Patrick D Broxton

Interests

Research

- Snow Hydrology (especially Snow forest interactions, snow mapping, and snow modelling), Mountain Ecohydrology, Seasonal and Subseasonal Streamflow Forecasting, creating Decision Support Systems

Courses

Scholarly Contributions

Journals/Publications

• Broxton, P. D., Van Leeuwen, W. J., Svoma, B., Walter, J., & Biederman, J. A. (2023). Subseasonal to Seasonal Streamflow Forecasting in a Semiarid Watershed. Journal of the American Water Resources Association, 59(6), 1493–1510.
• DeFlorio, M. J., Sengupta, A., Castellano, C. M., Wang, J., Zhang, Z., Gershunov, A., Guirguis, K., Luna, N. R., Clemesha, R. E., Pan, M., & others, . (2024). From California’s Extreme Drought to Major Flooding: Evaluating and Synthesizing Experimental Seasonal and Subseasonal Forecasts of Landfalling Atmospheric Rivers and Extreme Precipitation during Winter 2022/23. Bulletin of the American Meteorological Society, 105(1), E84--E104.
• Dwivedi, R., Biederman, J. A., Broxton, P. D., Lee, K., & Leeuwen, W. J. (2023). Snowtography quantifies effects of forest cover on net water input to soil at sites with ephemeral or stable seasonal snowpack in Arizona, USA. Ecohydrology, 16(2), e2494.
• Dwivedi, R., Biederman, J. A., Broxton, P. D., Lee, K., Leeuwen, W. J., & Pearl, J. K. (2023). Forest density and snowpack stability regulate root zone water stress and percolation differently at two sites with contrasting ephemeral vs. stable seasonal snowpacks. Journal of Hydrology, 624, 129915.
• Hoopes, C. A., Castro, C. L., Behrangi, A., Ehsani, M. R., & Broxton, P. (2023). Improving prediction of mountain snowfall in the southwestern United States using machine learning methods. Meteorological Applications, 30(6), e2153.
• Lewis, G., Harpold, A., Krogh, S. A., Broxton, P., & Manley, P. N. (2023). The prediction of uneven snowpack response to forest thinning informs forest restoration in the central Sierra Nevada. Ecohydrology, 16(7), e2580.
• Song, Y., Broxton, P. D., Behrangi, A., & Ehsani, M. (2021). Assessment of Snowfall Accumulation from Satellite and Reanalysis Products Using SNOTEL Observations in Alaska. Remote Sensing, 13(15), 2922.
• Broxton, P. D., Moeser, C. D., & Harpold, A. (2021). Accounting for Fine‐Scale Forest Structure is Necessary to Model Snowpack Mass and Energy Budgets in Montane Forests. Water Resources Research, 57(12). doi:10.1029/2021wr029716
• Broxton, P. D., & Leeuwen, W. J. (2020). Structure from motion of multi-angle RPAS imagery complements larger-scale airborne lidar data for cost-effective snow monitoring in mountain forests. Remote Sensing, 12(14), 2311.
• Broxton, P. D., Leeuwen, W. J., & Biederman, J. A. (2020). Forest cover and topography regulate the thin, ephemeral snowpacks of the semiarid Southwest United States. Ecohydrology, 13(4), e2202.
• Harpold, A. A., Krogh, S. A., Kohler, M., Eckberg, D., Sterle, G., Broxton, P. D., & Greenberg, J. A. (2020). Increasing the efficacy of forest thinning for snow using high‐resolution modeling: A proof of concept in the Lake Tahoe Basin, California, USA. Ecohydrology, 13(4). doi:10.1002/eco.2203
  More info

  Forest manipulation using forest thinning is one of the few means to manage water resources supplying downstream populations that are derived from snow-covered montane forests. The challenges of simulating processes at the tree scale have precluded generalizable recommendations for removing tree canopy to maximize snow water resources. Here, we apply the high-resolution Snow Physics and Lidar Mapping (SnowPALM) model to simulate snow mass and energy budgets at 1-m scale over a 1,200 by 1,200 m domain on the west shore of Lake Tahoe, Sierra Nevada, USA. The SnowPALM model verifies well against observations of snow depth and snow temperature in open and under canopy locations. This supported the application of SnowPALM under “virtual thinning” experiments, where all trees
• Krogh, S. A., Broxton, P. D., Manley, P. N., & Harpold, A. A. (2020). Using process based snow modeling and lidar to predict the effects of forest thinning on the northern Sierra Nevada snowpack. Frontiers in Forests and Global Change, 3, 21.
• Moeser, C. D., Broxton, P. D., Harpold, A., & Robertson, A. (2020). Estimating the effects of forest structure changes from wildfire on snow water resources under varying meteorological conditions. Water Resources Research, 56(11), e2020WR027071.
• Broxton, P. D., Van, L., & Biederman, J. A. (2019). Improving snow water equivalent maps with machine learning of snow survey and lidar measurements. Water Resources Research, 55(5), 3739--3757.
• Wang, Y., Broxton, P., Fang, Y., Behrangi, A., Barlage, M., Zeng, X., & Niu, G. (2019). A wet-bulb temperature-based rain-snow partitioning scheme improves snowpack prediction over the drier western United States. Geophysical Research Letters, 46(23), 13825--13835.
• Dawson, N., Broxton, P., & Zeng, X. (2018). Evaluation of remotely sensed snow water equivalent and snow cover extent over the contiguous United States. Journal of Hydrometeorology, 19(11), 1777--1791.
• Perdrial, J., Brooks, P. D., Swetnam, T., Lohse, K. A., Rasmussen, C., Litvak, M., Harpold, A. A., Zapata-Rios, X., Broxton, P., Mitra, B., & others, . (2018). A net ecosystem carbon budget for snow dominated forested headwater catchments: linking water and carbon fluxes to critical zone carbon storage. Biogeochemistry, 138, 225--243.
• Zeng, X., Broxton, P., & Dawson, N. (2018). Snowpack change from 1982 to 2016 over conterminous United States. Geophysical Research Letters, 45(23), 12--940.
• Broxton, P. D., Zeng, X., & Dawson, N. (2017). The impact of a low bias in snow water equivalent initialization on CFS seasonal forecasts. Journal of Climate, 30(21), 8657--8671.
• Dawson, N., Broxton, P., & Zeng, X. (2017). A new snow density parameterization for land data initialization. Journal of Hydrometeorology, 18(1), 197--207.
• Broxton, P. D., Dawson, N., & Zeng, X. (2016). Linking snowfall and snow accumulation to generate spatial maps of SWE and snow depth. Earth and Space Science, 3(6), 246--256.
• Broxton, P. D., Zeng, X., & Dawson, N. (2016). Why do global reanalyses and land data assimilation products underestimate snow water equivalent?. Journal of Hydrometeorology, 17(11), 2743--2761.
• Hazenberg, P., Broxton, P., Gochis, D., Niu, G., Pangle, L. A., Pelletier, J. D., Troch, P. A., & Zeng, X. (2016). Testing the hybrid-3-D hillslope hydrological model in a controlled environment. Water Resources Research, 52(2), 1089--1107.
• Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A., Niu, G., Williams, Z., Brunke, M. A., & Gochis, D. (2016). A gridded global data set of soil, intact regolith, and sedimentary deposit thicknesses for regional and global land surface modeling. Journal of Advances in Modeling Earth Systems, 8(1), 41--65.
• Broxton, P. D., Harpold, A. A., Biederman, J. A., Troch, P. A., Molotch, N. P., & Brooks, P. D. (2015). Quantifying the effects of vegetation structure on snow accumulation and ablation in mixed-conifer forests. Ecohydrology, 8(6), 1073--1094.
• Brunke, M. A., Broxton, P., Pelletier, J., Gochis, D., Hazenberg, P., LAWRENCE, D. M., Leung, L. R., NIU, G., TROCH, P. A., & Zeng, X. (2015). Implementing and Testing Variable Soil Thickness in the Community Land Model Version 4.5. Assessing and Improving the Representation of Hydrologic Processes in Atmospheric, Ocean, and Land Modeling and Dataset Generation, 100.
• Hazenberg, P., Fang, Y., Broxton, P., Gochis, D., Niu, G., Pelletier, J. D., Troch, P. A., & Zeng, X. (2015). A hybrid-3D hillslope hydrological model for use in E arth system models. Water Resources Research, 51(10), 8218--8239.
• Broxton, P. D., Zeng, X., Scheftic, W., & Troch, P. A. (2014). A MODIS-based global 1-km maximum green vegetation fraction dataset. Journal of Applied Meteorology and Climatology, 53(8), 1996--2004.
• Broxton, P. D., Zeng, X., Sulla-Menashe, D., & Troch, P. A. (2014). A global land cover climatology using MODIS data. Journal of Applied Meteorology and Climatology, 53(6), 1593--1605.
• Broxton, P., Troch, P. A., Schaffner, M., Unkrich, C., & Goodrich, D. (2014). An all-season flash flood forecasting system for real-time operations. Bulletin of the American Meteorological Society, 95(3), 399--407.
• Scheftic, W., Zeng, X., Broxton, P., & Brunke, M. (2014). Intercomparison of seven NDVI products over the United States and Mexico. Remote Sensing, 6(2), 1057--1084.
• Broxton, P. D., Troch, P. A., & Lyon, S. W. (2009). On the role of aspect to quantify water transit times in small mountainous catchments. Water Resources Research, 45(8).
• Lyon, S. W., Troch, P. A., Broxton, P. D., Molotch, N. P., & Brooks, P. D. (2008). Monitoring the timing of snowmelt and the initiation of streamflow using a distributed network of temperature/light sensors. Ecohydrology, 1(3), 215-224.

Presentations

• Broxton, P. D. (2023, November). Snow Monitoring and Streamflow Forecasting in the Salt / Verde Basin. Colorado River Climate and Hydrology Work Group Meeting. Salt Lake City.
• Broxton, P., Van, L. W., & Biederman, J. (2021, December). Using high resolution modelling to predict the effects of forest change on snowpack in the semiarid Southwest US. AGU Fall Meeting.

Poster Presentations

• Broxton, P. D., van Leeuwen, W. J., Joel, B., & Kyle, H. (2023, April). Multitemporal Snow Water Equivalent, Thermal, and Radiation Monitoring using Drone and Ground Surveys to Inform High Resolution Snow Modelling in Arizona’s Forests. Western Snow Conference.
• Broxton, P. D., Unkrich, C. C., Hernandez, M., Goodrich, D., Guertin, D. P., & Williams, C. J. (2022, December). Improving Erosion Modelling in Cold Environments by adding a snow module to the KINEROS2-Rangeland Hydrology and Erosion Model. AGU Fall Meeting.