Temporal Assessment of Meteorological Drought Events Using Stationary and Nonstationary Drought Indices for Two Climate Regions in India.

This study attempts to build nonstationary indices for assessing meteorological drought in two different climate zones in India: the arid Saurashtra and Kutch and humid-tropical Coastal Karnataka. Time and climate indices are considered as covariates to develop nonstationary models using the generalized additive model in location, scale, and shape (GAMLSS) for the period, 1951–2004. A comparative study has been conducted to assess the statistical performance of stationary and nonstationary models on various time scales (3, 6, 12, and 24 months). The best model is selected to conduct copula-based bivariate drought analysis. For this purpose, drought properties such as drought severity, duration, and peak are calculated. The annual and seasonal rainfall departures are also analyzed, and more rainfall-deficient years are detected in Saurashtra and Kutch regions than in Coastal Karnataka. The nonstationary index performed better in capturing drought properties in statistical analysis over both the study areas at all time scales. The nonstationary drought index shows better consistency with historical drought and flood events than the stationary index. Cooccurrence and joint return periods are calculated and compared with univariate return periods. A significant difference is observed between bivariate and univariate return periods, and more risk is detected in Saurashtra and Kutch than in Coastal Karnataka. The impacts of rainfall and drought on the yield of major crops in study areas are also analyzed. The yield loss rate of bajra significantly correlates with the nonstationary standardized precipitation index (NSPI) in Saurashtra and Kutch, whereas rice yield has no significant correlation with the index in Coastal Karnataka. This new aspect of drought analysis provides feasible results in both arid and humid regions in a changing environment. El Niño-Southern Oscillation (ENSO) refers to changes in ocean temperatures and atmospheric pressure in the Pacific Ocean, while Indian Ocean Dipole (IOD) refers to differences in sea surface temperatures in the Indian Ocean. Both ENSO and IOD can affect rainfall and drought conditions in India, making them essential factors to consider in understanding and predicting drought events. The nonstationary drought index developed using climate indices could accurately assess the important aspects of drought, like how severe it was, how long it lasted, and when it reached its peak. By evaluating the return period of a specific drought severity and duration, decision makers can assess the likelihood of experiencing such an event in a given year or over a specific period. It helps to prioritize resources, plan adaptation measures, and design drought management strategies to mitigate potential impacts. If specific ENSO or IOD conditions are associated with prolonged drought periods, water resource managers can implement proactive measures like water conservation initiatives, irrigation scheduling, and crop diversification. This research examines the impact of rainfall and drought on major crop yields (bajra in arid regions and rice in humid-tropical regions). This knowledge can aid farmers and policymakers in predicting crop losses, optimizing irrigation strategies, and implementing timely interventions to minimize agricultural productivity losses. [ABSTRACT FROM AUTHOR]