WRF: Collaborative Research: Extended-range forecasts of atmospheric rivers for adaptive management of flood risk, water supply, and environmental flows in California

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This research will develop ways to improve the robustness of human and environmental water supplies in California and similar regions with highly variable climates. Adaptive control policies that explicitly account for uncertainty in forecasts of extreme storm events will be designed. This work will explicitly tailor water system operations for regional storm track patterns and associated forecast errors for those storm types. Expected outcomes include three major scientific advancements: 1) characterization and modeling of spatial and temporal uncertainty in extended-range forecasts of cold-season precipitation, temperature, and floods at different locations and lead times 2) this improved knowledge of forecast error structure will be coupled with computational approaches for water resources control policy design to develop adaptive policies that are robust to forecast uncertainty; 3) determination of how forecast-informed control policies should be designed for long-term climate uncertainty represented by decadal-scale droughts and floods. Through these outcomes, this work will support a shift toward integrating state-of-the-art climate information with decision-making in the water sector of California, and findings from this work will be transferable to other semi-arid regions with similar flood regimes. This work will enable extensive interactions and technology transfer with key stakeholders in water management agencies in California throughout the project to promote the translation of research findings and methods into practice. All software and data analysis performed for this project will be open source and hosted on GitHub, enabling researchers around the world to reproduce and extend the project's findings. These software tools will be designed to support analyses on desktop computers as well as high-performance computing clusters, including NSF XSEDE resources, to support a range of decision-making processes. The team will advance graduate and undergraduate education in the key areas of data science and software design, identified in recent National Academy reports as critical issues for scientific reproducibility and workforce preparedness. These education expansions include of the PIs' statistics courses at Cornell and water resources engineering courses at UC Davis, incorporating simplified data analysis and modeling tasks from this project. Educational developments will be shared broadly through the ASCE Task Committee on Education (ECSTATIC).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.