Improving Streamflow Simulation in Mountainous Regions Using Multi-Sources Snow Remote Sensing Data

Snow is one of the most significant essential climate variables in global climate change study, and snowmelt accounts for a large part of the streamflow in mountainous regions on the Tibetan Plateau. However, previously researchers often calculate snow water equivalent using precipitation data, which is more unreliable in solid precipitation. This study is designed to combine a hydrological model and snow remote sensing data to improve the streamflow simulation in Lhasa river basin. Firstly, this study proposes a method to produce a set of snow cover maps with high temporal and spatial resolution from multiple satellite imagery (MODIS, Landsat, and Sentinel-1. Secondly, by comparing with this snow cover product, we evaluated the capability of a Geomorphology-Based Hydrological Model (GBHM) on snow cover simulation. Third, by margining the remotely sensed snow cover maps into the GBHM, the improvement of spring streamflow simulation was validated against in situ gauge observation. Finally, the GBHM simulated snow water equivalent (SWE), which was constrained by the water balance, was employed to assess the performance of SWE products in this basin. This study provide a method to estimate streamflow reliably in snow-covered mountainous regions, as well as to evaluate SWE at basin scale.