Snow Pillows, LiDAR, and Streamgauges: Incorporating Snow and Streamflow Observations in the Basin Water Balance

Prior studies have suggested that point measurements of SWE may not be spatially representative of the basin snowpack. The development of remotely-sensed snow observations, such as the Airborne Snow Observatory (ASO) and MODIS-based snow cover estimates, allows for relating point and distributed estimates of snow. We examine how SWE at courses and pillows compares with distributed ASO SWE in water year 2014 in a highelevation basin in Yosemite National Park. We find that peak basin-mean ASO SWE was less than that found by averaging regional snow pillows, but that it melted at a slower rate than pillow SWE during the ablation season. Based on this, we develop an approach for bias-correcting historical snow pillow indices of SWE to better represent the basin mean. We then calibrate an ensemble of lumped hydrologic models to infer basin-mean precipitation from streamflow and SWE. Models calibrated to only streamflow observations have substantial uncertainty in inferred precipitation. Including appropriate SWE observations in the calibration is found to reduce this uncertainty. However, calibrating to fractional snow cover in addition to streamflow did not improve the consistency of the models. We suggest that this approach can improve understanding of water balance components in high-elevation, sparsely-measured basins.

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