Accurate quantification of the terrestrial water cycle relies on combinations of multisource datasets. This analysis uses data from remotely sensed, in-situ, and reanalysis records to quantify the terrestrial water budget/balance and component uncertainties in the upper Chao Phraya River Basin from May 2002 to April 2020. Three closure techniques are applied to merge independent records of water budget components, creating up to 72 probabilistic realizations of the monthly water budget for the upper Chao Phraya River Basin. An artificial neural network (ANN) model is used to gap-fill data in and between GRACE and GRACE-FO-based terrestrial water storage anomalies. The ANN model performed well with r $\ge$ 0.95, NRMSE $=$ 0.24 − 0.37, and NSE $\ge$ 0.89 during the calibration and validation phases. The cumulative residual error in the water budget ensemble mean accounts for ~15% of the ensemble mean for both the precipitation and evapotranspiration. An increasing trend of 0.03 mm month⁻¹ in the residual errors may be partially attributable to increases in human activity and the relative redistribution of biases among other water budget variables. All three closure techniques show similar directions of constraints (i.e., wet or dry bias) in water budget variables with slightly different magnitudes. Our quantification of water budget residual errors may help benchmark regional hydroclimate models for understanding the past, present, and future status of water budget components and effectively manage regional water resources, especially during hydroclimate extremes.

To efficiently mitigate the multitude of direct and indirect impacts, basin-scale drought characterization is imperative, particularly for India, where water scarcity is continually increasing. We jointly used the GRACE gravity data, PCR-GLOBWB model outputs, and in situ data to quantify the deficits based on land water storage (LWS) and groundwater storage (GWS) in Peninsular India for 35 years from January 1980 to December 2014. The results showed that the study basins experienced high interannual variations despite the minimal linear LWS trends (0.26–0.56 mm yr⁻¹). GWS showed a slow but persistent response (longest deficit spanning ~6 years) to the seasonal variations in the hydrological fluxes and remained the major contributor to LWS. We demonstrated (1) the potential of the PCR-GLOBWB model to analyze LWS and its segregated components beyond GRACE data records, (2) the LWS-based index as a better drought indicator than traditional drought indices, and (3) GRACE-LWS as a proxy indicator of real-time groundwater monitoring without relying upon the intermittent in situ observations. This study underscores the need to revise the water allocation strategies and irrigation systems to maintain the sustainability of groundwater systems and may serve as a holistic framework for remotely monitoring the water beneath our feet, especially in the data-limited regions globally.

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.