Coping with predictive uncertainties in optimization of sustainable water resources
Year: 2007
Type: article
Abstract: This paper presents a reliability-based water resources management framework that utilizes stochastic optimization techniques to account for uncertainties associated with the prediction of water demand, surface water availability, baseline groundwater levels, a non-anthropogenic reservoir water budget, and hydrological/hydrogeological properties. Except for the hydrogeological properties, these uncertainties are partially caused by uncertainties in prediction of future climate conditions. The framework was developed to manage a water supply system that serves about two million people in the Northern Tampa Bay region in Florida, USA, while protecting wetland ecology and preventing seawater intrusion. The supply sources include about 180 groundwater production wells, three streamflow withdrawals, a regional reservoir, and a desalination plant. The developed method maximizes the reliability of achieving the goals that all protected wetlands in the area are healthy by maintaining high groundwater levels. The framework involves: (1) a distribution system simulation model to represent the water supply operation under an Optimal Regional Operation Plan (OROP), (2) a Monte Carlo simulation model to generate realizations of climatic events, water demand, available surface water quantity, and (3) a unit response matrix (URM) that relates groundwater level response to groundwater extraction. An operator response model simulates how water supply operators adjust the optimized rates of groundwater extraction, surface water withdrawal, and reservoir inflow/outflow according to meeting the water demand in all circumstances. The reliability optimization problem is solved using a differential evolutionary algorithm.
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1910323978