HR: 09:45h
AN: H21G-08 INVITED     [Abstracts]
TI: The worth of data for optimal groundwater management under uncertainty
AU: * Feyen, L
EM: luc.feyen@jrc.it
AF: Land Management Unit, Institute for Environment and Sustainability, JRC, European Commission, Land Management Unit, TP261, Institute for Environment and Sustainability, JRC, Ispra, 21020 Italy
AU: Gorelick, S M
EM: gorelick@pangea.stanford.edu
AF: Department of Geological and Environmental Sciences, Stanford University, Department of Geological and Environmental Sciences, Stanford University, Stanford, CA 94305 United States
AB: We address three interrelated concerns that underlie the design of optimal groundwater resources management. First is maximizing profits from groundwater production while limiting the decline of water levels. Second is accounting for uncertainty in the predicted water level response to pumping due to uncertainty in the hydraulic parameters and the boundary conditions. Third is optimizing the data collection strategy so that new hydraulic property measurements or observations of system state variables provide the greatest economic benefit to the water manager. The three interrelated concerns are addressed using an optimal design, data collection, and Bayesian decision-making framework. The design component employs stochastic simulation-optimization to plan regionally distributed groundwater pumping. To compensate for predictive uncertainty, the system is forced to under perform as a form of insurance. Collecting data from the aquifer system can potentially reduce predictive uncertainty and increase safe water production. The primary obstacle to the design of optimal measurement campaigns lies in quantifying the economic worth of hydrogeological information. We employ Bayesian data worth analysis to find the best locations of sequential measurements of the groundwater system that maximize the combined benefits of groundwater production and data collection. We show that techniques for guiding additional measurements that involve only the probabilities of the outcomes without considering their consequences, i.e., that are based on the highest expected reduction of uncertainty, are not necessarily most cost-effective to the water manager.
DE: 1829 Groundwater hydrology
DE: 1869 Stochastic hydrology
DE: 1873 Uncertainty assessment (3275)
DE: 1880 Water management (6334)
SC: Hydrology [H]
MN: Fall Meeting 2005