This project studies water systems in a changing climate through the lens of Sustainability Science, which provides a framework where all systems can move endogenously through time with interactions.
This study will develop an analytical system for the prediction of outcomes and feedbacks among the climate, biogeochemical, and social systems controlling water quality in the Great Lakes region. The focus will be on the expected impact of climate-change-related extreme events on nutrient loading to the Great Lakes, and the development of management systems that are robust and support adaptation in this context. We will select specific analytical scenarios, such as increased drought, extremes in springtime precipitation, changes in snowmelt patterns, and rapid shifts in human water use. A 50-year retrospective analysis will identify feedbacks and parameterize models to predict future changes, and a prognostic analysis will project impacts for 100 years. We will leverage ongoing water quality monitoring and modeling efforts, and perform a gap analysis for additional physical, ecological, biogeochemical, and social data needs. The majority of additional data collection will focus on linking the physical and biogeochemical systems with issues of governance through institutions, information, and incentives, as they shape behavior and policy-making. Overall, the project will (1) enhance understanding of the expected impacts of climate-change-induced extreme events on water quality, with the Great Lakes as a case study; and (2) create an analytical framework for integrating the human and biogeochemical controls on water quality that transcends the individual SBE, Bio, Eng, and Geo perspectives.
*This project was funded throught the University of Michigan and completed by NE CASC Fellow Alex Bryan.
Bryan, Alexander M., Steiner A. L., and Posselt D. J.; 2015 Regional modeling of surface-atmosphere interactions and their impact on Great Lakes hydroclimate, Journal of Geophysical Research: Atmospheres, 01/2015, 120(3):1044-1064