Freshwater is central to agriculture, industry, residential development and other aspects of the US economy, provides essential ecosystem goods and services for society, and maintains the health of the biota that inhabit aquatic ecosystems. Existing freshwater resources are being challenged by increasingly unsustainable land use and water use practices. The distribution, abundance and quality of freshwater supplies will undoubtedly also be affected by projected climate change and variability. Unless landscapes are managed proactively in the future, sustaining even the present level of ecosystem goods and services that aquatic systems provide will be impossible. Among the most pressing environmental challenges related to freshwater are how to formulate and implement sustainable, science-based, strategies to adapt to climate variation, land use change, and other consequences of human development. To address this challenge, we will develop an integrative mechanistic model accounting explicitly for human-landscape interactions. Our coupled human-landscape model incorporates linkages and feedbacks among atmospheric, terrestrial, aquatic, and social processes to predict the potential impact of climate variability, climate change, land use, and human activity on water resources at decadal to centennial scales in the Central Great Plains region of North America. This part of the United States has longstanding water quality and quantity concerns resulting from extreme climate variability, intensive water uses and land uses. Furthermore, the regional socio-economic system is challenged by dramatic population shifts, concentration of land tenure, dependency on highly variable and limited water supplies, economic uncertainty, and strong cultural resistance to top-down government regulation. Both human and natural systems in this area depend on adequate freshwater for survival, but are fragile, quickly and dramatically affected by climate fluctuations, and potentially facing disaster given either natural or anthropogenically-driven climate scenarios. In the first three components of the project, we develop and interactively couple mechanistic models of the three systems controlling water supply and water quality ? the hydrosystem, the aquatic ecosystem, and the human system. In the fourth research component, these three system models are integrated in two ways. First, we use an agent-based decision model to evaluate wholesystem (hydrosystem, aquatic ecosystem, human system) response to climate variation scenarios derived from historical climate data and downscaled climate projections. Second, we use policy optimization modeling to identify the most effective strategies to achieve sustainability within a culturally appropriate context.
This research will produce benefits to society by providing tools and frameworks that will guide policy formulation and implementation of incentives and regulations to ensure the sustainability of coupled hydrological, ecological, and human systems in the Central Great Plains and beyond. Our research has the potential to transform the science supporting water sustainability efforts on several fronts. Specifically, our research will: (a) improve our ability to translate climate model outputs into more understandable and relevant weather-scale events ; (b) produce a better understanding of how and why landowners make water-use decisions; (c) develop a spatially-explicit, landscape-scale synthesis of existing ecological data to understand how climate changes and watershed alterations impact riverscape-scale biodiversity, (d) facilitate understanding of how climate change will act in conjunction with and exacerbate a range of other stressors to impact the functions of wetlands, and (e) produce a usable policy optimization model to help governments move towards societally acceptable and achievable water sustainability. These advances will directly contribute to the sustainability of economically important agricultural systems, biologically significant ecosystems, urban population clusters, and clean water supplies.