Temporal Analysis of Rainfall Patterns Over India: Insights from IMERG, GSMap, and IMD Data

Water vapor is a fundamental element of the Earth's hydrological cycle and has a direct impact on agriculture, water resource management, and food security. Although ground-based systems like rain gauges can offer high-precision precipitation observations, their low spatial coverage calls for satellite-based products like GSMaP and IMERG to monitor large-scale precipitation. This research evaluates the precision and reliability of GSMaP and IMERG datasets against high-resolution IMD ground-based observations over the Indian subcontinent. The datasets were standardized to 0.25° spatial resolution, preprocessed to remove negligible precipitation values, and tested using statistical performance measures, such as mean bias (MB), root mean square error (RMSE), and Pearson's correlation coefficient (R). In addition, visual methods such as sector distribution maps and time-series analysis were utilized to compare spatial and temporal trends. Outcomes show that, although there is great promise of satellite-based precipitation products for hydrological modeling, disaster risk management, and climate adaptation strategies, biases and errors still occur. The research highlights the necessity of bias-correction methods and multi-source data integration to improve the precision of precipitation estimation, ultimately facilitating enhanced climate resilience and sustainable water resource management.