Drought assessment in any region primarily hinges on precipitation deficiency, which is subsequently propagated to various components and sectors, leading to different drought types. In countries such as India, an intricate relationship between various governing factors, drought types, and their quantification methodologies make it elusive to timely initiate government relief measures. This also prevents comprehensive inclusion of the integrated effect of the principal drivers of drought, resulting in ambiguous categorization of severity, where groundwater storage variability is often neglected despite its significant role in irrigation. Here, we developed a multivariate Joint Drought Index (JDI) combining satellite and model-based standardized indices of precipitation and evapotranspiration (SPEI), soil moisture (SSI), groundwater (SGI), and surface runoff (SRI) with different temporal scales by employing two robust methods, principal component analysis (PCA) and Gaussian copula, and applied the index to highly drought-prone Marathwada region from central India. Our novel approach of using different scale combinations of integrated indices for two primary seasons (Kharif and Rabi) provides more realistic drought intensities than multiple univariate indices, by incorporating the response from each index, representing the seasonal drought conditions corroborating with the seasonal crop yields. JDI, with both methods, successfully identified two major drought events in 2015 and 2018, while effectively capturing the groundwater drought. Moreover, despite the high correlation between JDI using PCA and copula, we observed a significant difference in the intensities reported by these methods, where copula detected exceptional drought conditions more frequently than PCA. JDI effectively detected the onset, duration, and termination of drought, where the improved accuracy of drought detection can play a critical role in policy formation and socioeconomic security of the related stakeholders. Seasonal agriculture drought categorization for holistic quantification of drought conditions as presented in this study should provide broad methodological implications on drought monitoring and mitigation measures, especially for agriculture-dominated regions in semiarid climates.

The complex attribution of climatic, hydrologic, and anthropogenic drivers to vegetation and agricultural production and their consequential societal impacts are not well understood, especially in socioeconomically sensitive states like Maharashtra, India. Here, we analyzed trends and variability in the MODIS leaf area index (LAI) time series, along with spatiotemporal patterns in precipitation, groundwater storage, agriculture statistics, and irrigation infrastructure, to identify their influences on the vegetation response and discuss their implications for farmers. The state showed greening in all biomes except forests, with a net gain of 17.478 × 10³ km² of leaf area during 2003–2019, where more than 70% of the trend in LAI is represented in croplands. Maximum greening was observed in irrigated croplands, attributable to increased crop productivity, whereas inadequate irrigation facilities with erratic rainfall patterns and droughts were primarily responsible for cropland browning. We discerned the dynamics and variability of vegetation response by incorporating a spectrum of synergistic feedbacks from multiple confounding drivers and found that uneven distribution of water availability across the administrative divisions governed the quantitative distinction in leaf area change. Despite the observed greening trends, the state witnessed a high number of farmer suicides related to droughts and agriculture failures hampering their socioeconomic security; therefore, improved irrigation infrastructure and comprehensive policy interventions are crucial for abatement of farmer distress.

Drought is a complex and multidimensional phenomenon affecting the global population. The widespread impacts of drought propagate through the climatic and hydrological cycle and affect the socio-economic security of the related stakeholders, especially farmers. Countries like India use several indices to determine the severity of the drought for governmental relief and mitigation measures, which is crucial for farmers facing agricultural stress and failures. However, the use of single or several separate drought indices cannot capture the combined effect of principal drivers responsible for the drought, where the effect of groundwater availability for agriculture is often neglected despite its heavy use in irrigation through groundwater extraction. In this study, we focus on the multidimensional response of drought in a single joint index to better capture the spatiotemporal variability in drought severity. The semi-arid region of Marathwada from central India, which frequently faces drought and is infamous for farmer suicides due to agriculture failures is taken as the study area. The response of hydroclimatic variables viz. precipitation, evapotranspiration, soil moisture, surface runoff, and groundwater storage were captured in their respective standardized indices (SPEI, SSI, SRI, and SGI respectively) which were then used to construct the Joint Drought Index (JDI) using two principal methods: 1) Principal Component Analysis (PCA) and 2) Gaussian copula. Both the methods were found to be capable of identifying the severity of the drought along with its onset, duration, and termination. Although individual indices such as SPI can sometimes acknowledge the meteorological response better, the JDI has the potential of capturing the response of multiple hydrological variables together at once for drought monitoring and assessment. During the period between 2003 to 2020, the drought of 2015 was identified as exceptionally severe in both the methods, where copula could better accommodate the severity of every integrated index whereas PCA averages the response of the variables to drought by allocating the weights to each index for each month.