INTEGRATION OF REMOTE SENSING DATA ON PRECIPITATION, EVAPOTRANSPIRATION & LEAF AREA INDEX INTO THE DISTRIBUTED GLOBAL HYDROLOGICAL MODEL PCR-GLOBWB

Using the Indus, Ganges, Brahmaputra, Mahi, Narmada, Tapi and Godavari River basins as an example, the present study examines the extent to which the remote sensing hydrological information can help improve the hydrological modelling. The leaf area index (LAI) derived from satellite remote sensing was incorporated into the PCR-GLOBWB model to add an inter-annually dynamic vegetation cycle. Four different precipitation products (CRU TS 3.21, APHRODITE, corrected-APHRODITE, and CHIRPS) were used in the different simulations to assess which product helps in better simulation of the historical river discharge in the aforesaid basins. An ensemble evapotranspiration product based on six different remote sensing ET models, was combined with the model to correct for the bias. It was concluded that the remote sensing LAI and Upper Indus Ganges Brahmaputra (UIGB)-corrected-APHRODITE precipitation products have a significant impact on the model. The P-RSLAI-ET-cor-APHRO model run performed significantly better in simulating the temporal variability of the river discharge at daily and monthly scales across the most gauging stations, while the P-RSLAI-cor-APHRO run captures the inter-annual variability and magnitude of discharge fairly well. The model improvements indicate that the incorporation of remote sensing hydrological information into the hydrological model did not only provide greater model accuracy and better representation of historic river flow but can also, to an extent, assisted in representing the the model related uncertainties in the simulations