Recently, a National Academy of Science (NAS) report on Advancing Climate Change Research called for integrative and interdisciplinary research that supports decision-making at local and regional scales. Local and regional land use decisions are likely to feedback on regional climate, either by mitigating or exacerbating regional climate change within the broader context of global climate change. According to NAS, the future research should lead to adaptation strategies for risk management, while being flexible and responsive to new knowledge. Further, the future research should acknowledge the considerable inertia, long lags in climate change processes, and potential feedbacks of local processes such as land use on regional climate.  In response to this identified gap by NAS, we postulate in this paper that a Complex Systems approach that recognizes nonlinearities and feedback loops between components could potentially provide a powerful framework for understanding the dynamics and lags and inertia in the coupled social-ecological systems. In this study, we present a complex systems’ informed System Dynamics Model (SDM) that is designed as an integrated assessment model to simulate the impacts of policy and governance design interventions in the linked social-ecological and climatic system of the Lake Champlain Basin (LCB). A dilemma that many fresh water systems across the globe face, more frequent and more intense flooding events are expected in LCB under fossil-fuel intensive climate change scenarios. Coupled with climatic change, increasing agricultural landscape and rapid urbanization in LCB, the Lake Champlain under such worse case scenarios can abruptly switch to a eutrophic state unless proactive adaptive management strategies are implemented in the multi-jurisdictional (Vermont, New York and Quebec) basin covering approximately 21,326 square kilometers. The adaptive management framework is understood as based on an experimental attitude toward environmental valuation and decision-making (Norton and Toman 1997). Adaptive management theorists propose that policy-makers often have to act under uncertainty, so policy and governance systems should be designed as probes of the system, capable of reducing uncertainty for the future through social and policy learning (Gundersen 1995; Gunderson and Holling 2002; Gunderson, Holling et al. 1995; Holling 1978; Holling 1992; Lee 1993; Norton and Steinemann 2001; Walters 1986). In this context, we demonstrate the potential of SDMs in simulating the impacts of different adaptive management interventions on the coupled social-ecological system in LCB. The system dynamic approach to modeling the coupled natural and human systems allows us to identify tipping points and thresholds that can lead to irreversible states and which might not be apparent through studies of any single system component in isolation. We also discuss the broader implications of this integrated assessment modeling for improving scientific understanding of the human-environment systems and designing effective responses to climate change.