Threats to future water “security” are increasingly assessed through not just the lens of water and water quality, but how these may unequally expand across sociodemographic and ethnic landscapes. Evidence to date indicates that low-carbon, climate mitigation policies and targets provide marginal benefits to water scarcity trends. Therefore, effective measures require integrated solutions to co-evolving system-wide features of supply, demand, nutrient loading, and conveyance, and avoid inequities and unjust transitions. Based on our current assessment with the MIT System for the Triage of Risks from Environmental and Socioeconomic Stressors (STRESS) platform, we find co-existing areas of water stress, water quality, poverty, and minority populations are extensive – particularly in the south and southeast United States – but with important granular hotspots in populated areas. Therefore, an underlying question and scientific challenge is to understand and quantify the extent that natural and human-forced drivers affect (or benefit) these landscapes – and what are the salient response patterns amidst climatic and human-forced uncertainties?

In view of these considerations, we have conducted a suite of simulations with a linked model system that resolves the contiguous United States at over 2,100 basins and includes a water management module as well as a parsimonious water-quality model. The experimental simulations combine altered landscapes of water supply, demand, nutrient loadings, and conveyance landscapes sequentially and successively. These altered landscapes reflect plausible changes in human-forced climate patterns, land use and management (cultivated for food, agriculture, and bioenergy), water demands (domestic, industrial, energy, and agriculture), as well as water-system efficiencies. Overall, we find that uniform and large-scale patterns of these drivers produce heterogenous and complex responses across U.S. basins, but with important exceptions. These heterogenous response features, however, can be ascribed to precursory conditions of the basins’ environments, and thus indicate potentially predictable consequences. We demonstrate these predictable features through a series of future scenarios generated by our multi-sector dynamical prediction framework.