Discharge estimation in high-mountain regions with improved methods using multisource remote sensing: A case study of the Upper Brahmaputra River

River discharge is an important variable in the water cycle that is related to water supply, irrigation, and flood forecasting. However, gauging stations are extremely limited across most high-mountain regions such as the Tibetan Plateau (TP), known as the Asia's water towers. Remote sensing, in combination with partial in situ discharge measurements, bridges the gap in monitoring river discharge over ungauged and poorly gauged basins. Of great importance for the successful retrieval of river discharge using remote sensing are river width (water surface area) and water level (water surface elevation), but it is challenging to retrieve accurate discharge values for high-mountain regions because of narrow river channels, complex terrain, and limited observations from a single satellite platform. Here, we used 1237 high-spatial-resolution images (Landsat series and Sentinel 1/2) to derive water surface areas with the Google Earth Engine (GEE), and satellite altimetry (Jason-2/3 and Satellite with Argos and AltiKa (SARAL/Altika)) to derive water levels for the Upper Brahmaputra River (UBR, the Yarlung Zangbo River in China) in the TP where the river width is typically less than 400 m. Using three power function equations, discharge was estimated for cross-sections around the four gauging stations in the UBR with triangular cross-sections outperforming their trapezoidal counterparts. It was also found that the equation combining both river width and water level produced the best discharge estimates whereas the other two equations (requiring either river width or water level as the input data) were complementary and could be used to extend the time series of discharge estimates. The Nash-Sutcliffe efficiency coefficient values for the discharge estimates range from 0.68 to 0.98 during the study period 2000-2017. The proposed method is feasible to estimate discharge in the UBR and potentially other high-mountain rivers globally.