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.
<p><strong>Green water assessment is evolving as a significant aspect of hydrological science since its existence is critical for crop production in rain-fed areas. The green water scarcity index (GWSI), which is based on evapotranspiration and effective rainfall, can assist researchers in understanding the water requirements of agriculture and the current water stress condition. To generate a GWSI map of India from 2017 to 2019 at monthly and yearly scales, this study employed Indian Meteorological Department (IMD) gridded rainfall and TerraClimate-based actual evapotranspiration</strong><strong> data products. The results showed that India experienced low GWSI throughout the monsoon season, as was to be expected, but interestingly, there were no high GWSI values (> 0.9) during the summer months, as seen in the winter. India experienced average GWSI values of 0.87, 0.86, and 0.83 in 2017, 2018, and 2019, respectively. In comparison to other years, 2019 has a lower GWSI, and rest years have similar GWSI values in the July and December months. In contrast to how almost all months in all years have similar GWSI values, the substantial discrepancy is only seen in September 2019. Due to the high frequency of rainfall events in September 2019, the ER rate has increased, which has led to a decrease in the GWSI in India's month of September 2019. According to the findings of this study, the monsoon has less of an impact on GWSI scarcity. India experiences green water scarcity all year round, necessitating extensive irrigation for agriculture. The lack of </strong><strong>gree water resources enabled the transition away from rainfed agriculture cultivation. This research will aid in determining the precise condition of water stress in the targeted region, as well as the zoning of water-scarce regions, so that future irrigation planning can be done appropriately.</strong></p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.