to estimate spatially distributed parameter fields via model calibration have been fraught with difficulty. This paper examines the factors that can influence the ability to infer spatial properties of a distributed model when the only information available for model evaluation is catchment streamflow response. In particular, we investigate the conditions under which spatial variability in parameters and rainfall cause sufficiently strong variations in the streamflow hydrographs to justify their representation in catchment models and whether such information can be detected via commonly used model performance measures. Our results show that spatial variability in parameter and precipitation fields can, indeed, have a detectable impact on the properties of the streamflow hydrograph but that this impact can be so greatly diminished by the damping and dispersive effects of routing that it is virtually nondetectable by conventional performance measures by the time the water reaches the catchment outlet. And although measures based on information theory may be able to detect subtle variations of this kind, the information may not ultimately be useful in the face of model structure and data errors. The only reasonable way forward therefore is to explore other kinds of catchment information (including multiple interior flow gauging locations) for use in estimation of spatially distributed parameter fields. Copyright 2011 by the American Geophysical Union.
• Bulygina, N., & Gupta, H. (2010). How Bayesian data assimilation can be used to estimate the mathematical structure of a model. Stochastic Environmental Research and Risk Assessment, 24(6), 925-937.
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  Abstract: In previous work, we presented a method for estimation and correction of non-linear mathematical model structures, within a Bayesian framework, by merging uncertain knowledge about process physics with uncertain and incomplete observations of dynamical input-state-output behavior. The resulting uncertainty in the model input-state-output mapping is expressed as a weighted combination of an uncertain conceptual model prior and a data-derived probability density function, with weights depending on the conditional data density. Our algorithm is based on the use of iterative data assimilation to update a conceptual model prior using observed system data, and thereby construct a posterior estimate of the model structure (the mathematical form of the equation itself, not just its parameters) that is consistent with both physically based prior knowledge and with the information in the data. An important aspect of the approach is that it facilitates a clear differentiation between the influences of different types of uncertainties (initial condition, input, and mapping structure) on the model prediction. Further, if some prior assumptions regarding the structural (mathematical) forms of the model equations exist, the procedure can help reveal errors in those forms and how they should be corrected. This paper examines the properties of the approach by investigating two case studies in considerable detail. The results show how, and to what degree, the structure of a dynamical hydrological model can be estimated without little or no prior knowledge (or under conditions of incorrect prior information) regarding the functional forms of the storage-streamflow and storage-evapotranspiration relationships. The importance and implications of careful specification of the model prior are illustrated and discussed. © 2010 Springer-Verlag.
• Ebtehaj, M., Moradkhani, H., & Gupta, H. V. (2010). Improving robustness of hydrologic parameter estimation by the use of moving block bootstrap resampling. Water Resources Research, 46(7).
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  Abstract: Modeling of natural systems typically involves conceptualization and parameterization to simplify the representations of the underlying process. Objective methods for estimation of the model parameters then require optimization of a cost function, representing a measure of distance between the observations and the corresponding model predictions, typically by calibration in a static batch mode and/or via some dynamic recursive optimization approach. Recently, there has been a focus on the development of parameter estimation methods that appropriately account for different sources of uncertainty. In this context, we introduce an approach to sample the optimal parameter space that uses nonparametric block bootstrapping coupled with global optimization. We demonstrate the applicability of this procedure via a case study, in which we estimate the parameter uncertainty resulting from uncertainty in the forcing data and evaluate its impacts on the resulting streamflow simulations. Copyright 2010 by the American Geophysical Union.
• Gupta, H. V., Troch, P. A., Wagener, T., Sivapalan, M., McGlynn, B. L., Harman, C. J., Kumar, P., Rao, P. S., Basu, N. B., & Wilson, J. S. (2010). The future of hydrology: An evolving science for a changing world: OPINION. Water Resources Research, 46(5). doi:10.1029/2009wr008906
• H., T., Lyon, S. W., Gupta, H. V., & Troch, P. A. (2010). Multicriteria design of rain gauge networks for flash flood prediction in semiarid catchments with complex terrain. Water Resources Research, 46(11).
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  Abstract: Despite the availability of weather radar data at high spatial (1 km 2) and temporal (5-15 min) resolution, ground-based rain gauges continue to be necessary for accurate estimation of storm rainfall input to catchments during flash flood events, especially in mountainous catchments. Given economical considerations, a long-standing problem in catchment hydrology is to establish optimal placement of a small number of rain gauges to acquire data on both rainfall depth and spatiotemporal variability of intensity during extreme storm events. Using weather radar observations and a dense network of 40 tipping bucket rain gauges, this study examines whether it is possible to determine a reliable "best" set of rain gauge locations for the Sabino Canyon catchment near Tucson, Arizona, USA, given its complex topography and dominant storm track pattern. High-quality rainfall data are used to evaluate all possible configurations of a "practical" network having from one to four rain gauges. A multicriteria design strategy is used to guide rain gauge placement, by simultaneously minimizing the residual percent bias and maximizing the coefficient of correlation between the estimated and true mean areal rainfall and minimizing the normalized spatial mean squared error between the estimated and true spatiotemporal rainfall distribution. The performance of the optimized rain gauge network was then compared against randomly designed network ensembles by evaluating the quality of streamflows predicted using the Kinematic Runoff and Erosion (KINEROS2) event-based rainfall-runoff model. Our results indicate that the multicriteria strategy provided a robust design by which a sparse but accurate network of rain gauges could be implemented for semiarid basins such as the one studied. Copyright 2010 by the American Geophysical Union.
• Martinez, G. F., & Gupta, H. V. (2010). Toward improved identification of hydrological models: A diagnostic evaluation of the "abcd" monthly water balance model for the conterminous United States. Water Resources Research, 46(8).
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  Abstract: Continental-scale water balance (WB) assessments are important for characterizing hydrologic systems and understanding regional-scale dynamics and for identifying hydroclimatic trends and systematic data biases. However, it is not clear whether existing models can reproduce the catchment dynamics observed in nature. Nor has our ability to evaluate model results kept pace with computational and data processing abilities. Consequently, methods for diagnostic model evaluation and improvement remain weak. There is a need for well-conceived, systematic strategies to guide model selection, establish data requirements, estimate parameters, and evaluate and track model performance. We examine these challenges in the context of monthly WB modeling for the conterminous United States by applying the "abcd" model to 764 catchments selected for their comprehensive coverage of hydrogeological conditions. By examining diagnostically relevant components of model error, we evaluate the details of its spatial variability across the continental United States. Model performance, parameters, and structures are found to be correlated with hydroclimatic variables. However, our results indicate a need for the conventional identification approach to be improved. Because they do not constrain models to reproduce important hydrological behaviors, reported values of NSE or r2 performance can be misleading. Further, we must establish suitable model hypotheses with appropriate spatiotemporal scale for each hydroclimatic region. Until these issues are resolved, such models cannot reliably be used to infer the spatiotemporal dynamics of continental-scale water balance or to regionalize model structures and parameters to ungaged locations. Copyright 2010 by the American Geophysical Union.
• Pokhrel, P., & Gupta, H. V. (2010). On the use of spatial regularization strategies to improve calibration of distributed watershed models. Water Resources Research, 46(1).
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  Abstract: Hydrologic models require the specification of unknown model parameters via calibration to historical input-output data. For spatially distributed models, the large number of unknowns makes the calibration problem poorly conditioned. Spatial regularization can help to stabilize the problem by facilitating inclusion of additional information. While a common regularization approach is to apply a scalar multiplier to the prior estimate of each parameter field, this can cause problems by simultaneously changing both the mean and the variance of the distribution. This paper explores a multiple-criteria regularization approach that facilitates adjustment of the mean, variance, and shape of the parameter distribution, using prior information to constrain the problem while providing sufficient degrees of freedom to enable model performance improvements. We also test simple squashing functions to help in maintaining conceptually reasonable parameter values throughout the spatial domain. We apply the method to three basins in the context of the Distributed Model Intercomparison Project (DMIP2), obtaining considerable performance improvements at the basin outlet. However, the prior parameter estimates are found to give much better performance at the interior points (treated as ungauged), suggesting that the spatial information has not been properly exploited. The results also suggest that basin outlet hydrographs may not be particularly sensitive to spatial parameter variability and that an overall basin mean value may be sufficient for flow forecasting at the outlet, although not at the interior points. We discuss weaknesses in our study approach and suggest diagnostically more powerful strategies to be pursued. Copyright 2010 by the American Geophysical Union.
• Vos, N. D., Rientjes, T. H., & Gupta, H. V. (2010). Diagnostic evaluation of conceptual rainfall-runoff models using temporal clustering. Hydrological Processes, 24(20), 2840-2850.
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  Abstract: Given the structural shortcomings of conceptual rainfall-runoff models and the common use of time-invariant model parameters, these parameters can be expected to represent broader aspects of the rainfall-runoff relationship than merely the static catchment characteristics that they are commonly supposed to quantify. In this article, we relax the common assumption of time-invariance of parameters, and instead seek signature information about the dynamics of model behaviour and performance. We do this by using a temporal clustering approach to identify periods of hydrological similarity, allowing the model parameters to vary over the clusters found in this manner, and calibrating these parameters simultaneously. The diagnostic information inferred from these calibration results, based on the patterns in the parameter sets of the various clusters, is used to enhance the model structure. This approach shows how diagnostic model evaluation can be used to combine information from the data and the functioning of the hydrological model in a useful manner. © 2010 John Wiley & Sons, Ltd.
• Wagener, T., Sivapalan, M., Troch, P. A., McGlynn, B. L., Harman, C. J., Gupta, H. V., Kumar, P., Suresh, P., Basu, N. B., & Wilson, J. S. (2010). The future of hydrology: An evolving science for a changing world. Water Resources Research, 46(5).
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  Abstract: Human activities exert global-scale impacts on our environment with significant implications for freshwater-driven services and hazards for humans and nature. Our approach to the science of hydrology needs to significantly change so that we can understand and predict these implications. Such an adjustment is a necessary prerequisite for the development of sustainable water resource management strategies and to achieve long-term water security for people and the environment. Hydrology requires a paradigm shift in which predictions of system behavior that are beyond the range of previously observed variability or that result from significant alterations of physical (structural) system characteristics become the new norm. To achieve this shift, hydrologists must become both synthesists, observing and analyzing the system as a holistic entity, and analysts, understanding the functioning of individual system components, while operating firmly within a well-designed hypothesis testing framework. Cross-disciplinary integration must become a primary characteristic of hydrologic research, catalyzing new research and nurturing new educational models. The test of our quantitative understanding across atmosphere, hydrosphere, lithosphere, biosphere, and anthroposphere will necessarily lie in new approaches to benchmark our ability to predict the regional hydrologic and connected implications of environmental change. To address these challenges and to serve as a catalyst to bring about the necessary changes to hydrologic science, we call for a long-term initiative to address the regional implications of environmental change. Copyright © 2010 by the American Geophysical Union.
• Bulygina, N., & Gupta, H. (2009). Estimating the uncertain mathematical structure of a water balance model via Bayesian data assimilation. Water Resources Research, 45(2).
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  Abstract: When constructing a hydrological model at the macroscale (e.g., watershed scale), the structure of this model will inherently be uncertain because of many factors, including the lack of a robust hydrological theory at that scale. In this work, we assume that a suitable conceptual model structure for the hydrologic system has already been determined; that is, the system boundaries have been specified, the important state variables and input and output fluxes to be included have been selected, the major hydrological processes and geometries of their interconnections have been identified, and the continuity equation (mass balance) has been assumed to hold. The remaining structural identification problem that remains, then, is to select the mathematical form of the dependence of the output on the inputs and state variables, so that a computational model can be constructed for making simulations and/or predictions of the system input-state-output behavior. The conventional approach to this problem is to preassume some fixed (and possibly erroneous) mathematical forms for the model output equations. We show instead how Bayesian data assimilation can be used to directly estimate (construct) the form of these mathematical relationships such that they are statistically consistent with macroscale measurements of the system inputs, outputs, and (if available) state variables. The resulting model has a stochastic rather than deterministic form and thereby properly represents both what we know (our certainty) and what we do not know (our uncertainty) about the underlying structure and behavior of the system. Further, the Bayesian approach enables us to merge prior beliefs in the form of preassumed model equations with information derived from the data to construct a posterior model. As a consequence, in regions of the model space for which observational data are available, the errors in preassumed mathematical form of the model can be corrected, improving model performance. For regions where no such data are available the "prior" theoretical assumptions about the model structure and behavior will dominate. The approach, entitled Bayesian estimation of structure, is used to estimate water balance models for the Leaf River Basin, Mississippi, at annual, monthly, and weekly time scales, conditioned on the assumption of a simple single-state-variable conceptual model structure. Inputs to the system are uncertain observed precipitation and potential evapotranspiration, and outputs are estimated probability distributions of actual evapotranspiration and streamflow discharge. Results show that the models estimated for the annual and monthly time scales perform quite well. However, model performance deteriorates for the weekly time scale, suggesting limitations in the assumed form of the conceptual model. Copyright 2009 by the American Geophysical Union.
• Clark, M., Hreinsson, E. Ö., Martinez, G., Tait, A., Slater, A., Hendrikx, J., Owens, I., Gupta, H., Schmidt, J., & Woods, R. (2009). Simulations of seasonal snow for the south Island, New Zealand. Journal of Hydrology New Zealand, 48(2), 41-58.
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  Abstract: Seasonal snow simulations are produced for the South Island of New Zealand using a relatively simple temperature-index snow model. Results show that the snow simulations are broadly consistent with the observed snow climatology, especially with respect to estimates of snow volume and snow duration. For the parameter sets tested, snow simulations were more realistic for those for which there is strong seasonal variability in melt and where the temperature threshold for snow accumulation is set to 1°C. However, we find strong interactions among the different parameters in the snow model that cannot be resolved with the available snow data. In the short-term, detailed basin-specific studies are necessary to refine model parameter values. © New Zealand Hydrological Society (2009).
• Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology, 377(1-2), 80-91.
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  Abstract: The mean squared error (MSE) and the related normalization, the Nash-Sutcliffe efficiency (NSE), are the two criteria most widely used for calibration and evaluation of hydrological models with observed data. Here, we present a diagnostically interesting decomposition of NSE (and hence MSE), which facilitates analysis of the relative importance of its different components in the context of hydrological modelling, and show how model calibration problems can arise due to interactions among these components. The analysis is illustrated by calibrating a simple conceptual precipitation-runoff model to daily data for a number of Austrian basins having a broad range of hydro-meteorological characteristics. Evaluation of the results clearly demonstrates the problems that can be associated with any calibration based on the NSE (or MSE) criterion. While we propose and test an alternative criterion that can help to reduce model calibration problems, the primary purpose of this study is not to present an improved measure of model performance. Instead, we seek to show that there are systematic problems inherent with any optimization based on formulations related to the MSE. The analysis and results have implications to the manner in which we calibrate and evaluate environmental models; we discuss these and suggest possible ways forward that may move us towards an improved and diagnostically meaningful approach to model performance evaluation and identification. © 2009 Elsevier B.V. All rights reserved.
• Gupta, H., & Bulygina, N. (2009). Estimating the uncertain mathematical structure of a water balance model via Bayesian data assimilation: BAYESIAN ESTIMATION OF MODEL STRUCTURE. Water Resources Research, 45(12). doi:10.1029/2007wr006749
• Herbst, M., Gupta, H. V., & Casper, M. C. (2009). Mapping model behaviour using Self-Organizing Maps. Hydrology and Earth System Sciences, 13(3), 395-409.
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  Abstract: Hydrological model evaluation and identification essentially involves extracting and processing information from model time series. However, the type of information extracted by statistical measures has only very limited meaning because it does not relate to the hydrological context of the data. To overcome this inadequacy we exploit the diagnostic evaluation concept of Signature Indices, in which model performance is measured using theoretically relevant characteristics of system behaviour. In our study, a Self- Organizing Map (SOM) is used to process the Signatures extracted from Monte-Carlo simulations generated by the distributed conceptual watershed model NASIM. The SOM creates a hydrologically interpretable mapping of overall model behaviour, which immediately reveals deficits and trade-offs in the ability of the model to represent the different functional behaviours of the watershed. Further, it facilitates interpretation of the hydrological functions of the model parameters and provides preliminary information regarding their sensitivities. Most notably, we use this mapping to identify the set of model realizations (among the Monte-Carlo data) that most closely approximate the observed discharge time series in terms of the hydrologically relevant characteristics, and to confine the parameter space accordingly. Our results suggest that Signature Index based SOMs could potentially serve as tools for decision makers inasmuch as model realizations with specific Signature properties can be selected according to the purpose of the model application. Moreover, given that the approach helps to represent and analyze multi-dimensional distributions, it could be used to form the basis of an optimization framework that uses SOMs to characterize the model performance response surface. As such it provides a powerful and useful way to conduct model identification and model uncertainty analyses. © Author(s) 2009.
• Kling, H., & Gupta, H. (2009). On the development of regionalization relationships for lumped watershed models: The impact of ignoring sub-basin scale variability. Journal of Hydrology, 373(3-4), 337-351.
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  Abstract: Lumped precipitation-runoff models represent the watershed as a single, homogeneous unit, thereby ignoring spatial variability in forcing inputs and physical properties. In spite of this, spatially distributed models, which account for the variability of such factors, generally do not provide better simulations of catchment outlet runoff. This is at least in part because lumped models are easier to calibrate. However, it is often unclear whether the optimal (calibrated) parameters of a lumped model take on values that are consistent with underlying physical properties. In this study we explore the hypothesis that optimized lumped parameters are "contaminated" by noise due to the lumped representation of the watershed. We conduct a series of virtual experiments in which a daily time-step conceptual precipitation-runoff model is applied, with both lumped and distributed spatial discretizations, to 49 Austrian mesoscale basins. The experiments examine the impacts of different degrees of spatial variability in the inputs and physical properties, as well as varying complexity of the model structure. The usage of lumped models results in optimal parameters that include a considerable degree of noise, because the parameters implicitly compensate for the deficiencies in the spatial discretization. Most of the noise is attributable to neglecting the spatial variability in the physical properties, while the spatial variability of the inputs is of less importance. Further, the noise increases with system complexity, where parameter interactions significantly magnify the noise. This noise in the lumped parameters diminishes the correlation with catchment properties, even when a theoretically strong relationship exists, thereby complicating parameter regionalization as used, for example, for prediction in ungauged basins. © 2009 Elsevier B.V. All rights reserved.
• Mahmoud, M., Liu, Y., Hartmann, H., Stewart, S., Wagener, T., Semmens, D., Stewart, R., Gupta, H., Dominguez, D., Dominguez, F., Hulse, D., Letcher, R., Rashleigh, B., Smith, C., Street, R., Ticehurst, J., Twery, M., Delden, H. v., Waldick, R., , White, D., et al. (2009). A formal framework for scenario development in support of environmental decision-making. Environmental Modelling and Software, 24(7), 798-808.
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  Abstract: Scenarios are possible future states of the world that represent alternative plausible conditions under different assumptions. Often, scenarios are developed in a context relevant to stakeholders involved in their applications since the evaluation of scenario outcomes and implications can enhance decision-making activities. This paper reviews the state-of-the-art of scenario development and proposes a formal approach to scenario development in environmental decision-making. The discussion of current issues in scenario studies includes advantages and obstacles in utilizing a formal scenario development framework, and the different forms of uncertainty inherent in scenario development, as well as how they should be treated. An appendix for common scenario terminology has been attached for clarity. Major recommendations for future research in this area include proper consideration of uncertainty in scenario studies in particular in relation to stakeholder relevant information, construction of scenarios that are more diverse in nature, and sharing of information and resources among the scenario development research community. © 2008 Elsevier Ltd.
• Pielke, R., & Gupta, H. V. (2009). Climate Change: The Need to Consider Human Forcings Other than Greenhouse Gases. EOS Transactions.
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  Pielke Sr R, K Beven, G Brasseur, J Calvert, M Chahine, R Dickerson, D Entekhabi, E Foufoula-Georgiou, HV Gupta, V Gupta, W Krajewski, EP Krider, WKM Lau, J McDonnell, W Rossow, J Schaake, S Sorooshian, E Wood (2010), Climate Change: The Need to Consider Human Forcings Other than Greenhouse Gases, EOS Transactions, Vol. 90, No. 45, 10 November 2009
• Pielke, R., Beven, K., Brasseur, G., Calvert, J., Chahine, M., Dickerson, R. R., Entekhabi, D., Foufolia-Georgiou, E., Gupta, H., Gupta, V., Karajewski, W., Kirder, E. P., Lau, K. M., McDonnell, J., Rossow, W., Scheeke, J., Smith, J., Sorroshian, S., & Wood, E. (2009). Climate change: The need to consider human forcings besides greenhouse gases. Eos, 90(45), 413-.
• Troch, P. A., Martinez, G. F., Pauwels, V. R., Durcik, M., Sivapalan, M., Harman, C., Brooks, P. D., Gupta, H., & Huxman, T. (2009). Climate and vegetation water use efficiency at catchment scales. Hydrological Processes, 23(16), 2409-2414.
• Vrugt, J. A., J.F., C., Gupta, H. V., & Robinson, B. A. (2009). Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?. Stochastic Environmental Research and Risk Assessment, 23(7), 1011-1026.
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  Abstract: In recent years, a strong debate has emerged in the hydrologic literature regarding what constitutes an appropriate framework for uncertainty estimation. Particularly, there is strong disagreement whether an uncertainty framework should have its roots within a proper statistical (Bayesian) context, or whether such a framework should be based on a different philosophy and implement informal measures and weaker inference to summarize parameter and predictive distributions. In this paper, we compare a formal Bayesian approach using Markov Chain Monte Carlo (MCMC) with generalized likelihood uncertainty estimation (GLUE) for assessing uncertainty in conceptual watershed modeling. Our formal Bayesian approach is implemented using the recently developed differential evolution adaptive metropolis (DREAM) MCMC scheme with a likelihood function that explicitly considers model structural, input and parameter uncertainty. Our results demonstrate that DREAM and GLUE can generate very similar estimates of total streamflow uncertainty. This suggests that formal and informal Bayesian approaches have more common ground than the hydrologic literature and ongoing debate might suggest. The main advantage of formal approaches is, however, that they attempt to disentangle the effect of forcing, parameter and model structural error on total predictive uncertainty. This is key to improving hydrologic theory and to better understand and predict the flow of water through catchments. © Springer-Verlag 2008.
• Vrugt, J. A., J.F., C., Gupta, H. V., & Robinson, B. A. (2009). Response to comment by Keith Beven on "Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?". Stochastic Environmental Research and Risk Assessment, 23(7), 1061-1062.
• Abramowitz, G., & Gupta, H. (2008). Toward a model space and model independence metric. Geophysical Research Letters, 35(5).
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  Abstract: Understanding the relationship between computer-based models and the environment they simulate is becoming increasingly important as we try to predict how the earth's climate will change. As a surrogate for the representation of uncertainty in a prediction problem, it is common to use the range of behaviour from a set of models (an ensemble), and the ensemble mean as the 'best guess' prediction. We suggest a 'model space' metric, which, by providing one relevant definition of model independence, could allow us to begin to understand the relationship between model spread and prediction uncertainty. This in turn could allow the minimisation of bias from the inclusion of similar models in ensembles and quantification of how much independent information each model contributes to the prediction problem. Copyright 2008 by the American Geophysical Union.
• Clark, M. P., Slater, A. G., Rupp, D. E., Woods, R. A., Vrugt, J. A., Gupta, H. V., Wagener, T., & Hay, L. E. (2008). Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models. WATER RESOURCES RESEARCH, 44.
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  The problems of identifying the most appropriate model structure for a given problem and quantifying the uncertainty in model structure remain outstanding research challenges for the discipline of hydrology. Progress on these problems requires understanding of the nature of differences between models. This paper presents a methodology to diagnose differences in hydrological model structures: the Framework for Understanding Structural Errors (FUSE). FUSE was used to construct 79 unique model structures by combining components of 4 existing hydrological models. These new models were used to simulate streamflow in two of the basins used in the Model Parameter Estimation Experiment (MOPEX): the Guadalupe River (Texas) and the French Broad River (North Carolina). Results show that the new models produced simulations of streamflow that were at least as good as the simulations produced by the models that participated in the MOPEX experiment. Our initial application of the FUSE method for the Guadalupe River exposed relationships between model structure and model performance, suggesting that the choice of model structure is just as important as the choice of model parameters. However, further work is needed to evaluate model simulations using multiple criteria to diagnose the relative importance of model structural differences in various climate regimes and to assess the amount of independent information in each of the models. This work will be crucial to both identifying the most appropriate model structure for a given problem and quantifying the uncertainty in model structure. To facilitate research on these problems, the FORTRAN-90 source code for FUSE is available upon request from the lead author.
• Gupta, H. V., Wagener, T., & Liu, Y. (2008). Reconciling theory with observations: Elements of a diagnostic approach to model evaluation. Hydrological Processes, 22(18), 3802-3813.
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  Abstract: This paper discusses the need for a well-considered approach to reconciling environmental theory with observations that has clear and compelling diagnostic power. This need is well recognized by the scientific community in the context of the 'Predictions in Ungaged Basins' initiative and the National Science Foundation sponsored 'Environmental Observatories' initiative, among others. It is suggested that many current strategies for confronting environmental process models with observational data are inadequate in the face of the highly complex and high order models becoming central to modern environmental science, and steps are proposed towards the development of a robust and powerful 'Theory of Evaluation'. This paper presents the concept of a diagnostic evaluation approach rooted in information theory and employing the notion of signature indices that measure theoretically relevant system process behaviours. The signature-based approach addresses the issue of degree of system complexity resolvable by a model. Further, it can be placed in the context of Bayesian inference to facilitate uncertainty analysis, and can be readily applied to the problem of process evaluation leading to improved predictions in ungaged basins. Copyright © 2008 John Wiley & Sons, Ltd.
• Gupta, H., Yatheendradas, S., Wagener, T., Unkrich, C., Goodrich, D., Schaffner, M., & Stewart, A. (2008). Understanding uncertainty in distributed flash flood forecasting for semiarid regions: SEMIARID FLASH FLOODS. Water Resources Research, 44(5). doi:10.1029/2007wr005940
• Liu, Y., Gupta, H., Springer, E., & Wagener, T. (2008). Linking science with environmental decision making: Experiences from an integrated modeling approach to supporting sustainable water resources management. Environmental Modelling and Software, 23(7), 846-858.
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  Abstract: The call for more effective integration of science and decision making is ubiquitous in environmental management. While scientists often complain that their input is ignored by decision makers, the latter have also expressed dissatisfaction that critical information for their decision making is often not readily available or accessible to them, or not presented in a usable form. It has been suggested that scientists need to produce more "usable" information with enhanced credibility, legitimacy, and saliency to ensure the adoption of research results. In basin-scale management of coupled human-water systems, water resources managers, like other decision makers, are frequently confronted with the need to make major decisions in the face of high system complexity and uncertainty. The integration of useful and relevant scientific information is necessary and critical to enable informed decision-making. This paper describes the main aspects of what has been learned in the process of supporting sustainable water resources planning and management in the semi-arid southwestern United States by means of integrated modeling. Our experience indicates that particular attention must be paid to the proper definition of focus questions, explicit conceptual modeling, a suitable modeling strategy, and a formal scenario analysis approach in order to facilitate the development of "usable" scientific information. We believe that these lessons and insights can be useful to other scientific efforts in the broader area of linking environmental science with decision making. © 2007 Elsevier Ltd. All rights reserved.
• Pokhrel, P., Gupta, H. V., & Wagener, T. (2008). A spatial regularization approach to parameter estimation for a distributed watershed model. Water Resources Research, 44(12).
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  Abstract: Environmental models have become increasingly complex with greater attention being given to the spatially distributed representation of processes. Distributed models have large numbers of parameters to be specified, which is typically done either by recourse to a priori methods based on observable physical watershed characteristics, by calibration to watershed input-state-output data, or by some combination of both. In the case of calibration, the high dimensionality of the parameter search space poses a significant identifiability problem. This article discusses how this problem can be addressed, utilizing additional information about the parameters through a process known as regularization. Regularization, in its broadest sense, is a mathematical technique that utilizes additional information or constraints about the parameters to reduce problems related to over-parameterization. This article develops and applies a regularization approach to the calibration of a version of the Hydrology Laboratory Distributed Hydrologie Model (HL-DHM) developed by the US National Weather Service. A priori parameter estimates derived using the approach by Koren et al. (2000) were used to develop regularization relationships to constrain the feasible parameter space and enable existing global optimization techniques to be applied to solve the calibration problem. In a case study for the Blue River basin, the number of unknowns to be estimated was reduced from 858 to 33, and this calibration strategy improved the model performance while preserving the physical realism of the model parameters. Our results also suggest that the commonly used parameter field "multiplier" approach may often not be appropriate. Copyright 2008 by the American Geophysical Union.
• White, D., Wagener, T., Twery, M. J., Ticehurst, J. L., Street, R., Stewart, S., Stewart, R. N., Smith, C., Semmens, D. J., Rashleigh, B., Mahmoud, M., Liu, Y., Letcher, R., Hulse, D., Hartmann, H., Gupta, H. V., Dominguez, D., & Delden, H. V. (2008). Formal Scenario Development for Environmental Impact Assessment Studies. Developments in Integrated Environmental Assessment, 3, 145-162. doi:10.1016/s1574-101x(08)00609-1
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  Abstract Scenario analysis is a process of evaluating possible future events through the consideration of alternative plausible, though not equally likely, states (scenarios). The analysis is designed to enable improved decision making and assessment through a more rigorous evaluation of possible outcomes and their implications. For environmental impact and integrated assessment studies, the process of scenario development typically involves making explicit and/or implicit assumptions about potential future conditions, such as climate change, land cover and land use changes, population growth, economic development and technological changes. Realistic assessment of scenario impacts often requires complex integrated modelling frameworks that represent environmental and socioeconomic systems to the best of our knowledge, including assumptions about plausible future conditions. In addition, scenarios have to be developed in a context relevant to the stakeholders involved, and include estimation and communication of uncertainties, to establish transparency, credibility and relevance of scenario results among the stakeholders. This paper reviews the state of the art of scenario development and analysis, proposes a formal approach to scenario development in environmental studies and discusses existing issues. Major recommendations for future research in this area include proper consideration of uncertainty involved in scenario studies, construction of scenarios of a more variable nature, and sharing of information and resources among the scenario development research community.
• Yatheendradas, S., Wagener, T., Gupta, H., Unkrich, C., Goodrich, D., Schaffner, M., & Stewart, A. (2008). Understanding uncertainty in distributed flash flood forecasting for semiarid regions. Water Resources Research, 44(5).
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  Abstract: Semiarid flash floods pose a significant danger for life and property in many dry regions around the world. One effective way to mitigate flood risk lies in implementing a real-time forecast and warning system based on a rainfall-runoff model. This study used a semiarid, physics-based, and spatially distributed watershed model driven by high-resolution radar rainfall input to evaluate such a system. The predictive utility of the model and dominant sources of uncertainty were investigated for several runoff events within the U.S. Department of Agriculture Agricultural Research Service Walnut Gulch Experimental Watershed located in the southwestern United States. Sources of uncertainty considered were rainfall estimates, watershed model parameters, and initial soil moisture conditions. Results derived through a variance-based comprehensive global sensitivity analysis indicated that the high predictive uncertainty in the modeled response was heavily dominated by biases in the radar rainfall depth estimates. Key model parameters and initial model states were identified, and we generally found that modeled hillslope characteristics are more influential than channel characteristics in small semiarid basins. We also observed an inconsistency in the parameter sets identified as behavioral for different events, which suggests that model calibration to historical data is unlikely to consistently improve predictive performance for different events and that real-time parameter updating may be preferable. Copyright 2008 by the American Geophysical Union.
• Yilmaz, K. K., Gupta, H. V., & Wagener, T. (2008). A process-based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model. Water Resources Research, 44(9).
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  Abstract: Distributed hydrological models have the potential to provide improved streamflow forecasts along the entire channel network, while also simulating the spatial dynamics of evapotranspiration, soil moisture content, water quality, soil erosion, and land use change impacts. However, they are perceived as being difficult to parameterize and evaluate, thus translating into significant predictive uncertainty in the model results. Although a priori parameter estimates derived from observable watershed characteristics can help to minimize obstacles to model implementation, there exists a need for powerful automated parameter estimation strategies that incorporate diagnostic information regarding the causes of poor model performance. This paper investigates a diagnostic approach to model evaluation that exploits hydrological context and theory to aid in the detection and resolution of watershed model inadequacies, through consideration of three of the four major behavioral functions of any watershed system; overall water balance, vertical redistribution, and temporal redistribution (spatial redistribution was not addressed). Instead of using classical statistical measures (such as mean squared error), we use multiple hydrologically relevant "signature measures" to quantify the performance of the model at the watershed outlet in ways that correspond to the functions mentioned above and therefore help to guide model improvements in a meaningful way. We apply the approach to the Hydrology Laboratory Distributed Hydrologic Model (HL-DHM) of the National Weather Service and show that diagnostic evaluation has the potential to provide a powerful and intuitive basis for deriving consistent estimates of the parameters of watershed models. Copyright 2008 by the American Geophysical Union.
• Abramowitz, G., Pitman, A., Gupta, H., Kowalczyk, E., & Wang, Y. (2007). Systematic bias in land surface models. Journal of Hydrometeorology, 8(5), 989-1001.
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  Abstract: A neural network-based flux correction technique is applied to three land surface models. It is then used to show that the nature of systematic model error in simulations of latent heat, sensible heat, and the net ecosystem exchange of CO2 is shared between different vegetation types and indeed different models. By manipulating the relationship between the dataset used to train the correction technique and that used to test it, it is shown that as much as 45% of per-time-step model root-mean-square error in these flux outputs is due to systematic problems in those model processes insensitive to changes in vegetation parameters. This is shown in the three land surface models using flux tower measurements from 13 sites spanning 2 vegetation types. These results suggest that efforts to improve the representation of fundamental processes in land surface models, rather than parameter optimization, are the key to the development of land surface model ability. © 2007 American Meteorological Society.
• Gupta, H., Abramowitz, G., Pitman, A., Kowalczyk, E., & Wang, Y. (2007). Systematic Bias in Land Surface Models. Journal of Hydrometeorology, 8(5), 989-1001. doi:10.1175/jhm628.1
• Liu, Y., & Gupta, H. V. (2007). Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework. Water Resources Research, 43(7).
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  Abstract: Despite significant recent developments in computational power and distributed hydrologic modeling, the issue of how to adequately address the uncertainty associated with hydrological predictions remains a critical and challenging one. This issue needs to be properly addressed for hydrological modeling to realize its maximum practical potential in environmental decision-making processes. Arguably, the key to properly addressing hydrologic uncertainty is to understand, quantify, and reduce uncertainty involved in hydrologic modeling in a cohesive, systematic manner. Although general principles and techniques on addressing hydrologic uncertainty are emerging in the literature, there exist no well-accepted guidelines about how to actually implement these principles and techniques in various hydrologic settings in an integrated manner. This paper reviews, in relevant detail, the common data assimilation methods that have been used in hydrologic modeling to address problems of state estimation, parameter estimation, and system identification. In particular, the paper discusses concepts, methods, and issues involved in hydrologic data assimilation from a systems perspective. An integrated hierarchical framework is proposed for pursuing hydrologic data assimilation in several progressive steps to maximally reduce uncertainty in hydrologic predictions. Copyright 2007 by the American Geophysical Union.
• Schaefli, B., & Gupta, H. V. (2007). Do Nash values have value?. Hydrological Processes, 21(15), 2075-2080.
• Wagener, T., Stewart, S., Mahmoud, M., Liu, Y., Hartmann, H., & Gupta, H. V. (2007). Scenario Development for Water Resources Planning and Management. IAHS-AISH publication, 192-198.
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  We report progress on a novel effort to develop a unified framework for constructing scenarios for water resource management. The framework comprises five iterative phases: scenario definition, scenario construction, scenario analysis, scenario assessment, and risk management. While the scenario framework can be applied to most water resource applications, we place particular emphasis on semiarid environments and forces not typically considered in the traditional water management process such as unforeseen changes in government institutions, or second-order effects of climate change on environmental systems. The main objective of scenario development for water resources is to inform policy-makers about the implications of various water management strategies. In addition, scenarios can consider the, possible effects of external drivers, such as changes in political institutions, or large-scale environmental change that may be especially important in developing countries.
• Wagener, T., Suzuki, K., Schaake, J. C., Rosbjerg, D., Kunstmann, H., Kadota, A., Hall, A., Gupta, H. V., Franks, S. W., Farias, C. A., Celeste, A. B., Boegh, E., & Bastidas, L. A. (2007). RNN-based inflow forecasting applied to reservoir operation via implicit stochastic optimization. IAHS-AISH publication, 452-462.
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  A Recurrent Neural Network (RNN) is proposed for monthly reservoir inflow forecasting. In order to verify its performance and applicability, the network is used to assist reservoir operations carried out by Implicit Stochastic Optimization (ISO). The ISO approach defines the release at each month conditioned on the month's initial storage and the forecasted inflow for the month. This inflow is determined by the RNN. For comparison, optimal ISO-based releases assuming the inflows as perfect forecasts are also conducted. The RNN estimates the current-period inflow as a function of the previous inflow and current forecasted rainfall. The excellent accuracy obtained by the RNN suggests that it is very effective for one-month-ahead forecasting of reservoir inflows. Furthermore, the optimal reservoir releases obtained by the ISO using the RNN-based forecasts were shown to be highly correlated with those using perfect forecasts and superior to those obtained by standard rules of operation.
• Yadav, M., Wagener, T., & Gupta, H. (2007). Regionalization of constraints on expected watershed response behavior for improved predictions in ungauged basins. Advances in Water Resources, 30(8), 1756-1774.
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  Abstract: Approaches to modeling the continuous hydrologic response of ungauged basins use observable physical characteristics of watersheds to either directly infer values for the parameters of hydrologic models, or to establish regression relationships between watershed structure and model parameters. Both these approaches still have widely discussed limitations, including impacts of model structural uncertainty. In this paper we introduce an alternative, model independent, approach to streamflow prediction in ungauged basins based on empirical evidence of relationships between watershed structure, climate and watershed response behavior. Instead of directly estimating values for model parameters, different hydrologic response behaviors of the watershed, quantified through model independent streamflow indices, are estimated and subsequently regionalized in an uncertainty framework. This results in expected ranges of streamflow indices in ungauged watersheds. A pilot study using 30 UK watersheds shows how this regionalized information can be used to constrain ensemble predictions of any model at ungauged sites. Dominant controlling characteristics were found to be climate (wetness index), watershed topography (slope), and hydrogeology. Main streamflow indices were high pulse count, runoff ratio, and the slope of the flow duration curve. This new approach provided sharp and reliable predictions of continuous streamflow at the ungauged sites tested. © 2007 Elsevier Ltd. All rights reserved.
• Abramowitz, G., Gupta, H., Pitman, A., Wang, Y., Leuning, R., Cleugh, H., & Hsu, K. (2006). Neural Error Regression Diagnosis (NERD): A tool for model bias identification and prognostic data assimilation. Journal of Hydrometeorology, 7(1), 160-177.
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  Abstract: Data assimilation in the field of predictive land surface modeling is generally limited to using observational data to estimate optimal model states or restrict model parameter ranges. To date, very little work has attempted to systematically define and quantify error resulting from a model's inherent inability to simulate the natural system. This paper introduces a data assimilation technique that moves toward this goal by accounting for those deficiencies in the model itself that lead to systematic errors in model output. This is done using a supervised artificial neural network to "learn" and simulate systematic trends in the model output error. These simulations in turn are used to correct the model's output each time step. The technique is applied in two case studies, using fluxes of latent heat flux at one site and net ecosystem exchange (NEE) of carbon dioxide at another. Root-mean-square error (rmse) in latent heat flux per time step was reduced from 27.5 to 18.6 W m-2 (32%) and monthly from 9.91 to 3.08 W m-2 (68%). For NEE, rmse per time step was reduced from 3.71 to 2.70 μmol m-2 s-1 (27%) and annually from 2.24 to 0.11 μmol m-2 s-1 (95%). In both cases the correction provided significantly greater gains than single criteria parameter estimation on the same flux. © 2006 American Meteorological Society.
• Bastidas, L. A., Hogue, T. S., Sorooshian, S., Gupta, H. V., & Shuttleworth, W. J. (2006). Parameter sensitivity analysis for different complexity land surface models using multicriteria methods. Journal of Geophysical Research D: Atmospheres, 111(20).
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  Abstract: A multicriteria algorithm, the MultiObjective Generalized Sensitivity Analysis (MOGSA), was used to investigate the parameter sensitivity of five different land surface models with increasing levels of complexity in the physical representation of the vegetation (BUCKET, CHASM, BATS 1, Noah, and BATS 2) at five different sites representing crop land/ pasture, grassland, rain forest, cropland, and semidesert areas. The methodology allows for the inclusion of parameter interaction and does not require assumptions of independence between parameters, while at the same time allowing for the ranking of several single-criterion and a global multicriteria sensitivity indices. The analysis required on the order of 50 thousand model runs. The results confirm that parameters with similar "physical meaning" across different model structures behave in different ways depending on the model and the locations. It is also shown that after a certain level an increase in model structure complexity does not necessarily lead to better parameter identifiability, i.e., higher sensitivity, and that a certain level of overparameterization is observed. For the case of the BATS 1 and BATS 2 models, with essentially the same model structure but a more sophisticated vegetation model, paradoxically, the effect on parameter sensitivity is mainly reflected in the sensitivity of the soil-related parameter. Copyright 2006 by the American Geophysical Union.
• Duan, Q., Schaake, J., Andréassian, V., Franks, S., Goteti, G., Gupta, H. V., Gusev, Y. M., Habets, F., Hall, A., Hay, L., Hogue, T., Huang, M., Leavesley, G., Liang, X., Nasonova, O. N., Noilhan, J., Oudin, L., Sorooshian, S., Wagener, T., & Wood, E. F. (2006). Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops. Journal of Hydrology, 320(1-2), 3-17.
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  Abstract: The Model Parameter Estimation Experiment (MOPEX) is an international project aimed at developing enhanced techniques for the a priori estimation of parameters in hydrologic models and in land surface parameterization schemes of atmospheric models. The MOPEX science strategy involves three major steps: data preparation, a priori parameter estimation methodology development, and demonstration of parameter transferability. A comprehensive MOPEX database has been developed that contains historical hydrometeorological data and land surface characteristics data for many hydrologic basins in the United States (US) and in other countries. This database is being continuously expanded to include more basins in all parts of the world. A number of international MOPEX workshops have been convened to bring together interested hydrologists and land surface modelers from all over world to exchange knowledge and experience in developing a priori parameter estimation techniques. This paper describes the results from the second and third MOPEX workshops. The specific objective of these workshops is to examine the state of a priori parameter estimation techniques and how they can be potentially improved with observations from well-monitored hydrologic basins. Participants of the second and third MOPEX workshops were provided with data from 12 basins in the southeastern US and were asked to carry out a series of numerical experiments using a priori parameters as well as calibrated parameters developed for their respective hydrologic models. Different modeling groups carried out all the required experiments independently using eight different models, and the results from these models have been assembled for analysis in this paper. This paper presents an overview of the MOPEX experiment and its design. The main experimental results are analyzed. A key finding is that existing a priori parameter estimation procedures are problematic and need improvement. Significant improvement of these procedures may be achieved through model calibration of well-monitored hydrologic basins. This paper concludes with a discussion of the lessons learned, and points out further work and future strategy. © 2005 Elsevier Ltd. All rights reserved.
• Gupta, H. V., Hogue, T. S., Bastidas, L. A., & Sorooshian, S. (2006). Evaluating model performance and parameter behavior for varying levels of land surface model complexity: LSM MODEL PERFORMANCE AND PARAMETER BEHAVIOR. Water Resources Research, 42(8). doi:10.1029/2005wr004440
• Hogue, T. S., Bastidas, L. A., Gupta, H. V., & Sorooshian, S. (2006). Evaluating model performance and parameter behavior for varying levels of land surface model complexity. Water Resources Research, 42(8).
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  Abstract: This paper investigates model performance and parameter behavior for a range of land surface model (LSM) complexity across a variety of vegetated surfaces. Although LSMs are used routinely in regional and global climate (and weather) prediction, there has been limited rigorous testing of these models across a range of biomes. A systems-based approach is used to compare five commonly used LSMs (BUCKET, CHASM, BATS1e, BATS2, and Noah) across five different vegetated sites (pasture, short grass, cropland, tropical rain forest, and semiarid desert). Results indicate that there is no "perfect" model and that additional complexity (defined as additional physical representation in a model) does not necessarily equate to improved performance. In general, the medium complexity BATS1e model has the most consistent performance, with overall lower errors across the sites. Results also indicate that prescribed parameter meanings are not consistent across the various LSM formulations. A comparison of BATS1e and BATS2 parameters reveals significant differences in behavior across the study sites. These findings have key implications for general application of a single model across a range of global biomes and for model intercomparison studies where parameters are preassigned to participating models. Copyright 2006 by the American Geophysical Union.
• Hogue, T. S., Gupta, H., & Sorooshian, S. (2006). A 'User-Friendly' approach to parameter estimation in hydrologic models. Journal of Hydrology, 320(1-2), 202-217.
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  Abstract: The goal of this paper is to analyze the reliability of the Multi-step Automated Calibration Scheme (MACS) over a variety of climatic and hydrologic conditions. The authors developed MACS for the estimation of parameters for hydrologic models; be it for 'fine-tuning' of a priori estimates, or for estimating parameters without a priori knowledge of the system. Optimization methods have advanced over the last few decades, and although they are used extensively by the research community, operational hydrologists have been less eager to implement automated calibration procedures. The authors, under cooperative agreement with the National Weather Service (NWS) Hydrology Laboratory have collaborated to develop a progressive calibration strategy, using 'in-house' NWS algorithms to optimize parameters for the hydrologic models used in operational streamflow forecasting: the Sacramento Soil Moisture Accounting (SAC-SMA) and the SNOW-17 model. The method, though developed within the NWS forecasting system, can be easily adapted to any hydrologic modeling system. In our current work, MACS has been tested on 20 NWS forecast points (or basins), located in various hydrologic and climatic regimes (five different River Forecast Centers (RFCs)) across the United States. Over half of the basins tested (11) consist of multi-tiered systems in the Western US (i.e. the hydrologic models are run over several elevation zones for one forecast point). The results show comparable, reliable calibrations, similar in quality to the traditional manual techniques, over all of the hydro-climatic regimes used for this study. MACS, generally, produces simulations with desirable performance measures, including improved Nash-Sutcliffe efficiency and lower percent bias. MACS performs well in all regions, even over the complex terrain in the western regions of the United States. © 2005 Elsevier Ltd. All rights reserved.
• Morin, E., Goodrich, D. C., Maddox, R. A., Gao, X., Gupta, H. V., & Sorooshian, S. (2006). Spatial patterns in thunderstorm rainfall events and their coupling with watershed hydrological response. Advances in Water Resources, 29(6), 843-860.
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  Abstract: Weather radar systems provide detailed information on spatial rainfall patterns known to play a significant role in runoff generation processes. In the current study, we present an innovative approach to exploit spatial rainfall information of air mass thunderstorms and link it with a watershed hydrological model. Observed radar data are decomposed into sets of rain cells conceptualized as circular Gaussian elements and the associated rain cell parameters, namely, location, maximal intensity and decay factor, are input into a hydrological model. Rain cells were retrieved from radar data for several thunderstorms over southern Arizona. Spatial characteristics of the resulting rain fields were evaluated using data from a dense rain gauge network. For an extreme case study in a semi-arid watershed, rain cells were derived and fed as input into a hydrological model to compute runoff response. A major factor in this event was found to be a single intense rain cell (out of the five cells decomposed from the storm). The path of this cell near watershed tributaries and toward the outlet enhanced generation of high flow. Furthermore, sensitivity analysis to cell characteristics indicated that peak discharge could be a factor of two higher if the cell was initiated just a few kilometers aside. © 2005 Elsevier Ltd. All rights reserved.
• Vrugt, J. A., Gupta, H. V., Dekker, S. C., Sorooshian, S., Wagener, T., & Bouten, W. (2006). Application of stochastic parameter optimization to the Sacramento Soil Moisture Accounting model. Journal of Hydrology, 325(1-4), 288-307.
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  Abstract: Hydrological models generally contain parameters that cannot be measured directly, but can only be meaningfully inferred by calibration against a historical record of input-output data. While considerable progress has been made in the development and application of automatic procedures for model calibration, such methods have received criticism for their lack of rigor in treating uncertainty in the parameter estimates. In this paper, we apply the recently developed Shuffled Complex Evolution Metropolis algorithm (SCEM-UA) to stochastic calibration of the parameters in the Sacramento Soil Moisture Accounting model (SAC-SMA) model using historical data from the Leaf River in Mississippi. The SCEM-UA algorithm is a Markov Chain Monte Carlo sampler that provides an estimate of the most likely parameter set and underlying posterior distribution within a single optimization run. In particular, we explore the relationship between the length and variability of the streamflow data and the Bayesian uncertainty associated with the SAC-SMA model parameters and compare SCEM-UA derived parameter values with those obtained using deterministic SCE-UA calibrations. Most significantly, for the Leaf River catchments under study our results demonstrate that most of the 13 SAC-SMA parameters are well identified by calibration to daily streamflow data suggesting that this data contains more information than has previously been reported in the literature. © 2006 Elsevier B.V. All rights reserved.
• Vrugt, J. A., Gupta, H. V., Nualláin, B. Ó., & Bouten, W. (2006). Real-time data assimilation for operational ensemble streamflow forecasting. Journal of Hydrometeorology, 7(3), 548-565.
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  Abstract: Operational flood forecasting requires that accurate estimates of the uncertainty associated with model-generated streamflow forecasts be provided along with the probable flow levels. This paper demonstrates a stochastic ensemble implementation of the Sacramento model used routinely by the National Weather Service for deterministic streamflow forecasting. The approach, the simultaneous optimization and data assimilation method (SODA), uses an ensemble Kalman filter (EnKF) for recursive state estimation allowing for treatment of streamflow data error, model structural error, and parameter uncertainty, while enabling implementation of the Sacramento model without major modification to its current structural form. Model parameters are estimated in batch using the shuffled complex evolution metropolis stochastic-ensemble optimization approach (SCEM-UA). The SODA approach was implemented using parallel computing to handle the increased computational requirements. Studies using data from the Leaf River, Mississippi, indicate that forecast performance improvements on the order of 30% to 50% can be realized even with a suboptimal implementation of the filter. Further, the SODA parameter estimates appear to be less biased, which may increase the prospects for finding useful regionalization relationships. © 2006 American Meteorological Society.
• Zehe, E., Wagener, T., Gupta, H. V., Freer, J., Beven, K., & Bardossy, A. (2006). Predictions in Ungauged Basins: Promise and Progress. IAHS-AISH publication.
• Demarty, J., Ottlé, C., Braud, I., Olioso, A., Frangi, J. P., Gupta, H. V., & Bastidas, L. A. (2005). Constraining a physically based Soil-Vegetation-Atmosphere Transfer model with surface water content and thermal infrared brightness temperature measurements using a multiobjective approach. Water Resources Research, 41(1), 1-15.
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  Abstract: This article reports on a multiobjective approach which is carried out on the physically based Soil-Vegetation-Atmosphere Transfer (SVAT) model. This approach is designed for (1) analyzing the model sensitivity to its input parameters under various environmental conditions and (2) assessing input parameters through the combined assimilation of the surface water content and the thermal infrared brightness temperature. To reach these goals, a multiobjective calibration iterative procedure (MCIP) is applied on the Simple Soil Plant Atmosphere Transfer-Remote Sensing (SiSPAT-RS) model. This new multiobjective approach consists of performing successive contractions of the feasible parameter space with the multiobjective generalized sensitivity analysis algorithm. Results show that the MCIP is an original and pertinent approach both for improving model calibration (i.e., reducing the a posteriori preferential ranges) and for driving a detailed SVAT model using various calibration data. The usefulness of the water content of the upper 5 cm and the thermal infrared brightness temperature for retrieving quantitative information about the main input surface parameters is also underlined. This study opens perspectives in the combined assimilation of various multispectral remotely sensed observations, such as passive microwaves and thermal infrared signals. Copyright 2005 by the American Geophysical Union.
• Gupta, H. V., Demarty, J., Ottlé, C., Braud, I., Olioso, A., Frangi, J. P., & Bastidas, L. A. (2005). Constraining a physically based Soil-Vegetation-Atmosphere Transfer model with surface water content and thermal infrared brightness temperature measurements using a multiobjective approach: CONSTRAINING A PHYSICALLY BASED SVAT MODEL. Water Resources Research, 41(1). doi:10.1029/2004wr003695
• Gupta, H., Hogue, T. S., Bastidas, L., Sorooshian, S., Mitchell, K., & Emmerich, W. (2005). Evaluation and Transferability of the Noah Land Surface Model in Semiarid Environments. Journal of Hydrometeorology, 6(1), 68-84. doi:10.1175/jhm-402.1
• Hogue, T. S., Bastidas, L., Gupta, H., Sorooshian, S., Mitchell, K., & Emmerich, W. (2005). Evaluation and transferability of the Noah land surface model in semiarid environments. Journal of Hydrometeorology, 6(1), 68-84.
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  Abstract: This paper investigates the performance of the National Centers for Environmental Prediction (NCEP) Noah land surface model at two semiarid sites in southern Arizona. The goal is to evaluate the transferability of calibrated parameters (i.e., direct application of a parameter set to a "similar" site) between the sites and to analyze model performance under the various climatic conditions that can occur in this region. A multicriteria, systematic evaluation scheme is developed to meet these goals. Results indicate that the Noah model is able to simulate sensible heat, ground heat, and ground temperature observations with a high degree of accuracy, using the optimized parameter sets. However, there is a large influx of moist air into Arizona during the monsoon period, and significant latent heat flux errors are observed in model simulations during these periods. The use of proxy site parameters (transferred parameter set), as well as traditional default parameters, results in diminished model performance when compared to a set of parameters calibrated specifically to the flux sites. Also, using a parameter set obtained from a longer-time-frame calibration (i.e., a 4-yr period) results in decreased model performance during nonstationary, short-term climatic events, such as a monsoon or El Niño. Although these results are specific to the sites in Arizona, it is hypothesized that these results may hold true for other case studies. In general, there is still the opportunity for improvement in the representation of physical processes in land surface models for semiarid regions. The hope is that rigorous model evaluation, such as that put forth in this analysis, and studies such as the Project for the Intercomparison of Land-Surface Processes (PILPS) San Pedro-Sevilleta, will lead to advances in model development, as well as parameter estimation and transferability, for use in long-term climate and regional environmental studies. © 2005 American Meteorological Society.
• Liu, Y., Gupta, H. V., Sorooshian, S., Bastidas, L. A., & Shuttleworth, W. J. (2005). Constraining land surface and atmospheric parameters of a locally coupled model using observational data. Journal of Hydrometeorology, 6(2), 156-172.
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  Abstract: In coupled land surface-atmosphere modeling, the possibility and benefits of constraining model parameters using observational data bear investigation. Using the locally coupled NCAR Single-column Community Climate Model (NCAR SCCM), this study demonstrates some feasible, effective approaches to constrain parameter estimates for coupled land-atmosphere models and explores the effects of including both land surface and atmospheric parameters and fluxes/variables in the parameter estimation process, as well as the value of conducting the process in a stepwise manner. The results indicate that the use of both land surface and atmospheric flux variables to construct error criteria can lead to better-constrained parameter sets. The model with "optimal" parameters generally performs better than when a priori parameters are used, especially when some atmospheric parameters are included in the parameter estimation process. The overall conclusion is that, to achieve balanced, reasonable model performance on all variables, it is desirable to optimize both land surface and atmospheric parameters and use both land surface and atmospheric fluxes/variables for error criteria in the optimization process. The results also show that, for a coupled land-atmosphere model, there are potential advantages to using a stepwise procedure in which the land surface parameters are first identified in offline mode, after which the atmospheric parameters are determined in coupled mode. This stepwise scheme appears to provide comparable solutions to a fully coupled approach, but with considerably reduced computational time. The trade-off in the ability of a model to satisfactorily simulate different processes simultaneously, as observed in most multicriteria studies, is most evident for sensible heat and precipitation in this study for the NCAR SCCM. © 2005 American Meteorological Society.
• Moradkhani, H., Hsu, K., Gupta, H., & Sorooshian, S. (2005). Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter. Water Resources Research, 41(5), 1-17.
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  Abstract: Two elementary issues in contemporary Earth system science and engineering are (1) the specification of model parameter values which characterize a system and (2) the estimation of state variables which express the system dynamic. This paper explores a novel sequential hydrologic data assimilation approach for estimating model parameters and state variables using particle filters (PFs). PFs have their origin in Bayesian estimation. Methods for batch calibration, despite major recent advances, appear to lack the flexibility required to treat uncertainties in the current system as new information is received. Methods based on sequential Bayesian estimation seem better able to take advantage of the temporal organization and structure of information, so that better compliance of the model output with observations can be achieved. Such methods provide platforms for improved uncertainty assessment and estimation of hydrologic model components, by providing more complete and accurate representations of the forecast and analysis probability distributions. This paper introduces particle filtering as a sequential Bayesian filtering having features that represent the full probability distribution of predictive uncertainties. Particle filters have, so far, generally been used to recursively estimate the posterior distribution of the model state; this paper investigates their applicability to the approximation of the posterior distribution of parameters. The capability and usefulness of particle filters for adaptive inference of the joint posterior distribution of the parameters and state variables are illustrated via two case studies using a parsimonious conceptual hydrologic model. Copyright 2005 by the American Geophysical Union.
• Moradkhani, H., Sorooshian, S., Gupta, H. V., & Houser, P. R. (2005). Dual state-parameter estimation of hydrological models using ensemble Kalman filter. Advances in Water Resources, 28(2), 135-147.
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  Abstract: Hydrologic models are twofold: models for understanding physical processes and models for prediction. This study addresses the latter, which modelers use to predict, for example, streamflow at some future time given knowledge of the current state of the system and model parameters. In this respect, good estimates of the parameters and state variables are needed to enable the model to generate accurate forecasts. In this paper, a dual state-parameter estimation approach is presented based on the Ensemble Kalman Filter (EnKF) for sequential estimation of both parameters and state variables of a hydrologic model. A systematic approach for identification of the perturbation factors used for ensemble generation and for selection of ensemble size is discussed. The dual EnKF methodology introduces a number of novel features: (1) both model states and parameters can be estimated simultaneously; (2) the algorithm is recursive and therefore does not require storage of all past information, as is the case in the batch calibration procedures; and (3) the various sources of uncertainties can be properly addressed, including input, output, and parameter uncertainties. The applicability and usefulness of the dual EnKF approach for ensemble streamflow forecasting is demonstrated using a conceptual rainfall-runoff model. © 2004 Elsevier Ltd. All rights reserved.
• Morin, E., Goodrich, D. C., Maddox, R. A., Gao, X., Gupta, H. V., & Sorooshian, S. (2005). Rainfall modeling for integrating radar information into hydrological model. Atmospheric Science Letters, 6(1), 23-30.
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  Abstract: A spatial rainfall model was applied to radar data of air mass thunderstorms to yield a rainstorm representation as a set of convective rain cells. The modeled rainfall was used as input into hydrological model, instead of the standard radar-grid data. This approach allows a comprehensive linkage between runoff responses and rainfall structures. Copyright © 2005 Royal Meteorological Society.
• Shamir, E., Imam, B., Gupta, H. V., & Sorooshian, S. (2005). Application of temporal streamflow descriptors in hydrologic model parameter estimation. Water Resources Research, 41(6), 1-16.
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  Abstract: This paper presents a parameter estimation approach based on hydrograph descriptors that capture dominant streamflow characteristics at three timescales (monthly, yearly, and record extent). The scheme, entitled hydrograph descriptors multitemporal sensitivity analyses (HYDMUS), yields an ensemble of model simulations generated from a reduced parameter space, based on a set of streamflow descriptors that emphasize the timescale dynamics of streamflow record. In this procedure the posterior distributions of model parameters derived at coarser timescales are used to sample model parameters for the next finer timescale. The procedure was used to estimate the parameters of the Sacramento soil moisture accounting model (SAC-SMA) for the Leaf River, Mississippi. The results indicated that in addition to a significant reduction in the range of parameter uncertainty, HYDMUS improved parameter identifiability for all 13 of the model parameters. The performance of the procedure was compared to four previous calibration studies on the same watershed. Although our application of HYDMUS did not explicitly consider the error at each simulation time step during the calibration process, the model performance was, in some important respects, found to be better than in previous deterministic studies. Copyright 2005 by the American Geophysical Union.
• Shamir, E., Imam, B., Morin, E., Gupta, H. V., & Sorooshian, S. (2005). The role of hydrograph indices in parameter estimation of rainfall-runoff models. Hydrological Processes, 19(11), 2187-2207.
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  Abstract: A reliable prediction of hydrologic models, among other things, requires a set of plausible parameters that correspond with physiographic properties of the basin. This study proposes a parameter estimation approach, which is based on extracting, through hydrograph diagnoses, information in the form of indices that carry intrinsic properties of a basin. This concept is demonstrated by introducing two indices that describe the shape of a streamflow hydrograph in an integrated manner. Nineteen mid-size (223-4790 km2) perennial headwater basins with a long record of streamflow data were selected to evaluate the ability of these indices to capture basin response characteristics. An examination of the utility of the proposed indices in parameter estimation is conducted for a five-parameter hydrologic model using data from the Leaf River, located in Fort Collins, Mississippi. It is shown that constraining the parameter estimation by selecting only those parameters that result in model output which maintains the indices as found in the historical data can improve the reliability of model predictions. These improvements were manifested in (a) improvement of the prediction of low and high flow, (b) improvement of the overall total biases, and (c) maintenance of the hydrograph's shape for both long-term and short-term predictions. Copyright © 2005 John Wiley & Sons, Ltd.
• Vrugt, J. A., G., C., Gupta, H. V., Bouten, W., & Verstraten, J. M. (2005). Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation. Water Resources Research, 41(1), 1-17.
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  Abstract: Hydrologic models use relatively simple mathematical equations to conceptualize and aggregate the complex, spatially distributed, and highly interrelated water, energy, and vegetation processes in a watershed. A consequence of process aggregation is that the model parameters often do not represent directly measurable entities and must therefore be estimated using measurements of the system inputs and outputs. During this process, known as model calibration, the parameters are adjusted so that the behavior of the model approximates, as closely and consistently as possible, the observed response of the hydrologic system over some historical period of time. In practice, however, because of errors in the model structure and the input (forcing) and output data, this has proven to be difficult, leading to considerable uncertainty in the model predictions. This paper surveys the limitations of current model calibration methodologies, which treat the uncertainty in the input-output relationship as being primarily attributable to uncertainty in the parameters and presents a simultaneous optimization and data assimilation (SODA) method, which improves the treatment of uncertainty in hydrologic modeling. The usefulness and applicability of SODA is demonstrated by means of a pilot study using data from the Leaf River watershed in Mississippi and a simple hydrologic model with typical conceptual components. Copyright 2005 by the American Geophysical Union.
• Wagener, T., & Gupta, H. V. (2005). Model identification for hydrological forecasting under uncertainty. Stochastic Environmental Research and Risk Assessment, 19(6), 378-387.
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  Abstract: Methods for the identification of models for hydrological forecasting have to consider the specific nature of these models and the uncertainties present in the modeling process. Current approaches fail to fully incorporate these two aspects. In this paper we review the nature of hydrological models and the consequences of this nature for the task of model identification. We then continue to discuss the history ("The need for more POWER"), the current state ("Learning from other fields") and the future ("Towards a general framework") of model identification. The discussion closes with a list of desirable features for an identification framework under uncertainty and open research questions in need of answers before such a framework can be implemented.
• Wagener, T., Schaake, J., Rosbjerg, D., Kunstmann, H., Hall, A., Gupta, H. V., Franks, S. W., Boegh, E., & Bastidas, L. A. (2005). IAHS-AISH Publication: Preface. IAHS-AISH publication.
• Yilmaz, K. K., Hogue, T. S., Hsu, K., Sorooshian, S., Gupta, H. V., & Wagener, T. (2005). Intercomparison of rain gauge, radar, and satellite-based precipitation estimates with emphasis on hydrologic forecasting. Journal of Hydrometeorology, 6(4), 497-517.
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  Abstract: This study compares mean areal precipitation (MAP) estimates derived from three sources: an operational rain gauge network (MAPG), a radar/ gauge multisensor product (MAPX), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) satellite-based system (MAPS) for the time period from March 2000 to November 2003. The study area includes seven operational basins of varying size and location in the southeastern United States. The analysis indicates that agreements between the datasets vary considerably from basin to basin and also temporally within the basins. The analysis also includes evaluation of MAPS in comparison with MAPG for use in flow forecasting with a lumped hydrologic model [Sacramento Soil Moisture Accounting Model (SAC-SMA)]. The latter evaluation investigates two different parameter sets, the first obtained using manual calibration on historical MAPG, and the second obtained using automatic calibration on both MAPS and MAPG, but over a shorter time period (23 months). Results indicate that the overall performance of the model simulations using MAPS depends on both the bias in the precipitation estimates and the size of the basins, with poorer performance in basins of smaller size (large bias between MAPG and MAPS) and better performance in larger basins (less bias between MAPG and MAPS). When using MAPS, calibration of the parameters significantly improved the model performance. © 2005 American Meteorological Society.
• Ajami, N. K., Gupta, H., Wagener, T., & Sorooshian, S. (2004). Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system. Journal of Hydrology, 298(1-4), 112-135.
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  Abstract: An important goal of spatially distributed hydrologic modeling is to provide estimates of streamflow (and river levels) at any point along the river system. To encourage collaborative research into appropriate levels of model complexity, the value of spatially distributed data, and methods suitable for model development and calibration, the US National Weather Service Hydrology Laboratory (NWSHL) is promoting the distributed modeling intercomparison project (DMIP). In particular, the project is interested in how spatially distributed estimates of precipitation provided by the next generation radar (NEXRAD) network, high resolution digital elevation models (DEM), soil, land-use and vegetation data can be integrated into an improved system for distributed hydrologic modeling that provides more accurate and informative flood forecasts. The goal of this study is to explore four questions: Can a semi-distributed approach improve the streamflow forecasts at the watershed outlet compared to a lumped approach? What is a suitable calibration strategy for a semi-distributed model structure, and how much improvement can be obtained? What is the minimum level of spatial complexity required, above which the improvement in forecast accuracy is marginal? What spatial details must be included to enable flow prediction at any point along the river network? The study compares lumped, semi-lumped and semi-distributed versions of the SAC-SMA (Sacramento Soil Moisture Accounting) model for the Illinois River basin at Watts (OK). A kinematic wave scheme is used to rout the flow along the river channel to the outlet. A Multi-step Automatic Calibration Scheme (MACS) using the Shuffled Complex Evolution (SCE-UA) optimization algorithm is applied for model calibration. The calibration results reveal that moving from a lumped model structure, driven by spatially averaged NEXRAD data over the entire basin, to a semi-distributed model structure, with forcing data averaged over each sub-basin while having identical parameters for all the sub-basins, improves the simulation results. However, varying the parameters between sub-basins does not further improve the simulation results, either at the outlet or at an interior testing point. © 2004 Elsevier B.V. All rights reserved.
• Demarty, J., Ottlé, C., Braud, I., Olioso, A., Frangi, J. P., Bastidas, L. A., & Gupta, H. V. (2004). Using a multiobjective approach to retrieve information on surface properties used in a SVAT model. Journal of Hydrology, 287(1-4), 214-236.
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  Abstract: The reliability of model predictions used in meteorology, agronomy or hydrology is partly linked to an adequate representation of the water and energy balances which are described in so-called SVAT (Soil Vegetation Atmosphere Transfer) models. These models require the specification of many surface properties which can generally be obtained from laboratory or field experiments, using time consuming techniques, or can be derived from textural information. The required accuracy of the surface properties depends on the model complexity and their misspecification can affect model performance. At various time and spatial resolutions, remote sensing provides information related to surface parameters in SVAT models or state variables simulated by SVAT models. In this context, the Simple Soil-Plant-Atmosphere Transfer-Remote Sensing (SiSPAT-RS) model was developed for remote sensing data assimilation objectives. This new version of the physically based SiSPAT model simulates the main surface processes (energy fluxes, soil water content profiles, temperatures) and remote sensing data in the visible, infrared and thermal infrared spectral domains. As a preliminary step before data assimilation in the model, the objectives of this study were (1) to apply a multiobjective approach for retrieving quantitative information about the surface properties from different surface measurements and (2) to determine the potential of the SiSPAT-RS model to be applied with 'little' a priori information about input parameters. To reach these goals, the ability of the Multiobjective Generalized Sensitivity Analysis (MOGSA) algorithm to determine and quantify the most influential input parameters of the SiSPAT-RS model on several simulated output variables, was investigated. The results revealed the main influential input parameters according to different contrasted environmental conditions, and contributed to the reduction of their a priori uncertainty range. A procedure for specifying surface properties from MOGSA results was tested on the thermal and hydraulic soil parameters, and evaluated through the SiSPAT-RS model performance. Although slightly lower than a reference simulation, the performance were satisfactory and suggested that complex SVAT models can be driven with little a priori information on soil properties, as in a future context of remote sensing data assimilation. Measurement acquired on a winter wheat field of the ReSeDA (Remote Sensing Data Assimilation) experiment were used in this study. © 2004 Elsevier B.V. All rights reserved.
• Georgakakos, K. P., Seo, D., Gupta, H., Schaake, J., & Butts, M. B. (2004). Towards the characterization of streamflow simulation uncertainty through multimodel ensembles. Journal of Hydrology, 298(1-4), 222-241.
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  Abstract: Distributed hydrologic modeling holds significant promise for improved estimates of streamflow with high spatial resolution. However, uncertainty in model structure and parameters, which are distributed in space, and in operational weather radar rainfall estimates, which comprise the main input to the models, contributes to significant uncertainty in distributed model streamflow simulations over a wide range of space and time scales. Using the simulations produced for the Distributed Model Intercomparison Project (DMIP), this paper develops and applies sample-path methods to characterize streamflow simulation uncertainty by diverse distributed hydrologic models. The emphasis in this paper is on the model parameter and structure uncertainty given radar rainfall forcing. Multimodel ensembles are analyzed for six application catchments in the Central US to characterize model structure uncertainty within the sample of models (both calibrated and uncalibrated) participating in DMIP. Ensembles from single distributed and lumped models are also used for one of the catchments to provide a basis to characterize the impact of parametric uncertainty versus model structure uncertainty in flow simulation statistics. Two main science questions are addressed: (a) what is the value of multimodel streamflow ensembles in terms of the probabilistic characterization of simulation uncertainty? And (b) how do probabilistic skill measures of multimodel versus single-model ensembles compare? Discussed also are implications for the operational use of streamflow ensembles generated by distributed hydrologic models. The results support the serious consideration of ensemble simulations and predictions created by diverse models in real time flow prediction. © 2004 Published by Elsevier B.V.
• Gupta, H. V., Vrugt, J. A., Wagener, T., & Gupta, H. V. (2004). Advances in automatic methods for identification of hydrologic models. JoH.
• Hogue, T., Wagener, T., Schaake, J., Duan, Q., Hall, A., Gupta, H. V., Leavesley, G., & Andreassian, V. (2004). A New Phase of the Model Parameter Estimation Experiment (MOPEX). EOS Transactions.
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  Hogue T, T Wagener, J Schaake, Q Duan, A Hall, HV Gupta, G Leavesley and V Andreassian (2004), A New Phase of the Model Parameter Estimation Experiment (MOPEX), EOS Transactions 85(22), 217-218
• Liu, Y., Gupta, H. V., Sorooshian, S., & Bastidas, L. A. (2004). Explore parameter sensitivities and model calibration in a locally coupled environment. Bulletin of the American Meteorological Society, 3365-3367.
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  Abstract: A locally coupled Single Column Model (SCM) was used for sensitivity analysis and model calibration. The sensitivity analysis was used to identify 32 land-surface parameters which appeared to be more or less sensitive in the locally coupled environment. The multi-objective sensitive analysis shows that the land surface-atmosphere interactions could have significant influences on the model parameter sensitivities. The calibration results suggest that it is crucial to include both land-surface and atmospheric parameters in the calibration of a coupled land surface model.
• Liu, Y., Gupta, H. V., Sorooshian, S., Bastidas, L. A., & Shuttleworth, W. J. (2004). Exploring parameter sensitivities of the land surface using a locally coupled land-atmosphere model. Journal of Geophysical Research D: Atmospheres, 109(21), D21101 1-13.
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  Abstract: This paper presents a multicriteria analysis that explores the sensitivity of the land surface to changes in both land and atmospheric parameters, in terms of reproducing surface heat fluxes and ground temperature; for the land parameters, offline sensitivity analyses were also conducted for comparison to infer the influence of land-atmosphere interactions. A simple "one-at-a-time" sensitivity analysis was conducted first to filter out some insensitive parameters, followed by a multicriteria sensitivity analysis using the multiobjective generalized sensitivity analysis algorithm. The models used were the locally coupled National Center for Atmospheric Research (NCAR) single-column community climate model and the offline NCAR land surface model, driven and evaluated by a summer intensive operational periods (IOP) data set from the southern Great Plains. As expected, the results show that land-atmosphere interactions (with or without land-atmosphere parameter interactions) can have significant influences on the sensitivity of the land surface to changes in the land parameters, and the single-criterion sensitivities can be significantly different from the multicriteria sensitivity. These findings are mostly model and data independent and can be generally useful, regardless of the model/data dependence of the sensitivities of individual parameters. The exceptionally high sensitivities of the selected atmospheric parameters in a multicriteria sense (and in particular for latent heat) appeal for adequate attention to the specification of effective values of these parameters in an atmospheric model. Overall, this study proposes an effective framework of multicriteria sensitivity analysis beneficial to future studies in the development and parameter estimation of other complex (offline or coupled) land surface models. Copyright 2004 by the American Geophysical Union.
• Meixner, T., Gupta, H. V., Montanari, A., & Jackson, B. (2004). Understanding Hydrologic Model Uncertainty: A Report on the IAHS-PUB Workshop. EOS Transactions.
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  Meixner T, H Gupta, A Montanari and B Jackson (2004), Understanding Hydrologic Model Uncertainty: A Report on the IAHS-PUB Workshop, EOS Transactions, v85, p556
• Meixner, T., Gupta, H., Alberto, M., & Bethania, J. (2004). Understanding hydrologic model uncertainty: A report on the iahs-pub workshop. Eos, 85(51), 556-.
• Moradkhani, H., Hsu, K., Gupta, H. V., & Sorooshian, S. (2004). Improved streamflow forecasting using self-organizing radial basis function artificial neural networks. Journal of Hydrology, 295(1-4), 246-262.
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  Abstract: Streamflow forecasting has always been a challenging task for water resources engineers and managers and a major component of water resources system control. In this study, we explore the applicability of a Self Organizing Radial Basis (SORB) function to one-step ahead forecasting of daily streamflow. SORB uses a Gaussian Radial Basis Function architecture in conjunction with the Self-Organizing Feature Map (SOFM) used in data classification. SORB outperforms the two other ANN algorithms, the well known Multi-layer Feedforward Network (MFN) and Self-Organizing Linear Output map (SOLO) neural network for simulation of daily streamflow in the semi-arid Salt River basin. The applicability of the linear regression model was also investigated and concluded that the regression model is not reliable for this study. To generalize the model and derive a robust parameter set, cross-validation is applied and its outcome is compared with the split sample test. Cross-validation justifies the validity of the nonlinear relationship set up between input and output data. © 2004 Elsevier B.V. All rights reserved.
• Shuttleworth, W. J., Gupta, H. V., Liu, Y., Sorooshian, S., & Bastidas, L. A. (2004). Exploring parameter sensitivities of the land surface using a locally coupled land-atmosphere model: EXPLORING PARAMETER SENSITIVITIES. Journal of Geophysical Research: Atmospheres, 109(D21), n/a-n/a. doi:10.1029/2004jd004730
• Wagener, T., Gupta, H. V., Carpenter, K., James, B., Vázquez, R., Sorooshian, S., & Shuttleworth, J. (2004). A hydroarchive for the free exchange of hydrological software. Hydrological Processes, 18(2), 389-391.
• Wagener, T., Shaake, J., Leavesley, G. H., Hogue, T. S., Hall, A., Gupta, H. V., Duan, Q., & Andreassian, V. (2004). Model Parameter Experiment Begins New Phase. Eos, Transactions American Geophysical Union, 85(22), 217-218. doi:10.1029/2004eo220005
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  The Model Parameter Estimation Experiment (MOPEX) is an ongoing international project to help develop techniques for the a priori estimation of parameters used in land surface parameterization schemes of atmospheric and hydrological models. MOPEX is affiliated with both the Prediction in Ungauged Basins (PUBS) and the Global Energy and Water Cycle Experiment (GEWEX),and is supported by the GEWEX American Prediction Project (GAPP) as well as by individual participants. Current procedures for a priori parameter estimation are often based on relationships between model parameters and basin characteristics—that is, soils, vegetation, topography climate, geology, etc. These developed relationships have not been fully validated through rigorous testing using retrospective hydrometeorological data and corresponding land surface characteristics. This is partly because the necessary database needed for such testing has not been available. Moreover, there still exists a gap in our understanding of the links between model parameters and land surface characteristics.
• Beven, K., Young, P., Gupta, H., Thiemann, M., Trosset, M., & Sorooshian, S. (2003). Comment on "Bayesian recursive parameter estimation for hydrologic models" by M. Thiemann, M. Trosset, H. Gupta, and S. Sorooshian. Water Resources Research, 39(5), COM11-COM14+COM21-COM25.
• Gupta, H. V., Thiemann, M., Trosset, M., & Sorooshian, S. (2003). Reply to Comment on ‘Bayesian recursive parameter estimation for hydrologic models’ by Keith Beven and Peter Young. Water Resources Research.
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  Gupta HV, M Thiemann, M Trosset and S Sorooshian (2003), Reply to Comment on ‘Bayesian recursive parameter estimation for hydrologic models’ by Keith Beven and Peter Young, Water Resources Research, Vol. 39, No. 5, pp. 2.1-2.5, doi: 10.1029/2002WR001405
• Gupta, H. V., Vrugt, J. A., Bastidas, L. A., Bouten, W., & Sorooshian, S. (2003). Effective and efficient algorithm for multiobjective optimization of hydrologic models: MULTIOBJECTIVE OPTIMIZATION OF HYDROLOGIC MODELS. Water Resources Research, 39(8). doi:10.1029/2002wr001746
• Gupta, H. V., Vrugt, J. A., Bouten, W., & Hopmans, J. W. (2003). Toward Improved Identifiability of Soil Hydraulic Parameters: On the Selection of a Suitable Parametric Model. Vadose Zone Journal, 2(1), 98-113. doi:10.2136/vzj2003.9800
• Liu, Y., Bastidas, L. A., Gupta, H. V., & Sorooshian, S. (2003). Impacts of a parameterization deficiency on offline and coupled land surface model simulations. Journal of Hydrometeorology, 4(5), 901-914.
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  Abstract: Surface water and energy balance plays an important role in land surface models, especially in coupled land surface-atmospheric models due to the complicated interactions between land surfaces and the overlying atmosphere. The primary purpose of this paper is to demonstrate the significant negative impacts that a minor deficiency in the parameterization of canopy evaporation may have on offline and coupled land surface model simulations. In this research, using the offline NCAR Land Surface Model (LSM) and the locally coupled NCAR Single-column Community Climate Model (SCCM) as examples, intensive effort has been focused on the exploration of the mechanisms involved in the activation of unrealistically high canopy evaporation and thus unreasonable surface energy partitions because of a minor deficiency in the parameterization of canopy evaporation. The main causes responsible for exacerbating the impacts of the deficiency of the land surface model through the coupling of the two components are analyzed, along with possible impacts of land surface parameters in triggering the problems. Results from experimental runs show that, for a large number of randomly generated physically realistic land surface parameter sets, this model deficiency has caused the occurrences of negative canopy water with a significantly high frequency for both the offline NCAR LSM and the coupled NCAR SCCM, suggesting that land surface parameters are not the only important factors in triggering the problems associated with the model deficiency. In addition, the concurrence of intense solar radiation and enough precipitation is identified to be mainly responsible for exacerbating the negative impacts of the parameterization deficiency. Finally, a simple adjustment has been made in this study to effectively prevent the occurrences of negative canopy water storages, leading to significantly improved model performances.
• Vrugt, J. A., Bouten, W., Gupta, H. V., & Hopmans, J. W. (2003). Toward improved identifiability of soil hydraulic parameters: On the selection of a suitable parametric model. Vadose Zone Journal, 2(1), 98-113.
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  Abstract: We present a thorough identifiability analysis of the soil hydraulic parameters in the parametric models of Brooks and Corey (BC; Brooks and Corey, 1964), Mualem-van Genuchten (VG; van Genuchten, 1980), and Kosugi (KC; Kosugi, 1996, 1999) using the recently developed Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm (Vrugt et al., 2002b, and unpublished data). Because the SCEM-UA algorithm globally thoroughly exploits the parameter space and therefore explicitly accounts for parameter interdependence and nonlinearity of the employed parametric models, the algorithm is suited to generate a useful description of parameter uncertainty and its antithesis, parameter identifiability. A set of measured water retention characteristics of the UNSODA database (Leij et al., 1996) spanning a wide range of soil textures and three transient laboratory outflow experiments with decreasing flow rates were used to illustrate that a parameter identifiability analysis facilitates the selection of an adequate parametric model structure and provides useful information about the limitations of a model. Moreover, results suggest that one should be especially careful in establishing pedotransfer functions without knowledge of the underlying posterior uncertainty associated with the soil hydraulic parameters using direct estimation methods.
• Vrugt, J. A., Bouten, W., Gupta, H. V., & Sorooshian, S. (2003). Correction to “Toward improved identifiability of hydrologic model parameters: The information content of experimental data”. Water Resources Research.
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  Vrugt JA, W Bouten, HV Gupta and S. Sorooshian (2003), Correction to “Toward improved identifiably of hydrologic model parameters: The information content of experimental data”, Water Resources Research, Vol. 39, No. 3, pp. 10.1
• Vrugt, J. A., Bouten, W., Gupta, H. V., & Sorooshian, S. (2003). Erratum: Toward improved identifiability of hydrologic model parameters: The information content of experimental data (Water Resources Research (2003) 39: 3 (1054) DOI: 10.1029/2003WR001962). Water Resources Research, 39(3), COR11.
• Vrugt, J. A., Gupta, H. V., Bastidas, L. A., Bouten, W., & Sorooshian, S. (2003). Effective and efficient algorithm for multiobjective optimization of hydrologic models. Water Resources Research, 39(8), SWC51-SWC519.
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  Abstract: Practical experience with the calibration of hydrologic models suggests that any single-objective function, no matter how carefully chosen, is often inadequate to properly measure all of the characteristics of the observed data deemed to be important. One strategy to circumvent this problem is to define several optimization criteria (objective functions) that measure different (complementary) aspects of the system behavior and to use multicriteria optimization to identify the set of nondominated, efficient, or Pareto optimal solutions. In this paper, we present an efficient and effective Markov Chain Monte Carlo sampler, entitled the Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm, which is capable of solving the multiobjective optimization problem for hydrologic models. MOSCEM is an improvement over the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm, using the concept of Pareto dominance (rather than direct single-objective function evaluation) to evolve the initial population of points toward a set of solutions stemming from a stable distribution (Pareto set). The efficacy of the MOSCEM-UA algorithm is compared with the original MOCOM-UA algorithm for three hydrologic modeling case studies of increasing complexity.
• Vrugt, J. A., Gupta, H. V., Bouten, W., & Sorooshian, S. (2003). A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resources Research, 39(8), SWC11-SWC116.
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  Abstract: Markov Chain Monte Carlo (MCMC) methods have become increasingly popular for estimating the posterior probability distribution of parameters in hydrologic models. However, MCMC methods require the a priori definition of a proposal or sampling distribution, which determines the explorative capabilities and efficiency of the sampler and therefore the statistical properties of the Markov Chain and its rate of convergence. In this paper we present an MCMC sampler entitled the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA), which is well suited to infer the posterior distribution of hydrologic model parameters. The SCEM-UA algorithm is a modified version of the original SCE-UA global optimization algorithm developed by Duan et al. [1992]. The SCEM-UA algorithm operates by merging the strengths of the Metropolis algorithm, controlled random search, competitive evolution, and complex shuffling in order to continuously update the proposal distribution and evolve the sampler to the posterior target distribution. Three case studies demonstrate that the adaptive capability of the SCEM-UA algorithm significantly reduces the number of model simulations needed to infer the posterior distribution of the parameters when compared with the traditional Metropolis-Hastings samplers.
• Wagener, T., McIntyre, N., Lees, M. J., Wheater, H. S., & Gupta, H. V. (2003). Towards reduced uncertainty in conceptual rainfall-runoff modelling: Dynamic identifiability analysis. Hydrological Processes, 17(2), 455-476.
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  Abstract: Conceptual modelling requires the identification of a suitable model structure and the estimation of parameter values through calibration against observed data. A lack of objective approaches to evaluate model structures and the inability of calibration procedures to distinguish between the suitability of different parameter sets are major sources of uncertainty in current modelling procedures. This paper presents an approach analysing the performance of the model in a dynamic fashion resulting in an improved use of available information. Model structures can be evaluated with respect to the failure of individual components, and periods of high information content for specific parameters can be identified. The procedure is termed dynamic identifiability analysis (DYNIA) and is applied to a model structure built from typical conceptual components. © 2003 John Wiley & Sons, Ltd.
• Andersen, J., Sandholt, I., Jensen, K. H., Refsgaard, J. C., & Gupta, H. (2002). Perspectives in using a remotely sensed dryness index in distributed hydrological models at the river-basin scale. Hydrological Processes, 16(15), 2973-2987.
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  Abstract: In a previous study a spatially distributed hydrological model, based on the MIKE SHE code, was constructed and validated for the 375 000 km2 Senegal River basin in West Africa. The model was constructed using spatial data on topography, soil types and vegetation characteristics together with time-series of precipitation from 112 stations in the basin. The model was calibrated and validated based on river discharge data from nine stations in the basin for 11 years. Calibration and validation results suggested that the spatial resolution of the input data in parts of the area was not sufficient for a satisfactory evaluation of the modelling performance. The study further examined the spatial patterns in the model input and output, and it was found that particularly the spatial resolution of the precipitation input had a major impact on the model response. In an attempt to improve the model performance, this study examines a remotely sensed dryness index for its relationship to simulated soil moisture and evaporation for six days in the wet season 1990. The index is derived from observations of surface temperature and vegetation index as measured by the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor. The correlation results between the index and the simulation results are of mixed quality. A sensitivity analysis, conducted on both estimates, reveals significant uncertainties in both. The study suggests that the remotely sensed dryness index with its current use of NOAA AVHRR data does not offer information that leads to a better calibration or validation of the simulation model in a spatial sense. The method potentially may become more suitable with the use of the upcoming high-resolution temporal Meteosat Second Generation data. © 2002 John Wiley and Sons, Ltd.
• Gupta, H. V., Meixner, T., Bastidas, L. A., & Bales, R. C. (2002). Multicriteria parameter estimation for models of stream chemical composition: MULTICRITERIA PARAMETER ESTIMATION FOR MODELS. Water Resources Research, 38(3), 9-1-9-9. doi:10.1029/2000wr000112
• Gupta, H., Sorooshian, S., Gao, X., Imam, B., Hsu, K., Bastidas, L., Jailun, L. i., & Mahani, S. (2002). The challenge of predicting flash floods from thunderstorm rainfall. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 360(1796), 1363-1371.
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  PMID: 12804254;Abstract: A major characteristic of the hydrometeorology of semi-arid regions is the occurrence of intense thunderstorms that develop very rapidly and cause severe flooding. In summer, monsoon air mass is often of subtropical origin and is characterized by convective instability. The existing observational network has major deficiencies for those regions in providing information that is important to run-off generation. Further, because of the complex interactions between the land surface and the atmosphere, mesoscale atmospheric models are currently able to reproduce only general features of the initiation and development of convective systems. In our research, several interrelated components including the use of satellite data to monitor precipitation, data assimilation of a mesoscale regional atmospheric model, modification of the land component of the mesoscale model to better represent the semi-arid region surface processes that control run-off generation, and the use of ensemble forecasting techniques to improve forecasts of precipitation and run-off potential are investigated. This presentation discusses our ongoing research in this area; preliminary results including an investigation related to the unprecedented flash floods that occurred across the Las Vegas valley (Nevada, USA) in July of 1999 are discussed.
• Hsu, K., Gupta, H. V., Gao, X., Sorooshian, S., & Imam, B. (2002). Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis. Water Resources Research, 38(12), 381-3817.
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  Abstract: Artificial neural networks (ANNs) can be useful in the prediction of hydrologic variables, such as streamflow, particularly when the underlying processes have complex nonlinear interrelationships. However, conventional ANN structures suffer from network training issues that significantly limit their widespread application. This paper presents a multivariate ANN procedure entitled self-organizing linear output map (SOLO), whose structure has been designed for rapid, precise, and inexpensive estimation of network structure/parameters and system outputs. More important, SOLO provides features that facilitate insight into the underlying processes, thereby extending its usefulness beyond forecast applications as a tool for scientific investigations. These characteristics are demonstrated using a classic rainfall-runoff forecasting problem. Various aspects of model performance are evaluated in comparison with other commonly used modeling approaches, including multilayer feedforward ANNs, linear time series modeling, and conceptual rainfall-runoff modeling.
• Leplastrier, M., Pitman, A. J., Gupta, H., & Xia, Y. (2002). Exploring the relationship between complexity and performance in a land surface model using the multicriteria method. Journal of Geophysical Research D: Atmospheres, 107(20), XXXI-XXXII.
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  Abstract: The performance of five modes of a land surface model, the Chameleon Surface Model (CHASM), was investigated after calibration via the multicriteria method to monthly totals of evaporation and runoff from the Valdai data set. The use of CHASM allows for an exploration into the relationship between surface energy balance complexity and optimal performance by isolating the impacts of different parameterizations of the surface energy balance. When compared to quantities used within the calibration process, CHASM's performance was significantly increased with calibration over default simulations regardless of calibration length or mode complexity. Within the calibration period, CHASM's performance increased with increasing complexity in the representation of the surface energy balance. Outside the calibration period there was little improvement to simulations from additional complexity in the surface energy balance representation above the simplest mode. Calibration is shown to reduce the scatter between modes suggesting that some of the differences between models in PILPS Phase 2d may be explained by the specification of parameter values. For simulations of quantities not used in calibration, performance can be reduced as a result of calibration. This implies that evaporation and runoff may not be the best quantities for calibration in order to improve model performance. It is suggested that the best quantities to calibrate may be mode and model specific. Copyright 2002 by the American Geophysical Union.
• Leplastrier, M., Pitman, A. J., Gupta, H., & Xia, Y. (2002). Exploring the relationship between complexity and performance in a land surface model using the multicriteria method. Journal of Geophysical Research: Atmospheres, 107(D20). doi:10.1029/2001jd000931
• Meixner, T., Bastidas, L. A., Gupta, H. V., & Bales, R. C. (2002). Multicriteria parameter estimation for models of stream chemical composition. Water Resources Research, 38(3), 91-99.
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  Abstract: The inability to develop an accurate and precise parameter estimation method for catchment hydrochemical models has been a persistent problem. We investigate the use of multicriteria calibration techniques and the selection of the criteria as a first step in solving this problem. We applied a multicriteria search algorithm to the Alpine Hydrochemical Model (AHM) of the Emerald Lake watershed, Sequoia National Park, California. A total of 21 chemical and hydrologic criteria were available for determining model performance. Four subsets of these criteria were selected for the multicriteria analysis using three different methods. The first set used the four least correlated observations of stream chemical composition. A second set of criteria was determined by using the four species with the least correlated root mean square error criteria values (p < 0.05 for each pair). Finally, two sets were chosen using the results of a multicriteria sensitivity analysis. The most accurate and precise results were observed using criteria selected using results from a sensitivity analysis, with the correlation analyses being a poor method for selecting criteria. This set of criteria emphasizes attributes of the model structure, the observations, and our understanding of the processes influencing watershed hydrology and water quality. Also, our results gave improved estimates of several hydrologic and biogeochemical processes in addition to identifying a flaw in the current representation of mineral weathering within the AHM, as applied to the Emerald Lake watershed.
• Sorooshian, S., Gao, X., Hsu, K., Maddox, R. A., Hong, Y., Gupta, H. V., & Imam, B. (2002). Diurnal variability of tropical rainfall retrieved from combined GOES and TRMM satellite information. Journal of Climate, 15(9), 983-1001.
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  Abstract: Recent progress in satellite remote-sensing techniques for precipitation estimation, along with more accurate tropical rainfall measurements from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) instruments, have made it possible to monitor tropical rainfall diurnal patterns and their intensities from satellite information. One year (August 1998-July 1999) of tropical rainfall estimates from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) systems were used to produce monthly means of rainfall diurnal cycles at hourly and 1° × 1° scales over a domain (30°S-30°N, 80°E-10°W) from the Americas across the Pacific Ocean to Australia and eastern Asia. The results demonstrate pronounced diurnal variability of tropical rainfall intensity at synoptic and regional scales. Seasonal signals of diurnal rainfall are presented over the large domain of the tropical Pacific Ocean, especially over the ITCZ and South Pacific convergence zone (SPCZ) and neighboring continents. The regional patterns of tropical rainfall diurnal cycles are specified in the Amazon, Mexico, the Caribbean Sea, Calcutta, Bay of Bengal, Malaysia, and northern Australia. Limited validations for the results include comparisons of 1) the PERSIANN-derived diurnal cycle of rainfall at Rondonia, Brazil, with that derived from the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE) radar data; 2) the PERSIANN diurnal cycle of rainfall over the western Pacific Ocean with that derived from the data of the optical rain gauges mounted on the TOGA-moored buoys: and 3) the monthly accumulations of rainfall samples from the orbital TMI and PR surface rainfall with the accumulations of concurrent PERSIANN estimates. These comparisons indicate that the PERSIANN-derived diurnal patterns at the selected resolutions produce estimates that are similar in magnitude and phase.
• Sorooshian, S., Sorooshian, S., Bales, R., Bales, R., Gupta, H. V., Gupta, H. V., Woodard, G., Woodard, G., Washburne, J., & Washburne, J. (2002). A Brief History and Mission of SAHRA: a National Science Foundation Science and Technology Center on Sustainability of semi-Arid Hydrology and Riparian Areas. Hydrological Processes.
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  Sorooshian S, R Bales, HV Gupta, G Woodard and J Washburne (2002), A Brief History and Mission of SAHRA: a National Science Foundation Science and Technology Center on Sustainability of semi-Arid Hydrology and Riparian Areas, Hydrological Processes, 16, 3293-3295, doi: 10.1002/hyp.5067
• Vrugt, J. A., Bouten, W., Gupta, H. V., & Sorooshian, S. (2002). Toward improved identifiability of hydrologic model parameters: The information content of experimental data. Water Resources Research, 38(12), 481-4813.
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  Abstract: We have developed a sequential optimization methodology, entitled the parameter identification method based on the localization of information (PIMLI) that increases information retrieval from the data by inferring the location and type of measurements that are most informative for the model parameters. The PIMLI approach merges the strengths of the generalized sensitivity analysis (GSA) method [Spear and Hornberger, 1980], the Bayesian recursive estimation (BARE) algorithm [Thiemann et al., 2001], and the Metropolis algorithm [Metropolis et al., 1953]. Three case studies with increasing complexity are used to illustrate the usefulness and applicability of the PIMLI methodology. The first two case studies consider the identification of soil hydraulic parameters using soil water retention data and a transient multistep outflow experiment (MSO), whereas the third study involves the calibration of a conceptual rainfall-runoff model.
• Xia, Y., Pitman, A. J., Gupta, H. V., Leplastrier, M., Henderson-Sellers, A., & Bastidas, L. A. (2002). Calibrating a land surface model of varying complexity using multicriteria methods and the Cabauw dataset. Journal of Hydrometeorology, 3(2), 181-194.
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  Abstract: The multicriteria methodology, which provides a means to estimate optimal ranges for land surface model parameter values via calibration, is evaluated. Following calibration, differences between schemes resulting from effective parameter values can be isolated from differences resulting from scheme structure or scheme parameterizations. The method is applied to the Project for the Intercomparison of Land Surface Parameterization Schemes (PILPS) phase-2a data from the Cabauw site in the Netherlands using the Chameleon Surface Model (CHASM) as the surrogate for a range of land surface schemes. Simulations are performed calibrating six modes of CHASM, representing a range of land surface complexity, against observed net radiation and latent and sensible heat fluxes. The six modes range from a simple bucket model to a complex mosaic-type structure with separate energy balances for each mosaic tile and explicit treatment of transpiration, canopy interception, and bare-ground evaporation. Results demonstrate that the performance of CHASM depends on the complexity of the representation of the surface energy balance. If the multicriteria method is used with two observed variables, the performance of the model improves little with incremental increases in complexity until the most complex version of the model is reached. If the multicriteria method is used with three observed variables, the most complex mode is shown to calibrate more accurately and more precisely than the simple modes. In all cases, every calibrated mode performs better than simulations using the default PILPS phase-2a parameters. The performance of the most complex mode of CHASM suggests that more complex representations of the surface energy balance generally improve the calibrated performance of land surface schemes. However, all modes, when calibrated, retain a residual error that most likely is due to parameterization errors included in the scheme. Most error is contained in the simulation of the latent heat flux, which suggests that, to improve CHASM further, the representation of the surface hydrological processes should be developed. Thus, the multicriteria method provides a means to assess the performance of a single model or group of land surface models and provides guidance as to the directions scheme development should take.
• Boyle, D. P., Gupta, H. V., Sorooshian, S., Koren, V., Zhang, Z., & Smith, M. (2001). Toward improved streamflow forecasts: Value of semidistributed modeling. Water Resources Research, 37(11), 2749-2759.
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  Abstract: The focus of this study is to assess the performance improvements of semidistributed applications of the U.S. National Weather Service Sacramento Soil Moisture Accounting model on a watershed using radar-based remotely sensed precipitation data. Specifically, performance comparisons are made within an automated multicriteria calibration framework to evaluate the benefit of "spatial distribution" of the model input (precipitation), structural components (soil moisture and streamflow routing computations), and surface characteristics (parameters). A comparison of these results is made with those obtained through manual calibration. Results indicate that for the study watershed, there are performance improvements associated with semidistributed model applications when the watershed is partitioned into three subwatersheds; however, no additional benefit is gained from increasing the number of subwatersheds from three to eight. Improvements in model performance are demonstrably related to the spatial distribution of the model input and streamflow routing. Surprisingly, there is no improvement associated with the distribution of the surface characteristics (model parameters).
• Houser, P. R., Gupta, H. V., Shuttleworth, W. J., & Famiglietti, J. S. (2001). Multiobjective calibration and sensitivity of a distributed land surface water and energy balance model. Journal of Geophysical Research D: Atmospheres, 106(D24), 33421-33433.
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  Abstract: The feasibility of using spatially distributed information to improve the predictive ability of a spatially distributed land surface water and energy balance model (LSM) was explored at the U. S. Department of Agriculture Agricultural Research Service (USDA-ARS) Walnut Gulch Experimental Watershed in southeastern Arizona. The inclusion of spatially variable soil and vegetation information produced unrealistic simulations that were inconsistent with observations, which was likely an artifact of both discretely assigning a single set of parameters to a given area and inadequate knowledge of spatially varying parameter values. Because some of the model parameters were not measured or are abstract quantities a multiobjective least squares strategy was used to find catchment averaged parameter values that minimize the prediction error of latent heat flux, soil heat flux, and surface soil moisture. This resulted in a substantial improvement in the model's spatially distributed performance and yielded valuable insights into the interaction and optimal selection of model parameters. Copyright 2001 by the American Geophysical Union.
• Sen, O. L., Bastidas, L. A., Shuttleworth, J. W., Yang, Z. L., Gupta, H. V., & Sorooshian, S. (2001). Impact of field-calibrated vegetation parameters on GCM climate simulations. Quarterly Journal of the Royal Meteorological Society, 127(574), 1199-1223.
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  Abstract: This paper describes a study in which, for the first time, advanced systems-engineering parameter-estimation techniques were applied to data from several field studies to estimate the preferred set of parameters for some of the most common biomes represented in an advanced Soil-Vegetation-Atmosphere Transfer (SVAT) scheme (BATS2, a recent version of the Biosphere-Atmosphere Transfer Scheme); the effect on modelled climate was also investigated. Observational data from field sites in Brazil, Canada, Arizona and Kansas/Oklahoma in the USA, and the Netherlands were chosen as representative of tropical rain forest, coniferous forest, semi-arid vegetation, agricultural crops, and grassland biomes, respectively. Together, these five biomes make up 50% of the land area represented in BATS2. Multi-criteria calibration algorithms do not produce a unique set of model parameters and, when different combinations of the available objective functions at each site are considered, the number of solutions increases substantially. The need for a single parameter-set for each site (biome) is an important practical issue that was necessarily addressed in this study. A procedure was defined in which optimized parameter-sets were successively discarded by successively applying a cut-off threshold to single observable objective functions following a preference hierarchy. In this study, only the vegetation-related parameters are calibrated for each of the five biomes and implemented into BATS2; however, in a separate experiment, the effect of including soil parameters in the optimization was investigated. When the calibrated parameters are adopted and used in BATS2, there are significant changes between the climates calculated in an eight-year run with Version 3 of the Community Climate Model and in an equivalent eight-year run in which the original default parameters were used. The overall conclusion of this exploratory study is that advanced parameter-estimation techniques and appropriate field data can be used successfully to improve representation of surface exchanges and the modelled climate given by a GCM, by defining appropriate values for vegetation-related parameters in an advanced SVAT scheme.
• Sivakumar, B., Sorooshian, S., Gupta, H. V., & Gao, X. (2001). A chaotic approach to rainfall disaggregation. Water Resources Research, 37(1), 61-72.
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  Abstract: The importance of high-resolution rainfall data to understanding the intricacies of the dynamics of hydrological processes and describing them in a sophisticated and accurate way has been increasingly realized. The last decade has witnessed a number of studies and numerous approaches to the possibility of transformation of rainfall data from one scale to another, nearly unanimously pointing to such a possibility. However, an important limitation of such approaches is that they treat the rainfall process as a realization of a stochastic process, and therefore there seems to be a lack of connection between the structure of the models and the underlying physics of the rainfall process. The present study introduces a new framework based on the notion of deterministic chaos to investigate the behavior of the dynamics of rainfall transformation between different temporal scales aimed toward establishing this connection. Rainfall data of successively doubled resolutions (i.e., 6, 12, 24, 48, 96, and 192 hours) observed at Leaf River basin, in the state of Mississippi, United States of America, are studied. The correlation dimension method is employed to investigate the presence of chaos in the rainfall transformation. The finite and low correlation dimensions obtained for the distributions of weights between rainfall data of different scales indicate the existence of chaos in the rainfall transformation, suggesting the applicability of a chaotic model. The formulation of a simple chaotic disaggregation model and its application to the Leaf River rainfall data provides encouraging results with practical potential. The disaggregation model results themselves indicate the presence of chaos in the dynamics of rainfall transformation, providing support for the results obtained using the correlation dimension method.
• Thiemann, M., Trosset, M., Gupta, H., & Sorooshian, S. (2001). Bayesian recursive parameter estimation for hydrologic models. Water Resources Research, 37(10), 2521-2535.
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  Abstract: The uncertainty in a given hydrologic prediction is the compound effect of the parameter, data, and structural uncertainties associated with the underlying model. In general, therefore, the confidence in a hydrologic prediction can be improved by reducing the uncertainty associated with the parameter estimates. However, the classical approach to doing this via model calibration typically requires that considerable amounts of data be collected and assimilated before the model can be used. This limitation becomes immediately apparent when hydrologic predictions must be generated for a previously ungauged watershed that has only recently been instrumented. This paper presents the framework for a Bayesian recursive estimation approach to hydrologic prediction that can be used for simultaneous parameter estimation and prediction in an operational setting. The prediction is described in terms of the probabilities associated with different output values. The uncertainty associated with the parameter estimates is updated (reduced) recursively, resulting in smaller prediction uncertainties as measurement data are successively assimilated. The effectiveness and efficiency of the method are illustrated in the context of two models: a simple unit hydrograph model and the more complex Sacramento soil moisture accounting model, using data from the Leaf River basin in Mississippi.
• Wagener, T., Boyle, D. P., Lees, M. J., Wheater, H. S., Gupta, H. V., & Sorooshian, S. (2001). A framework for development and application of hydrological models. Hydrology and Earth System Sciences, 5(1), 13-26.
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  Abstract: Many existing hydrological modelling procedures do not make best use of available information, resulting in non-minimal uncertainties in model structure and parameters, and a lack of detailed information regarding model behaviour. A framework is required that balances the level of model complexity supported by the available data with the level of performance suitable for the desired application. Tools are needed that make optimal use of the information available in the data to identify model structure and parameters, and that allow a detailed analysis of model behaviour. This should result in appropriate levels of model complexity as a function of available data, hydrological system characteristics and modelling purpose. This paper introduces an analytical framework to achieve this, and tools to use within it, based on a multi-objective approach to model calibration and analysis. The utility of the framework is demonstrated with an example from the field of rainfall-runoff modelling.
• Boyle, D. P., Gupta, H. V., & Sorooshian, S. (2000). Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods. Water Resources Research, 36(12), 3663-3674.
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  Abstract: Automatic methods for model calibration seek to take advantage of the speed and power of digital computers, while being objective and relatively easy to implement. However, they do not provide parameter estimates and hydrograph simulations that are considered acceptable by the hydrologists responsible for operational forecasting and have therefore not entered into widespread use. In contrast, the manual approach which has been developed and refined over the years to result in excellent model calibrations is complicated and highly labor-intensive, and the expertise acquired by one individual with a specific model is not easily transferred to another person (or model). In this paper, we propose a hybrid approach that combines the strengths of each. A multicriteria formulation is used to "model" the evaluation techniques and strategies used in manual calibration, and the resulting optimization problem is solved by means of a computerized algorithm. The new approach provides a stronger test of model performance than methods that use a single overall statistic to aggregate model errors over a large range of hydrologic behaviors. The power of the new approach is illustrated by means of a case study using the Sacramento Soil Moisture Accounting model.
• Goodrich, D., Chebouni, A., Goff, B., & Gupta, H. V. (2000). Preface paper to the Semi-Arid Land-Surface-Atmosphere (SALSA) Program special issue. Agricultural And Forest Meteorology.
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  Goodrich DC, Chehbouni A, Goff B, and several authors including Gupta H (2000). Preface paper to the Semi-Arid Land-Surface-Atmosphere (SALSA) Program special issue, Agricultural And Forest Meteorology, 105(1-3), pp. 3-20.
• Govindaraju, R. S., & Gupta, H. V. (2000). Artificial Neural Networks in Hydrology I: Preliminary Concepts. ASCE Journal of Hydrologic Engineering.
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  Govindaraju RS, et al. (ASCE Task Committee on Artificial Neural Networks in Hydrology) (2000), Artificial Neural Networks in Hydrology I: Preliminary Concepts, ASCE Journal of Hydrologic Engineering, 5(2), pp. 115-123
• Govindaraju, R. S., & Gupta, H. V. (2000). Artificial Neural Networks in Hydrology II: Hydrologic Applications. ASCE Journal of Hydrologic Engineering.
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  Govindaraju RS, et al (ASCE Task Committee on Artificial Neural Networks in Hydrology) (2000), Artificial Neural Networks in Hydrology II: Hydrologic Applications, ASCE Journal of Hydrologic Engineering, 5 (2), pp. 124-137
• Hogue, T. S., Sorooshian, S., Gupta, H., Holz, A., & Braatz, D. (2000). A multistep automatic calibration scheme for river forecasting models. Journal of Hydrometeorology, 1(6), 524-542.
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  Abstract: Operational flood forecasting models vary in complexity, but nearly all have parameters for which values must be estimated. The traditional and widespread manual calibration approach requires considerable training and experience and is typically laborious and time consuming. Under the Advanced Hydrologic Prediction System modernization program. National Weather Service (NWS) hydrologists must produce rapid calibrations for roughly 4000 forecast points throughout the United States. The classical single-objective automatic calibration approach, although fast and objective, has not received widespread acceptance among operational hydrologists. In the work reported here, University of Arizona researchers and NWS personnel have collaborated to combine the strengths of the manual and automatic calibration strategies. The result is a multistep automatic calibration scheme (MACS) that emulates the progression of steps followed by NWS hydrologists during manual calibration and rapidly provides acceptable parameter estimates. The MACS approach was tested on six operational basins (drainage areas from 671 to 1302 km2) in the North Central River Forecast Center (NCRFC) area. The results were found to compare favorably with the NCRFC manual calibrations in terms of both visual inspection and statistical measures, such as daily root-mean-square error and percent bias by flow group. Further, implementation of the MACS procedure requires only about 3-4 person hours per basin, in contrast to the 15-20 person hours typically required using the manual approach. Based on this study, the NCRFC has opted to perform further testing of the MACS procedure at a large number of forecast points that constitute the Grand River (Michigan) forecast group. MACS is a time-saving, reliable approach that can provide calibrations that are of comparable quality to the NCRFC's current methods.
• Sorooshian, S., Hsu, K., Gao, X., Gupta, H. V., Imam, B., & Braithwaite, D. (2000). Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bulletin of the American Meteorological Society, 81(9), 2035-2046.
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  Abstract: PERSIANN, an automated system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, has been developed for the estimation of rainfall from geosynchronous satellite longwave infared imagery (GOES-IR) at a resolution of 0.25° × 0.25° every half-hour. The accuracy of the rainfall product is improved by adaptively adjusting the network parameters using the instantaneous rain-rate estimates from the Tropical Rainfall Measurement Mission (TRMM) microwave imager (TMI product 2A12), and the random errors are further reduced by accumulation to a resolution of 1° × 1° daily. The authors' current GOES-IR - TRMM TMI based product, named PERSIANN-GT, was evaluated over the region 30°S-30°N, 90°E-30°W, which includes the tropical Pacific Ocean and parts of Asia, Australia, and the Americas. The resulting rain-rate estimates agree well with the National Climatic Data Center radar-gauge composite data over Florida and Texas (correlation coefficient p > 0.7). The product also compares well (p ̃ 0.77-0.90) with the monthly World Meteorological Organization gauge measurements for 5° × 5° grid locations having high gauge densities. The PERSIANN-GT product was evaluated further by comparing it with current TRMM products (3A11, 3B31, 3B42, 3B43) over the entire study region. The estimates compare well with the TRMM 3B43 1° × 1° monthly product, but the PERSIANN-GT products indicate higher rainfall over the western Pacific Ocean when compared to the adjusted geosynchronous precipitation index-based TRMM 3B42 product.
• Bastidas, L. A., Gupta, H. V., Sorooshian, S., Shuttleworth, W. J., & Yang, Z. L. (1999). Sensitivity analysis of a land surface scheme using multicriteria methods. Journal of Geophysical Research D: Atmospheres, 104(D16), 19481-19490.
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  Abstract: Attempts to model surface-atmosphere interactions with greater physical realism have resulted in complex land surface schemes (LSS) with large numbers of parameters. A companion paper describes a multicriteria calibration procedure for extracting plot-scale estimates of the preferred ranges of these parameters from the various observational data sets that are now available. A complementary procedure is presented in this paper that provides an objective determination of the multicriteria sensitivity of the modeled variables to the parameters, thereby allowing the number of calibration parameters and hence the computational effort to be reduced. Two case studies are reported for the BATS model using data sets of typical quality but very different location and climatological regime (ARM-CART and Tucson). The sensitivity results were found to be consistent with the physical properties of the different environments, thereby supporting the reasonableness of the model formulation. Further, when the insensitive parameters are omitted from the calibration process, there is little degradation in the quality of the model description and little change in the preferred range of the remaining parameters. Copyright 1999 by the American Geophysical Union.
• Gupta, H. V., Bastidas, L. A., Sorooshian, S., Shuttleworth, W. J., & Yang, Z. L. (1999). Parameter estimation of a land surface scheme using multicriteria methods. Journal of Geophysical Research D: Atmospheres, 104(D16), 19491-19503.
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  Abstract: Attempts to create models of surface-atmosphere interactions with greater physical realism have resulted in land surface schemes (LSS) with large numbers of parameters. The hope has been that these parameters can be assigned typical values by inspecting the literature. The potential for using the various observational data sets that are now available to extract plot-scale estimates for the parameters of a complex LSS via advanced parameter estimation methods developed for hydrological models is explored in this paper. Results are reported for two case studies using data sets of typical quality but very different location and climatological regime (ARM-CART and Tucson). The traditional single-criterion methods were found to be of limited value. However, a multicriteria approach was found to be effective in constraining the parameter estimates into physically plausible ranges when observations on at least one appropriate heat flux and one properly selected state variable are available. Copyright 1999 by the American Geophysical Union.
• Gupta, H. V., Sorooshian, S., & Yapo, P. O. (1999). Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. Journal of Hydrologic Engineering, 4(2), 135-143.
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  Abstract: The usefulness of a hydrologic model depends on how well the model is calibrated. Therefore, the calibration procedure must be conducted carefully to maximize the reliability of the model. In general, manual procedures for calibration can be extremely time-consuming and frustrating, and this has been a major factor inhibiting the widespread use of the more sophisticated and complex hydrologic models. A global optimization algorithm entitled shuffled complex evolution recently was developed that has proved to be consistent, effective, and efficient in locating the globally optimal model parameters of a hydrologic model. In this paper, the capability of the shuffled complex evolution automatic procedure is compared with the interactive multilevel calibration multistage semiautomated method developed for calibration of the Sacramento soil moisture accounting streamflow forecasting model of the U.S. National Weather Service. The results suggest that the state of the art in automatic calibration now can be expected to perform with a level of skill approaching that of a well-trained hydrologist. This enables the hydrologist to take advantage of the power of automated methods to obtain good parameter estimates that are consistent with the historical data and to then use personal judgment to refine these estimates and account for other factors and knowledge not incorporated easily into the automated procedure. The analysis also suggests that simple split-sample testing of model performance is not capable of reliably indicating the existence of model divergence and that more robust performance evaluation criteria are needed.
• Hsu, K., Gupta, H. V., Gao, X., & Sorooshian, S. (1999). Estimation of physical variables from multichannel remotely sensed imagery using a neural network: Application to rainfall estimation. Water Resources Research, 35(5), 1605-1618.
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  Abstract: Satellite-based remotely sensed data have the potential to provide hydrologically relevant information about spatially and temporally varying physical variables. A methodology for estimating such variables from multichannel remotely sensed data is presented; the approach is based on a modified counterpropagation neural network (MCPN) and is both effective and efficient at building complex nonlinear input-output function mappings from large amounts of data. An application to high-resolution estimation of the spatial and temporal variation of surface rainfall using geostationary satellite infrared and visible imagery is presented. Test results also indicate that spatially and temporally sparse ground-based observations can be assimilated via an adaptive implementation of the MCPN method, thereby allowing on-line improvement of the estimates.
• Liming, X. u., Sorooshian, S., Gao, X., & Gupta, H. V. (1999). A cloud-patch technique for identification and removal of no-rain clouds from satellite infrared imagery. Journal of Applied Meteorology, 38(8), 1170-1181.
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  Abstract: A new cloud-patch method for the identification and removal of no-rain cold clouds from infrared (IR) imagery is presented. A cloud patch is defined as a cluster of connected IR imagery pixels that are colder than a given IR brightness temperature threshold. The threshold is derived through a combination of the rainfall field estimated from microwave observations and the IR data closely coincident with microwave sensor satellite overpasses. Seven cloud-patch features are used to describe cloud-top properties, including six IR based and one VIS based. The ID3 algorithm is used to extract structural knowledge from a training dataset and to produce classification rules expressed explicitly on the values of various patch features; these rules can be used to explain the physical principles underlying the cloud classification. The method was evaluated for the Japanese islands and surrounding oceans using AIP/1 data for June (training period) and July-August (evaluation period) 1989. The results of identifying no-rain cloud patches are very good for both periods in spite of the change in rainfall regime from frontal to subtropical convective. Nearly 20% of the total pixels and 60% of the no-rain cloud pixels were removed with negligible rain losses due to misclassification. Moreover, visible data were found to be useful for enhancing the no-rain cold patch identification and thereby reducing the rain loss.
• Meixner, T., Gupta, H. V., Bastidas, L. A., & Bales, R. C. (1999). Sensitivity analysis using mass flux and concentration. Hydrological Processes, 13(14-15), 2233-2244.
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  Abstract: Sensitivity analysis for hydrochemical models requires consideration of the multivariate nature of watershed response. A robust multiobjective generalized sensitivity analysis (MOGSA) procedure, recently developed at the University of Arizona, was used to fully investigate the single objective parameter sensitivity of the Alpine Hydrochemical Model (AHM). A total of 20000 simulations for a two-year period were conducted for the Emerald Lake watershed in Sequoia National Park, California. For each simulation 21 objective functions were evaluated: they were discharge and both concentration and mass flux for ten chemical species. The MOGSA procedure revealed that only 2000 simulations were necessary to establish the parameters sensitive to mass flux or concentration. We found significant differences in parameter sensitivity for concentration versus mass flux objective functions. For example, a snowpack elution parameter and a number of hydrologic parameters were sensitive for Cl- concentration, while only the snowpack elution parameter was sensitive for Cl- mass flux. By using mass flux instead of concentration fewer mineral weathering parameters and more soil exchange parameters were sensitive. Mass flux calculations emphasize the spring snowmelt and peak discharge events of the early summer. Our results indicate that using mass instead of concentration permits better identification of the model parameters that most affect stream conditions during peak springtime flows and that some combination of mass flux and concentration objectives should be used in evaluating model performance.
• Sorooshian, S., Gupta, H. V., Hogue, T. S., Holz, A., Braatz, D., & Gupta, H. V. (1999). A multi-step automatic calibration scheme (MACS) for river forecasting models utilizing the national weather service river forecast system (NWSRFS). Water Resources Research.
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  Final Report to the Hydrologic Research Laboratory of the National Weather Service Grant NA #87WHO582
• Yunchun, H. u., Gao, X., Shuttleworth, W. J., Gupta, H., Mahfouf, J., & Viterbo, P. (1999). Soil-moisture nudging experiments with a single-column version of the ECMWF model. Quarterly Journal of the Royal Meteorological Society, 125(557), 1879-1902.
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  Abstract: The soil-moisture nudging technique suggests using model forecast errors in near-surface air temperature and relative humidity to re-initialize (update) soil moisture in atmospheric models. This study investigates the application of soil-moisture nudging using a single-column version of the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The model was applied at 16 sites selected to sample a range of climates and land covers across the globe, with atmospheric forcing taken from the ECMWF operational analysis for 15 June 1994 and 15 December 1994. When observation errors are set to zero, the Optimal Interpolation technique for deriving estimates of nudging coefficients shows computational instability because strong correlation between near-surface temperature and near-surface relative humidity forecast errors makes the coefficient matrix of the linear equations ill-posed. Therefore, Principal Component Analysis (PCA) is used as a pre-processor to identify the independent and dependent components of temperature and relative humidity errors. With PCA, the soil-moisture nudging coefficients appropriate to the dominant principal component become stable and effective for correcting soil moisture within days. Such coefficients, when derived for the northern hemisphere summer over the 16 sites, are sufficiently consistent to propose using their all-site, daily-average values in a globally applicable soil-moisture analysis scheme. Tests of this method at the First ISLSCP (International Satellite Land Surface Climatology Program) Field Experiment (FIFE) site confirm that soil-moisture nudging can provide good estimates of near-surface weather variables and surface fluxes in a numerical weather prediction (NWP) model which gives poor simulation of precipitation (thereby poor soil moisture). However, the PCA method is unable to give an accurate determination of the location of the soil moisture within soil layers that are accessible to the atmosphere. Further, these tests show that, if the NWP model has good simulation of precipitation but poor simulation of surface radiation, soil-moisture nudging could wrongly vary the soil moisture so as to provide good estimates of the surface sensible-heat flux, but poor estimates of surface latent-heat flux. An approach to distinguish error sources from surface radiation and soil moisture is necessary for further improvement.
• Gupta, H. V., Sorooshian, S., & Yapo, P. O. (1998). Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information. Water Resources Research, 34(4), 751-763.
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  Abstract: Several contributions to the hydrological literature have brought into question the continued usefulness of the classical paradigm for hydrologic model calibration. With the growing popularity of sophisticated 'physically based' watershed models (e.g., landsurface hydrology and hydrochemical models) the complexity of the calibration problem has been multiplied many fold. We disagree with the seemingly widespread conviction that the model calibration problem will simply disappear with the availability of more and better field measurements. This paper suggests that the emergence of a new and more powerful model calibration paradigm must include recognition of the inherent multiobjective nature of the problem and must explicitly recognize the role of model error. The results of our preliminary studies are presented. Through an illustrative case study we show that the multiobjective approach is not only practical and relatively simple to implement but can also provide useful information about the limitations of a model.
• Houser, P. R., Shuttleworth, W. J., Famiglietti, J. S., Gupta, H. V., Syed, K. H., & Goodrich, D. C. (1998). Integration of soil moisture remote sensing and hydrologic modeling using data assimilation. Water Resources Research, 34(12), 3405-3420.
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  Abstract: The feasibility of synthesizing distributed fields of soil moisture by the novel application of four-dimensional data assimilation (4DDA) applied in a hydrological model is explored. Six 160-km 2 push broom microwave radiometer (PBMR) images gathered over the Walnut Gulch experimental watershed in southeast Arizona were assimilated into the Topmodel-based Land-Atmosphere Transfer Scheme (TOPLATS) using several alternative assimilation procedures. Modification of traditional assimilation methods was required to use these high-density PBMR observations. The images were found to contain horizontal correlations that imply length scales of several tens of kilometers, thus allowing information to be advected beyond the area of the image. Information on surface soil moisture also was assimilated into the subsurface using knowledge of the surface-subsurface correlation. Newtonian nudging assimilation procedures are preferable to other techniques because they nearly preserve the observed patterns within the sampled region but also yield plausible patterns in unmeasured regions and allow information to be advected in time.
• Sorooshian, S., Hsu, K., & Gupta, H. V. (1998). Streamflow Forecasting Using Artificial Neural Networks. Water resources engineering, 967-972.
• Thiemann, M., Sorooshian, S., Gupta, H. V., & Funke, R. (1998). Calibration of a Semi-Distributed Rainfall-Runoff Model Using Global Optimization Strategies. Water resources engineering, 1362-1367.
• Winchell, M., Gupta, H. V., & Sorooshian, S. (1998). On the simulation of infiltration- and saturation-excess runoff using radar-based rainfall estimates: Effects of algorithm uncertainty and pixel aggregation. Water Resources Research, 34(10), 2655-2670.
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  Abstract: The effects of uncertainty in radar-estimated precipitation input on simulated runoff generation from a medium-sized (100-km2) basin in northern Texas are investigated. The radar-estimated rainfall was derived from Next Generation Weather Radar (NEXRAD) Level II base reflectivity data and was supplemented by ground-based rain-gauge data. Two types of uncertainty in the precipitation estimates are considered: (1) those arising from the transformation of reflectivity to rainfall rate and (2) those due to the spatial and temporal representation of the 'true' rainfall field. The study explicitly differentiates between the response of simulated saturation-excess runoff and infiltration-excess runoff to these uncertainties. The results indicate that infiltration-excess runoff generation is much more sensitive than saturation-excess runoff generation to both types of precipitation uncertainty. Furthermore, significant reductions in infiltration-excess runoff volume occur when the temporal and spatial resolution of the precipitation input is decreased. A method is developed to relate this storm-dependent reduction in runoff volume to the spatial heterogeneity of the highest-intensity rainfall periods during a storm.
• Yapo, P. O., Gupta, H. V., & Sorooshian, S. (1998). Multi-objective global optimization for hydrologic models. Journal of Hydrology, 204(1-4), 83-97.
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  Abstract: The development of automated (computer-based) calibration methods has focused mainly on the selection of a single-objective measure of the distance between the model-simulated output and the data and the selection of an automatic optimization algorithm to search for the parameter values which minimize that distance. However, practical experience with model calibration suggests that no single-objective function is adequate to measure the ways in which the model fails to match the important characteristics of the observed data. Given that some of the latest hydrologic models simulate several of the watershed output fluxes (e.g. water, energy, chemical constituents, etc.), there is a need for effective and efficient multi-objective calibration procedures capable of exploiting all of the useful information about the physical system contained in the measurement data time series. The MOCOM-UA algorithm, an effective and efficient methodology for solving the multiple-objective global optimization problem, is presented in this paper. The method is an extension of the successful SCE-UA single-objective global optimization algorithm. The features and capabilities of MOCOM-UA are illustrated by means of a simple hydrologic model calibration study.
• Hsu, K., Gao, X., Sorooshian, S., & Gupta, H. V. (1997). Precipitation estimation from remotely sensed information using artificial neural networks. Journal of Applied Meteorology, 36(9), 1176-1190.
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  Abstract: A system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) is under development at The University of Arizona. The current core of this system is an adaptive Artificial Neural Network (ANN) model that estimates rainfall rates using infrared satellite imagery and ground-surface information. The model was initially calibrated over the Japanese Islands using remotely sensed infrared data collected by the Geostationary Meteorological Satellite (GMS) and ground-based data collected by the Automated Meteorological Data Acquisition System (AMeDAS). The model was then validated for both the Japanese Islands (using GMS and AMeDAS data) and the Florida peninsula (using GOES-8 and NEXRAD data). An adaptive procedure is used to recursively update the network parameters when ground-based data are available. This feature dramatically improves the estimation performance in response to the diverse precipitation characteristics of different geographical regions and time of year. The model can also be successfully updated using only spatially and/or temporally limited observation data such as ground-based rainfall measurements. Another important feature is a procedure that provides insights into the functional relationships between the input variables and output rainfall rate.
• Hsu, K., Gupta, H. V., & Sorooshian, S. (1997). Application of a recurrent neural network to rainfall-runoff modeling. Proceedings of the Annual Water Resources Planning and Management Conference, 68-73.
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  Abstract: The lumped daily rainfall-runoff process for the Leaf River Basin in Mississippi was modeled using two different Artificial Neural Network (ANN) model structures. Our results indicate that both structures, the popular Three Layer Feedforward Neural Network (TLFNN) and the Recurrent Neural Network (RNN), perform well. However, the TLFNN requires trial-and-error testing to identify the appropriate number of time-delayed input variables to the model. Further, it is not suitable for distributed watershed modeling; i.e., when distributed precipitation information (multiple gages or radar images) is available. The RNN structure provides a representation of the dynamic internal feedbacks loops in the system, thereby eliminating the need for lagged inputs and resulting in a reduction in the number of network weights (and hence training time). The suitability of RNN's for distributed watershed modeling is discussed.
• Yapo, P. O., Gupta, H. V., Sorooshian, S., Yapo, P. O., & Gupta, H. V. (1997). A multiobjective global optimization algorithm with application to calibration of hydrologic models. Advances in Calibration of Watershed Models.
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  This research was partially supported by grants from the National Science Foundation (grants EAR-9415347 and EAR-9418147), the Hydrologic Research Laboratory of the U.S. National Weather Service (grants NA37WH0385, NA47WH0408 and NA57WH0575), and by the National Aeronautics and Space Administration (NASA-EOS grant NAGW2425). Financial assistance was also provided to Dr. Patrice Yapo by The University of Arizona Graduate College. These sources of assistance are gratefully acknowledged.
• Gao, X., Sorooshian, S., & Gupta, H. V. (1996). Sensitivity analysis of the biosphere-atmosphere transfer scheme. Journal of Geophysical Research D: Atmospheres, 101(D3), 7279-7289.
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  Abstract: The biosphere-atmosphere transfer scheme (BATS) land surface parameterization was developed to provide a realistic representation of hydrometeorology at the atmosphere-land interface for coupling with general circulation models. This paper presents the results of a comprehensive sensitivity analysis of BATS for the conditions of midlatitude land surface and climatology. The analysis employs visual "feature-curve" representations to study the sensitivity of key model outputs to variations over a range of model parameters, atmospheric forcings, and model initialization conditions. The analysis provides insights into the behavior of the model and indicates that (1) the dominant energy flux shifts from being the soil sensible heat flux in arid/semiarid regions to evapotranspiration in rain forest regions; (2) in off-line studies the absence of important feedback mechanisms may result in unrealistic results at extreme climatic conditions; (3) the model functions which determine the atmosphere-controlled and soil moisture limited latent heat fluxes from soil and vegetation may need to be refined in order to obtain a more realistic modeling of the energy fluxes from the land surface; and (4) certain components of the model can possibly be simplified. The results also show that the influence of errors in the initial state estimates can persist for several years, indicating that caution should be exercised when attempting to validate or calibrate BATS using incomplete observation data and short-term simulation runs. Copyright 1996 by the American Geophysical Union.
• Yapo, P. O., Gupta, H. V., & Sorooshian, S. (1996). Automatic calibration of conceptual rainfall-runoff models: Sensitivity to calibration data. Journal of Hydrology, 181(1-4), 23-48.
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  Abstract: The identification of hydrologic models requires that appropriate data be selected for model calibration. In the research presented here, the shuffled complex evolution (SCE-UA) global optimization method was used to calibrate the NWSRFS-SMA conceptual rainfall-runoff flood forecasting model of the US National Weather Service, using a 40-year record of historical data. Based on 344 calibration runs using different lengths of data from different sections of the historical record, we conclude that approximately 8 years of data are required to obtain calibrations that are relatively insensitive to the period selected. Further, the reduction in parameter uncertainty is maximal when the wettest data periods on record are used. A residual analysis is used to compare the performance of the daily root mean square (DRMS) and heteroscedastic maximum likelihood error (HMLE) objective functions. The results suggest that the factor currently limiting model performance is the unavailability of strategies that explicitly account for model error during calibration.
• Hsu, K., Gupta, H. V., & Sorooshian, S. (1995). Artificial neural network modeling of the rainfall-runoff process. Water Resources Research, 31(10), 2517-2530.
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  Abstract: This study presents a new procedure (entitled linear least squares simplex) for identifying the structure and parameters of three-layer feed forward artificial neural network (ANN) models and demonstrates the potential of such models for simulating the nonlinear hydrologic behaviour of watersheds. The nonlinear ANN model approach is shown to provide a better representation of the rainfall-runoff relationships of the medium-size Leaf River basin near Collins, Mississippi, than the linear ARMAX (autorergressive moving average with exogenous inputs) time series approach or the conceptual SAC-SMA (Sacramento soil moisture accounting) model. The ANN approach does provide a viable and effective alternative to the ARMAX time series approach for developing input-output simulation and forecasting models in situations that do not require modeling of the internal structure of the watershed. -from Authors
• Duan, Q., Sorooshian, S., & Gupta, V. K. (1994). Optimal use of the SCE-UA global optimization method for calibrating watershed models. Journal of Hydrology, 158(3-4), 265-284.
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  Abstract: The difficulties involved in calibrating conceptual watershed models have, in the past, been partly attributable to the lack of robust optimization tools. Recently, a global optimization method known as the SCE-UA (shuffled complex evolution method developed at The University of Arizona) has shown promise as an effective and efficient optimization technique for calibrating watershed models. Experience with the method has indicated that the effectiveness and efficiency of the algorithm are influenced by the choice of the algorithmic parameters. This paper first reviews the essential concepts of the SCE-UA method and then presents the results of several experimental studies in which the National Weather Service river forecast system-soil moisture accounting (NWSRFS-SMA) model, used by the National Weather Service for river and flood forecasting, was calibrated using different algorithmic parameter setups. On the basis of these results, the recommended values for the algorithmic parameters are given. These values should also help to provide guidelines for other users of the SCE-UA method. © 1994.
• Duan, Q. Y., Gupta, V. K., & Sorooshian, S. (1993). Shuffled complex evolution approach for effective and efficient global minimization. Journal of Optimization Theory and Applications, 76(3), 501-521.
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  Abstract: The degree of difficulty in solving a global optimization problem is in general dependent on the dimensionality of the problem and certain characteristics of the objective function. This paper discusses five of these characteristics and presents a strategy for function optimization called the shuffled complex evolution (SCE) method, which promises to be robust, effective, and efficient for a broad class of problems. The SCE method is based on a synthesis of four concepts that have proved successful for global optimization: (a) combination of probabilistic and deterministic approaches; (b) clustering; (c) systematic evolution of a complex of points spanning the space, in the direction of global improvement; and (d) competitive evolution. Two algorithms based on the SCE method are presented. These algorithms are tested by running 100 randomly initiated trials on eight test problems of differing difficulty. The performance of the two algorithms is compared to that of the controlled random search CRS2 method presented by Price (1983, 1987) and to a multistart algorithm based on the simplex method presented by Nelder and Mead (1965). © 1993 Plenum Publishing Corporation.
• SOROOSHIAN, S., DUAN, Q., & GUPTA, V. (1993). CALIBRATION OF RAINFALL-RUNOFF MODELS - APPLICATION OF GLOBAL OPTIMIZATION TO THE SACRAMENTO SOIL-MOISTURE ACCOUNTING MODEL. WATER RESOURCES RESEARCH, 29(4), 1185-1194.
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  Conceptual rainfall-runoff models are difficult to calibrate by means of automatic methods; one major reason for this is the inability of conventional procedures to locate the globally optimal set of parameters. This paper investigates the consistency with which two global optimization methods, the shuffled complex evolution (SCE-UA) method (developed by the authors) and the multistart simplex (MSX) method, are able to find the optimal parameter set during calibration of the Sacramento soil moisture accounting model (SAC-SMA) of the National Weather Service River Forecast System (NWSRFS). In the first phase of this study, error-free synthetic data are used to conduct a comparative evaluation of the algorithms under ''ideal'' conditions. In 10 independent trials of each algorithm in which 13 parameters of the SAC-SMA model were optimized simultaneously, the SCE-UA method achieved a 100% success rate in locating the precise global optimum (i.e., the ''true'' parameter values) while the MSX method failed in all trials even with more than twice the number of function evaluations. In the second phase, historical data from the Leaf River watershed are used to conduct a comparative evaluation of the algorithms under ''real'' conditions, using two different estimation criteria, DRMS and HMLE; the SCE-UA algorithm obtained consistently lower function values and more closely grouped parameter estimates, while using one-third fewer function evaluations than the MSX algorithm.
• Yapo, P., Sorooshian, S., & Gupta, V. (1993). A Markov chain flow model for flood forecasting. Water Resources Research, 29(7), 2427-2436.
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  Abstract: With the new approach, flood forecasting is possible by focusing on a preselected range of streamflows. In addition, the approach introduces a multiobjective (two-criterion) function of the assessment of model performance. The two criteria are: 1) the probability of issuing a false alarm and 2) the probability of failing to predict a flood event. The goal is to minimize both criteria simultaneously. Three versions of the model are presented: a first-order Markov chain model, a second-order Markov chain model, and a first-order Markov chain with rainfall as exogenous input model. These models compared favorably to time series models, using data from two watersheds (a semiarid watershed and a temperate watershed), when evaluated in terms of the multiobjective performance criterion. -from Authors
• Duan, Q., Sorooshian, S., & Gupta, V. (1992). Effective and efficient global optimization for conceptual rainfall- runoff models. Water Resources Research, 28(4), 1015-1031.
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  Abstract: Results are presented that establish clearly the nature of the multiple optima problem for the research conceptual rainfall-runoff (CRR) model SIXPAR. These results suggest that the CRR model optimization problem is more difficult than had been previously thought and that currently used local search procedures have a very low probability of successfully finding the optimal parameter sets. The performance of three existing global search procedures are evaluated on the model SIXPAR. A powerful new global optimization procedure is presented, entitled the shuffled complex evolution (SCE-UA) method, which was able to consistently locate the global optimum of the SIXPAR model, and appears to be capable of efficiently and effectively solving the CRR model optimization problem. -from Authors
• RITZI, R., SOROOSHIAN, S., & GUPTA, V. (1991). ON THE ESTIMATION OF PARAMETERS FOR FREQUENCY-DOMAIN MODELS. WATER RESOURCES RESEARCH, 27(5), 873-882.
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  The problem of estimating parameters for frequency domain models is considered. Past approaches have most commonly based estimation criteria upon modulus and phase transformations of the model and sample frequency response functions. As an alternative, a complex vector estimation criterion is proposed and is implemented in a nonlinear, Gauss-Marquardt optimization algorithm. When compared to the previous methods of fitting modulus and phase transformations, the complex vector estimation methodology has less bias and variance and is more robust.
• Gupta, V. K., & Sorooshian, S. (1985). AUTOMATIC CALIBRATION OF CONCEPTUAL CATCHMENT MODELS USING DERIVATIVE-BASED OPTIMIZATION ALGORITHMS.. Water Resources Research, 21(4), 473-485.
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  Abstract: A method is discussed for explicit computation of the derivatives based on an analysis of the modality of behavior present in such models. In the method the threshold structures are not replaced by smoothing functions, thereby preserving the conceptual integrity of these models. The discussion includes a theoretical analysis of a single linear reservoir (with threshold) model which provides some insights into the issue of the threshold parameter identifiability. Simulation study comparisons of the Newton (derivative based) and the Simplex (direct search) optimization algorithms indicate that the former is more efficient, especially when the number of parameters to be optimized is large.
• Gupta, V. K., & Sorooshian, S. (1985). The relationship between data and the precision of parameter estimates of hydrologic models. Journal of Hydrology, 81(1-2), 57-77.
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  Abstract: This paper presents a discussion of the relationship between data used for hydrologic model calibration and the precision of model parameter estimates. The analysis is conducted within the framework of the maximum likelihood approach to model selection. The concept of "information" is discussed and the relationship between information and parameter uncertainty is examined. This analysis provides some interesting insights into the role that the quantity and quality of the data used play in the identification procedure. Based on this, a method for selecting data sets suitable for model calibration is suggested. The ideas discussed are illustrated by means of simulation studies using a conceptual-type rainfall-runoff model. © 1985.
• SOROOSHIAN, S., & GUPTA, V. (1985). THE ANALYSIS OF STRUCTURAL IDENTIFIABILITY - THEORY AND APPLICATION TO CONCEPTUAL RAINFALL-RUNOFF MODELS. WATER RESOURCES RESEARCH, 21(4), 487-495.
• Gupta, V. K., & Sorooshian, S. (1983). UNIQUENESS AND OBSERVABILITY OF CONCEPTUAL RAINFALL-RUNOFF MODEL PARAMETERS: THE PERCOLATION PROCESS EXAMINED.. Water Resources Research, 19(1), 269-276.
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  Abstract: The percolation equation of the soil moisture accounting model of the National Weather Service River Forecast System is discussed. It is shown that a logical reparameterization of this equation can result in conditions that improve the chances of obtaining unique parameter estimates.
• Sorooshian, S., & Gupta, V. K. (1983). Automatic calibration of conceptual rainfall-runoff models: the question of parameter observability and uniqueness.. Water Resources Research, 19(1), 260-268.
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  Abstract: Reasons for the inability to obtain unique and conceptually realistic parameter sets for conceptual rainfall-runoff models are examined. The problem is first posed in a framework that allows a more consistent and logical analysis of the related aspects. Response surface studies demonstrate that choice of an objective function that better explains some of the stochastic properties of the errors in the output results in a smoother, better shaped response surface; hence the chances of obtaining unique and realistic parameter estimates are improved.-from Authors
• Sorooshian, S., Gupta, V. K., & Fulton, J. L. (1983). Evaluation of maximum likelihood parameter estimation techniques for conceptual rainfall-runoff models: influence of calibration data variability and length on model credibility.. Water Resources Research, 19(1), 251-259.
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  Abstract: The success of an automatic calibration procedure is highly dependent on the choice of the objective function and the nature (quantity and quality) of the data used. The objective function should be selected on the basis of the stochastic properties of the errors present in the data and in the model. In this paper we compare the performance of two maximum likelihood estimators, the AMLE, which assumes the presence of first lag autocorrelated homogeneous variance errors, and the HMLE, which assumes the presence of uncorrelated inhomogeneous variance errors, to the commonly used simple least squares criterion, SLS.-from Authors

Proceedings Publications

• Nearing, G. S., Kratzert, F., Klotz, D., Hoedt, P. J., Klambauer, G., Hochreiter, S., Gupta, H. V., Nevo, S., & Matias, Y. (2020). A Deep Learning Architecture for Conservative Dynamical Systems: Application to Rainfall-Runoff Modeling. In AI for Earth Sciences Workshop at NEURIPS 2020.
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  Nearing G, Kratzert F, Klotz D, Hoedt PJ, Klambauer G, Hochreiter S, Gupta H, Nevo S, and Matias Y (2020), A Deep Learning Architecture for Conservative Dynamical Systems: Application to Rainfall-Runoff Modeling, AI for Earth Sciences Workshop at NEURIPS 2020.
• Guertin, D. P., Razavi, S., UnKrich, C. L., Burns, I. S., Goodrich, D. C., Gupta, H. V., & Meles, M. B. (2019, Summer). Uncertainty and Parameter Sensitivity of the KINEROS2 Physically-Based Distributed Sediment and Runoff Model. In Proceedings of the 2019 Federal Interagency Sedimentation and Hydrologic Modeling Conference.
• Meles, M. B., Goodrich, D., Unkrich, C. L., Burns, S., Gupta, H. V., Razavi, S., & Guertin, D. P. (2019, Winter). Uncertainty and Parameter Sensitivity of Physically Distributed Sediment and Runoff KINEROS2 Model. In SEDHYD 2019: Federal Interagency Sedimentation and Hydrologic Modeling Conference.
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  [CT-356] Bitew MW, D Goodrich, CL Unkrich, S Burns, HV Gupta, S Razavi and DP Guertin (2019), Uncertainty and Parameter Sensitivity of Physically Distributed Sediment and Runoff KINEROS2 Model, presented at SEDHYD 2019: Federal Interagency Sedimentation and Hydrologic Modeling Conference, Reno, Nevada, June 24-28.
• Nearing, G., Nearing, G. S., Gupta, H. V., Shen, C., Chen, X., & Gupta, H. V. (2019). Machine Learning in Hydrologic Modeling II. In AGU.
• Nearing, G., Nearing, G. S., Gupta, H. V., Shen, C., Chen, X., & Gupta, H. V. (2019). Machine Learning in Hydrologic Modeling III Posters. In AGU.
• Gupta, H. V., & Gupta, H. V. (2017). Learning with Models & Data: A Maximum Entropy Approach (Invited Presentation). In Global Institute for Water Security.
• Gupta, H. V., Aye, K. T., Balakrishnan, R., Rajagopal, S., Rajagopal, S., Nguwi, Y., & Gupta, H. V. (2014). A study of key critical success factors (CSFs) for Enterprise Resource Planning (ERP) systems. In NA.
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  Corporate adopt Enterprise Resource Planning System hoping to increase the efficiency and productivity of employees and ultimately increase sales revenue. However, many companies end up paying high price to implement the Enterprise Resource Planning System without benefiting from such systems. Most of these failures arise with Small and Medium Enterprises and resulted in many Small and Medium Enterprises refuse to take the initial step and invest in an Enterprise Resource Planning System. There is a lack of consolidation of literature documenting the success and failure factors behind it. This paper aims at consolidating the key critical success factors that typically govern the outcome of implementing Enterprise Resource Planning System in Small and Medium Enterprises. In this paper we attempt to relate the main critical success factors with the management decisions. Further, the critical success factors are ranked based on the importance to the success of the Enterprise Resource Planning System implementation.
• Gupta, H. V., Aye, K. T., Balakrishnan, R., Rajagopal, S., Rajagopal, S., Nguwi, Y., & Gupta, H. V. (2014). Formulating, implementing and evaluating ERP in small and medium scale industries. In NA.
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  To increase the efficiency and productivity, Enterprise Resource Planning (ERP) systems are implemented in corporations. However, the implementation of ERP can be extremely tricky. Due to this, many small and medium enterprises (SMEs) fail to identify the importance of ERP and address its challenge. ERP has a big role to play particularly in developing countries. There is a need to motivate SMEs to implement ERP and make it a successful business in developing countries. This need is evident as SMEs play a vital role in the economic development and stability in the developing countries. However, only a handful of details are publicly available about the success and failure factors of the SMEs in the developing countries. To motivate SMEs, this work aims to provide a list of key critical success factors (CSF) found in various ERP systems in SME related literatures. We are proposing a generic matrix of the important CSFs found in literature. This work identified the top 3 CSFs to be the top management support, change management and project management. This work further proposes a model consisting of different phases of ERP implementation to be followed. In this paper, we attempt to relate the main CSFs with the management decisions. Further, the CSFs are ranked based on the correlation with the success of ERP implementation. This can benefit the SMEs which are considering or implementing ERP better.
• Paturel, J., Diello, P., Mahe, G., Dezetter, A., Yacouba, H., Barbier, B., Karambiri, H., Yilmaz, K. K., Yucel, I., Gupta, H. V., Wagener, T., Yang, D., Yang, D., Savenije, H. H., Savenije, H. H., Neale, C., Kunstmann, H., Pomeroy, J. W., & Gupta, H. V. (2009). Hydrological modelling and climate-man-environment interrelationships in the Burkinabe Sahel.. In NA.
• Yilmaz, K. K., Yucel, I., Gupta, H. V., Wagener, T., Yang, D., Yang, D., Savenije, H. H., Savenije, H. H., Neale, C., Kunstmann, H., Pomeroy, J. W., & Gupta, H. V. (2009). New approaches to hydrological prediction in data-sparse regions. Proceedings of Symposium HS.2 at the Joint Convention of The International Association of Hydrological Sciences (IAHS) and The International Association of Hydrogeologists (IAH) held in Hyderabad, India, 6-12 September 2009.. In IAHS.
• Houser, P., Belvedere, D., Pozzi, W., Imam, B., Schiffer, R., Welty, C., Lawford, R., Schlosser, C., Gupta, H., Vorosmarty, C., & Matthews, D. (2007, January). WaterNet: The NASA water cycle solutions network. In International Geoscience and Remote Sensing Symposium (IGARSS), 2462-2464.
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  Abstract: The goal of the WaterNet project is to improve and optimize the sustained ability of water cycle researchers, stakeholders, organizations and networks to interact, identify, harness, and extend NASA research results to augment decision support tools and meet national needs. WaterNet is being build by engaging relevant NASA water cycle research resources and community-of-practice organizations to develop what we term an "actionable database" that can be used to communicate and connect NASA Water cycle research Results (NWRs) towards the improvement of water-related Decision Support Tools (DSTs). An actionable database includes enough sufficient knowledge about its nodes and their heritage so that connections between these nodes are identifiable and robust. Recognizing the many existing highly valuable water-related science and application networks, we will focus the balance of our efforts on enabling their interoperability in a solutions network context. We initially focus on identification, collection, and analysis of the two end points, these being the NWRs and water related DSTs. We will then develop strategies to connect these two end points via innovative communication strategies, improved user access to NASA resources, improved water cycle research community appreciation for DST requirements, and improved identification of pathways for progress. Finally, we will develop relevant benchmarking and metrics, to understand the network's characteristics, to optimize its performance, and to establish sustainability. The WaterNet will deliver numerous preevaluation reports that will identify the pathways for improving the collective ability of the water cycle community to routinely harness NWRs that address crosscutting water cycle challenges. ©2007 IEEE.
• Majid, M., Fethi, L., Boegh, E., Kunstmann, H., Wagener, T., Wagener, T., Hall, A., Bastidas, L. A., Franks, S. W., Gupta, H. V., Rosbjerg, D., Schaake, J., Schaake, J., & Gupta, H. V. (2007). Analysis of dry periods for dam operation in northern Tunisia.. In NA.
• Gupta, H. V., Beven, K., Wagener, T., & Gupta, H. V. (2006). Model Calibration and Uncertainty Estimation. In NA.
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  All rainfall-runoff models are, by definition, simplifications of the real-world system under investigation. The model components are aggregated descriptions of real-world hydrologic processes. One consequence of this is that the model parameters often do not represent directly measurable entities, but must be estimated using measurements of the system response through a process known as model calibration. The objective of this calibration process is to obtain a model with the following characteristics: (i) the input-state-output behavior of the model is consistent with the measurements of catchment behavior, (ii) the model predictions are accurate (i.e. they have negligible bias) and precise (i.e. the prediction uncertainty is relatively small), and (iii) the model structure and behavior are consistent with current hydrologic understanding of reality. This article describes the historic development leading to current views on model calibration, and the algorithms and techniques that have been developed for estimating parameters, thereby enabling the model to mimic the behavior of the hydrologic system. Manual techniques as well as automatic algorithms are addressed. The automatic approaches range from purely random techniques, to local and global search algorithms. An overview of multiobjective and recursive algorithms is also presented. Although it would be desirable to reduce the total output prediction error to zero (i.e. the difference between observed and simulated system behavior) this is generally impossible owing to the unavoidable uncertainties inherent in any rainfall-runoff modeling procedure. These uncertainties stem mainly from the inability of calibration procedures to uniquely identify a single optimal parameter set, from measurement errors associated with the system input and output, and from model structural errors arising from the aggregation of real-world processes into a mathematical model. Some commonly used approaches to estimate these uncertainties and their impacts on the model predictions are discussed. The article ends with a brief discussion about the current status of calibration and how well we are able to represent the effects of uncertainty in the modeling process, and some potential directions.
• Afouda, A., Lawin, E. A., Lebel, T., Peugeot, C., Seguis, L., Franks, S. W., Wagener, T., Bøgh, E., Gupta, H. V., Bastidas, L. A., Nobre, C., Galvao, C. O., Galvao, C. O., Lawin, E. A., & Gupta, H. V. (2005). A rainfall-runoff model based on the Least Action Principle.. In NA.
• Boyle, D. P., Naranjo, R., Lamorey, G., Bassett, S., Gupta, H., & Brookshire, D. (2005, January). Development of an integrated hydologic model to explore the feasibility of water banking and markets in the Southwestern U.S.. In MODSIM05 - International Congress on Modelling and Simulation: Advances and Applications for Management and Decision Making, Proceedings, 615-619.
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  Abstract: The American West is the fastest growing region in the United States. It also has a significant portion of the region that is arid and is currently experiencing an extensive drought. The drought may continue for many years to come. Water development in the western U.S. has traditionally been aimed at ensuring water supplies in the face of climatic and anthropogenic change. However, the development of "new" supplies through reservoir development and other infrastructure will no longer be possible. Essentially water that has always been scarce is becoming scarcer. This ever-increasing scarcity is contributing to an increasing number of conflicts between traditional water users such as farmers and ranchers and environmental and recreation users, while urban demand continues to increase. Water 2025: Preventing Crisis and Conflict in the West addresses these issues and calls for the development of water markets and banks for the reallocation of water in order to more efficiently provide water amongst the competing needs. The 2025 U.S. Department of Interior publication calls directly for the development of water markets and banking systems to address the ever-increasing water shortages in the Western U.S. Researchers at the Desert Research Institute (DRI) are conducting research within the U.S. National Science Foundation (NSF) Science and Technology Center (STC) for Sustainability of semi-Arid Hydrology and Riparian Areas (SAHRA) aimed at developing an integrated physical and engineering hydrologic model for the purpose of investigating the feasibility of water banking and markets in the Rio Grande watershed. The main components of the integrated model include a detailed representation of system behavior in (1) headwater areas (snow accumulation and melt); (2) surface reservoirs and conveyance systems (operational surface reservoirs, river routing, and diversions/returns); (3) regional and near river aquifers; (4) agricultural demand and uses; and, in future work, (5) urban and industrial demand and uses. Existing models of several of these components have been identified and are being included, to some degree, in the initial integrated model. Coupling of existing and new models of each component to create the final completely integrated model requires a detailed understanding of the problem requirements (what questions are being asked) and the data (what information is available in the hydrologic data). Once developed and tested, the integrated model will be used with an economic "Market" model and "Behavior" model to investigate several water market and banking scenarios. The integrated physical model will need to be sufficiently distributed to allow for the tracking of water movement, possibly at the ditch level for an irrigation district. The model may need to be tightly coupled to account for ground/surface water. Whether, in fact, this needs to be done, will depend on the specific scenarios that are to be tested and explored. The engineering/infrastructure module must represent the capabilities of water movement and storage for the hydrologic setting of the water bank. The institutional module will consist of the legal and regulatory framework. Finally, the economic module will be a trading institution. All of these models must be coupled and interactive for a true water banking system to exist and represent the actual physical hydrology with legal/economic institutions for water resources management. The linked models will be used to study feedbacks between physical processes, water resources management institutions and economic decisions. Once the component modules are developed and tested, they will be used for scenario analysis, all within the context of water markets and banking as policy solutions to allow for more efficient reallocation of water in semi-arid environments. It is hoped that this effort will represent a significant step for water resources systems, and the experience gained is expected to guide development of more robust interfaces which can inform policy. The work described in this paper represents the current state of the development of the integrated hydrologic model.
• Duan, Q., Schaake, J., Schaake, J., Andreassian, A., Franks, S. W., Gupta, H. V., Gusev, Y. M., Habets, F., Hall, A., Hall, A., Hay, L. E., Hogue, T. S., Huang, M., Huang, M., Leavesley, G. H., Leavesley, G. H., Liang, X., Nasonova, O. N., Noilhan, J., , Noilhan, J., et al. (2005). Model Parameter Estimation Experiment (MOPEX): Overview and Summary of the Second and Third Workshop Results. In NA.
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  Model Parameter Estimation Experiment (MOPEX) is an international project aimed to develop enhanced techniques for the a priori estimation of parameters in hydrologic models and in land surface parameterization schemes of atmospheric models. MOPEX science strategy involves three major steps: data preparation, a priori parameter estimation methodology development, and demonstration of parameter transferability. A comprehensive MOPEX database has been developed that contains historical hydrometeorological data and land surface characteristics data for many hydrologic basins in the United States and in other countries. This database is continuing to be expanded to include more basins in all parts of the world. A number of international MOPEX workshops have been convened to bring together interested hydrologists and land surface modelers from all over world to exchange knowledge and experience in developing a priori parameter estimation techniques. This paper describes the results from the second and third MOPEX workshops. The specific objective of those workshops is to examine the state of a priori parameter estimation techniques and how they can be potentially improved with observations from well-monitored hydrologic basins. Participants of these MOPEX workshops were given data for 12 basins in the Southeastern United States and were asked to carry out a series ofmore » numerical experiments using a priori parameters as well as calibrated parameters developed for their respective hydrologic models. Eight different models have carried all out the required numerical experiments and the results from those models have been assembled for analysis in this paper. This paper presents an overview of the MOPEX experiment design. The experimental results are analyzed and the important lessons from the two workshops are discussed. Finally, a discussion of further work and future strategy is given.« less
• Kane, A., Franks, S. W., Wagener, T., Bøgh, E., Gupta, H. V., Bastidas, L. A., Nobre, C., Galvao, C. O., Galvao, C. O., & Gupta, H. V. (2005). The climatic vulnerability of flows on the Senegal River and the consequences thereof.. In NA.
• Lienou, G., Mahe, G., Paturel, J., Servat, E., Lubes-niel, H., Sighomnou, D., Ekodeck, G. E., Dezetter, A., Franks, S. W., Wagener, T., Bøgh, E., Gupta, H. V., Bastidas, L. A., Nobre, C., Galvao, C. O., Galvao, C. O., Gupta, H. V., & Ekodeck, G. E. (2005). Changes in the hydrological regime of the rivers of southern Cameroun: an impact of climate variability in the equatorial zone.. In NA.
• Mahe, G., Olivry, J. C., Servat, E., Franks, S. W., Wagener, T., Bøgh, E., Gupta, H. V., Bastidas, L. A., Nobre, C., Galvao, C. O., Galvao, C. O., & Gupta, H. V. (2005). Sensitivity of West-African rivers to climatic and environmental changes: extremes and paradoxes.. In NA.
• Wagener, T., Franks, S. W., Gupta, H. V., Bøgh, E., Bastidas, L. A., Nobre, C., Galvao, C. O., Galvao, C. O., & Gupta, H. V. (2005). Regional hydrological impacts of climate change : Impact assessment and decision making. In NA.
• Wagener, T., Gupta, H. V., Sorooshian, S., Webb, B. W., Arnell, N., Onof, C., Onof, C., Macintyre, N., Gurney, R. J., Kirby, C., & Gupta, H. V. (2004). Stochastic formulation of a conceptual hydrological model.. In NA.
• Hogue, T. S., Sorooshian, S., Gupta, H. V., & Bastidas, L. A. (2001). Evaluation of parameter sensitivity for different levels of land-surface model complexity. In American Meteorological Society Annual Meeting, Albuquerque, New Mexico, January 8-11, 2001.
• Hogue, T. S., Sorooshian, S., Holz, A., Gupta, H. V., & Braatz, D. (2000). Use of the NWSRFS OPT3 for Calibration of the SAC-SMA Model. In 15th Conference on Hydrology, American Meteorology Society, Long Beach, California, January 9-14, 2000.
• Hsu, K., Gupta, H. V., & Sorooshian, S. (1998, January). Streamflow forecasting using artificial neural networks. In International Water Resources Engineering Conference - Proceedings, 2, 967-972.
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  Abstract: A variety of ANN models are being tested for their performance in forecasting daily streamflow from rainfall measurements. In this study, a new ANN structure, called SOLO (Self Organizing feature map with Linear Output) is compared to the conventional time-delay neural network (TDNN) and recurrent neural network (RNN) structures. Results for the Leaf River watershed in Mississippi indicate that the SOLO structure provides equivalent or superior performance across the full range of flow levels (base flow recessions to peaks). Further, the SOLO structure is considerably less costly (in terms of effort and computational requirements) to identify and train.
• Thiemann, M., Gupta, H., Funke, R., & Sorooshian, S. (1998, January). Calibration of a semi-distributed rainfall-runoff model using global optimization strategies. In International Water Resources Engineering Conference - Proceedings, 2, 1362-1367.
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  Abstract: The physically based, semi-distributed Regionalized Rainfall Runoff Model `R3' has been designed to simulate watershed behavior using information derived from observable watershed characteristics. While some of this information can be directly measured in the field, in practice other aspects may not be easy to obtain due to the lack of measurement techniques or nonobservable model parameters. The R3 model therefore relies on calibration for the accurate definition of some of its model parameters. Previous attempts to estimate the 7 model parameters and the initial wetness were performed with the global gradient search algorithm `Globex', but the results proved to be unsatisfying. A recently developed evolution-based global search algorithm `Shuffled Complex Evolution' (SCE-UA) has been reported to be capable of localizing the global optimum even for `difficult' response surfaces. In this study, a comparison between these two algorithms was performed with historical data. The results showed the SCE-UA algorithm to be significantly superior to the Globex method in terms of efficiency and effectiveness.
• Gupta, H. V., Hsu, K., & Sorooshian, S. (1997, January). Superior training of artificial neural networks using weight-space partitioning. In IEEE International Conference on Neural Networks - Conference Proceedings, 3, 1919-1923.
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  Abstract: LLSSIM (Linear Least Squares SIMplex)is a new algorithm for batch training of three-layer feedforward Artificial Neural Networks (ANN), based on a partitioning of the weight space. The input-hidden weights are trained using a `Multi-Start Downhill Simplex' global search algorithm, and the hidden-output weights are estimated using `conditional linear least squares'. Monte-carlo testing shows that LLSSIM provides globally superior weight estimates with significantly fewer function evaluations than the conventional back propagation, adaptive back propagation, and conjugate gradient strategies.
• Winchell, M., Gupta, H. V., Sorooshian, S., & Gupta, H. V. (1997). EFFECTS OF RADAR-ESTIMATED PRECIPITATION UNCERTAINTY ON DIFFERENT RUNOFF-GENERATION MECHANISMS. In NA.
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  Primary financial support for this research was granted by the Natural and Man-Made Hazard Mitigation Program of the National Science Foundation (BCS-9307411), with partial support from the Hydrologic Research Laboratory of the National Weather Service (NA57WH0575) and the NASA -EOS project (NAGW2425). The aforementioned support is greatly appreciated.
• Hsu, K., Gupta, H. V., Sorooshian, S., & Gupta, H. V. (1996). A SUPERIOR TRAINING STRATEGY FOR THREE-LAYER FEEDFORWARD ARTIFICIAL NEURAL NETWORKS. In IEEE 1997 International Conference on Neural Networks.
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  This research was partially supported by grants from the Hydrologic Research Laboratory of the U.S. National Weather Service (Grant no. NA37WH0385), the NASA -EOS Interdisciplinary Research Program (IDP -88 -086), and the NOAA Research Program (NA16RC0119 -0). The first author greatly appreciates the fellowship support provided by the NASA Global Change Program (Grant No. NGT- 30045).

Presentations

• Chavez, M. A., Ehret, U., & Gupta, H. V. (2023). UNITE: A toolbox for unified diagnostic evaluation of physics-based, data-driven and hybrid models based on information theory. 2023 EGU General Assembly, Vienna, Austria, 23–28 April. Vienna, Austria: EGU.
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  Chaves MA, U Ehret & HV Gupta (2023), UNITE: A toolbox for unified diagnostic evaluation of physics-based, data-driven and hybrid models based on information theory, Abstract, EGU23-4039, Session HS1.3.2 – Bridging physical, analytical, information-theoretic and machine learning approaches to system dynamics and predictability in Hydrology and Earth System Sciences, 2023 EGU General Assembly, Vienna, Austria, April 23-28.
• Gauch, M., Gupta, H. V., Kratzert, F., Mai, J., Tolson, B., Nearing, G., Houben, D., & Al, E. (2023). Peeking Inside Hydrologists' Minds: Comparing Human Judgment and Quantitative Metrics of Hydrographs. 2023 EGU General Assembly, Vienna, Austria, 23–28 April. Vienna, Austria: EGU.
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  Gauch M, HV Gupta et al. (2023), Peeking Inside Hydrologists' Minds: Comparing Human Judgment and Quantitative Metrics of Hydrographs, Abstract, EGU23-12261, Session HS1.3.1 – Revisiting good modelling practices – where are we today and where to tomorrow? 2023 EGU General Assembly, Vienna, Austria, April 23-28.
• Gauch, M., Kratzert, F., Mai, J., Tolson, B., Nearing, G., Houben, D., Gupta, H. V., & Al, E. (2022). Rate my Hydrograph: Evaluating the Conformity of Expert Judgement and Quantitative Metrics. 2022 EGU General Assembly, Vienna, Austria, 3–8 April 2022. Vienna, Austria: EGU.
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  Gauch M, F Kratzert, J Mai, B Tolson, G Nearing, D Houben, H Gupta, S Hochreiter, D Klotz (2022), Rate my Hydrograph: Evaluating the Conformity of Expert Judgement and Quantitative Metrics, Session HS1.3.1, Revisiting good modelling practices – where are we today? Virtual PICO, 2022 EGU General Assembly, Vienna, Austria, 3–8 April 2022.
• Gharari, S., & Gupta, H. V. (2022). Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data. 2022 EGU General Assembly, Vienna, Austria, 3–8 April 2022. Vienna, Austria: EGU.
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  Gharari S and HV Gupta (2022), Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data, Session HS1.3.2 – Bridging physical, analytical, information-theoretic and machine learning approaches to system dynamics and predictability in Hydrology and Earth System Sciences, 2022 EGU General Assembly, Vienna, Austria, 3–8 April 2022.
• Gupta, H. V. (2022). How Can We Use Models to Facilitate Scientific Discovery?. Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan, Iran. (Virtual Talk) Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan, Iran.
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  [IT-103] Gupta HV (2022 invited), How Can We Use Models to Facilitate Scientific Discovery? Invited Talk (Virtual) at Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan, Iran, Mar 3-4.
• Gupta, H. V. (2022). How Can We Use Models to Facilitate Scientific Discovery?. Tripods Weekly Seminar, Dept. of Mathematics, The University of Arizona. Dept. of Mathematics, The University of Arizona.
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  Gupta HV (2022 invited), How Can We Use Models to Facilitate Scientific Discovery? Invited presentation at Tripods Weekly Seminar, Dept. of Mathematics, The University of Arizona, Tucson, Arizona, Feb 14.
• Gupta, H. V. (2023). Towards Physical-Conceptual Modeling of Mass, Energy and Information Flows Using Machine Learning Technology. 2023 Borland Lecture, AGU Hydrology Days, Colorado State U, Fort Collins CO, Mar 21.. Colorado State U, Fort Collins CO.
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  Gupta HV (2023 invited), Towards Physical-Conceptual Modeling of Mass, Energy and Information Flows Using Machine Learning Technology,2023 Borland Lecture, AGU Hydrology Days, Colorado State U, Fort Collins CO, Mar 21.
• Gupta, H. V. (2023). Towards Physical-Conceptual Modeling of Mass, Energy and Information Flows Using Machine Learning Technology. Hydrology and Atmospheric Sciences Seminar Series, University of Arizona, Tucson AZ, Feb 8. Hydrology and Atmospheric Sciences Department, University of Arizona, Tucson AZ.
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  Gupta HV (2023 invited), Towards Physical-Conceptual Modeling of Mass, Energy and Information Flows Using Machine Learning Technology,Hydrology and Atmospheric Sciences Seminar Series, University of Arizona, Tucson AZ, Feb 8.
• Khatami, S., Di Baldassarre, G., Gupta, H. V., Moallemi, E., & Pool, S. (2022). Suggesting a new diagram and convention for characterising and reporting model performance. 2022 EGU General Assembly, Vienna, Austria, 3–8 April 2022. Vienna, Austria: EGU.
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  Khatami S, G Di Baldassarre, H Gupta, E Moallemi, S Pool (2022), Suggesting a new diagram and convention for characterising and reporting model performance, Session HS1.3.1, Revisiting good modelling practices – where are we today? Virtual PICO, 2022 EGU General Assembly, Vienna, Austria, 3–8 April 2022.
• Loritz, R., & Gupta, H. V. (2023). Extrapo…what? Predictions beyond the support of the training data. 2023 EGU General Assembly, Vienna, Austria, 23–28 April. Vienna, Austria: EGU.
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  Loritz R and H Gupta (2023), Extrapo…what? Predictions beyond the support of the training data, Abstract, EGU23-1526, Session HS3.3 – Deep Learning in Hydrology, 2023 EGU General Assembly, Vienna, Austria, April 23-28.
• Sheikholeslami, R., Puy, A., Gupta, H. V., & et al, . (2022). How certain are we about the model-based estimations of global irrigation water withdrawal?. 2022 EGU General Assembly, Vienna, Austria, 3–8 April 2022. Vienna, Austria: EGU.
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  Sheikholeslami R, A Puy, HV Gupta, JW Hall, S Lo Piano, J Meier, F Pappenberger, A Porporato, G Vico, A Saltelli (2022), How certain are we about the model-based estimations of global irrigation water withdrawal? Session HS3.6: Advances in diagnostics, sensitivity, uncertainty analysis, and hypothesis testing of Earth and Environmental Systems models, 2022 EGU General Assembly, Vienna, Austria, 3–8 April 2022.
• Shen, C., Gupta, H. V., & Al, E. (2023). Differentiable modeling to unify machine learning and physical models and advance Geosciences. 2023 EGU General Assembly, Vienna, Austria, 23–28 April. Vienna, Austria: EGU.
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  Shen C, HV Gupta et al. (2023), Differentiable modeling to unify machine learning and physical models and advance Geosciences, Abstract, EGU23-15968, Session HS1.3.2 – Bridging physical, analytical, information-theoretic and machine learning approaches to system dynamics and predictability in Hydrology and Earth System Sciences, 2023 EGU General Assembly, Vienna, Austria, April 23-28.
• Wang, Y. H., & Gupta, H. V. (2023). Comparison of Physics-Informed Mass-Conserving Perceptron against Data-Driven Neural Network and Physical-Conceptual Models in Modeling the Hydrologic Systems. El Dia Del Agua y Atmosphera, Dept. of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Mar 28. Dept. of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson.
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  Wang YH and H Gupta (2023), Comparison of Physics-Informed Mass-Conserving Perceptron against Data-Driven Neural Network and Physical-Conceptual Models in Modeling the Hydrologic Systems, El Dia Del Agua y Atmosphera, Dept. of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Mar 28.
• Wang, Y. H., & Gupta, H. V. (2023).

  Developing Catchment-Scale Physical-Conceptual Rainfall-Runoff Models Using Machine-Learning

  . Google Flood Forecasting Meets Machine Learning, Virtual poster session, Jan 10-11.. Virtual poster session.
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  Wang YH and H Gupta (2022), Developing Catchment-Scale Physical-Conceptual Rainfall-Runoff Models Using Machine-Learning, Google Flood Forecasting Meets Machine Learning, Virtual poster session, Jan 10-11.
• Wang, Y. H., Guo, B., & Gupta, H. V. (2023). A Mathematical Model for Multi-component PFAS Solute Transport in Unsaturated Media. El Dia Del Agua y Atmosphera, Dept. of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Mar 28. Dept. of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson.
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  Saleem H, B Guo and H Gupta (2023), A Mathematical Model for Multi-component PFAS Solute Transport in Unsaturated Media, El Dia Del Agua y Atmosphera, Dept. of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Mar 28.
• De La Fuente, L., Ehsani, M. R., Gupta, H. V., & Condon, L. (2022). Hydro-LSTM: A Hydrological Approach to LSTM Machine Learning based Modeling. El Dia Del Agua y Atmosphera, Dept. of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Mar 22. Dept. of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson.
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  De la Fuente L, Ehsani MR, H Gupta and L Condon (2022), Hydro-LSTM: A Hydrological Approach to LSTM Machine Learning based Modeling,El Dia Del Agua y Atmosphera, Dept. of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Mar 22
• De la Fuente, L., Ehsani, M. R., Gupta, H. V., & Condon, L. (2022).

  Towards the Hydrological Interpretability of Long-Short Term Memory Machine Learning Modeling

  . 2022 Fall Meeting of the American Geophysical Union, Dec 12-16.. Fall Meeting of the American Geophysical Union, San Francisco CA.
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  De la Fuente L, MR Ehsani, H Gupta and LE Condon (2022), Towards the Hydrological Interpretability of Long-Short Term Memory Machine Learning Modeling, Session H079: Physics-Informed Machine Learning in Hydrology and Land Surface Processes, 2022 Fall Meeting of the American Geophysical Union, Dec 12-16.
• Ehsani, M. R., Behrangi, A., Zarei, A., Gupta, H. V., Zarei, A., Gupta, H. V., Ehsani, M. R., & Behrangi, A. (2022).

  NowCasting-Nets: Representation Learning to Mitigate Latency Gap of Satellite Precipitation Products Using Convolutional and Recurrent Neural Networks

  . 2022 Fall Meeting of the American Geophysical Union, Dec 12-16.. Fall Meeting of the American Geophysical Union, San Francisco CA.
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  Ehsani MR, A Zarei, H Gupta and A Behrangi (2022), NowCasting-Nets: Representation Learning to Mitigate Latency Gap of Satellite Precipitation Products Using Convolutional and Recurrent Neural Networks, Session H115: Utilizing Precipitation Datasets and Quantifying Associated Uncertainties in Hydrometeorological and Climate Impact Applications, 2022 Fall Meeting of the American Geophysical Union, Dec 12-16.
• Gupta, H. V. (2022). Auto Encoders and their Relevance to Physically Based Modeling. presentation to ML Group at Institute for Water and River Basin Management, Karlsruhe Institute of Technology, Karlsruhe, Germany, Sept 9. Institute for Water and River Basin Management, Karlsruhe Institute of Technology, Karlsruhe, Germany.
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  Gupta HV (2022 invited), Auto Encoders and their Relevance to Physically Based Modeling, presentation to ML Group at Institute for Water and River Basin Management, Karlsruhe Institute of Technology, Karlsruhe, Germany, Sept 9.
• Gupta, H. V. (2022). Towards Physical-Conceptual Modeling of Mass, Energy and Information Flows Using Machine Learning Technology. 13th International Excellence Talk, Lecture series organized by the International Scholars and Welcome Office, The International Excellence Grants Program, Hosted by Institute for Water and River Basin Management (IWH-HYD), Karlsruhe Institute of Technology, Karlsruhe, Germany, Sept 29.. Institute for Water and River Basin Management (IWH-HYD), Karlsruhe Institute of Technology, Karlsruhe, Germany,.
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  Gupta HV (2022 invited), Towards Physical-Conceptual Modeling of Mass, Energy and Information Flows Using Machine Learning Technology,13th International Excellence Talk, Lecture series organized by the International Scholars and Welcome Office, The International Excellence Grants Program, Hosted by Institute for Water and River Basin Management (IWH-HYD), Karlsruhe Institute of Technology, Karlsruhe, Germany, Sept 29.
• Gupta, H. V. (2022). Towards Physical-Conceptual Modeling of Mass, Energy and Information Flows Using Machine Learning Technology. University of Arizona RTG in Data Driven Discovery Showcase, Dept. of Mathematics, The University of Arizona, Tucson, Arizona, Mar 1.. University of Arizona RTG in Data Driven Discovery Showcase, Dept. of Mathematics, The University of Arizona, Tucson, Arizona.
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  Gupta HV (2022 invited), Towards Physical-Conceptual Modeling of Mass, Energy and Information Flows Using Machine Learning Technology,presentation at University of Arizona RTG in Data Driven Discovery Showcase, Dept. of Mathematics, The University of Arizona, Tucson, Arizona, Mar 1.
• Gupta, H. V. (2022). How Can We Use Models to Facilitate Scientific Discovery?. Invited Panelist at Hydroinformatics Roundtable, Frontiers in Hydrology Meeting, San Juan, Puerto Rico, June 19-24.. Frontiers in Hydrology Meeting, San Juan, Puerto Rico: AGU-CUAHSI.
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  Gupta HV (2022 invited), How Can We Use Models to Facilitate Scientific Discovery? Invited Panelist at Hydroinformatics Roundtable, Frontiers in Hydrology Meeting, San Juan, Puerto Rico, June 19-24.
• Gupta, H. V. (2022). How Can We Use Models to Facilitate Scientific Discovery?. Session on “Hydrocomplexity: Addressing Challenges in Hydrology from a Holistic Perspective”, Frontiers in Hydrology Meeting, San Juan, Puerto Rico. Frontiers in Hydrology Meeting, San Juan, Puerto Rico: AGU-CUAHSI.
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  Gupta HV (2022 invited), How Can We Use Models to Facilitate Scientific Discovery? Invited presentation at Session on “Hydrocomplexity: Addressing Challenges in Hydrology from a Holistic Perspective”, Frontiers in Hydrology Meeting, San Juan, Puerto Rico, June 19-24.
• Shen, C., Gupta, H. V., Bandai, T., & Kifer, D. (2022).

  Breaking down the imaginary barrier between machine learning and process-based modeling with differentiable modeling

  . 2022 Fall Meeting of the American Geophysical Union, Dec 12-16.. Fall Meeting of the American Geophysical Union, San Francisco CA.
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  Shen C, HV Gupta, T Bandai and D Kifer (2022), Breaking down the imaginary barrier between machine learning and process-based modeling with differentiable modeling, Session H078: Physics-Based and Hybrid (Physics ML) Modeling for Watershed Function, 2022 Fall Meeting of the American Geophysical Union, Dec 12-16.
• Wang, Y. H., & Gupta, H. V. (2022). Bridging the Gap between Physical-Conceptual Modeling and Machine Learning for Catchment-Scale Rainfall-Runoff Modeling. El Dia Del Agua y Atmosphera, Dept. of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Mar 22. Dept. of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson.
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  Wang YH and H Gupta (2022), Bridging the Gap between Physical-Conceptual Modeling and Machine Learning for Catchment-Scale Rainfall-Runoff Modeling, El Dia Del Agua y Atmosphera, Depr. of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Mar 22
• Wang, Y. H., & Gupta, H. V. (2022).

  Developing Catchment-Scale Physical-Conceptual Rainfall-Runoff Models Using Machine-Learning

  . 2022 Fall Meeting of the American Geophysical Union, Dec 12-16.. Fall Meeting of the American Geophysical Union, San Francisco CA.
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  Wang YH and H Gupta (2022), Developing Catchment-Scale Physical-Conceptual Rainfall-Runoff Models Using Machine-Learning, Session H079: Physics-Informed Machine Learning in Hydrology and Land Surface Processes, 2022 Fall Meeting of the American Geophysical Union, Dec 12-16.
• De La Fuente, L., & Gupta, H. V. (2021). Towards A Multi-Representational Approach to Characterizing Hydrological Processes at Large Scales. 2021 Fall Meeting of the American Geophysical Union, Online, Dec 13-17. Online: AGU.
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  De la Fuente L and HV Gupta (2021), Towards A Multi-Representational Approach to Characterizing Hydrological Processes at Large Scales, Session XX: Machine Learning in Hydrological Modeling, presented at 2021 Fall Meeting of the American Geophysical Union, Online, Dec 13-17.
• De La Fuente, L., Ehsani, M. R., & Gupta, H. V. (2021). Hydro-LSTM: A Hydrological Approach for the LSTM Model. 2nd Workshop on Knowledge Guided Machine Learning (KGML2021): A Framework for Accelerating Scientific Discovery, online, August 9-11. Online.
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  De la Fuente L, MR Ehsani and HV Gupta (2021), Hydro-LSTM: A Hydrological Approach for the LSTM Model, presented at 2nd Workshop on Knowledge Guided Machine Learning (KGML2021): A Framework for Accelerating Scientific Discovery, online, August 9-11
• De la Fuente, L., & Gupta, H. V. (2021). Transferring Learning Between Machine Learning and Physics-Based Approaches to Improve Characterization of Watershed Behavior. El Dia Del Agua y Atmosphera, Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Mar 31/April 1. Online: Department of Hydrology and Atmospheric Sciences, The University of Arizona,.
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  De la Fuente L and H Gupta (2021), Transferring Learning Between Machine Learning and Physics-Based Approaches to Improve Characterization of Watershed Behavior, presented at El Dia Del Agua y Atmosphera, Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Mar 31/April 1
• Gharari, S., & Gupta, H. V. (2021). Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data. 2021 Fall Meeting of the American Geophysical Union, Online, Dec 13-17. Online: AGU.
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  [CT-389] Gharari S and HV Gupta (2021), Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data, Session XX: Machine Learning in Hydrological Modeling, presented at 2021 Fall Meeting of the American Geophysical Union, Online, Dec 13-17.
• Gupta, H. V. (2021). Accurate Probabilistic Estimation of One-D Entropy from Random Samples. University of Arizona RTG in Data Driven Discovery Showcase, Dept. of Mathematics. University of Arizona, Dept. of Mathematics.
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  Gupta HV (2021 invited), Accurate Probabilistic Estimation of One-D Entropy from Random Samples, Invited presentation at University of Arizona RTG in Data Driven Discovery Showcase, Dept. of Mathematics, The University of Arizona, Tucson, Arizona, Mar 10.
• Gupta, H. V. (2021). How Can We Use Models to Facilitate Scientific Discovery?. University of Arizona, Department of Civil & Architectural Engineering & Mechanics Seminar. University of Arizona, Department of Civil & Architectural Engineering & Mechanics.
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  Gupta HV (2021 invited), How Can We Use Models to Facilitate Scientific Discovery?, Invited presentation at Department of Civil & Architectural Engineering & Mechanics, The University of Arizona, Tucson, Arizona, Nov 12.
• Partington, D., Thyer, M., Shanafield, M., McInerney, D., Westra, S., Maier, H., Simmons, C., Croke, B., Jakeman, T., Gupta, H. V., & Kavetski, D. (2021). Modelling hydrological change due to wildfires. 24th International Congress on Modelling and Simulation, Dec 5-10..
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  Partington D, M Thyer, M Shanafield, D McInerney, S Westra, H Maier, C Simmons, B Croke, T Jakeman, H Gupta, D Kavetski (2021), Modelling hydrological change due to wildfires, 24th International Congress on Modelling and Simulation, Dec 5-10.
• Saleem, H., Guo, B., & Gupta, H. V. (2021). A Mathematical Model for the Transport of Multi-Component PFAS In Unsaturated Porous Media. El Dia Del Agua y Atmosphera, Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Mar 31/April 1. Online: Department of Hydrology and Atmospheric Sciences, The University of Arizona,.
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  Saleem H, B Guo and H Gupta (2021), A Mathematical Model for the Transport of Multi-Component PFAS In Unsaturated Porous Media, presented at El Dia Del Agua y Atmosphera, Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Mar 31/April 1
• Thyer, M., Gupta, H. V., Westra, S., & Al, E. (2021). Accelerating development of the next generation of conceptual hydrological models to support decision-making under change. 24th International Congress on Modelling and Simulation, Dec 5-10..
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  Thyer M, H Gupta, S Westra, D McInerney, H Maier, D Kavetski, T Jakeman, B Croke, C Simmons, M Shanafield, Partington D (2021), Accelerating development of the next generation of conceptual hydrological models to support decision-making under change, 24th International Congress on Modelling and Simulation, Dec 5-10.
• Wang, Y. H., & Gupta, H. V. (2021). Exploring the Integration of Process-Based and Deep Learning Approaches for Modeling Snowpack Dynamics. 2021 Fall Meeting of the American Geophysical Union, Online, Dec 13-17. Online: AGU.
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  Wang YH and HV Gupta (2021), Exploring the Integration of Process-Based and Deep Learning Approaches for Modeling Snowpack Dynamics, Session XX: Machine Learning in Hydrological Modeling, presented at 2021 Fall Meeting of the American Geophysical Union, Online, Dec 13-17.
• Wang, Y. H., Gupta, H. V., Zeng, X., & Niu, G. (2021). Developing Regional Snow Model Using Long Short-Term Memory Networks Applied to Large Pixel-Scale Datasets. El Dia Del Agua y Atmosphera, Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Mar 31/April 1. Online: Department of Hydrology and Atmospheric Sciences, The University of Arizona,.
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  Wang YH, H Gupta, X Zeng and GY Niu (2021), Developing Regional Snow Model Using Long Short-Term Memory Networks Applied to Large Pixel-Scale Datasets, presented at El Dia Del Agua y Atmosphera, Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Mar 31/April 1
• Wang, Y. H., Gupta, H. V., Zeng, X., & Niu, G. (2021). Hypothesis Testing Using Long Short-Term Memory Networks for Improving Understanding of Continental- and Regional-Scale Snowpack Dynamics. 2021 Fall Meeting of the American Geophysical Union, Online, Dec 13-17. Online: AGU.
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  Wang YH, HV Gupta , X Zeng and GY Niu (2021), Hypothesis Testing Using Long Short-Term Memory Networks for Improving Understanding of Continental- and Regional-Scale Snowpack Dynamics, Session H081: Physics-informed Machine Learning in Hydrology, presented at 2021 Fall Meeting of the American Geophysical Union, Online, Dec 13-17.
• De la Fuente, L., & Gupta, H. V. (2020, Spring). Robust data splitting for hydrological modeling: Implementation of machine learning techniques. El Dia Del Agua y Atmosphera, Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, April. Tucson AZ: Department of Hydrology and Atmospheric Sciences, The University of Arizona.
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  L de la Fuentes and HV Gupta (2020), Robust data splitting for hydrological modeling: Implementation of machine learning techniques, presented at El Dia Del Agua y Atmosphera, Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, April
• De la Fuente, L., Gupta, H. V., & Nearing, G. S. (2020, Fall). Transferring Learning Between Machine Learning and Physics-Based Approaches to Improve Characterization of Watershed Behavior. 2020 Fall Meeting of the American Geophysical Union, Online, Dec 7-11. Online: American Geophysical Union.
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  L De la Fuente, H Gupta, GS Nearing (2020), Transferring Learning Between Machine Learning and Physics-Based Approaches to Improve Characterization of Watershed Behavior, Session H081: Machine Learning in Hydrological Modeling, presented at 2020 Fall Meeting of the American Geophysical Union, Online, Dec 1-17.
• Guo, D., Zheng, D., Maier, H., & Gupta, H. V. (2020, Spring). Robustness of Conceptual Rainfall-Runoff Models: How this Varies across Australian Catchments?. 2020 Meeting of The European Geophysical Union, Vienna, Austria, May 3-8. Vienna, Austria: European Geophysical Union.
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  D Guo, Zheng F, H Maier and HV Gupta (2020), Robustness of Conceptual Rainfall-Runoff Models: How this Varies across Australian Catchments? presented at Session HS2.5.2 on Large-sample hydrology: characterising and understanding hydrological diversity, of the 2020 Meeting of The European Geophysical Union, Vienna, Austria, May 3-8.
• Gupta, H. V., Wang, Y. H., Broxton, P. D., Fang, Y., Behrangi, A., Zeng, X., & Niu, G. (2020, Fall). Toward Improving Snowpack Prediction and Snow Cover Fraction Parameterization in Land Surface Models. 2020 Fall Meeting of the American Geophysical Union, Online, Dec 7-11. Online: American Geophysical Union.
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  H Gupta, YH Wang , PD Broxton, Y Fang, A Behrangi, X Zeng and GY Niu (2020), Toward Improving Snowpack Prediction and Snow Cover Fraction Parameterization in Land Surface Models, Session C029: Quantifying Spatial and Temporal Variability of Snow and Snow Processes, presented at 2020 Fall Meeting of the American Geophysical Union, Online, Dec 1-17.
• Ji, L., Ji, L., Baker, V. R., Baker, V. R., Gupta, H. V., Gupta, H. V., Ferre, P. A., Ferre, P. A., Liu, T., & Liu, T. (2020, Fall). Machine learning analysis of morphometry-extreme flood links in the Lower Colorado River Basin. 2020 Fall Meeting of the American Geophysical Union, Online, Dec 7-11. Online: American Geophysical Union.
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  L Ji, VR Baker, HV Gupta, PA Ferre, T Liu (2020), Machine learning analysis of morphometry-extreme flood links in the Lower Colorado River Basin, Session NH004 - Data Science and Machine Learning for Natural Hazard Sciences, presented at 2020 Fall Meeting of the American Geophysical Union, Online, Dec 1-17.
• Liu, T., McGuire, L., Wei, H., Wengers, F. K., Gupta, H. V., Ji, L., & Goodrich, D. C. (2020, Fall). Hydrological recovery after a severe wildfire in a chaparral dominated, mountainous watershed. 2020 Fall Meeting of the American Geophysical Union, Online, Dec 7-11. Online: American Geophysical Union.
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  T Liu, L McGuire, H Wei, FK Rengers, H Gupta, L Ji, DC Goodrich (2020), Hydrological recovery after a severe wildfire in a chaparral dominated, mountainous watershed, Session H093: Point- to Catchment-Scale Effects of Wildfire on Hydrology, Water Resources, and Ecosystems, presented at 2020 Fall Meeting of the American Geophysical Union, Online, Dec 1-17.
• Luis, D. L., & Gupta, H. V. (2020, Spring). Robust data splitting for Hydrological Modeling: Implementation of Machine Learning techniques. 2020 EGU General Assembly, Vienna, Austria. Vienna, Austria: European Geosciences Union.
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  De la Fuente L, HV Gupta (2020), Robust data splitting for Hydrological Modeling: Implementation of Machine Learning techniques, presented at Session XX of the 2020 Meeting of The European Geophysical Union, Vienna, Austria, May 3-8.
• Meles, M. B., Goodrich, D. C., Unkrich, C. L., Burns, I. S., Gupta, H. V., Hirpa, F. A., Guertin, D. P., & Razavi, S. (2020, Fall). Understanding the Dynamics of Parameter Importance for Global Parameterization of Modeling Semi-Arid Watersheds. 2020 Fall Meeting of the American Geophysical Union, Online, Dec 7-11. Online: American Geophysical Union.
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  MB Meles, DC Goodrich, CL Unkrich, IS Burns, HV Gupta, FA Hirpa, DP Guertin, S Razavi (2020), Understanding the Dynamics of Parameter Importance for Global Parameterization of Modeling Semi-Arid Watersheds, Session H195: Diagnostics, Sensitivity, and Uncertainty Analysis of Earth and Environmental Models, presented at 2020 Fall Meeting of the American Geophysical Union, Online, Dec 1-17.
• Nearing, G. S., Kratzert, F., Pelissier, C. S., Klotz, D., Frame, J., & Gupta, H. V. (2020, Spring). Machine Learning is Central to the Future of Hydrology Modeling. 2020 Meeting of The European Geophysical Union, Vienna, Austria, May 3-8. Vienna, Austria: European Geophysical Union.
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  Nearing G, F Kratzert, C Pelissier, D Klotz, J Frame, H Gupta (2020), Machine Learning is Central to the Future of Hydrology Modeling, presented at HS1.2.1 (PICO) on Pathways towards solving the Unsolved Problems in Hydrology (UPH), of the 2020 Meeting of The European Geophysical Union, Vienna, Austria, May 3-8.
• Nearing, G. S., Kratzert, F., Sampson, A. K., Pelissier, C. S., Klotz, D., Prieto, C., Frame, J. M., & Gupta, H. V. (2020, Fall). What Is The Role Of Hydrological Science In The Age Of Machine Learning?. 2020 Fall Meeting of the American Geophysical Union, Online, Dec 7-11. Online: American Geophysical Union.
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  GS Nearing, F Kratzert, AK Sampson, C Pelissier, D Klotz, C Prieto, JM Frame, H Gupta (2020 invited), What Is The Role Of Hydrological Science In The Age Of Machine Learning? Session H080: Machine Learning in Hydrologic Forecasting, presented at 2020 Fall Meeting of the American Geophysical Union, Online, Dec 7-11.
• Wang, Y. H., Gupta, H. V., Broxton, P. D., Behrangi, A., Zeng, X., Fang, Y., & Niu, G. (2020, Spring). Developing the Snow Cover Fraction Schemes using Machine Learning Approach for Hydrological Predictions. 2020 Meeting of the American Meteorological Society, Phoenix Arizona, Jan 12-16.. Boston, MA: AMS.
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  Wang YH, HV Gupta, PD Broxton, A Behrangi, X Zeng, Y Fang, Guo-Yue Niu (2020), Developing the Snow Cover Fraction Schemes using Machine Learning Approach for Hydrological Predictions, Session on Applications of Machine Learning in Earth System Modeling, presented at 2020 Annual Meeting of the American Meteorological Society, Boston MA, Jan 12–16.
• Wang, Y. H., Gupta, H. V., Nearing, G. S., Zeng, X., & Niu, G. (2020, Fall). Hypothesis Testing using Long Short-Term Memory Networks Applied to Large Pixel-Scale Datasets. 2020 Fall Meeting of the American Geophysical Union, Online, Dec 7-11. Online: American Geophysical Union.
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  YH Wang, H Gupta, GS Nearing, X Zeng and GYue Niu (2020), Hypothesis Testing using Long Short-Term Memory Networks Applied to Large Pixel-Scale Datasets, Session H224: Machine Learning in Hydrological Modeling, presented at 2020 Fall Meeting of the American Geophysical Union, Online, Dec 1-17.
• Wang, Y. H., Niu, G., Gupta, H. V., Zeng, X., Broxton, P. D., Behrangi, A., Fang, Y., Fang, Y., Broxton, P. D., Behrangi, A., Zeng, X., Gupta, H. V., Wang, Y. H., & Niu, G. (2020, Spring). Toward Improving Snowpack Prediction and its Parameterization in Land Surface Models. El Dia Del Agua y Atmosphera, Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, April. Tucson AZ: Department of Hydrology and Atmospheric Sciences, The University of Arizona.
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  YH Wang, HV Gupta, P Broxton, Y Fang, A Behrangi, X Zeng, GYue Niu (2020), Toward Improving Snowpack Prediction and its Parameterization in Land Surface Models, presented at El Dia Del Agua y Atmosphera, Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, April
• Zheng, F., Guo, D., Maier, H., & Gupta, H. V. (2020, Spring). On the Robustness of Conceptual Rainfall-Runoff Models to Calibration and Evaluation Dataset Splits Selection: A Large Sample Investigation. 2020 Meeting of The European Geophysical Union, Vienna, Austria, May 3-8. Vienna, Austria: European Geophysical Union.
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  Zheng F, D Guo, H Maier and HV Gupta (2020), On the Robustness of Conceptual Rainfall-Runoff Models to Calibration and Evaluation Dataset Splits Selection: A Large Sample Investigation, presented at Session HS2.5.2 on Large-sample hydrology: characterising and understanding hydrological diversity, of the 2020 Meeting of The European Geophysical Union, Vienna, Austria, May 3-8.
• Bitew, M. W., Goodrich, D. C., Unkrich, C., Burns, S., Gupta, H. V., Razavi, S., & Guertin, D. P. (2019, Summer 2019). Uncertainty and Parameter Sensitivity of Physically Distributed Sediment and Runoff KINEROS2 Model. SEDHYD 2019: Federal Interagency Sedimentation and Hydrologic Modeling Conference, Reno, Nevada, June 24-28. Reno Nevada: Federal Interagency.
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  Bitew MW, D Goodrich, CL Unkrich, S Burns, HV Gupta, S Razavi and DP Guertin (2019), Uncertainty and Parameter Sensitivity of Physically Distributed Sediment and Runoff KINEROS2 Model, presented at SEDHYD 2019: Federal Interagency Sedimentation and Hydrologic Modeling Conference, Reno, Nevada, June 24-28.
• Bitew, M. W., Goodrich, D., Unkrich, C. L., Burns, S., Gupta, H. V., Razavi, S., & Guertin, D. P. (2019, Summer 2019). Uncertainty and Parameter Sensitivity of Physically Distributed Sediment and Runoff KINEROS2 Model. SEDHYD 2019: Federal Interagency Sedimentation and Hydrologic Modeling Conference. Reno, Nevada.
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  Bitew MW, D Goodrich, CL Unkrich, S Burns, HV Gupta, S Razavi and DP Guertin (2019), Uncertainty and Parameter Sensitivity of Physically Distributed Sediment and Runoff KINEROS2 Model, presented at SEDHYD 2019: Federal Interagency Sedimentation and Hydrologic Modeling Conference, Reno, Nevada, June 24-28.
• Gao, G., Zhang, J., Fu, B., & Gupta, H. V. (2019, Fall 2019). An elasticity approach to quantify the effects of climate variability and ecological restoration on sediment discharge change in the Loess Plateau, China. 2019 Fall Meeting of the American Geophysical Union. San Francisco, CA: American Geophysical Union.
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  Gao G, J Zhang, B Fu and HV Gupta (2019), An elasticity approach to quantify the effects of climate variability and ecological restoration on sediment discharge change in the Loess Plateau, China, Session H013 - Advances in quantifying impacts and extents of land-use/land-cover change on hydrology, presented at 2019 Fall Meeting of the American Geophysical Union, San Francisco CA, Dec 9-13.
• Gupta, H. V. (2019, Fall 2019). Transforming Earth Science by Bridging Machine Learning & Physics. 2019 Fall Meeting of the American Geophysical Union. San Francisco CA: American Geophysical Union.
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  Gupta HV (2019 invited), Transforming Earth Science by Bridging Machine Learning & Physics, Invited Talk ID: 486407, Session H023. Advancing Watershed Science Using Machine Learning, Diverse Data, And Mechanistic Modeling, presented at 2019 Fall Meeting of the American Geophysical Union, Washington DC, Dec 9-13.
• Gupta, H. V. (2019, Fall). Some Thoughts on Bridging Machine Learning & Physics. Uncertainty Quantification Group, Department of Mathematics, The University of Arizona. The University of Arizona: Department of Mathematics, The University of Arizona.
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  Gupta HV (2019 invited), Some Thoughts on Bridging Machine Learning & Physics, Presentation to Uncertainty Quantification Group, Dept. of Mathematics, The University of Arizona, Tucson, Arizona, Apr 10.
• Lahmers, T., Hazenberg, P., Gupta, H. V., Castro, C. L., Gochis, D. J., Dugger, A., & Yates, D. (2019, Spring). Enhancements to the WRF-Hydro Hydrologic Model Structure for Semi-Arid Environments. 2019 Meeting of the American Meteorological Society, Phoenix Arizona, Jan 6-10.. Phoenix, Arizona: AMS.
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  Lahmers TM, P Hazenberg, H Gupta, C Castro, D Gochis, A Dugger, D Yates, L Read, L Karsten, YH Wang , RJ Zamora and B Cosgrove (2019), Enhancements to the WRF-Hydro Hydrologic Model Structure for Semi-arid Environments, presented at 2019 Meeting of the American Meteorological Society, Phoenix Arizona, Jan 6-10.
• Lahmers, T., Hazenberg, P., Gupta, H. V., Castro, C. L., Gochis, D., Dugger, A., Yates, D., Read, L., Karsten, L., Wang, Y. H., Zamora, R. J., & Cosgrove, B. A. (2019, Fall 2019). Demonstrating the Added Values of Suction Losses for Channel Infiltration in WRF-Hydro Hydrologic Model and its Applications in Semiarid Region. 2019 Fall Meeting of the American Geophysical Union. San Franciso CA: American Geophysical Union.
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  Lahmers TM, P Hazenberg, H Gupta, C Castro, D Gochis, A Dugger, D Yates, L Read, L Karsten, YH Wang, RJ Zamora and BA Cosgrove (2019), Implementation and evaluation of channel infiltration in the NOAA National Water Model for semi-arid environments, Session H037 - Continental Scale Modeling: Process Heterogeneity from Summit to Sea, presented at 2019 Fall Meeting of the American Geophysical Union, San Francisco CA, Dec 9-13.
• Meles, M. B., Goodrich, D. C., Gupta, H. V., & Burns, I. S. (2019, Fall 2019). The Dynamics of the Uncertainty and Parameter Sensitivity in Modeling Semi-arid Systems. 2019 Fall Meeting of the American Geophysical Union. Washington DC: American Geophysical Union.
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  Meles MB, DC Goodrich, H Gupta and IS Burns (2019), The Dynamics of the Uncertainty and Parameter Sensitivity in Modeling Semi-arid Systems, Session H011 - Advances in Models, Algorithms, and Data Observation in Hydrology and Water Energy System Operation, presented at 2019 Fall Meeting of the American Geophysical Union, Washington DC, Dec 9-13.
• Mitchell, M., Hazenberg, P., Lahmers, T., Burns, S., Castro, C. L., Gochis, D. J., Goodrich, D., Gupta, H. V., & Korgaonkarz, Y. (2019, Spring). Observed and Simulated Channel Network Infiltration Losses in the Semi-Arid Walnut Gulch Experimental Watershed. 2019 Meeting of the American Meteorological Society, Phoenix Arizona, Jan 6-10.. Phoenix, Arizona: AMS.
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  Mitchell M, P Hazenberg, TM Lahmers, S Burns, CL Castro, D Gochis, DC Goodrich, HV Gupta and Y Korgaonkarz (2019), Observed and Simulated Channel Network Infiltration Losses in the Semi-Arid Walnut Gulch Experimental Watershed, Arizona, presented at 2019 Meeting of the American Meteorological Society, Phoenix Arizona, Jan 6-10.
• Mitchell, M., Hazenberg, P., Lahmers, T., Burns, S., Castro, C. L., Gochis, D., Goodrich, D. C., Gupta, H. V., & Korgaonkar, Y. (2019, January). Observed and Simulated Channel Network Infiltration Losses in the Semi-Arid Walnut Gulch Experimental Watershed. 99th Annual American Meteorological Society Meeting.
• Nearing, G. S., Gupta, H. V., Kratzert, F., & Sampson, A. K. (2019, Fall 2019). Physically-Based Machine Learning for Hydrological Modeling. 2019 Fall Meeting of the American Geophysical Union. San Francisco, CA: American Geophysical Union.
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  Nearing GS, HV Gupta, F Kratzert and AK Sampson (2019) Physically-Based Machine Learning for Hydrological Modeling, Session H093: Machine Learning in Hydrological Modeling, presented at 2019 Fall Meeting of the American Geophysical Union, San Francisco CA, Dec 9-13.
• Pechlivanidis, I., Gupta, H. V., & Bosshard, T. (2019, Spring). How to identify a representative subset of hydro-climatic simulations for impact modelling studies?. 2019 Meeting of The European Geophysical Union, Vienna, Austria, Apr 23-28. Vienna, Austria: European Geophysical Union.
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  Pechlivanidis IG, HV Gupta and T. Bosshard (2019), How to identify a representative subset of hydro-climatic simulations for impact modelling studies? presented at Session HS2.4.5 Hydrological change: Regional hydrological behaviour under transient climate and land use conditions, of the 2019 Meeting of The European Geophysical Union, Vienna, Austria, Apr 7-12.
• Wang, Y. H., Fang, Y., Broxton, P. D., Behrangi, A., Zeng, X., Gupta, H. V., & Niu, G. (2019, Fall). Developing a New Snow Cover Fraction Scheme for Hydrological Predictions. 2019 Fall Meeting of the American Geophysical Union. San Francisco, CA: AGU.
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  Wang YH, Y Fang, PD Broxton, A Behrangi, X Zeng, HV Gupta, Guo-Yue Niu (2019), Developing a New Snow Cover Fraction Scheme for Hydrological Predictions, Session H025 - Applications in Snow Hydrology: Linking Seasonal Snow to Natural Processes and Society, presented at 2019 Fall Meeting of the American Geophysical Union, San Francisco CA, Dec 9-13.
• Wang, Y. H., Gupta, H. V., Lahmers, T., Castro, C. L., Unkrich, C. L., Goodrich, D. C., & Hazenberg, P. (2019, Spring). Examining parameter identifiability of the WRF-Hydro hydrologic model. El Dia del Agua y Atmosphera, Department of Hydrology and Atmospheric Sciences, The University of Arizona, 25 March. Tucson AZ: Department of Hydrology and Atmospheric Sciences, The University of Arizona.
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  Wang YH, HV Gupta, TM Lahmers, CL Castro, CL Unkrich, DC Goodrich and P Hazenberg (2019), Examining parameter identifiability of the WRF-Hydro hydrologic model, presented at El Dia del Agua y Atmosphera, Department of Hydrology and Atmospheric Sciences, The University of Arizona, 25 March.
• Wang, Y. H., Hazenberg, P., Gupta, H. V., Castro, C. L., Lahmers, T., Unkrich, C., & Goodrich, D. (2019, Spring). Demonstrating the Added Values of Suction Losses for Channel Infiltration in WRF-Hydro Hydrologic Model and its Applications in Semiarid Region. 2019 Meeting of the American Meteorological Society, Phoenix Arizona, Jan 6-10.. Phoenix, Arizona: AMS.
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  Wang YH, P Hazenberg, H Gupta, C Castro, T Lahmers, C Unkrich and D Goodrich (2019), Demonstrating the Added Values of Suction Losses for Channel Infiltration in WRF-Hydro Hydrologic Model and its Applications in Semiarid Region, session on "Integrated Metrics and Benchmarking for Next-Generation Hydro/Land-Surface Modeling of the Water Cycle”, presented at 2019 Meeting of the American Meteorological Society, Phoenix Arizona, Jan 6-10.
• Wang, Y. H., Niu, G., Fang, Y., Gupta, H. V., Broxton, P. D., Zeng, X., Behrangi, A., Behrangi, A., Broxton, P. D., Zeng, X., Fang, Y., Gupta, H. V., Wang, Y. H., & Niu, G. (2019, Fall). Investigation of Constructing a Snow Cover Fraction Scheme for Land Surface Model. 2019 Annual Meeting of the Arizona Hydrological Society, Casino del Sol, Tucson AZ, Sept 25–27. Tucson AZ: AHS.
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  Wang YH, HV Gupta, PD Broxton, A Behrangi, X Zeng, Y Fang, Guo-Yue Niu (2019), Investigation of Constructing a Snow Cover Fraction Scheme for Land Surface Model, presented at 2019 Annual Meeting of the Arizona Hydrological Society, Casino del Sol, Tucson AZ, Sept 25–27.
• Zhang, J., Gupta, H. V., Gao, G., Fu, B., Zhang, X., & Li, R. (2019, Fall 2019). A Universal Multi-fractal Approach to Assessment of Spatiotemporal Extreme Precipitation over the Loess Plateau of China. 2019 Fall Meeting of the American Geophysical Union. Washington DC: American Geophysical Union.
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  Zhang J, H Gupta, G Gao, B Fu, X Zhang and Rui Li (2019), A Universal Multi-fractal Approach to Assessment of Spatiotemporal Extreme Precipitation over the Loess Plateau of China, Session H080 - Hydrology of Arid and Semi-arid Environments, presented at 2019 Fall Meeting of the American Geophysical Union, San Francisco CA, Dec 9-13.
• Ehret, U., Darscheid, P., Nearing, G., & Gupta, H. V. (2018, Spring). An information perspective on hydrological learning and prediction. General Assembly of the European Geosciences Union in Vienna/Austria, April.. Vienna/Austria: European Geosciences Union.
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  Ehret U, P Darscheid, G Nearing and H Gupta (2018), An information perspective on hydrological learning and prediction, General Assembly of the European Geosciences Union in Vienna/Austria, April.
• Guse, B., Pfannerstill, M., Fohrer, N., & Gupta, H. V. (2018, Spring 2018). Improving parameter identifiability by computing weighted performance criteria based on daily sensitivity time series. General Assembly of the European Geosciences Union in Vienna/Austria, April.. Vienna/Austria: EGU.
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  Guse B, M Pfannerstill, N Fohrer and H Gupta (2018), Improving parameter identifiability by computing weighted performance criteria based on daily sensitivity time series, session HS1.4 – Advances in Diagnostics, Sensitivity, and Uncertainty Analysis of Earth and Environmental Systems Models, General Assembly of the European Geosciences Union in Vienna/Austria, April.
• Guse, B., Pfannerstill, M., Fohrer, N., & Gupta, H. V. (2018, Spring). Improving parameter identifiability by computing weighted performance criteria based on daily sensitivity time series. General Assembly of the European Geosciences Union in Vienna/Austria, April.. Vienna/Austria: European Geosciences Union.
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  Guse B, M Pfannerstill, N Fohrer and H Gupta (2018), Improving parameter identifiability by computing weighted performance criteria based on daily sensitivity time series, General Assembly of the European Geosciences Union in Vienna/Austria, April.
• Huo, X., Niu, G., Gupta, H. V., Duan, Q., & Gong, W. (2018, Fall 2018). Investigation of the Robustness of Parameter Sensitivity Estimates from Different Spatial Samples. 2018 Fall Meeting of the American Geophysical Union. Washington DC: American Geophysical Union.
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  Huo X, GY Niu, H Gupta, Q Duan and W Gong (2018), Investigation of the Robustness of Parameter Sensitivity Estimates from Different Spatial Samples, session H044 – Data integration, inverse methods, and data valuation across a range of scales in hydrogeophysics, presented at 2018 Fall Meeting of the American Geophysical Union, Washington DC, Dec 10-14.
• Lahmers, T., Gupta, H. V., Hazenberg, P., Castro, C. L., Gochis, D. J., Yates, D., Dugger, A., & Goodrich, D. C. (2018, Spring). Enhancements to the WRF-Hydro Hydrologic Model Structure for Semi-Arid Environments. 32nd AMS Conference on Hydrology, 98th Annual AMS Meeting, Austin, Texas, 7-11 Jan.. Austin, Texas: AMS.
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  Lahmers TM, H Gupta, P Hazenberg, CL Castro, DJ Gochis, D Yates, A Dugger, DC Goodrich (2018), Enhancements to the WRF-Hydro Hydrologic Model Structure for Semi-Arid Environments, presented at 32nd AMS Conference on Hydrology, 98th Annual AMS Meeting, Austin, Texas, 7-11 Jan.
• Lahmers, T., Hazenberg, P., Gupta, H. V., Castro, C. L., Gochis, D. J., Dugger, A., Yates, D., Lahmers, T., Hazenberg, P., Gupta, H. V., Castro, C. L., Gochis, D. J., Dugger, A., & Yates, D. (2018, Fall 2018). Enhancements to the WRF-Hydro Hydrologic Model Structure for Semi-Arid Environments. 2018 Fall Meeting of the American Geophysical Union. Washington, DC: AGU.
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  Lahmers TM, P Hazenberg, H Gupta, C Castro, D Gochis, A Dugger, D Yates, L Read, L Karsten, YH Wang, RJ Zamora and B Cosgrove (2018), Enhancements to the WRF-Hydro Hydrologic Model Structure for Semi-arid Environments, session on Research, Development and Evaluation of the National Water Model and Facilitation of Community Involvement, presented at 2018 Fall Meeting of the American Geophysical Union, Washington DC, Dec 10-14.
• Loritz, R., Gupta, H. V., Jackisch, C., Westhoff, M., Kleidon, A., Ehret, U., & Zehe, E. (2018, Spring). On the dynamic nature of hydrological similarity. General Assembly of the European Geosciences Union in Vienna/Austria, April.. Vienna/Austria: European Geosciences Union.
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  Loritz R, H Gupta, C Jackisch, M Westhoff, A Kleidon, U Ehret and E Zehe (2018), On the dynamic nature of hydrological similarity, session HS2.1.3 - Catchment Organisation, Similarity, and Evolution, General Assembly of the European Geosciences Union in Vienna/Austria, April.
• Maier, H. R., Zheng, F., Wu, W., Dandy, G. C., Gupta, H. V., & Zhang, T. (2018, Summer 2018). Does Predictive Validation Increase or Decrease the Uncertainty Associated with Environmental Model Outputs?. 9th International Congress on Environmental Modelling and Software "Modelling for Sustainable Food-Energy-Water Systems" of the International Environmental Modelling & Software Society. Fort Collins, Colorado.
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  Maier HR, F Zheng, W Wu, GC Dandy, HV Gupta and T Zhang (2018), Does Predictive Validation Increase or Decrease the Uncertainty Associated with Environmental Model Outputs?, presented at the biennial 9th International Congress on Environmental Modelling and Software "Modelling for Sustainable Food-Energy-Water Systems" of the International Environmental Modelling & Software Society, Fort Collins, Colorado, June 24-28.
• Nearing, G., & Gupta, H. V. (2018, Spring). Philosophical Foundations of Hydrological Uncertainty. General Assembly of the European Geosciences Union in Vienna/Austria, April.. Vienna/Austria: European Geosciences Union.
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  Nearing G and H Gupta (2018), Philosophical Foundations of Hydrological Uncertainty, session HS1.4 - Advances in Diagnostics, Sensitivity, and Uncertainty Analysis of Earth and Environmental Systems Models, General Assembly of the European Geosciences Union in Vienna/Austria, April.
• Razavi, S., & Gupta, H. V. (2018, Summer). Revisiting the Fundamental Basis of Global Sensitivity Analysis for Dynamical Environmental Models. 9th International Congress on Environmental Modelling and Software "Modelling for Sustainable Food-Energy-Water Systems" of the International Environmental Modelling & Software Society, Fort Collins, Colorado, June 24-28.. Fort Collins, Colorado: Environmental Modelling and Software.
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  Razavi S and HV Gupta (2018), Revisiting the Fundamental Basis of Global Sensitivity Analysis for Dynamical Environmental Models, presented at the biennial 9th International Congress on Environmental Modelling and Software "Modelling for Sustainable Food-Energy-Water Systems" of the International Environmental Modelling & Software Society, Fort Collins, Colorado, June 24-28.
• Razavi, S., Sheikholeslami, R., Gupta, H. V., & Haghnegahdar, A. (2018, Fall). VARS-TOOL: A Toolbox for Comprehensive, Efficient, and Robust Global Sensitivity Analysis. 2018 Fall Meeting of the American Geophysical Union. Washington, DC: AGU.
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  Razavi S, R Sheikholeslami, H Gupta and A Haghnegahdar (2018), VARS-TOOL: A toolbox for Comprehensive, Efficient, and Robust Sensitivity and Uncertainty Analysis, presented at 2018 Fall Meeting of the American Geophysical Union, Washington DC, Dec 10-14.
• Sheikholeslami, R., Razavi, S., Gupta, H. V., Becker, W., & Haghnegahdar, A. (2018, Summer). Addressing Curse of Dimensionality in Global Sensitivity Analysis of Large Environmental Models: An Automated Grouping Strategy. 9th International Congress on Environmental Modelling and Software "Modelling for Sustainable Food-Energy-Water Systems" of the International Environmental Modelling & Software Society, Fort Collins, Colorado, June 24-28.. Fort Collins, Colorado: Environmental Modelling and Software.
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  Sheikholeslami R, S Razavi, HV Gupta, W Becker and A Haghnegahdar (2018), Addressing Curse of Dimensionality in Global Sensitivity Analysis of Large Environmental Models: An Automated Grouping Strategy, presented at the biennial 9th International Congress on Environmental Modelling and Software "Modelling for Sustainable Food-Energy-Water Systems" of the International Environmental Modelling & Software Society, Fort Collins, Colorado, June 24-28.
• Wang, Y. H., Hazenberg, P., Gupta, H. V., Castro, C. L., Lahmers, T., Unkrich, C., & Goodrich, D. (2018, Fall 2018). Demonstrating the Added Values of Suction Losses for Channel Infiltration in WRF-Hydro Hydrologic Model and its Applications in Semiarid Region. 2018 Fall Meeting of the American Geophysical Union. Washington DC: American Geophysical Union.
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  Wang YH, P Hazenberg, H Gupta, C Castro, T Lahmers, C Unkrich and D Goodrich (2018), Demonstrating the Added Values of Suction Losses for Channel Infiltration in WRF-Hydro Hydrologic Model and its Applications in Semiarid Region, session H046: Diagnostics, Sensitivity and Uncertainty Analysis of Earth and Environmental Modeling, presented at 2018 Fall Meeting of the American Geophysical Union, Washington DC, Dec 10-14.
• Gupta, H. V. (2016, Fall). Learning with Models & Data: A Maximum Entropy Approach. 2017 Annual Meeting of the American Meteorological Society. Seattle, WA.
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  Gupta HV (2017 invited), Learning with Models & Data: A Maximum Entropy Approach, 2017 Robert E Horton Lecture presented at the 2017 Annual Meeting of the American Meteorological Society, Seattle, Washington, Jan 23-27.
• Gupta, H. V. (2017, Fall). Challenges with Existing Methods for Model Sensitivity Analysis. Uncertainty Quantification Group, Department of Mathematics, The University of Arizona. The University of Arizona: Department of Mathematics, The University of Arizona.
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  Gupta HV (2017 invited), Challenges with Existing Methods for Model Sensitivity Analysis, Presentation to Uncertainty Quantification Group, Department of Mathematics, The University of Arizona, Tucson, Arizona, Oct 4.
• Gupta, H. V. (2017, Fall). Explaining the Karakoram Anomaly. Swiss Climate Summer School, Oeschger Centre for Climate Change Research (OCCR), ETH Zurich, 3-8 September. Oeschger Centre for Climate Change Research: ETH Zurich.
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  Bashir F, X Zeng, H Gupta and P Hazenberg (2017), Explaining the Karakoram Anomaly, presented at the Swiss Climate Summer School, Oeschger Centre for Climate Change Research (OCCR), ETH Zurich, 3-8 September
• Gupta, H. V. (2017, Spring). Learning with Models & Data: A Maximum Entropy Approach. 2017 Annual Meeting of the American Meteorological Society, Seattle, Washington, Jan 23-27. Seattle, Washington: American Meteorological Society.
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  Gupta HV (2017 invited), Learning with Models & Data: A Maximum Entropy Approach, 2017 Robert E Horton Lecture presented at the 2017 Annual Meeting of the American Meteorological Society, Seattle, Washington, Jan 23-27.
• Guse, B., Pfannerstill, M., Gafurov, A., Fohrer, N., & Gupta, H. V. (2017, Spring). How does the sensitivity signal change when using the rate of change of a hydrological variable instead of its magnitude?. 2017 Meeting of The European Geophysical Union, Vienna, Austria, Apr 23-28. Vienna, Austria: European Geophysical Union.
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  Guse B, M Pfannerstill, A Gafurov, N Fohrer and H Gupta (2017), How does the sensitivity signal change when using the rate of change of a hydrological variable instead of its magnitude?, presented at Session HS1.5: Advances in Sensitivity and Uncertainty Analysis of Earth and Environmental Models, of the 2017 Meeting of The European Geophysical Union, Vienna, Austria, Apr 23-28.
• Lahmers, T., Castro, C. L., Gupta, H. V., Gochis, D. J., Dugger, A., & Smith, M. (2017, Spring). Enhancing the NOAA National Water Center WRF-Hydro model architecture to improve representation of the Midwest and Southwest CONUS climate regions. 31st Conference on Hydrology, Seattle, WA, American Meteorological Society.. Seattle, WA: American Meteorological Society..
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  Lahmers TM, CL Castro, H Gupta, DJ Gochis, A Dugger, and M Smith (2017), Enhancing the NOAA National Water Center WRF-Hydro model architecture to improve representation of the Midwest and Southwest CONUS climate regions, 31st Conference on Hydrology, Seattle, WA, American Meteorological Society.
• Loritz, R., Neuper, M., Gupta, H. V., & Zehe, E. (2017, Spring). How to handle spatial heterogeneity in hydrological models. 2017 Meeting of The European Geophysical Union, Vienna, Austria, Apr 23-28. Vienna, Austria: European Geophysical Union.
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  Loritz R, M Neuper, H Gupta and E Zehe (2017), How to handle spatial heterogeneity in hydrological models, presented at Session HS2.1.2: On the interaction of models and hydrological knowledge: the battle of reducing uncertainty and increasing realism, of the 2017 Meeting of The European Geophysical Union, Vienna, Austria, Apr 23-28.
• Pechlivanidis, I., Gupta, H. V., & Bosshard, T. (2017, Spring). Can we really identify a representative subset of hydro-climatic simulations for impact modeling studies?. 2017 Meeting of The European Geophysical Union, Vienna, Austria, Apr 23-28. Vienna, Austria: European Geophysical Union.
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  Pechlivanidis IG, HV Gupta and T. Bosshard (2017), Can we really identify a representative subset of hydro-climatic simulations for impact modeling studies?, presented at Session CL3.08 From climate to impacts: Linking models and projecting impacts across sectors, of the 2017 Meeting of The European Geophysical Union, Vienna, Austria, Apr 23-28.
• Razavi, S., & Gupta, H. V. (2017, Summer). A Comprehensive, Efficient, and Robust Approach for Global Sensitivity Analysis. 2017 IAHS Scientific Assembly, Port Elizabeth, South Africa, 10-14 July.. Port Elizabeth, South Africa: IAHS.
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  Razavi S and H Gupta (2017), A Comprehensive, Efficient, and Robust Approach for Global Sensitivity Analysis, presented at Session W2: Quantifying uncertainty in hydrological systems: A Bayesian point of view, of the 2017 IAHS Scientific Assembly, Port Elizabeth, South Africa, 10-14 July.
• Roy, T., Valdes, J. B., Capdevilla, A. S., Gupta, H. V., Lyon, B., & Durcik, M. (2017, Spring). Comparison of two bias correction schemes in the context of climate change impacts assessment in the Mara River basin. El Dia Del Agua y Atmosphera, Department of Hydrology and Atmospheric Sciences, The University of Arizona, 27 March. Department of Hydrology and Atmospheric Sciences, The University of Arizona: Department of Hydrology and Atmospheric Sciences, The University of Arizona.
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  Roy T, JB Valdes, A Serrat-Capdevila, HV Gupta, B Lyon and M Durcik (2017), Comparison of two bias correction schemes in the context of climate change impacts assessment in the Mara River basin, presented at El Dia Del Agua y Atmosphera, Department of Hydrology and Atmospheric Sciences, The University of Arizona, 27 March.
• Roy, T., Valdes, J. B., Lyon, B., Demaria, E. M., Capdevilla, A. S., Durcik, M., & Gupta, H. V. (2017, Fall). Short-Term Climate Change Impacts on Mara Basin Hydrology. 2017 Fall Meeting of the American Geophysical Union, New Orleans, Mississippi, Dec. New Orleans, Mississippi: American Geophysical Union.
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  Roy T, JB Valdés, B Lyon, EM Demaria, R Valdés-Pineda, A Serrat-Capdevila, M Durcik & H Gupta (2017), Short-Term Climate Change Impacts on Mara Basin Hydrology, presented at 2017 Fall Meeting of the American Geophysical Union, New Orleans, Mississippi, Dec XX-XX.
• Alemayehu, T., van Griensven, A., Bauwens, W., & Gupta, H. V. (2016, Summer). Calibration of hydrological model in data scarce basins: coupling of flow signatures with remote sensing evapotranspiration. 8th International Congress on Environmental Modelling and Software.
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  Alemayehu T, A van Griensven, W Bauwens and H Gupta (2016), Calibration of hydrological model in data scarce basins: coupling of flow signatures with remote sensing evapotranspiration, presented at Session B5: Managing Uncertainty, 8th International Congress on Environmental Modelling and Software, Toulouse, France, July 10-14
• Gharari, S., & Gupta, H. V. (2016, Spring). How certain are the process parameterizations in our models?. Session HS1.6: Advances in Sensitivity and Uncertainty Analysis of Earth and Environmental Systems Models of the 2016 Meeting of The European Geophysical Union. Vienna, Austria: European Geosciences Union.
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  Gharari S, and H Gupta (2016), How certain are the process parameterizations in our models?, presented at Session HS1.6: Advances in Sensitivity and Uncertainty Analysis of Earth and Environmental Systems Models of the 2016 Meeting of The European Geophysical Union, Vienna, Austria, Apr 17-22.
• Gupta, H. V. (2016, Fall). A General Theory of Learning with Models and Data. 2016 Distinguished Lecturer series on “Breakthroughs in Water Security”, Global Institute for Water Security, University of Saskatchewan, Saskatoon, Canada. University of Saskatchewan, Saskatoon, Canada: University of Saskatchewan, Saskatoon, Canada.
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  Gupta HV (2016 invited), A General Theory of Learning with Models and Data, 2016 Distinguished Lecturer series on “Breakthroughs in Water Security”, Global Institute for Water Security, University of Saskatchewan, Saskatoon, Canada, Sept 21.
• Gupta, H. V. (2016, Fall). Sustainable Water ActioN (SWAN): Building Research Links between the EU and the USA. EAB Meeting of the HAS Department.
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  Gupta HV (2016 invited), Sustainable Water ActioN (SWAN): Building Research Links between the EU and the USA, presented at EAB Meeting of the HAS Department, University of Arizona, Oct 17.
• Gupta, H. V. (2016, Fall). Towards A General Theory of Learning with Models and Data. Departmental Seminar, Department of Hydrology and Atmospheric Sciences.
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  Gupta HV (2016 invited), Towards A General Theory of Learning with Models and Data, Departmental Seminar, Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Arizona, Oct 27.
• Gupta, H. V. (2016, Spring). Information Theory and the Hydrological Sciences: Models, Data, Uncertainty and Learning. International Workshop on 'Information Theory and the Earth Sciences.
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  Gupta HV (2016 invited), Information Theory and the Hydrological Sciences: Models, Data, Uncertainty and Learning, International Workshop on 'Information Theory and the Earth Sciences' The Schneefernerhaus, Garmisch-Partenkirchen, Germany, April 24-28.
• Guse, B., Pfannerstill, M., Gafurov, A., Fohrer, N., & Gupta, H. V. (2016, Spring). Demasking the integrated value of discharge – Advanced sensitivity analysis on the components of hydrological models. Session HS1.6: Advances in Sensitivity and Uncertainty Analysis of Earth and Environmental Systems Models of the 2016 Meeting of The European Geophysical Union. Vienna, Austria: European Geosciences Union.
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  Guse B, M Pfannerstill, A Gafurov, N Fohrer and H Gupta (2016), Demasking the integrated value of discharge – Advanced sensitivity analysis on the components of hydrological models, presented at Session HS1.6: Advances in Sensitivity and Uncertainty Analysis of Earth and Environmental Models of the 2016 Meeting of The European Geophysical Union, Vienna, Austria, Apr 17-22.
• Guse, B., Pfannerstill, M., Strauch, M., Reusser, D., Ludtke, S., Volk, M., Gupta, H. V., & Fohrer, N. (2016, Spring). Advancing sensitivity analysis to precisely characterise temporal parameter dominance. Session HS1.6: Advances in Sensitivity and Uncertainty Analysis of Earth and Environmental Systems Models of the 2016 Meeting of The European Geophysical Union. Vienna, Austria: European Geosciences Union.
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  Guse B, M Pfannerstill, M Strauch, D Reusser, S Lüdtke, M Volk, H Gupta and N Fohrer (2016), Advancing sensitivity analysis to precisely characterise temporal parameter dominance, presented at Session HS1.6: Advances in Sensitivity and Uncertainty Analysis of Earth and Environmental Systems Models of the 2016 Meeting of The European Geophysical Union, Vienna, Austria, Apr 17-22
• Haghnegahdar, A., Razavi, S., & Gupta, H. V. (2016, Spring). A multi-model multi-objective study to evaluate the role of metric choice on sensitivity assessment. Session HS1.6: Advances in Sensitivity and Uncertainty Analysis of Earth and Environmental Systems Models of the 2016 Meeting of The European Geophysical Union. Vienna, Austria: European Geosciences Union.
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  Haghnegahdar A, Razavi S and H Gupta (2016), A multi-model multi-objective study to evaluate the role of metric choice on sensitivity assessment, presented at Session HS1.6: Advances in Sensitivity and Uncertainty Analysis of Earth and Environmental Systems Models of the 2016 Meeting of The European Geophysical Union, Vienna, Austria, Apr 17-22.
• Lahmers, T., Castro, C. L., Gupta, H. V., Gochis, D., Dugger, A., & Smith, M. (2016, Fall). Enhancing the NOAA National Water Center WRF-Hydro model architecture to improve representation of the Midwest and Southwest CONUS climate regions. 2016 Fall Meeting of the American Geophysical Union.
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  Lahmers TM, CL Castro, HV Gupta, DJ Gochis, A Dugger & M Smith (2016), Enhancing the NOAA National Water Center WRF-Hydro model architecture to improve representation of the Midwest and Southwest CONUS climate regions, Session H044: Forecasting Hydrology at Continental Scale, 2016 Fall Meeting of the American Geophysical Union, San Francisco CA, Dec 12-16
• Marcais, J., Gupta, H. V., Troch, P. A., & deDreusy, J. (2016, Fall). Coupling machine learning with mechanistic models to study runoff production and river flow at the hillslope scale. 2016 Fall Meeting of the American Geophysical Union.
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  Marçais J, H Gupta, P Troch & JR de Dreuzy (2016), Coupling machine learning with mechanistic models to study runoff production and river flow at the hillslope scale, Session NG013: The Interface between Models and Data, 2016 Fall Meeting of the American Geophysical Union, San Francisco CA, Dec 12-16
• Razavi, S., & Gupta, H. V. (2016, Fall). A New Framework for Effective and Efficient Global Sensitivity Analysis of Hydrologic and Environmental Systems Models. Session on ‘Advances in Uncertainty Analysis Methods for Hydrologic Modeling’ of the HIC 2016 12th International Conference on Hydroinformatics, Songdo Convensia, Incheon, Korea. Incheon, Korea.
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  Razavi S and HV Gupta (2016), A New Framework for Effective and Efficient Global Sensitivity Analysis of Hydrologic and Environmental Systems Models, presented at Session on ‘Advances in Uncertainty Analysis Methods for Hydrologic Modeling’ of the HIC 2016 12th International Conference on Hydroinformatics, Songdo Convensia, Incheon, Korea, Aug 21-26
• Razavi, S., & Gupta, H. V. (2016, Spring). A New Framework for Effective and Efficient Global Sensitivity Analysis of Hydrologic and Environmental Systems Models. Session on ‘Advanced Research for River Water Modeling’ of the ASCE-EWRI 2016 Conference, West Palm Beach, Florida. West Palm Beach, Florida: ASCE-EWRI Conference.
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  Razavi S and HV Gupta (2016), A New Framework for Effective and Efficient Global Sensitivity Analysis of Hydrologic and Environmental Systems Models, presented at Session on ‘Advanced Research for River Water Modeling’ of the ASCE-EWRI 2016 Conference, West Palm Beach, Florida, May 22-26
• Razavi, S., Gupta, H. V., & Haghnegahdar, A. (2016, Spring). What Constitutes a “Good” Sensitivity Analysis? Elements and Tools for a Robust Sensitivity Analysis with Reduced Computational Cost. Session HS1.6: Advances in Sensitivity and Uncertainty Analysis of Earth and Environmental Systems Models of the 2016 Meeting of The European Geophysical Union. Vienna, Austria: European Geosciences Union.
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  Razavi S, H Gupta and A Haghnegahdar (2016), What Constitutes a “Good” Sensitivity Analysis? Elements and Tools for a Robust Sensitivity Analysis with Reduced Computational Cost, presented at Session HS1.6: Advances in Sensitivity and Uncertainty Analysis of Earth and Environmental Systems Models of the 2016 Meeting of The European Geophysical Union, Vienna, Austria, Apr 17-22.
• Roy, T., Gupta, H. V., Serrat-Capdevilla, A., & Valdes, J. B. (2016, Fall). Using satellite based actual evapotranspiration estimates to improve streamflow forecasting. 2016 Fall Meeting of the American Geophysical Union.
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  Roy T, H Gupta, A Serrat-Capdevila and J Valdes (2016), Using satellite based actual evapotranspiration estimates to improve streamflow forecasting, 2016 Fall Meeting of the American Geophysical Union, San Francisco CA, Dec 12-16
• Gharari, S., Hrachowitz, M., Fenecia, F., Gao, H., Gupta, H. V., & Savenije, H. (2015, Apr 12-17). Can we construct back parametrizations of a given model structure using large sample hydrology?. 2015 Meeting of European Geosciences Union Session HS2.3.12, ‘Open data and Virtual laboratories in comparative hydrology and multi-basin modelling’. Vienna, Austria: European Geosciences Union.
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  Gharari S, M Hrachowitz, F Fenicia, H Gao, H Gupta, HHG Savenije (2015), Can we construct back parametrizations of a given model structure using large sample hydrology? presented at Session HS2.3.12, ‘Open data and Virtual laboratories in comparative hydrology and multi-basin modelling’ of the 2015 Meeting of European Geosciences Union, Vienna, Austria, Apr 12-17
• Gharari, S., Hrachowitz, M., Fenecia, F., Gao, H., Gupta, H. V., & Savenije, H. (2015, Apr 12-17). Progressive evaluation of incorporating information into a model building process: from scratch to FLEX-TOPO. 2015 Meeting of European Geosciences Union Session HS1.7: ‘Data & Models, Induction & Prediction, Information & Uncertainty: Towards a common framework for model building and predictions in the Geosciences’. Vienna, Austria: European Geosciences Union.
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  Gharari S, M Hrachowitz, F Fenicia, H Gao, H Gupta, HHG Savenije (2015), Progressive evaluation of incorporating information into a model building process: from scratch to FLEX-TOPO, presented at Session HS1.7: ‘Data & Models, Induction & Prediction, Information & Uncertainty: Towards a common framework for model building and predictions in the Geosciences’ of the 2015 Meeting of European Geosciences Union, Vienna, Austria, Apr 12-17
• Gharari, S., Hrachowitz, M., Fenecia, F., Gao, H., Gupta, H. V., & Savenije, H. (2015, Fall). How certain are the process parameterizations in our models?. Session H085: Predictions, Models and Hydrological Information: How Much Certainty Should We Expect in an Uncertain World? 2015 Fall Meeting of the American Geophysical Union, San Francisco CA. San Francisco CA: American Geophysical Union.
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  Gharari S, M Hrachowitz, F Fenicia, H Gao, H Gupta, HHG Savenije (2015) How certain are the process parameterizations in our models? Session H085: Predictions, Models and Hydrological Information: How Much Certainty Should We Expect in an Uncertain World? 2015 Fall Meeting of the American Geophysical Union, San Francisco CA, Dec 14-18
• Gharari, S., Hrachowitz, M., Fenicia, F., Gao, H., Gupta, H. V., & Savenije, H. (2015, Spring). Can we construct back parametrizations of a given model structure using large sample hydrology?. Session HS2.3.12, ‘Open data and Virtual laboratories in comparative hydrology and multi-basin modelling’ of the 2015 Meeting of The European Geophysical Union, Vienna, Austria. Vienna, Austria: European Geosciences Union.
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  Gharari S, M Hrachowitz, F Fenicia, H Gao, H Gupta, HHG Savenije (2015), Can we construct back parametrizations of a given model structure using large sample hydrology? presented at Session HS2.3.12, ‘Open data and Virtual laboratories in comparative hydrology and multi-basin modelling’ of the 2015 Meeting of The European Geophysical Union, Vienna, Austria, Apr 12-17
• Gupta, H. V. (2015, Fall). Models, Data, Uncertainty and Learning: How Information is Coded into Dynamical Geophysical Models. Inaugural 3D Modelling Conference “Saying goodbye to a 2D Earth”, Margaret River.
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  Gupta HV (2015 invited), Models, Data, Uncertainty and Learning: How Information is Coded into Dynamical Geophysical Models, Keynote address at Inaugural 3D Modelling Conference “Saying goodbye to a 2D Earth”, Margaret River, Western Australia, August 2-7.
• Gupta, H. V. (2015, Fall). Models, Data, Uncertainty and Learning: How Information is Coded into Dynamical Geophysical Models. Invited talk at ARC Centre of Excellence for Climate System Science. University of New South Wales, Sydney, Australia: University of New South Wales, Sydney, Australia.
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  Gupta HV (2015 invited), Models, Data, Uncertainty and Learning: How Information is Coded into Dynamical Geophysical Models, Invited talk at ARC Centre of Excellence for Climate System Science, University of New South Wales, Sydney, Australia, Jul 27th 2015.
• Gupta, H. V. (2015, Fall). Models, Data, Uncertainty and Learning: How Information is Coded into Dynamical Geophysical Models. Keynote address at Inaugural 3D Modelling Conference “Saying goodbye to a 2D Earth”. Margaret River, Western Australia: University of Perth.
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  Gupta HV (2015 invited), Models, Data, Uncertainty and Learning: How Information is Coded into Dynamical Geophysical Models, Keynote address at Inaugural 3D Modelling Conference “Saying goodbye to a 2D Earth”, Margaret River, Western Australia, August 2-7.
• Gupta, H. V. (2015, Fall). Uncertainty, Information and Learning: How Information is Coded into Dynamical Geophysical Models. Invited talk at Los Alamos National laboratories (LANL) 2015 Frontiers in Geoscience Colloquium Series. Los Alamos National laboratories (LANL) , Los Alamos, New Mexico: Los Alamos National laboratories (LANL) , Los Alamos, New Mexico.
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  Gupta HV (2015 invited), Uncertainty, Information and Learning: How Information is Coded into Dynamical Geophysical Models, Los Alamos National laboratories (LANL) 2015 Frontiers in Geoscience Colloquium Series, Los Alamos, New Mexico, June 8-9.
• Haghnegahdar, A., Razavi, S., Wheater, H., & Gupta, H. V. (2015, Fall). Sensitivity analysis and insights into hydrologic processes and uncertainty at different scales. Session on Multiscale Dependency and Uncertainty in Modeling of Surface and Subsurface Environments, 2015 Fall Meeting of the American Geophysical Union, San Francisco CA. 2015 Fall Meeting of the American Geophysical Union, San Francisco CA: American Geophysical Union.
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  Haghnegahdar A, S Razavi, H Wheater & H Gupta (2015) Sensitivity analysis and insights into hydrologic processes and uncertainty at different scales, Paper# H23F-1638, Session XXX: Multiscale Dependency and Uncertainty in Modeling of Surface and Subsurface Environments, 2015 Fall Meeting of the American Geophysical Union, San Francisco CA, Dec 14-18
• Lahmers, T., Castro, C. L., & Gupta, H. V. (2015, Fall). Optimization of precipitation and streamflow forecasts in the southwest Contiguous US for warm season convection. 2015 Fall Meeting of the American Geophysical Union, San Francisco CA. San Francisco CA: American Geophysical Union.
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  Lahmers T, C Castro and H Gupta (2015) Optimization of precipitation and streamflow forecasts in the southwest Contiguous US for warm season convection, Session XXX: xxx. 2015 Fall Meeting of the American Geophysical Union, San Francisco CA, Dec 14-18
• Mathevet, T., Gupta, H. V., Andréassian, V., Perrin, C., & Le Moine, N. (2015, Fall). A Multi-objective Intercomparison of 2 Conceptual Rainfall-Runoff Models on 2000+ Watersheds Worldwide. Session H036: Efficient Diagnostics, Sensitivity and Uncertainty Analysis of Complex Environmental Models, 2015 Fall Meeting of the American Geophysical Union, San Francisco CA. San Francisco CA: American Geophysical Union.
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  Mathevet T, H Gupta, V Andréassian, C Perrin & N Le Moine (2015) A Multi-objective Intercomparison of 2 Conceptual Rainfall-Runoff Models on 2000+ Watersheds Worldwide, Session H036: Efficient Diagnostics, Sensitivity and Uncertainty Analysis of Complex Environmental Models, 2015 Fall Meeting of the American Geophysical Union, San Francisco CA, Dec 14-18
• Mathevet, T., Kumar, R., Gupta, H. V., Vaze, J., & Andreassian, V. (2015, Apr 12-17). Large Sample Hydrology: Building an international sample of watersheds to improve consistency and robustness of model evaluation. 2015 Meeting of European Geosciences Union Session HS2.3.12, ‘Open data and Virtual laboratories in comparative hydrology and multi-basin modelling’. Vienna, Austria: European Geosciences Union.
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  Mathevet T, R Kumar, H Gupta, J Vaze and V Andréassian (2015), Large Sample Hydrology: Building an international sample of watersheds to improve consistency and robustness of model evaluation, presented at Session HS2.3.12, ‘Open data and Virtual laboratories in comparative hydrology and multi-basin modelling’ of the 2015 Meeting of The European Geosciences Union, Vienna, Austria, Apr 12-17
• Mathevet, T., Kumar, R., Gupta, H. V., Vaze, J., & Andréassian, V. (2015, Spring). Large Sample Hydrology: Building an international sample of watersheds to improve consistency and robustness of model evaluation. Session HS2.3.12, ‘Open data and Virtual laboratories in comparative hydrology and multi-basin modelling’ of the 2015 Meeting of The European Geophysical Union, Vienna, Austria. Vienna, Austria: European Geosciences Union.
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  Mathevet T, R Kumar, H Gupta, J Vaze and V Andréassian (2015), Large Sample Hydrology: Building an international sample of watersheds to improve consistency and robustness of model evaluation, presented at Session HS2.3.12, ‘Open data and Virtual laboratories in comparative hydrology and multi-basin modelling’ of the 2015 Meeting of The European Geophysical Union, Vienna, Austria, Apr 12-17
• Niyazi, F., & Gupta, H. V. (2015, Apr 12-17). Investigation of Land Use Effect on Nash Model Parameters. 2015 Meeting of European Geosciences Union Session HS2.4.3: ‘Hydrological change: Regional hydrological behaviour under transient climate and land use’. Vienna, Austria: European Geosciences Union.
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  Niyazi F and HV Gupta (2015), Investigation of Land Use Effect on Nash Model Parameters, presented at Session HS2.4.3: ‘Hydrological change: Regional hydrological behaviour under transient climate and land use’ of the 2015 Meeting of European Geosciences Union, Vienna, Austria, Apr 12-17
• Razavi, S., & Gupta, H. V. (2015, Apr 12-17). A New Framework for Effective and Efficient Global Sensitivity Analysis of Earth and Environmental Systems Models. 2015 Meeting of European Geosciences Union Session HS1.7: ‘Data & Models, Induction & Prediction, Information & Uncertainty: Towards a common framework for model building and predictions in the Geosciences’. Vienna, Austria: European Geosciences Union.
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  Razavi S and HV Gupta (2015), A New Framework for Effective and Efficient Global Sensitivity Analysis of Earth and Environmental Systems Models, presented at Session HS1.7: ‘Data & Models, Induction & Prediction, Information & Uncertainty: Towards a common framework for model building and predictions in the Geosciences’ of the 2015 Meeting of The European Geosciences Union, Vienna, Austria, Apr 12-17
• Razavi, S., & Gupta, H. V. (2015, May 3-7, 2015). A Critical Look at Sensitivity Analysis of Hydrologic Systems Models: A New Framework for Global Sensitivity Analysis. AGU-GAC-MAC-CGU Session H012: ‘General Hydrology’ of the AGU-GAC-MAC-CGU Joint Assembly. Montreal, Canada: American Geophysical Union.
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  Razavi S and HV Gupta (2015), A Critical Look at Sensitivity Analysis of Hydrologic Systems Models: A New Framework for Global Sensitivity Analysis, presented at Session H012: ‘General Hydrology’ of the AGU-GAC-MAC-CGU Joint Assembly, Montreal, Canada, May 3-7, 2015
• Razavi, S., & Gupta, H. V. (2015, Summer). A Critical Look at Sensitivity Analysis of Hydrologic Systems Models: A New Framework for Global Sensitivity Analysis. Session H012: ‘General Hydrology’ of the AGU-GAC-MAC-CGU Joint Assembly, Montreal, Canada. Montreal, Canada: AGU-GAC-MAC-CGU.
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  Razavi S and HV Gupta (2015), A Critical Look at Sensitivity Analysis of Hydrologic Systems Models: A New Framework for Global Sensitivity Analysis, presented at Session H012: ‘General Hydrology’ of the AGU-GAC-MAC-CGU Joint Assembly, Montreal, Canada, May 3-7
• Rodriguez, D., Gupta, H. V., & Mendiondo, E. M. (2015, Fall). Assessing Uncertainties in Surface Water Security: A Probabilistic Multi­model Resampling approach. Session H119: Water Resources, Climate Change, and Sustainability: Breakthroughs in Process Understanding, Data Availability, and Impact Assessment. 2015 Fall Meeting of the American Geophysical Union, San Francisco CA. San Francisco CA: American Geophysical Union.
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  Rodrigues DBB, HV Gupta and EM Mendiondo, (2015) Assessing Uncertainties in Surface Water Security: A Probabilistic Multi­model Resampling approach. Session H119: Water Resources, Climate Change, and Sustainability: Breakthroughs in Process Understanding, Data Availability, and Impact Assessment. 2015 Fall Meeting of the American Geophysical Union, San Francisco CA, Dec 14-18
• Roy, T., Lahmers, T., Tso, M., & Gupta, H. V. (2015, Fall). SPSM: A physically-based snowpack accounting model. 2015 Annual Symposium of the Arizona Hydrological Society, Phoenix AZ. Phoenix AZ: Arizona Hydrological Society.
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  Roy T, T Lahmers, M Tso and H Gupta (2015) SPSM: A physically-based snowpack accounting model, Annual Symposium of the Arizona Hydrological Society, Phoenix AZ, Sept 16-19
• Roy, T., Roy, T., Serrat-Capdevilla, A., Serrat-Capdevilla, A., Gupta, H. V., Gupta, H. V., Valdes, J. B., & Valdes, J. B. (2015, Fall). Streamflow Forecasting using Satellite Products: A Benchmark Approach. Can We Reduce Uncertainty by using Multiple Products and Multiple Models?. 2015 Fall Meeting of the American Geophysical Union, San Francisco CA. San Francisco CA: American Geophysical Union.
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  Roy T, A Serrat-Capdevila, H Gupta and J Valdes (2015) Streamflow Forecasting using Satellite Products: A Benchmark Approach. Can We Reduce Uncertainty by using Multiple Products and Multiple Models? Paper H51H-1471, Session XXX: xxx. 2015 Fall Meeting of the American Geophysical Union, San Francisco CA, Dec 14-18
• Roy, T., Serrat-Capdevilla, A., Gupta, H. V., & Valdes, J. B. (2015, Summer). Estimating uncertainties in streamflow forecasts using a Bayesian multi-model and multi-product approach. 2015 UCOWR/NIWR/CUAHSI Annual Conference “Water is Not for Gambling: Utilizing Science to Reduce Uncertainty”, Green Valley Ranch Resort Henderson, NV. Green Valley Ranch Resort Henderson, NV: UCOWR/NIWR/CUAHSI Annual Conference.
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  Roy T, A Serrat-Capdevila, H Gupta and J Valdes (2015) Estimating uncertainties in streamflow forecasts using a Bayesian multi-model and multi-product approach, 2015 UCOWR/NIWR/CUAHSI Annual Conference “Water is Not for Gambling: Utilizing Science to Reduce Uncertainty”, Green Valley Ranch Resort Henderson, NV, June 16-18
• Gharari, S., Fenecia, F., Gao, H., Gupta, H. V., & Savenije, H. (2014, Apr 28-May 2). Bottom-up or top-down? Introducing constraints might be a way forward,. 2014 Meeting of European Geosciences Union Session HS2.3.3 ‘Synthesis of bottom-up and top-down modelling approaches for a better understanding of catchment hydrology’. Vienna, Austria: European Geosciences Union.
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  Gharari S, F Fenicia, H Gao, HV Gupta and HHG Savenije (2014), Bottom-up or top-down? Introducing constraints might be a way forward, presented at Session HS2.3.3 ‘Synthesis of bottom-up and top-down modelling approaches for a better understanding of catchment hydrology’ of the 2014 Meeting of The European Geophysical Union, Vienna, Austria, Apr 28-May 2
• Gharari, S., Hrachowitz, M., Fenecia, F., Gao, H., Gupta, H. V., & Savenije, H. (2014, Apr 28-May 2). Progressive evaluation of incorporating information into a model building process. 2014 Meeting of European Geosciences Union Session HS1.5/GI1.9 ‘Data & Models, Induction & Prediction, Information & Uncertainty: Towards a common framework for model building and predictions in the Geosciences’. Vienna, Austria: European Geosciences Union.
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  Gharari S, M Hrachowitz, F Fenicia, H Gao, HV Gupta and HHG Savenije (2014), Progressive evaluation of incorporating information into a model building process, presented at Session HS1.5/GI1.9 ‘Data & Models, Induction & Prediction, Information & Uncertainty: Towards a common framework for model building and predictions in the Geosciences’ of the 2014 Meeting of The European Geophysical Union, Vienna, Austria, Apr 28-May 2
• Gharari, S., Hrachowitz, M., Fenecia, F., Gao, H., Gupta, H. V., & Savenije, H. (2014, Dec 15-19). Progressive evaluation of incorporating information into a model building process: from scratch to FLEX-TOPO. 2014 AGU Chapman Conference on Catchment Spatial Organization and Complex Behavior. Luxembourg City, Luxembourg: American Geophysical Union.
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  Gharari S, M Hrachowitz, F Fenicia, H Gao, H Gupta, HHG Savenije (2014), Progressive evaluation of incorporating information into a model building process: from scratch to FLEX-TOPO, presented at 2014 AGU Chapman Conference on Catchment Spatial Organization and Complex Behavior, Luxembourg City, Luxembourg, Sept 23-26
• Gharari, S., Hrachowitz, M., Fenecia, F., Gao, H., Gupta, H. V., & Savenije, H. (2014, Dec 15-19). Progressive evaluation of incorporating information into a model building process: from scratch to FLEX-TOPO. 2014 Fall Meeting of the American Geophysical Union Session H110: Understanding the Interface Between Models and Data. San Francisco CA: American Geophysical Union.
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  Gharari S, M Hrachowitz, F Fenicia, H Gao, H Gupta, HHG Savenije (2014), Progressive evaluation of incorporating information into a model building process: from scratch to FLEX-TOPO, Session H110: Understanding the Interface Between Models and Data (2014 AGU Fall Meeting), 2014 Fall Meeting of the American Geophysical Union, San Francisco CA, Dec 15-19.
• Gupta, H. V. (2014, Fall). Uncertainty Quantification and Learning in Geophysical Modeling: How Information is Coded into Dynamical Models. Invited talk at Session IN017: Frontiers in Uncertainty Quantification for Geophysical Modeling, 2014 Fall Meeting of the American Geophysical Union. Fall Meeting of the American Geophysical Union, San Francisco: American Geophysical Union.
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  Gupta HV (2014 invited), Uncertainty Quantification and Learning in Geophysical Modeling: How Information is Coded into Dynamical Models, Session IN017: Frontiers in Uncertainty Quantification for Geophysical Modeling, 2014 Fall Meeting of the American Geophysical Union, San Francisco Dec 15-19.
• Gupta, H. V. (2014, Spring). Using Models and Data to Learn: The Need for a Perspective based in Characterization of Information. Dalton Medal Lecture at the 2014 Meeting of The European Geosciences Union. 2014 Meeting of The European Geosciences Union, Vienna, Austria: European Geosciences Union.
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  Gupta HV (2014 invited), Using Models and Data to Learn: The Need for a Perspective based in Characterization of Information, Dalton Medal Lecture at the 2014 Meeting of The European Geosciences Union, Vienna, Austria, Apr 28-May 2.
• Gupta, H. V., & Serrat-Capdevilla, A. (2014, Summer). Participatory knowledge generation for decision making. Invited talk at ‘International Conference On Data, Information And Knowledge For Water Governance In Networked Societies’. University of Seville, Seville, Spain: University of Seville, Seville, Spain.
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  Gupta HV and A Serrat-Capdevila (2014 invited), Participatory knowledge generation for decision making, presented at the ‘International Conference On Data, Information And Knowledge For Water Governance In Networked Societies’, organized by the Sustainable Water ActioN (SWAN) INCO-LAB project team at the University of Seville (Spain), June 9-11 2014.
• Nearing, G. S., & Gupta, H. V. (2014, Feb 2-6). Information-Theoretic Perspective on Benchmarking with Inductive Models. 2014 Annual Meeting of the American Meteorological Society Session on "Integrated Metrics and Benchmarking For Next Generation Hydro/Land Surface Modeling of the Water Cycle". Atlanta, GA: American Meteorological Society.
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  Nearing GS and H Gupta (2014), Information-Theoretic Perspective on Benchmarking with Inductive Models, presented at the Annual Meeting of the American Meteorological Society, session on "Integrated Metrics and Benchmarking For Next Generation Hydro/Land Surface Modeling of the Water Cycle", Atlanta, GA, Feb 2-6
• Razavi, S., & Gupta, H. V. (2014, Apr 28-May 2). Towards More Efficient and Effective Global Sensitivity Analysis. 2014 Meeting of European Geosciences Union Session HS1.5/GI1.9: ‘Data & Models, Induction & Prediction, Information & Uncertainty: Towards a common framework for model building and predictions in the Geosciences’. Vienna, Austria: European Geosciences Union.
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  Razavi S and HV Gupta (2014), Towards More Efficient and Effective Global Sensitivity Analysis, presented at Session HS1.5/GI1.9: ‘Data & Models, Induction & Prediction, Information & Uncertainty: Towards a common framework for model building and predictions in the Geosciences’ of the 2014 Meeting of The European Geophysical Union, Vienna, Austria, Apr 28-May 2
• Razavi, S., & Gupta, H. V. (2014, Dec 15-19). What Do We Mean By Sensitivity Analysis? The Need For A Comprehensive Characterization Of Sensitivity In Earth System Models. 2014 Fall Meeting of the American Geophysical Union Session H106: Uncertainty and sensitivity in models and observations and their impacts on decision making related to geological, hydrological and environmental applications. San Francisco CA: American Geophysical Union.
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  Razavi S and HV Gupta (2014), What Do We Mean By Sensitivity Analysis? The Need For A Comprehensive Characterization Of Sensitivity In Earth System Models, Session H106: Uncertainty and sensitivity in models and observations and their impacts on decision making related to geological, hydrological and environmental applications (2014 AGU Fall Meeting), 2014 Fall Meeting of the American Geophysical Union, San Francisco CA, Dec 15-19.
• Rodrigues, D. B., Gupta, H. V., & Mendiondo, E. M. (2014, Apr 28-May 2). Assessing uncertainties in superficial water provision by different bootstrap-based techniques. 2014 Meeting of European Geosciences Union Session HS2.4.2: ‘Hydrological extremes: from droughts to floods’. Vienna, Austria: European Geosciences Union.
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  Rodrigues DBB, HV Gupta and EM Mendiondo (2014), Assessing uncertainties in superficial water provision by different bootstrap-based techniques, presented at Session HS2.4.2: ‘Hydrological extremes: from droughts to floods’ of the 2014 Meeting of The European Geophysical Union, Vienna, Austria, Apr 28-May 2
• Roy, T., Serrat-Capdevilla, A., Valdes, J. B., Durcik, M., Gupta, H. V., & Mukherjee, R. (2014, Dec 15-19). Multi-model and multi-product streamflow forecasting in the African basins. 2014 El Dia Del Agua, Department of Hydrology and Water Resources, The University of Arizona. Tucson, Arizona: Department of Hydrology and Water Resources, The University of Arizona.
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  Roy T, A Serrat-Capdevila, JB Valdes, M Durcik, HV Gupta and R Mukherjee (2014), Multi-model and multi-product streamflow forecasting in the African basins, presented at El Dia Del Agua, Department of Hydrology and Water Resources, The University of Arizona, Apr 9
• Yang, Z., Gupta, H. V., & Dominguez, F. (2014, Dec 15-19). Urban effects on regional climate: A case study in the Phoenix-Tucson Corridor. 2014 Fall Meeting of the American Geophysical Union Session H106: Uncertainty and sensitivity in models and observations and their impacts on decision making related to geological, hydrological and environmental applications. San Francisco CA: American Geophysical Union.
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  Yang Z, F Dominguez and HV Gupta (2014), Urban effects on regional climate: A case study in the Phoenix-Tucson Corridor, Session H106: Uncertainty and sensitivity in models and observations and their impacts on decision making related to geological, hydrological and environmental applications (2014 AGU Fall Meeting), 2014 Fall Meeting of the American Geophysical Union, San Francisco CA, Dec 15-19.
• Clark, M., Kavetski, D., Fenecia, F., & Gupta, H. V. (2013, Spring). A common framework for the development and analysis of process-based hydrological models. Session HS 1.2: 'Data & Models, Induction & Prediction, Information & Uncertainty: Towards a common framework for model building and predictions in the Geosciences' organized jointly by Hydrological Sciences, Geosciences and Atmospheric Sciences, 2013 EGU General Assembly, Vienna, Austria. Vienna, Austria: European Geosciences Union.
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  Clark M, D Kavetski, F Fenicia and HV Gupta (2013), A common framework for the development and analysis of process-based hydrological models, Presentation at Session HS 1.2: 'Data & Models, Induction & Prediction, Information & Uncertainty: Towards a common framework for model building and predictions in the Geosciences' organized jointly by Hydrological Sciences, Geosciences and Atmospheric Sciences, 2013 EGU General Assembly, Vienna, Austria, Apr 7-12.
• Gong, W., Gupta, H. V., & Yang, D. (2013, Spring). On Evaluating the Information Content of Observation Data. Session HS 1.2: 'Data & Models, Induction & Prediction, Information & Uncertainty: Towards a common framework for model building and predictions in the Geosciences' organized jointly by Hydrological Sciences, Geosciences and Atmospheric Sciences, 2013 EGU General Assembly, Vienna, Austria. Vienna, Austria: European Geosciences Union.
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  Gong W, HV Gupta, Yang D (2013), On Evaluating the Information Content of Observation Data, Presentation at Session HS 1.2: 'Data & Models, Induction & Prediction, Information & Uncertainty: Towards a common framework for model building and predictions in the Geosciences' organized jointly by Hydrological Sciences, Geosciences and Atmospheric Sciences, 2013 EGU General Assembly, Vienna, Austria, Apr 7-12.
• Gong, W., Yang, D., Gupta, H. V., & Nearing, G. S. (2013, Fall). Recent Advances of Information Entropy Estimation Method for Practical Hydrological Variables. Session H050: ‘Information & Uncertainty in Data & Models: Towards a common framework for model building and prediction’ of the 2013 Fall Meeting of the American Geophysical Union, San Francisco. 2013 Fall Meeting of the American Geophysical Union, San Francisco: American Geophysical Union.
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  Gong W, D Yang, HV Gupta and GS Nearingt (2013), Recent Advances of Information Entropy Estimation Method for Practical Hydrological Variables, presented at Session H050: ‘Information & Uncertainty in Data & Models: Towards a common framework for model building and prediction’ of the 2013 Fall Meeting of the American Geophysical Union, San Francisco, Dec 9-13
• Gupta, H. V. (2013, Fall). An Atmospheric Circulation Pattern-Based Assessment of The Impacts of Projected Climate Change, or How I spent my Sabbatical!. Invited talk at at Department of Hydrology and Water Resources, The University of Arizona. The University of Arizona, Tucson, AZ, USA: Department of Hydrology and Water Resources, The University of Arizona.
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  Gupta HV (2013 invited), An Atmospheric Circulation Pattern-Based Assessment of The Impacts of Projected Climate Change, or How I spent my Sabbatical! presented at Department of Hydrology and Water Resources, The University of Arizona, Tucson, AZ, USA, Oct 16.
• Gupta, H. V. (2013, Fall). Circulation Pattern-Based Assessment of Projected Climate Change for a Catchment in Spain. Invited talk at at at USDA-ARS, Marcopa County, AZ. USDA-ARS, Marcopa County, AZ: USDA-ARS, Marcopa County.
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  Gupta HV (2013 invited), Circulation Pattern-Based Assessment of Projected Climate Change, presented at USDA-ARS, Marcopa County, AZ, USA, Oct 7.
• Gupta, H. V. (2013, Spring). Sustainable Water ActioN (SWAN): Building Research Links between the EU and the USA. Invited talk at SWAN Workshop on “Challenges in Integrating Hydrologuc Science into Urban+ Decision Making”. The University of Arizona, Tucson, AZ, USA: The University of Arizona, Tucson, AZ, USA.
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  Gupta HV (2013 invited), Sustainable Water ActioN (SWAN): Building Research Links between the EU and the USA, presented at SWAN Workshop on “Challenges in Integrating Hydrologuc Science into Urban+ Decision Making”, Tucson, Arizona, April 29-May 3.
• Gupta, H. V. (2013, Spring). Sustainable Water ActioN (SWAN): Building Research Links between the EU and the USA. Invited talk at UofA Workshop on “Hydrologic, Ecologic, and Morphologic Processes of Engineered Watershed”s. The University of Arizona, Tucson, AZ, USA: The University of Arizona, Tucson, AZ, USA.
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  Gupta HV (2013 invited), Sustainable Water ActioN (SWAN): Building Research Links between the EU and the USA, presented at UofA Workshop on “Hydrologic, Ecologic, and Morphologic Processes of Engineered Watershed”s, Tucson, Arizona, May 8-9, 2013.
• Gupta, H. V., Nearing, G. S., Gong, W., Clark, M. P., & Vrugt, J. A. (2013, Fall). On The Need for an Information-Based Approach to Evaluating Model Structural Hypotheses. Invited talk at Session H009: ‘Best Practices in Model Verification and Uncertainty Analysis across Earth's Dynamic Systems’ of the 2013 Fall Meeting of the American Geophysical Union. 2013 Fall Meeting of the American Geophysical Union, San Francisco: American Geophysical Union.
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  Gupta HV, GS Nearing, W Gong, MP Clark and JA Vrugt (2013 invited), On The Need for an Information-Based Approach to Evaluating Model Structural Hypotheses, presented at Session H009: ‘Best Practices in Model Verification and Uncertainty Analysis across Earth's Dynamic Systems’ of the 2013 Fall Meeting of the American Geophysical Union, San Francisco, Dec 9-13, 2013.
• Gupta, H. V., Nearing, G. S., Gong, W., Weijs, S., & Ehret, U. (2013, Fall). An Information Theory Perspective on Uncertainty Quantification and Bayes Law. Invited talk at H050: ‘Information & Uncertainty in Data & Models: Towards a common framework for model building and prediction’ of the 2013 Fall Meeting of the American Geophysical Union. 2013 Fall Meeting of the American Geophysical Union, San Francisco: American Geophysical Union.
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  Gupta HV, GS Nearing, W Gong, SV Weijs and U Ehret (2013 invited), An Information Theory Perspective on Uncertainty Quantification and Bayes Law, presented at Session H050: ‘Information & Uncertainty in Data & Models: Towards a common framework for model building and prediction’ of the 2013 Fall Meeting of the American Geophysical Union, San Francisco, Dec 9-13, 2013.
• Gupta, H. V., Serrat-Capdevilla, A., Dominguez, F., Zeng, X., Valdes, J., Poupeau, F., & Schneier-Madanes, G. (2013, Fall). Sustainable Water ActioN (SWAN): Challenges in Integrating Hydrologic Science into Urban+ Decision Making. Oral presentation at the AHS 26th Annual Symposium on “Shifting Boundaries: Recalibrating the Hydrologic Approach”, Tucson, AZ. AHS 26th Annual Symposium, Tucson, AZ: Arizona Hydrological Society.
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  Gupta HV, A Serrat-Capdevilla, F Dominguez, X Zeng, J Valdes, F Poupeau and G Schneier-Madanes (2013), Sustainable Water ActioN (SWAN): Challenges in Integrating Hydrologic Science into Urban+ Decision Making, oral presentation at the AHS 26th Annual Symposium on “Shifting Boundaries: Recalibrating the Hydrologic Approach”, Tucson, AZ, Sept 18–21.
• Moreno, H. A., White, D. D., Gupta, H. V., Vivoni, E. R., & Sampson, D. A. (2013, Fall). Scaling up the Hydrologic Effects of Forest Thinning in Semi-Arid Basins of Arizona. Session H025: ‘Forests and the Hydrological Regime: After all these years what can we tell policy-makers about how changing tree cover influences runoff?’ of the 2013 Fall Meeting of the American Geophysical Union, San Francisco. 2013 Fall Meeting of the American Geophysical Union, San Francisco: American Geophysical Union.
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  Moreno HA, DD White, HV Gupta, ER Vivoni and DA Sampson (2013), Scaling up the Hydrologic Effects of Forest Thinning in Semi-Arid Basins of Arizona, presented at Session H025: ‘Forests and the Hydrological Regime: After all these years what can we tell policy-makers about how changing tree cover influences runoff?’ of the 2013 Fall Meeting of the American Geophysical Union, San Francisco, Dec 9-13
• Nearing, G. S., Gupta, H. V., Crow, W., & Gong, W. (2013, Fall). Information-Based Analysis of Data Assimilation. Invited talk at Session H041: ‘Hydrologic Data Assimilation’ of the 2013 Fall Meeting of the American Geophysical Union. 2013 Fall Meeting of the American Geophysical Union, San Francisco: American Geophysical Union.
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  Nearing GS, H Gupta, W Crow and W Gong (2013 invited), Information-Based Analysis of Data Assimilation, presented at Session H041: ‘Hydrologic Data Assimilation’ of the 2013 Fall Meeting of the American Geophysical Union, San Francisco, Dec 9-13
• Nearing, G., & Gupta, H. V. (2013, Spring). Extracting Information about Model Structure from Observations. Session HS 1.2: 'Data & Models, Induction & Prediction, Information & Uncertainty: Towards a common framework for model building and predictions in the Geosciences' organized jointly by Hydrological Sciences, Geosciences and Atmospheric Sciences, 2013 EGU General Assembly, Vienna, Austria. Vienna, Austria: European Geosciences Union.
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  Nearing G and H Gupta (2013), Extracting Information about Model Structure from Observations, Presentation at Session HS 1.2: 'Data & Models, Induction & Prediction, Information & Uncertainty: Towards a common framework for model building and predictions in the Geosciences' organized jointly by Hydrological Sciences, Geosciences and Atmospheric Sciences, 2013 EGU General Assembly, Vienna, Austria, Apr 7-12.
• Rodriguez, D., Gupta, H. V., & Mendiondo, E. M. (2013, Fall). A Blue/Green Water-based Accounting Framework for Assessment of Water Security. Session H056: ‘Managing and modeling for water security’ of the 2013 Fall Meeting of the American Geophysical Union, San Francisco. 2013 Fall Meeting of the American Geophysical Union, San Francisco: American Geophysical Union.
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  Rodrigues DBB, HV Gupta and EM Mendiondo (2013), A Blue/Green Water-based Accounting Framework for Assessment of Water Security, presented at Session H056: ‘Managing and modeling for water security’ of the 2013 Fall Meeting of the American Geophysical Union, San Francisco, Dec 9-13
• Rodriguez, D., Mendiondo, E. M., & Gupta, H. V. (2013, Fall). Water Footprint as a tool for freshwater ecosystem services assessment and water scarcity management. Oral presentation at the AHS 26th Annual Symposium on “Shifting Boundaries: Recalibrating the Hydrologic Approach”, Tucson, AZ. AHS 26th Annual Symposium, Tucson, AZ: Arizona Hydrological Society.
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  Rodrigues DBB, EM Mendiondo and HV Gupta (2013), Water Footprint as a tool for freshwater ecosystem services assessment and water scarcity management, submitted to the AHS 26th Annual Symposium on “Shifting Boundaries: Recalibrating the Hydrologic Approach”, Tucson, AZ, Sept 18–21.
• Sapriza, G. A., Jodar, J., Carrera, J., & Gupta, H. V. (2013, Spring). Sensitivity of Hydrological Model Simulations to Underling Assumptions in a Stochastic Downscaling method. Session HS 7.2: ‘Precipitation uncertainty and variability: observations, ensemble simulation and downscaling’, 2013 EGU General Assembly, Vienna, Austria. Vienna, Austria: European Geosciences Union.
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  Sapriza Azuri G, J Jodar, J Carrera and HV Gupta (2013), Sensitivity of Hydrological Model Simulations to Underling Assumptions in a Stochastic Downscaling method, Presentation at Session HS 7.2: ‘Precipitation uncertainty and variability: observations, ensemble simulation and downscaling’, 2013 EGU General Assembly, Vienna, Austria, Apr 7-12.
• Serrat-Capdevilla, A., Gupta, H. V., Boyanova, K., Cabello, V., Rodriguez, D., Yang, Z., Dominguez, F., Valdes, J., & Poupeau, F. (2013, Fall). A Transdisciplinary Approach to Sustainable Water Action. Oral presentation at the AHS 26th Annual Symposium on “Shifting Boundaries: Recalibrating the Hydrologic Approach”, Tucson, AZ. AHS 26th Annual Symposium, Tucson, AZ: Arizona Hydrological Society.
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  Serrat-Capdevilla A, HV Gupta, K Boyanova, V Cabello, D Bicca, Z Yang, F Dominguez, J Valdes, Franck Popeau (2013), A Transdisciplinary Approach to Sustainable Water Action, oral presentation at to the AHS 26th Annual Symposium on “Shifting Boundaries: Recalibrating the Hydrologic Approach”, Tucson, AZ, Sept 18–21.
• Yang, Z., Dominguez, F., & Gupta, H. V. (2013, Fall). Effects of urbanization on regional climate: A case study in Phoenix and Tucson Corridor. Oral presentation at the AHS 26th Annual Symposium on “Shifting Boundaries: Recalibrating the Hydrologic Approach”, Tucson, AZ. AHS 26th Annual Symposium, Tucson, AZ: Arizona Hydrological Society.
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  Yang Z, F Dominguez and H Gupta (2013), Effects of urbanization on regional climate: A case study in Phoenix and Tucson Corridor, submitted to the AHS 26th Annual Symposium on “Shifting Boundaries: Recalibrating the Hydrologic Approach”, Tucson, AZ, Sept 18–21.

Poster Presentations

• Cosgrove, B., Zamora, R., Wang, Y., Read, L., Yates, D., Dugger, A., Gochis, D., Castro, C. L., Gupta, H. V., Hazenberg, P., & Lahmers, T. M. (2020, January). Implementation and Evaluation of Channel Infiltration in NOAA National Water Model for Semiarid Environments. 34th Conference on Hydrology, 100th American Meteorological Society Meeting.
• Gupta, H. V., Durcik, M., Serrat-Capdevila, A. -., Valdes, R., Demaria, E. M., Lyon, B., Valdes, J. B., & Roy, T. (2017, December). Short-term climate change impacts on Mara basin hydrology. 2017 AGU Fall Meeting, Abstract H51J-1395. New Orleans.
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  The predictability of climate diminishes significantly at shorter time scales (e.g. decadal). Both natural variability as well as sampling variability of climate can obscure or enhance climate change signals in these shorter scales. Therefore, in order to assess the impacts of climate change on basin hydrology, it is important to design climate projections with exhaustive climate scenarios. In this study, we first create seasonal climate scenarios by combining (1) synthetic precipitation projections generated from a Vector Auto-Regressive (VAR) model using the University of East Anglia Climate Research Unit (UEA-CRU) data with (2) seasonal trends calculated from 31 models in the Coupled Model Intercomparison Project Phase 5 (CMIP). The seasonal climate projections are then disaggregated to daily level using the Agricultural Modern-Era Retrospective Analysis for Research and Applications (AgMERRA) data. The daily climate data are then bias-corrected and used as forcings to the land-surface model, Variable Infiltration Capacity (VIC), to generate different hydrological projections for the Mara River basin in East Africa, which are then evaluated to study the hydrologic changes in the basin in the next three decades (2020-2050).
• Smith, M., Dugger, A. A., Gochis, D. J., Gupta, H. V., Castro, C. L., & Lahmers, T. M. (2017, January). Enhancing the NOAA National Water Center WRF-Hydro model architecture to improve representation of the Midwest and Southwest CONUS climate regions. 97th American Meteorological Society meeting, 31st Conference on Hydrology.
• Boyanova, K., Niraula, R., Dominguez, F., Gupta, H. V., & Nedkov, S. (2016, Spring). Quantification of water-related ecosystem services in the Upper Santa Cruz Watershed. Poster at the EU-USA Sustainable Water Action Network (SWAN) International Conference on Open Knowledge: Bridging Perspectives to Address Water Challenges, (Tucson, Arizona, Feb 16-17, 2016). Doubletree Inn, Tucson, AZ, USA: The University of Arizona, Tucson, AZ, USA.
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  Boyanova K, R Niraula, F Dominguez, H Gupta and S Nedkov (2016), Quantification of water-related ecosystem services in the Upper Santa Cruz Watershed, presented at the EU-USA Sustainable Water Action Network (SWAN) International Conference on Open Knowledge: Bridging Perspectives to Address Water Challenges, (Tucson, Arizona, Feb 16-17, 2016)
• Poupeau, F., Gupta, H. V., Serrat-Capdevilla, A., Sans-Fuentes, M. A., Harris, S. W., & Hayde, L. G. (2016, Spring). Water Bankruptcy in the Land of Plenty; Steps Towards a Transatlantic and Transdisciplinary Assessment of Water Scarcity in Southern Arizona. Poster at the EU-USA Sustainable Water Action Network (SWAN) International Conference on Open Knowledge: Bridging Perspectives to Address Water Challenges, (Tucson, Arizona, Feb 16-17, 2016). Doubletree Inn, Tucson, AZ, USA: The University of Arizona, Tucson, AZ, USA.
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  Poupeau F, H Gupta, A Serrat-Capdevilla, MA Sans-Fuentes, S Harrs and LG Hayde (2016), Water Bankruptcy in the Land of Plenty; Steps Towards a Transatlantic and Transdisciplinary Assessment of Water Scarcity in Southern Arizona, presented at the EU-USA Sustainable Water Action Network (SWAN) International Conference on Open Knowledge: Bridging Perspectives to Address Water Challenges, (Tucson, Arizona, Feb 16-17, 2016)
• Boyanova, K., Niraula, R., Yang, Z., Dominguez, F., & Gupta, H. V. (2015, Summer). Quantification of ecosystem services in the Upper Santa Cruz Watershed, Arizona, USA. Presented at Session 44: Ecosystem Services: Supply, Flows and Demands in and Between Landscapes of the 9th IALE World Congress, Portland, Oregon USA. Portland, Oregon USA: IALE.
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  Boyanova K, R Niraula, Z Yang, F Dominguez and H Gupta (2015), Quantification of ecosystem services in the Upper Santa Cruz Watershed, Arizona, USA, presented at Session 44: Ecosystem Services: Supply, Flows and Demands in and Between Landscapes of the 9th IALE World Congress, Portland, Oregon USA, July 5-10
• Boyanova, K., Poupeau, F., Gupta, H. V., Serrat-Capdevilla, A., & Sans-Fuentes, M. A. (2015, Fall). Water Bankruptcy in the Land of Plenty; Steps Towards a Transatlantic and Transdisciplinary Assessment of Water Scarcity in Southern Arizona. Presented at the 8th Ecosystem Services Partnership (ESP) workshop on ‘Ecosystem Services for Nature, People and Prosperity’, Stellenbosch, South Africa. Stellenbosch, South Africa.
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  Boyanova K, F Poupeau, H Gupta, A Serrat-Capdevila, M Sans-Fuentes (2015), Water Bankruptcy in the Land Of Plenty: Steps towards a transatlantic and transdisciplinary assessment on the nature and causes of water scarcity in Southern Arizona, presented at the 8th Ecosystem Services Partnership (ESP) workshop on ‘Ecosystem Services for Nature, People and Prosperity’, Stellenbosch, South Africa, Nov 9-13
• Serrat-Capdevilla, A., Valdes, J., Gupta, H. V., Merino, M., Valdes, R., & Durcik, M. (2013, Fall). Real-Time Multi-Model Hydrologic Forecasts in Africa using Satellite Data. Poster presentation at the AHS 26th Annual Symposium on “Shifting Boundaries: Recalibrating the Hydrologic Approach”, Tucson, AZ. AHS 26th Annual Symposium, Tucson, AZ: Arizona Hydrological Society.
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  Serrat-Capdevilla A, J Valdes, HV Gupta, M Merino, K Ba, R Valdes, M Durcik (2013), Real-Time Multi-Model Hydrologic Forecasts in Africa using Satellite Data, poster presentation at the AHS 26th Annual Symposium on “Shifting Boundaries: Recalibrating the Hydrologic Approach”, Tucson, AZ, Sept 18–21.

Others

• Gupta, H. V. (2018, Fall 2018). Meet Jennifer McIntosh – Water and Life. UA Paw Prints Faculty Highlights Article, Parent & Family Programs Newsletter, Parent & Family Programs, Dean of Students Office.
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  Gupta H (2018), Meet Jennifer McIntosh – Water and Life, UA Paw Prints Faculty Highlights Article, Parent & Family Programs Newsletter, Parent & Family Programs, Dean of Students Office, 3rd August 2018.
• Ehret, U., Gupta, H. V., Nearing, G., Ruddell, B., Wellman, F., Kumar, R., Weijs, S., Jackson, B., & Abramowitz, G. (2017, July). International Workshop held on The Role of Information Theory in the Earth Sciences. Hydrology Section Newsletter of the American Geophysical Union.
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  Ehret U, H Gupta, G Nearing, B Ruddell, F Wellman, R Kumar, S Weijs, B Jackson and G Abramowitz (2017), International Workshop held on The Role of Information Theory in the Earth Sciences, Hydrology Section Newsletter of the American Geophysical Union, July 2017.
• Gupta, H. V. (2017, Fall 2017). International Workshop held on The Role of Information Theory in the Earth Sciences. Hydrology Section Newsletter of the American Geophysical Union.
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  Ehret U, H Gupta, G Nearing, B Ruddell, F Wellman, R Kumar, S Weijs, B Jackson and G Abramowitz (2017), International Workshop held on The Role of Information Theory in the Earth Sciences, Hydrology Section Newsletter of the American Geophysical Union, July 2017.
• Gupta, H. V., Humphrey, M., & Sorooshian, S. (2014, December). AAAS Program Review And Guidance For Utah-Wyoming Nsf Epscor RII Track-2 Award ‘Collaborative Research: Ci-Water Cyber-Infrastructure To Advance High Performance Water Resource Modeling. American Association for the Advancement of Science.
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  Gupta H, M Humphrey, S Sorooshian, H McInnis (2015), “AAAS Program Review And Guidance For Utah-Wyoming Nsf Epscor RII Track-2 Award ‘Collaborative Research: Ci-Water Cyber-Infrastructure To Advance High Performance Water Resource Modeling’”, 2014 AAAS Review Panel, Dec
• Duan, Q., Gupta, H. V., & Sorooshian, S. (2005, May). Shuffled Complex Evolution global optimization algorithm; Matlab version. Matlab Central. http://www.mathworks.com/matlabcentral/fileexchange/7671
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  Shuffled Complex Evolution global optimization algorithm; Matlab version available at Matlab Central: http://www.mathworks.com/matlabcentral/fileexchange/7671