J. D. Quinn, A. Hadjimichael, P. M. Reed, S. Steinschneider
Department of Energy, Office of Science, Earth & Environmental Systems Modeling, Program Acknowledged Support: No, other Non-DOE EESM source of support
Planning under deep uncertainty, when probabilistic characterizations of the future are unknown, is a major challenge in water resources management. Many planning frameworks advocate for “scenario-neutral” analyses in which alternative policies are evaluated over plausible future scenarios with no assessment of their likelihoods. Instead, these frameworks use sensitivity analysis to discover which uncertain factors have the greatest influence on performance. This knowledge can be used to design monitoring programs and adaptive policies that respond to changes in the critical uncertainties. However, scenario-neutral analyses make implicit assumptions about the range and independence of the uncertain factors that may not be consistent with the coupled human-hydrologic processes influencing the system. These assumptions could influence which factors are found to be most important and which policies are most robust, belying their neutrality; assuming uniformity and independence could have decision-relevant implications. This study illustrates these implications using a multistakeholder planning problem within the Colorado River Basin, where hundreds of rights holders vie for the river’s limited water under the law of prior appropriation. Variance-based sensitivity analyses are performed to assess users’ vulnerabilities to changing hydrologic conditions using four experimental designs: (1) scenario-neutral samples of hydrologic factors, centered on recent historical conditions, (2) scenarios informed by climate projections, (3) scenarios informed by paleohydrologic reconstructions, and (4) scenario-neutral samples of hydrologic factors spanning all previous experimental designs. Differences in sensitivities and user robustness rankings across the experiments illustrate the challenges of inferring the most consequential drivers of vulnerabilities to design effective monitoring programs and robust management policies.