Keywords
Abstract
Drought is an irregular hydro climatological phenomenon whose incidence varies significantly both spatially and temporally, particularly in the semi-arid areas of Maharashtra. The present study adopts a geospatial methodology for mapping the Kharif drought hotspot areas on a taluka scale using CHIRPS rainfall, SPI-4 and the Getis-Ord Gi* index. The monthly rainfall values from 2000 to 2025 were downloaded for 140 talukas of 14 drought-prone districts using Google Earth Engine. The precipitation amounts occurring from June to September were combined together to capture the Kharif season, and the SPI-4 values for each of the taluka-years were derived based on the gamma distribution. Years with SPI-4 values ≤ −1 were identified as the drought years and the number of drought incidences was calculated to determine the recurrence over the 26-year period. Getis-Ord Gi* was later applied to identify the spatial clustering of the recurrent drought. In all, 3,640 observations of taluka-years were made, out of which, 697 observations experienced drought conditions. There was marked variability in drought frequency in different talukas, with drought being observed in as many as seven consecutive years in some talukas during the study period. Using hotspot analysis, 29 out of 140 talukas (20.7%), which includes 9 talukas at 99% confidence level, 9 talukas at 95% confidence level and 11 talukas at 90% confidence level, were found to be significant high-recurrence drought hotspots. CHIRPS-based drought assessment was validated independently using ERA5-Land rainfall dataset. Correlation between the two datasets was moderate, with Pearson and Spearman correlation coefficients being 0.502 and 0.574, respectively. Agreement between the drought categories was 76.9% with Cohen’s Kappa coefficient being 0.348. Validation of historical drought periods using document records was also performed, and agreement was found for 2001-2004, 2008-2009 and 2014-2015. Thus, it can be concluded that Kharif drought has a spatial clustering tendency and not a random distribution pattern in the study region. The proposed framework based on spatial hotspot analysis along with temporal recurrence, independent rainfall data validation and historical agreement can be effectively used for identifying drought hotspots for drought management purposes.
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Copyright (c) 2026 Nilofar Jamadar, Niranjani P V (Author)