Improving snow simulation with more realistic vegetation parameters in a regional climate model in the Tianshan Mountains, Central Asia

Abstract

The snowpack evolution has a significant impact on the water cycle and energy exchange at the watershed and regional scales, especially in the mountainous area with complex topography and land surface properties. An accurate description of the vegetation parameters for the regional climate model (RCM) coupled with the land surface model (LSM) is necessary to achieve a more accurate simulation of the mountainous snow process. However, the default vegetation options could not update real-time in the RCM LSMs, causing large uncertainties in the snow mass estimation. Thus, this study investigated the effect of the key vegetation parameters on the snow simulation in the Tianshan Mountains (TS) through real-time updates with remotely sensed leaf area index (LAI), green vegetation fraction (FVEG) and land cover (LC) products in the Weather Research and Forecasting (WRF) model coupled with the Noah LSM with Multiparameterization Options (Noah-MP). The results demonstrated that more realistic vegetation parameters could improve the performance of snow simulation in the WRF/Noah-MP, especially in the forest regions. The underestimated vegetation parameters of the integrated remote sensing products caused an increased surface albedo and less snow interception, particularly in the snow ablation period, and less vegetation density could also reduce the net longwave radiation emitted from the canopy at the surface, causing a lower near-surface temperature and less snowmelt. Additionally, less snow interception and melted snow contributed to a larger snow water equivalent on the ground, such as in the Western TS and the high-altitude regions of the Ili Valley. The updating vegetation parameters' approach will help to provide information so as to accurately model the snow resources in the mountainous areas.