Simulating the hydrological regime of the snow fed and glaciarised Gilgit Basin in the Upper Indus using global precipitation products and a data parsimonious precipitation-runoff model.

In many high altitude river basins, the hydro-climatic regimes and the spatial and temporal distribution of precipitation are little known, complicating efforts to quantify current and future water availability. Scarce, or non-existent, gauged observations at high altitudes coupled with complex weather systems and orographic effects further prevent a realistic and comprehensive assessment of precipitation. Quantifying the contribution from seasonal snow and glacier melt to the river runoff for a high altitude, melt dependent region is especially difficult. Global scale precipitation products, in combination with precipitation-runoff modelling may provide insights to the hydro-climatic regimes for such data scarce regions. In this study two global precipitation products; the high resolution (0.1° × 0.1°), newly developed ERA5-Land, and a coarser resolution (0.55° × 0.55°) JRA-55, are used to simulate snow/glacier melts and runoff for the Gilgit Basin, a sub-basin of the Indus. A hydrological precipitation-runoff model, the Distance Distribution Dynamics (DDD), requires minimum input data and was developed for snow dominated catchments. The mean of total annual precipitation from 1995 to 2010 data was estimated at 888 mm and 951 mm by ERA5-Land and JRA-55, respectively. The daily runoff simulation obtained a Kling Gupta efficiency (KGE) of 0.78 and 0.72 with ERA5-Land and JRA-55 based simulations, respectively. The simulated snow cover area (SCA) was validated using MODIS SCA and the results are quite promising on daily, monthly and annual scales. Our result showed an overall contribution to the river flow as about 26% from rainfall, 37-38% from snow melt, 31% from glacier melt and 5% from soil moisture. These melt simulations are in good agreement with the overall hydro-climatic regimes and seasonality of the area. The proxy energy balance approach in the DDD model, used to estimate snow melt and evapotranspiration, showed robust behaviour and potential for being employed in data poor basins.