Refining snow-streamflow dynamics in a Tibetan Plateau basin by incorporating snow depth and topography

Snow plays a crucial role in land surface hydrological and energy processes. Accurately representing the snow-streamflow relationship is important for understanding how climate change affects alpine hydrology. However, most land surface models and hydrological models’ snow schemes overlook the influences of snow depth and topography, causing uncertainties in snow and related hydrological processes simulations. This issue is more pronounced on the Tibetan Plateau (TP) due to its shallow snow and complex topography. The challenge of how inadequate snow cover parameterization affects snow and streamflow simulations is a critical scientific question. This study targets the upstream areas of the Heihe River basin on the TP. Using multi-source observational datasets and the WRF-Hydro model, we incorporated seven pre-existing snow schemes that consider snow depth and topography into the WRF-Hydro to identify the optimized scheme. Comparing the results simulated with the default and optimized schemes, we quantified the improvement in the representation of the snow-streamflow relationship by considering snow depth and topography and revealed the influencing mechanisms of these two factors. Results show that the default scheme largely overestimates snow cover fraction (SCF). Accounting for snow depth alone reduces the monthly SCF bias by 6.20%. When both snow depth and topography are considered, the monthly SCF bias is reduced by 20.88%. Moreover, the default scheme underestimates the cold-season streamflow and overestimates the warm-season streamflow. The optimized scheme greatly enhances the accuracy of streamflow simulation, reducing the cold-season streamflow underestimation by 12.13% and lowering the warm-season streamflow overestimation by 8.84%. Furthermore, such incorporation reduces albedo overestimation, increases absorbed shortwave radiation, and changes land surface temperature (LST) and surface resistance (r_s). LST and r_s are key variables through which snow influences evapotranspiration and snow water equivalent, eventually altering the snow-streamflow relationship. These findings highlight the importance of considering snow depth and topography in numerical simulations for alpine areas and provide valuable scientific support for understanding the response of hydrological processes to snow change under climate warming.