Articles | Volume 18, issue 7
https://doi.org/10.5194/essd-18-5531-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-5531-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A 40-year high-resolution gridded meteorological dataset derived from station observations in the Reynolds Creek Experimental Watershed
Andrew R. Hedrick
CORRESPONDING AUTHOR
Northwest Watershed Research Center, US Department of Agriculture – Agricultural Research Service, Boise, Idaho 83702, USA
Brandon Stairs
Northwest Watershed Research Center, US Department of Agriculture – Agricultural Research Service, Boise, Idaho 83702, USA
C. Jason Williams
Northwest Watershed Research Center, US Department of Agriculture – Agricultural Research Service, Boise, Idaho 83702, USA
Joachim Meyer
Department of Geosciences, Boise State University, Boise, Idaho 83712, USA
James P. McNamara
Department of Geosciences, Boise State University, Boise, Idaho 83712, USA
Patrick Kormos
Idaho Snow Survey, US Department of Agriculture – Natural Resource Conservation Service, Boise, Idaho 83709, USA
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This study uses 48 airborne lidar surveys and an energy balance snow model to investigate the spatial accumulation of snow water equivalent (SWE) in a large 1,180 km2 headwater catchment throughout hydrologically variable years. Results quantify the variability of the date of peak SWE storage across the basin and indicate that multiple lidar acquisitions throughout the accumulation and melt season provide the most optimal information for water supply forecasting applications.
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Freshwater resupply from seasonal snow in the mountains is changing. Current water prediction methods from snow rely on historical data excluding the change and can lead to errors. This work presented and evaluated an alternative snow-physics-based approach. The results in a test watershed were promising, and future improvements were identified. Adaptation to current forecast environments would improve resilience to the seasonal snow changes and helps ensure the accuracy of resupply forecasts.
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This study uses 48 airborne lidar surveys and an energy balance snow model to investigate the spatial accumulation of snow water equivalent (SWE) in a large 1,180 km2 headwater catchment throughout hydrologically variable years. Results quantify the variability of the date of peak SWE storage across the basin and indicate that multiple lidar acquisitions throughout the accumulation and melt season provide the most optimal information for water supply forecasting applications.
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Remotely sensed properties of snow are dependent on accurate terrain information, which for a lot of the cryosphere and seasonal snow zones is often insufficient in accuracy. However, as we show in this paper, we can bypass this issue by optimally solving for the terrain by utilizing the raw radiance data returned to the sensor. This method performed well when compared to validation datasets and has the potential to be used across a variety of different snow climates.
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Freshwater resupply from seasonal snow in the mountains is changing. Current water prediction methods from snow rely on historical data excluding the change and can lead to errors. This work presented and evaluated an alternative snow-physics-based approach. The results in a test watershed were promising, and future improvements were identified. Adaptation to current forecast environments would improve resilience to the seasonal snow changes and helps ensure the accuracy of resupply forecasts.
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Short summary
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Climate change affects precipitation phase, which can propagate into changes in streamflow timing and magnitude. This study examines how variations in rainfall and snowmelt affect discharge. We found that annual discharge and stream cessation depended on the magnitude and timing of rainfall and snowmelt and on the snowpack melt-out date. This highlights the importance of precipitation timing and emphasizes the need for spatiotemporally distributed simulations of snowpack and rainfall dynamics.
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Short summary
This dataset provides hourly gridded estimates of meteorologic variables based on station observations between 1984 and 2023 in the Reynolds Creek Experimental Watershed, including air temperature, humidity, wind, incoming solar radiation, and precipitation. This dataset exists as a resource for energy balance modeling and independent validation of atmospheric reanalysis datasets and is publicly available in a cloud-optimized format at the Ag Data Commons data repository.
This dataset provides hourly gridded estimates of meteorologic variables based on station...
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