Hydrological model calibration in a Himalayan Catchment Using Remote sensing/Reanalysis Datasets.

Hydrological models are simplified mathematical representations of various hydrological processes and their interactions in a catchment. They are widely employed to simulate hydrological responses under diverse scenarios of climate, land use, land cover, and agricultural and soil management practices, which are helpful for planning water resources and management at the catchment scale. The parameters of the hydrological models are often optimised by calibrating them such that the observed and simulated streamflow match closely. Reliable prediction of hydrological variables of interest in ungauged or poorly gauged basins and addressing the uncertainty associated with the prediction is a challenging task as it is very difficult to calibrate the models due to the unavailability of measured hydrological responses. Escalating research interest in predicting hydrological fluxes at ungauged or poorly gauged catchments has been witnessed recently using distributed modelling, advanced scientific methods, and the availability of high-resolution satellite-based and reanalysis datasets used in model calibration. Additionally, the ability of remote sensing data sources to consider spatial variability is a further benefit in calibrating hydrological models, which lowers the level of uncertainty in the outputs. This proposed study focuses on calibrating 3 layered Variable Infiltration Capacity (VIC) model with soil moisture, and evapotranspiration obtained from different remote sensed and reanalysis data sets in the Upper Indus basin of the Hindukush Himalayan region. As the Upper Indus basin has limited meteorological stations and no gauging stations in the Indian mainland, the current study has much scope to quantify the water resources using different remote sensing/reanalysis datasets. The results expected from this study are to find the suitable variable and reanalysis product for the calibration of the VIC model and the uncertainty associated with various remote sensing and reanalysis products.