Spatiotemporal consistency and inconsistency of meteorological and agricultural drought identification: A case study of India

Abstract

Drought identification is challenging because the severity, intensity and frequency of extreme drought events are heterogenous. Incorporating multiple datasets is to generate standardized drought indices for effective drought assessment, monitoring, and develop early warning systems. This study aims to explore the spatio–temporal relationship among drought indices of precipitation, soil moisture, vegetation condition and health based on standardizing multiple drought indices with trend detection. The indices were derived through standardized drought analysis from 2001 to 2020 over India and regional level. Moreover, time series decomposition was utilized to remove seasonal and random components for trend detection. This study enhanced the comprehension of meteorological and agricultural drought conditions in India by standardizing and analyzing trends in multiple drought indices. The results revealed that the most severe drought periods for subregions were consistent for all indices with trend analysis. All indices also reported a significant decline in trend for the 2009 drought event across the subregions in India. These findings contributed to a better understanding of drought dynamics in the subregion. However, vegetation-based indices were not always consistent to the indices of precipitation and soil moisture due to ecosystem resilience or heat stress. This study offered policymakers diverse information, including correlations and variations of multiple drought indices to assist in detecting the most severe periods of drought at a regional scale.