Dynamic spatio-temporal reconstruction, evaluation and trend analysis of satellite-based rainfall: a comprehensive study in Samastipur, Bihar

Abstract High-resolution, satellite-retrieved precipitation products are useful input data for hydrological predictions and water resources management, especially in developing countries where the availability of ground-based rainfall measurements with high spatial coverage is very limited. This research explores the temporal variability of rainfall, crucial for understanding hydrology and water resource management, particularly in vulnerable regions like Bihar, India. satellite-based precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) was evaluated using observed rainfall at meteorological station using key statistical parameters and also carried long-term trend analysis during 2000–2023 by applying Mann–Kendall Test and estimating Standardized Anomaly Index (SAI). The results reveal that, Rainfall was underestimated by satellite product and Before bias correction, TRMM data exhibited significant discrepancies in rainfall estimates, with varying biases and mean errors across grid points. After bias correction, the agreement between TRMM and observed rainfall significantly improved, with Pearson correlation coefficients stabilizing between 0.8 and 1.0, bias reduced to − 0.1 to 0.2, and mean errors minimized to − 0.1 to 0.1. Additionally, root mean square error (RMSE) improved, and R 2 values, indicating enhanced reliability of the corrected data. The analysis of annual rainfall and the Standardized Anomaly Index (SAI) indicates significant variability without a clear trend. This variability exposes the region to extreme weather, with flooding in wet years like 2007, 2008, 2011 and 2021 and droughts in dry years such as 2005 and 2019. Thus, there is an urgent need for adaptive water management and agricultural strategies.