A Comparison and Ranking Study of Monthly Average Rainfall Datasets with IMD Gridded Data in India

Abstract

Precise rainfall measurement is essential for achieving reliable results in hydrologic applications. The technological advancement has brought numerous rainfall datasets that can be available to assess rainfall patterns. However, the suitability of a given dataset for a specific location remains an open question. The objective of this study is to find which rainfall datasets perform well in India at various spatial resolutions: pixel level, meteorological sub-divisions (MSDs) level, and India as a whole and temporal resolutions: monthly and yearly. This study performs skill metrics analysis on seven widely used rainfall datasets—GPM, CRU, CHIRPS, GLDAS, PERSIANN-CDR, SM2RAIN, and TerraClimate—using the Indian Meteorological Department’s (IMD) gridded data as a reference. The rule-based decision tree techniques are employed on the obtained skill metrics analysis values to find the good-performing rainfall dataset at each pixel value among all the datasets used. The MSD and pixel-wise analyses reveal that GPM performs well, while TerraClimate performed the most poorly in almost all MSDs. The analysis suggests that of the satellite-derived, gauged, and merged datasets, merged-type are the good-performing datasets at the MSD level, with approximately 17 MSDs demonstrating the same. The temporal analysis (in both month- and year-wise scales) also suggests that GPM is a good-performing dataset. This study obtained the optimal dataset for each pixel among the seven selected datasets. The GPM dataset typically ranks as a good-performing fit, followed by CHIRPS and then PERSIANN-CDR. Despite its finer resolution, the TerraClimate dataset ranks lowest at the pixel level. This research will aid in selecting the optimal dataset for MSDs and pixels to obtain reliable results for hydrologic and agricultural applications, which will contribute to sustainable development.