Modeling Spatial Distribution of Snow Water Equivalent by Combining Meteorological and Satellite Data with Lidar Maps

An accurate characterization of the water content of snowpack, or snow water equivalent (SWE), is necessary to quantify water availability and constrain hydrologic and land-surface models. Recently, airborne observations (e.g., lidar) have emerged as a promising method to accurately quantify SWE at high resolutions (scales of ∼100m and finer). However, the frequency of these observations is very low, typically once or twice per season in Rocky Mountains, Colorado. Here, we present a machine learning framework based on Random Forests to model temporally sparse lidar-derived SWE, enabling estimation of SWE at unmapped time points. We approximated the physical processes governing snow accumulation and melt as well as snow characteristics by obtaining fifteen different variables from gridded estimates of precipitation, temperature, surface reflectance, elevation, and canopy. Results showed that in the Rocky Mountains of Colorado, our framework is capable of modeling SWE with a higher accuracy when compared with estimates generated by the Snow Data Assimilation System (SNODAS). The mean value of the coefficient of determination (R2) using our approach was 0.57 and the root mean squared error (RMSE) was 13 cm, which was a significant improvement over SNODAS (mean R2 = 0.13, RMSE = 20 cm). We explored the relative importance of the input variables, and observed that at the spatial resolution of 800 m, meteorological variables are more important drivers of predictive accuracy than surface variables which characterize the properties of snow on the ground. This research provides a framework to expand the applicability of lidar-derived SWE to unmapped time points.