Recent debate over the scope of the U.S. Clean Water Act underscores the need to develop a robust body of scientific work that defines the connectivity between freshwater systems and people. Coupled natural and human systems (CNHS) modeling is one tool that can be used to study the complex, reciprocal linkages between human actions and ecosystem processes. Well-developed CNHS models exist at a conceptual level, but the mapping of these system representations in practice is limited. This study’s objective is to develop a general methodology to functionally capture feedbacks from human actions to the ecosystem and from the ecosystem back to human actions in lake catchment CNHS. To do so, we develop a coupling approach for models from diverse disciplines, including economics, agronomy, hydrology, limnology, and social psychology. We address extant challenges in CNHS modeling, which arise from differences in disciplinary approach, model structure, and spatiotemporal resolution.
Results/Conclusions
Our study yields a paired conceptual-empirical framework that captures defining human-natural feedbacks in freshwater lake catchments. Our approach couples key CNHS components, including land-use decisions, terrestrial nutrient cycling, hydrologic-solute transport, aquatic nutrient cycling, residential property values, and civic engagement. We develop an empirical workflow in which we instantiate these components using flexible, discipline-specific models that accommodate heterogeneous catchments within the same modeling framework. This ensures that differences in CNHS dynamics are driven by the systems themselves, rather than the modeling approach. We identify the critical variables that link models and present a workflow for passing data between models that differ in approach (e.g., quantitative vs. qualitative and process-based vs. statistical) and spatiotemporal resolution. In doing so, we create an integrated, multi-disciplinary tool that supports and advances cross-disciplinary dialogue that moves CNHS lake catchment modeling in a more systematic direction and, ultimately, provides a foundation for smart decision-making and policy.