Impact of Precipitation Uncertainty on High-Resolution Snow Simulations over Western US Mountain Regions

Snow plays a critical role in global and regional water resource, hydrological cycle, and climate system. Current model simulations of snow cover and mass suffer from large uncertainties in precipitation particularly over complex mountain terrains. Reducing the uncertainty requires accurate precipitation simulations/datasets with high resolutions that resolve topographic variations. This study seeks to assess the impact of precipitation uncertainty on high-resolution (4 km) seasonal snow simulations over the western US mountain regions based on the Noah-MP land surface model driven by various precipitation forcing datasets. We evaluate the modeled snow water equivalent and other properties (albedo, cover fraction, and depth) by comparing with satellite and in situ measurements. We quantify the discrepancies among different precipitation datasets and the induced uncertainty in snow simulations over the mountain areas. We further estimate the hydrological consequences caused by precipitation uncertainty in terms of changes in snow season and runoff, which has important implications for water resource management. Within the context of precipitation uncertainty, we also conduct sensitivity simulations to quantify the uncertainty from other key snow processes and associated impacts on snow simulations. This study aims to provide directions for future improvements in snow modeling over complex mountain regions.