Gaps Between Western U.S. Reservoir Inflows and Headwater Precipitation Timing, Amount, and Phase
Snowmelt from mountain headwater basins supplies approximately 80% of the runoff to reservoirs in the western United States (WUS), and is therefore a key component of western hydropower. Yet predicting runoff from hydrometeorological data in complex terrain (and its sensitivity to disturbances and extremes) remains highly uncertain. Basic science uncertainties include how inflows to reservoirs respond to shifts in precipitation timing, amount, phase (rain versus snow), the impacts of evolving forest/land cover due to disturbances, and the often overlooked role of groundwater in the mountain hydrologic cycle. Challenges to address these questions arise in validating and downscaling climate data for use in physics-based or AI-driven watershed models, assessing biases and process fidelity in these models, and scaling process understanding from plot/hillslope/intensive field campaigns to watershed and hydropower-relevant scales. Further, water management restrictions and policies drive reservoir operation guidelines such as stationary rule curves, with the potential to inhibit energy production and water supply.
Citation Formats
TY - DATA
AB - Snowmelt from mountain headwater basins supplies approximately 80% of the runoff to reservoirs in the western United States (WUS), and is therefore a key component of western hydropower. Yet predicting runoff from hydrometeorological data in complex terrain (and its sensitivity to disturbances and extremes) remains highly uncertain. Basic science uncertainties include how inflows to reservoirs respond to shifts in precipitation timing, amount, phase (rain versus snow), the impacts of evolving forest/land cover due to disturbances, and the often overlooked role of groundwater in the mountain hydrologic cycle. Challenges to address these questions arise in validating and downscaling climate data for use in physics-based or AI-driven watershed models, assessing biases and process fidelity in these models, and scaling process understanding from plot/hillslope/intensive field campaigns to watershed and hydropower-relevant scales. Further, water management restrictions and policies drive reservoir operation guidelines such as stationary rule curves, with the potential to inhibit energy production and water supply.
AU - Rudisill, William
A2 - Siirila-Woodburn, Erica
A3 - Feldman, Dan
DB - Energy-Water Resilience
DP - Open EI | National Laboratory of the Rockies
DO -
KW - large hydropower
KW - headwater
KW - precipitation
KW - timing
KW - phase
KW - runoff
KW - snowmelt
KW - mountain headwater
KW - hydropower
KW - inflows
KW - hydrologic cycle
KW - climate
KW - data
KW - physics-based
KW - AI
KW - watershed
LA - English
DA - 2026/01/15
PY - 2026
PB - LBNL
T1 - Gaps Between Western U.S. Reservoir Inflows and Headwater Precipitation Timing, Amount, and Phase
UR - https://ewr.openei.org/submissions/104
ER -
Rudisill, William, et al. Gaps Between Western U.S. Reservoir Inflows and Headwater Precipitation Timing, Amount, and Phase. LBNL, 15 January, 2026, Energy-Water Resilience. https://ewr.openei.org/submissions/104.
Rudisill, W., Siirila-Woodburn, E., & Feldman, D. (2026). Gaps Between Western U.S. Reservoir Inflows and Headwater Precipitation Timing, Amount, and Phase. [Data set]. Energy-Water Resilience. LBNL. https://ewr.openei.org/submissions/104
Rudisill, William, Erica Siirila-Woodburn, and Dan Feldman. Gaps Between Western U.S. Reservoir Inflows and Headwater Precipitation Timing, Amount, and Phase. LBNL, January, 15, 2026. Distributed by Energy-Water Resilience. https://ewr.openei.org/submissions/104
@misc{EWR_Dataset_104,
title = {Gaps Between Western U.S. Reservoir Inflows and Headwater Precipitation Timing, Amount, and Phase},
author = {Rudisill, William and Siirila-Woodburn, Erica and Feldman, Dan},
abstractNote = {Snowmelt from mountain headwater basins supplies approximately 80\% of the runoff to reservoirs in the western United States (WUS), and is therefore a key component of western hydropower. Yet predicting runoff from hydrometeorological data in complex terrain (and its sensitivity to disturbances and extremes) remains highly uncertain. Basic science uncertainties include how inflows to reservoirs respond to shifts in precipitation timing, amount, phase (rain versus snow), the impacts of evolving forest/land cover due to disturbances, and the often overlooked role of groundwater in the mountain hydrologic cycle. Challenges to address these questions arise in validating and downscaling climate data for use in physics-based or AI-driven watershed models, assessing biases and process fidelity in these models, and scaling process understanding from plot/hillslope/intensive field campaigns to watershed and hydropower-relevant scales. Further, water management restrictions and policies drive reservoir operation guidelines such as stationary rule curves, with the potential to inhibit energy production and water supply. },
url = {https://ewr.openei.org/submissions/104},
year = {2026},
howpublished = {Energy-Water Resilience, LBNL, https://ewr.openei.org/submissions/104},
note = {Accessed: 2026-06-17}
}
Details
Data from Jan 15, 2026
Last updated Jan 15, 2026
Submitted Jan 15, 2026
Contact
Dan Feldman
Authors
Keywords
large hydropower, headwater, precipitation, timing, phase, runoff, snowmelt, mountain headwater, hydropower, inflows, hydrologic cycle, climate, data, physics-based, AI, watershedDOE Project Details
Project Name White Papers on Ideas to Advance Energy-Water Resilience
Project Lead
Project Number WP-104
