S&T Project 8116 Final Report: Merging High-Resolution Airborne Snowpack Data with Existing Long-Term Hydrometeorological Observations to Improve Water Supply Forecasting
Mountain snowpack is a critical observation for seasonal streamflow forecasting in the Western United States. The Airborne Snow Observatory (ASO) methodology generates maps of snow depth, snow water equivalent, (SWE) and albedo from airborne lidar and spectrometer measurements that inform snowpack modeling. In this work, we evaluate how traditional snow pillow station-based estimates of basin-wide SWE compare to more spatially complete estimates from ASO, and the impact these differences have on streamflow modeling and forecasts. While the ASO flights provide excellent spatial resolution and coverage of high elevation areas, observations are limited to flight times. Assessing the relationship between stations, which monitor continuously, and ASO flights, which capture a snapshot in time, this project identified that approximately 5-10 ASO flights, combined with stations, are sufficient to define 95% of the variability in the remaining flights. The ASO data are also assimilated into SUMMA, or the Structure for Unifying Multiple Modeling Alternatives, to evaluate the potential improvement for water supply forecast models. Using several approaches for when flights were assimilated, results suggest that including ASO SWE estimates near peak SWE can substantially improve ensemble streamflow predictions by removing much of error that accumulated from uncertain precipitation through the winter season.
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| description | Mountain snowpack is a critical observation for seasonal streamflow forecasting in the Western United States. The Airborne Snow Observatory (ASO) methodology generates maps of snow depth, snow water equivalent, (SWE) and albedo from airborne lidar and spectrometer measurements that inform snowpack modeling. In this work, we evaluate how traditional snow pillow station-based estimates of basin-wide SWE compare to more spatially complete estimates from ASO, and the impact these differences have on streamflow modeling and forecasts. While the ASO flights provide excellent spatial resolution and coverage of high elevation areas, observations are limited to flight times. Assessing the relationship between stations, which monitor continuously, and ASO flights, which capture a snapshot in time, this project identified that approximately 5-10 ASO flights, combined with stations, are sufficient to define 95% of the variability in the remaining flights. The ASO data are also assimilated into SUMMA, or the Structure for Unifying Multiple Modeling Alternatives, to evaluate the potential improvement for water supply forecast models. Using several approaches for when flights were assimilated, results suggest that including ASO SWE estimates near peak SWE can substantially improve ensemble streamflow predictions by removing much of error that accumulated from uncertain precipitation through the winter season. |
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| landingPage | https://data.usbr.gov/catalog/4514/item/11054 |
| modified | 2021-09-30T21:45:46Z |
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| title | S&T Project 8116 Final Report: Merging High-Resolution Airborne Snowpack Data with Existing Long-Term Hydrometeorological Observations to Improve Water Supply Forecasting |
