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Accurately simulating the spatiotemporal distribution of mountain snow water equivalent improves estimates of available meltwater and benefits the water resource management community. In this paper we present the first integration of lidar-derived distributed snow depth data into a physics-based snow model using direct insertion. Over four winter seasons (2013–2016) the National Aeronautics and Space Administration/Jet Propulsion Laboratory (NASA/JPL) Airborne Snow Observatory (ASO) performed near-weekly lidar surveys throughout the snowmelt season to measure snow depth at high resolution over the Tuolumne River Basin above Hetch Hetchy Reservoir in the Sierra Nevada Mountains of California. The modeling component of the ASO program implements the iSnobal model to estimate snow density for converting measured depths to snow water equivalent and to provide temporally complete snow cover mass and thermal states between flights. Over the four years considered in this study, snow depths from 36 individual lidar flights were directly inserted into the model to provide updates of snow depth and distribution. Considering all updates to the model, the correlation between ASO depths and modeled depths with and without previous updates was on average r2 = 0.899 (root-mean-square error = 12.5 cm) and r2 = 0.162 (root-mean-square error = 41.5 cm), respectively. The precise definition of the snow depth distribution integrated with the iSnobal model demonstrates how the ASO program represents a new paradigm for the measurement and modeling of mountain snowpacks and reveals the potential benefits for managing water in the region.


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This document was originally published in Water Resources Research by Wiley on behalf of the American Geophysical Union. Copyright restrictions may apply. doi: 10.1029/2018WR023190