A policy tree optimization approach to dynamic adaptation under climate uncertainty

Water resources systems face a wide range of uncertainty in future hydroclimatic and socio-economic conditions, justifying an adaptive planning approach. Recent advances in dynamic adaptation have designed policies in which infrastructure and management actions are triggered by thresholds of indicator variables monitored over time. Typically, one or more of these components are prespecified, constraining the flexibility of policy design and evaluation. The opportunity exists for methods to identify policies combining the most relevant indicators, actions, and thresholds for dynamic adaptation to climate change. Here we present a generalized framework based on multi-objective policy tree optimization, a heuristic policy search method in which adaptation policies are represented as binary trees. We demonstrate this approach using an illustrative water resources planning problem in California where infrastructure expansion, reservoir operations, conservation rules, and conjunctive use are adapted over time to balance flood risk, water supply, and environmental objectives. To capture the uncertainty in nonstationary forcing, indicator variables include long-term hydroclimatic statistics from downscaled GCM projections along with uncertain land use and economic conditions. Policy robustness is determined by validation against a held-out scenario ensemble. A key focus of the results is comparing the indicators and actions selected by robust versus non-robust policies to identify cases where policies adapt to a signal rather than noise. This framework is supported by open source software and is generalizable across water resources systems challenged with adaptive planning under climate uncertainty.