Abstract:
Precipitation is the most critical input for hydrological models. In this paper we evaluate the usefulness and reliability of re-analysed and satellite-based precipitation datasets in driving a large-scale hydrological model for the Beas River basin, a mountainous region in northern India. The spatial and temporal distribution of gridded precipitation in India is compared with raingauge measurements by using three statistical tests. Then a large-scale glacio-hydrological model (GSM-WASMOD), which couples WASMOD-D and a glacier mass-balance module, is applied for the basin. The three precipitation datasets are used to drive the large-scale GSM-WASMOD for simulating the water balance of the Beas River basin for the period 1997–2001. The model results are compared and assessed based on Nash-Sutcliffe efficiency (NS) and relative volume error (VE). On average, the global gridded satellite-based dataset performs as well as the sparse raingauge data in this region, indicating that the satellite-based dataset can be used as a data source for water resources in basins with little or no ground-based measurements.
