San Francisco Estuary and Watershed Science

Management of the Sacramento River and Sacramento–San Joaquin Delta (SRD) is one of California’s greatest challenges, requiring trade-offs between valued components that serve a multiplicity of conflicting purposes. Trade-offs do not signal a failure to create clever enough models, or scenarios that find a single optimal solution. Rather, an optimal solution that meets multiple objectives does not exist. We demonstrate an improved method for multiple-objective allocation of water: “turn-taking” optimization (TTO) within a multi-model cloud computing framework. We apply TTO to an array of physical hydrologic models that are linked with the Ecological Flows Tool (EFT): a multi-species decision support framework to evaluate how specific components of the flow regime promote and balance favorable habitat conditions for 15 representative species and 31 indicators within the SRD. Applying the TTO approach incorporates the existing modelled representation of socio-economic water management criteria, priorities, and constraints — and optimizes water-release patterns each water year using a dynamically shifting set of EFT indicators. Rather than attempting to optimize conditions for all ecological indicators every year, TTO creates flexibility and opportunities for different indicators to be successful in different years, informed by the frequency with which each species’ ecological needs should be met. As an individual EFT indicator is successful in a particular year, its priority in one or more subsequent years is reduced (and vice versa). Comparing TTO to a Reference Case scenario based on current management practices, 12 EFT indicators are improved, 14 show no change, and 5 show a reduction in suitability. When grouped into nine species and life-history groups, performance improved in four (late-fall-run Chinook, winter-run Chinook, spring-run Chinook, and Fremont cottonwood), did not change in four (fall-run Chinook Salmon, Delta Smelt, Splittail, and Longfin Smelt), and was worse in one group (Steelhead).