资源环境科技发展态势分析平台

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 r(2) = 0.899 (root-mean-square error = 12.5 cm) and r(2) = 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.

Plan Language Summary In regions that depend primarily on snow to support life, water availability is becoming an increasingly important topic. National Aeronautics and Space Administration (NASA)'s Airborne Snow Observatory (ASO) is a new platform for estimating the amount of water stored in mountain snowpacks. Since 2013, the ASO has combined detailed measurements of snow depth from an aircraft with snowpack density estimates from a physics-based snow model to provide predictions of total snow water equivalent stored in the Tuolumne River Basin in the California Sierra Nevada. This work describes the process of updating the snow model using the measured ASO snow depths through a direct insertion process. When the distribution of all the snow in the basin is known more accurately, the model results are improved.