Forward Simulation of Snow Albedo Based on Snicar Model

Abstract

Snow albedo plays an important role in the global climate system due to its climatic feedback effects. Due to the limitations of remote sensing methods, the remotely sensed snow albedo products have significant data loss and error uncertainty. In view of the limitation of retrieval methods, this research utilizing SNICAR model to study the forward simulation of snow albedo. We verify and optimize the SNICAR forward simulation model at the plot scale, based on field measurements of the input variables of forward model such as snow grain size, snow density, water content and solar zenith angle, and combined with the snow grain size evolution model driven by meteorological elements. The results indicate that the MAE (mean absolute error), RMSE (root mean square error), R (Pearson correlation coefficient), and NSE (Nash-Sutcliffe efficiency coefficient) of the observed and simulated snow albedo by the optimized SNICAR model are 0.04, 0.05, 0.86 and 0.67, respectively. Our research validates and optimizes the snow albedo forward simulation model, which provides an effective simulation means acquiring the snow albedo data of continuous time series in alpine mountain regions.