CAREER: CAS-Climate: Forecast-informed Flexible Reservoir System Modeling Enabled by Artificial Intelligence Algorithms Using Subseasonal-to-Seasonal Hydroclimatological Forecasts

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Abrupt weather extremes, changing climate, and frequent natural hazards, such as floods and droughts, have created new challenges for the effective, sustainable, and flexible operation of our nation’s reservoir systems. To avoid reservoir failures due to insufficient operational flexibility and unpredictable water fluxes during extreme events, dam operators need two essential items: (1) accurate and reliable hydrological forecasts at extended lead times (ranging from days to months in the near future); and (2) powerful and adaptive decision support tools, which not only could assist real-time decision making about how much water to release at a certain time, but also allow reservoir operators to nimbly incorporate engineering constraints and hydroclimatological forecast scenarios into flexible release planning. Over the past four decades, significant scientific advancements have been made in deterministic forecasts, linear programming, optimization algorithms, and rule-based simulation models to guide reservoir operations. However, these approaches are unable to address future operational challenges due to current limitations in understanding the variabilities of Subseasonal-to-Seasonal (S2S) hydroclimatological forecasts and a lack of modeling capabilities that utilize ensemble forecasts for more effective water release decision-making. Therefore, the goals of this CAREER project are twofold: 1) to develop an integrated solution that can account for the spatial and temporal variability of precipitation and its uncertainty; and 2) to develop a novel Artificial Intelligence & Data Mining (AI&DM) decision support tool that allows reservoir operators to use improved ensemble forecasts to develop adaptive release strategies. This research targets enabling better response to, and mitigation of the impacts of, extreme weather events and climate uncertainty in reservoir operation and planning.The project will (1) leverage the advantages of state-of-the-art deep learning models to discover and correct the spatial and temporal errors associated with S2S precipitation forecasts from multiple forecasting models in the North American Multi-Model Ensemble dataset; and (2) develop an adaptive Ensemble Boosting Tree-based Predictive Control Model, which can effectively incorporate improved ensemble forecasts into scenario-based reservoir release simulations for planning purposes. Hydrological modeling and uncertainty analysis will be performed to help understand how meteorological uncertainty propagates from atmospheric conditions into water resources planning and infrastructure management. Large-scale hydrological validation experiments (over 671 watersheds) and reservoir simulations (over 316 dams) across the U.S. will be conducted. The results will be used to validate the improved forecasts, quantify the ensemble hydrological forecast uncertainty, and evaluate the forecast-informed reservoir decision support tool. The AI&DM models will be comprehensively tested in collaboration with the U.S. Bureau of Reclamation (USBR) and the U.S. Army Corps of Engineers (USACE), which are two major reservoir agencies in the USA. The expected outcomes are aimed to allow reservoir operators to develop suitable reservoir storage and release strategies that address sudden fluxes of incoming water or a lack of water supply, while simultaneously meeting various demands and constraints. Educational tasks are tightly coupled with research. Active learning activities will help graduate students develop the ability to tackle complex research problems. Undergraduate students will obtain skills in programming. Outreach includes hosting an annual “Water Festival” exhibit at the National Weather Museum and Science Center (NWMSC) in Norman, Oklahoma. During and beyond this CAREER project, museum visitors and children will witness the importance of hydrology, meteorology, water resources management, and the impacts of extreme weather and climate. The NSF-funded CUAHSI organization will also collaborate with the project to maximize the broader impacts of developed data, models, and algorithms via various educational and outreach activities.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.