High-Elevation Evapotranspiration Estimates during Drought: Using Streamflow and LiDAR Snow Observations to Close the Upper Tuolumne River Basin Water Balance
Abstract
High-elevation spatial and temporal distributions of key hydrologic variables, such as snow water equivalent (SWE), precipitation, basin storage and evapotranspiration (ET) are difficult to observe due to the sparse coverage of existing observational networks. Airborne LiDAR provides remotely sensed, high-resolution observations of snow depth, though there are uncertainties in the estimation of SWE from LiDAR due to uncertain snow density and uncertain baselines in areas with glaciers and permanent snowfields. Streamflow observations offer another perspective on these distributions, as streamflow spatially integrates the basin's snowmelt response minus ET and increases in storage. By comparing distributed streamflow observations from multiple nested and adjacent basins with LiDAR-based SWE estimates, we verify their hydrologic agreement and seek to infer largely unobserved quantities such as ET. In this study, we use LiDAR observations from the NASA Airborne Snow Observatory (ASO) over the upper Tuolumne River basin in Yosemite National Park, over water years 2013-2015, during a period of historic drought in California. Streamflow time series from multiple sub-basins are available from the Yosemite Hydroclimate Network. For each sub-basin in the Tuolumne domain, we compare ASO SWE volumes from each LiDAR flight plus subsequent precipitation inputs with streamflow volumes from the flight date to 30 September. This water balance approach shows that snowmelt plus precipitation exceeds streamflow by relatively consistent amounts (100-200 mm over the warm season) across subbasins and years. Simple hydrologic models (calibrated to match the streamflow, LiDAR SWE and precipitation inputs for each subbasin) and point soil moisture measurements suggest that this difference corresponds to ET, with basin storage changes relatively small in comparison. This application suggests that LiDAR snow observations may shed light on uncertain aspects of hydrologic science.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2016
- Bibcode:
- 2016AGUFM.C44A..06H
- Keywords:
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- 0736 Snow;
- CRYOSPHEREDE: 0740 Snowmelt;
- CRYOSPHEREDE: 0798 Modeling;
- CRYOSPHERE