Projections of nonstationary climate risks can vary considerably from one source to another, posing considerable communication and decision-analytical challenges. One such challenge is how to present trade-offs under deep uncertainty in a salient and interpretable manner. Some common approaches include analyzing a small subset of projections or treating all considered projections as equally likely. These approaches can underestimate risks, hide deep uncertainties, and are mostly silent on which assumptions drive decision-relevant outcomes. Here we introduce and demonstrate a transparent Bayesian framework for synthesizing deep uncertainties to inform climate risk management. The first step of this workflow is to generate an ensemble of simulations representing possible futures and analyze them through standard exploratory modeling techniques. Next, a small set of probability distributions representing subjective beliefs about the likelihood of possible futures is used to weight the scenarios. Finally, these weights are used to compute and characterize trade-offs, conduct robustness checks, and reveal implicit assumptions. We demonstrate the framework through a didactic case study analyzing how high to elevate a house to manage coastal flood risks.

It is widely acknowledged that distributed water systems (DWSs), which integrate distributed water supply and treatment with existing centralized infrastructure, can mitigate challenges to water security from extreme events, climate change, and aged infrastructure. However, there is a knowledge gap in finding beneficial DWS configurations, i.e., where and at what scale to implement distributed water supply. We develop a meso-scale representation model that approximates DWSs with reduced backbone networks, which enable efficient system emulation while preserving key physical realism. Moreover, system emulation allows us to build a multi-objective optimization model for computational policy search that addresses energy utilization and economic impacts. We demonstrate our models on a hypothetical DWS with distributed direct potable reuse (DPR) based on the City of Houston’s water and wastewater infrastructure. The backbone DWS with greater than 92% link and node reductions achieves satisfactory approximation of global flows and water pressures, to enable configuration optimization analysis. Results from the optimization model reveal case-specific as well as general opportunities, constraints, and their interactions for DPR allocation. Implementing DPR can be beneficial in areas with high energy intensities of water distribution, considerable local water demands, and commensurate wastewater reuse capacities. The meso-scale modeling approach and the multi-objective optimization model developed in this study can serve as practical decision-support tools for stakeholders to search for alternative DWS options in urban settings.