Evaluation of remote sensing and reanalysis based precipitation products for agro-hydrological studies in semi-arid tropics of Tamil Nadu

Abstract. This study provides a comprehensive evaluation of eight high spatial resolution gridded precipitation products in semi-arid regions of Tamil Nadu, India, focusing specifically on Coimbatore, Madurai, Tiruchirappalli, and Tuticorin, where both irrigated and rainfed agriculture is prevalent. The study regions lack sufficiently long-term and spatially representative observed precipitation data, essential for agro-hydrological studies and better understanding and managing the nexus between food production and water and soil 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 Reanalysis 2 (NCEP2), and European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis version 5 Land (ERA5-Land), Modern-Era Retrospective analysis for Research and Application version 2 (MERRA 2) against the station data. Linearly interpolated precipitation products were statistically evaluated at two spatial (grid and district-wise) and three temporal (daily, monthly, and yearly) resolutions for 2003–2014. Based on overall statistical metrics, ERA 5 Land was the best-performing precipitation product in Coimbatore, Madurai, and Tiruchirappalli, with MSWEP closely behind. In Tuticorin, however, MSWEP outperformed the others. On the other hand, MERRA2 and NCEP2 performed the worst in all the study regions, as indicated by their higher Root Mean Square Error (RMSE) and lower correlation values. Except in Coimbatore, most precipitation products underestimated the monthly monsoon precipitation, which highlights the need for a better algorithm for capturing the convective precipitation events. Also, the Percent Mean Absolute Error (%MAE) was higher in non-monsoon months, indicating that these product-based agro-hydrological modeling, like irrigation scheduling for water-scarce periods, may be less reliable. The ability of precipitation products to capture the extreme precipitation intensity differed from the overall statistical metrics, where MSWEP performed the best in Coimbatore and Madurai, PERSIANN CDR in Tiruchirappalli, and ERA5-Land in Tuticorin. This study offers crucial guidance for managing water resources in agricultural areas, especially in precipitation data-scarce regions, by helping to select suitable precipitation products and bias correction methods for agro-hydrological research.