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Investigating the Accuracy of Multiple Gridded Precipitation Products and Their Impact on Hydrological Model Behaviors in Indian River Basins
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DOI：10.1175/JHM-D-25-0009.1.png)
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Abstract
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This study evaluates the utility of gridded precipitation products (GPPs) in hydrological applications across major central and southern Indian river basins (IRBs). The basins were selected to capture diverse climatic regimes from 2001 to 2020 and assesses the value of each product for driving streamflow simulation using the Soil and Water Assessment Tool (SWAT). The GPP datasets include Indian Meteorological Department (IMD), Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM (GPM IMERG), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-climate data record (PERSIANN-CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), and Multi-Source Weighted-Ensemble Precipitation (MSWEP). Validation against rain gauge observations revealed varying magnitudes of agreement among GPPs, though no systematic errors were identified. At the grid cell, we observed significant disagreement between GPPs in western regions. At the subbasin level, MSWEP exhibited the greatest heterogeneity in precipitation estimates. For daily streamflow simulation performance, MSWEP demonstrated superior capability in driving hydrological model simulations, followed by IMD and IMERG. However, in the studied tropical monsoon basins, annual peak-flow analysis revealed that none of the GPPs adequately captured peak-flow values. We used Sobol' indices for global sensitivity analysis, identifying curve number (CN), base-flow alpha factor (ALPHA_BF), and saturated hydraulic conductivity (SOL_K) as key parameters. In conclusion, this study demonstrates that GPPs not only influence the accuracy of streamflow simulations but also play a significant role in impacting the sensitivity of SWAT model parameters, emphasizing the critical importance of careful precipitation data selection in hydrological modeling applications for optimal application and interpretation.
Keywords:
Hydrology
Remote sensing
Numerical analysis/modeling
Streamflow
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