Dynamic Adaptation to Climate Change: Framing the Challenge of Uncertainty Characterization for Human-Environmental Systems

Climate change introduces substantial uncertainty to water resources planning and raises the key question: when, or under what conditions, should adaptation occur? A number of recent studies aim to identify policies mapping future observations to actions—in other words, framing climate adaptation as an optimal control problem. Here we review a set of research gaps and opportunities in this area centered on the challenge of characterizing uncertainty, which prevents the direct identification of management interventions. These include exogenous uncertainty in forcing, model structure, and parameters propagated through a chain of climate and hydrologic models; endogenous uncertainty in human‐environmental system dynamics across multiple scales; and sampling uncertainty due to the finite length of historical observations and future projections. Recognizing these challenges, we propose several opportunities to improve the design of policies for dynamic climate adaptation, namely, how problem context and understanding of climate processes might assist with uncertainty quantification and experimental design, out‐of‐sample validation and robustness of optimized adaptation policies, and analysis of structural uncertainty in human-environmental feedbacks. While long-term water planning faces numerous and interacting sources of uncertainty, dynamic planning enables learning from new observations over time to better characterize the risk of hydrologic extremes and inform infrastructure and conservation measures.