Fusing Basin-Scale Airborne Observations with Simulations to Quantify Mountain Snow Water Storage During a Severe Drought

English Abstract:

One of the great unknowns in mountain hydrology is how much water is stored within a seasonal snowpack at the basin scale. Quantifying mountain water resources is critical for informed water resource management, but has proven elusive due to high spatial and temporal variability of mountain snow cover, complex terrain, accessibility constraints and limited in-situ networks. The Airborne Snow Observatory (ASO, aso.jpl.nasa.gov) uses airborne LiDAR techniques to derive snow depth combined with snow densities from a physically-based snow model to provide unprecedented basin-wide estimates of snow water mass (snow water equivalent, SWE). ASO was operational over selected basins in the Sierra Nevada in California during 2013 and 2014. Both years were very dry, with precipitation in 2013 at 75% of average, and 2014 – the driest year on record, 50% of average. Using over 10 weekly ASO acquisitions from peak SWE thru spring each year, we were able to provide water managers with reliable estimates of SWE volume, snow cover depletion and surface water input over the basin. However, shallow snow presents many difficulties for both LiDAR measurement of snow depth and for simulation modelling of precipitation distribution and snow density. The analysis presented here attempts to quantify and analyse these uncertainties. Our findings show that even during a drought year the LiDAR depth fields combined with the simulation model provide improved estimates of SWE volume in the basin. We also show the potential for the LiDAR depth fields to be used to improve precipitation estimates over this complex mountain region, and to update the snow distribution for improved density simulations. The NASA/JPL ASO program is the most important coupling of LiDAR remote sensing and snow hydrology in the last 50 years.