Evaluation of Remote Sensing and Reanalysis based Precipitation Products for hydrological studies in Semi-arid Tropics of Tamil Nadu

This study provides a comprehensive evaluation of eight high spatial resolution gridded precipitation products in Semi-Arid regions of Tamil Nadu in India, particularly focusing on Coimbatore, Madurai, Tiruchirapalli and Tuticorin. The study regions lack sufficiently long-term and spatially representative observed precipitation data, which is a crucial component for hydrological management. Hence, the present study evaluates the accuracy of five remote sensing-based precipitation products viz. Tropical Rainfall Measuring Mission (TRMM), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks – Climate Data Records (PERSIANN CDR), CPC MORPHing technique(CMORPH), Global Precipitation Measurement (GPM) and Multi-Source Weighted-Ensemble Precipitation (MSWEP) and three reanalysis-based precipitation products viz. National Center for Environmental Prediction (NCEP2) Reanalysis 2, European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis version 5 Land (ERA 5 Land), Modern-Era Retrospective analysis for Research and Application version 2 (MERRA 2) against the station data obtained from the archives of respective Public Works Department. Initially, precipitation products and ground station data were gridded to a common spatial resolution of 0.1 by linear interpolation. The products were then statistically evaluated at multiple spatial (grid and district-wise) and temporal (daily, weekly, monthly and yearly) resolutions for the period 2003-2014. We found that district-wise analysis at monthly and yearly temporal resolution provided better correlation and significantly reduced biases and errors. Evaluation results showed that in terms of overall statistical metrics, ERA 5 Land, MSWEP, PERSIANN CDR and GPM were the best-performing precipitation products, while NCEP2 performed the worst. ERA 5 Land and MSWEP better represented the daily rainfall characteristics with lower Mean Absolute Error and Root Mean Square Error. This study has significant implications for managing hydrological resources by providing valuable guidance when choosing alternative precipitation products in data-scarce regions.