Mounting evidence indicates that low-carbon, climate mitigation policies and targets promote marginal benefits to water scarcity and the impacts on water quality and equity remain in question. Therefore, insights toward truly effective measures require predictive tools that track the co-evolving, system-wide features of supply, demand, nutrient loading, and conveyance that all 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, protected lands, and minority populations are extensive – particularly across the lower Mississippi River basin as well as the southeast and southwest United States – but with other 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, what are the salient response patterns, and what optimal pathways exist amidst climatic and human-forced uncertainties?

In view of this, 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. 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. We find that plausible changes in land-use, nutrient loadings, and temperature convey the strongest downstream impacts to potable water supplies, which are unequally distributed. Thus, taken holistically, any “solution” to improved water quality, availability, and security will be a case of “minimized consequences”.  We further demonstrate the challenges these solutions present with findings that result from a series of future scenarios generated by our multi-sector dynamical framework.