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Land use change and climate variability have significantly altered the regional water cycle over the last century thereby affecting water security at a local to regional scale. Therefore, it is important to investigate how the climate, land use change, and water demand potentially influence the water security by applying the concept of water footprint. An integrated hydrological modeling framework using SWAT (Soil and Water Assessment Tool) model was developed by considering both anthropogenic (e.g. land use change, water demand) and climatic factors to quantify the spatio-temporal variability of water security indicators such as blue water scarcity, green water scarcity, Falkenmark index, and freshwater provision indicators in Savannah River Basin (SRB). The SRB witnesses a significant change in land use land cover (e.g. forest cover, urban area) as well as water demand (e.g. irrigation, livestock production). Overall our results reveal that, SRB witnessed a significant decrease in blue water due to the climate variability indicating that the precipitation has more control over the blue water resources. Whereas, green water was more sensitive to changes in land use pattern. In addition, the magnitude of various water security indicators are different within each county suggesting that water scarcity are controlled by various factors within a region. An integrated assessment of water footprint, environmental flow, anthropogenic factors, and climatic variables can provide useful information on the rising (how and where) of water related risk to human and ecological health. 

The atmospheric water supply and demand dynamics determine a region’s potential water resources. The hydrologic ratios, such as, aridity index, evaporation ratio and runoff coefficients are useful indicators to quantify the atmospheric water dynamics at watershed to regional scales. In this study, we developed a modeling framework using a machine learning approach to predict hydrologic ratios for watersheds located in contiguous United States (CONUS) by utilizing a set of climate, soil, vegetation, and topographic variables. Overall, the proposed modeling framework is able to simulate the hydrologic ratios at watershed scale with a considerable accuracy. The concept of non-parametric elasticity was applied to study the potential influence of the estimated hydrologic ratios on various drought characteristics (resilience, vulnerability, and exposure) for river basins located in CONUS. Spatial sensitivity of drought indicators to hydrologic ratios suggests that an increase in hydrologic ratios may result in augmentation of magnitude of drought indicators in majority of the river basins. Aridity index seems to have higher influence on drought characteristics in comparison to other hydrologic ratios. It was observed that the machine learning approach based on random forests algorithm can efficiently estimate the spatial distribution of hydrologic ratios provided sufficient data is available. In addition to that, the non-parametric based elasticity approach can identify the potential influence of hydrologic ratios on spatial drought characteristics.