HR: 1340h
AN: H13A-1321    [Abstracts]
TI: Hydrologic Scenario Uncertainty in a Comprehensive Assessment of Hydrogeologic Uncertainty
AU: Nicholson, T J
EM: tjn@nrc.gov
AF: U.S. Nuclear Regulatory Commission, Mail Stop T-9C34, Washington, DC 20555 United States
AU: * Meyer, P D
EM: philip.meyer@pnl.gov
AF: Pacific Northwest National Lab, 620 SW Fifth Ave Ste 810, Portland, OR 97204 United States
AU: Ye, M
EM: ming.ye@dri.edu
AF: Desert Research Institute, 755 E. Flamingo Road, Las Vegas, NV 89119 United States
AU: Neuman, S P
EM: neuman@hwr.arizona.edu
AF: University of Arizona, 1133 E. North Campus Drive, Tucson, AZ 85721 United States
AB: A method to jointly assess hydrogeologic conceptual model and parameter uncertainties has recently been developed based on a Maximum Likelihood implementation of Bayesian Model Averaging (MLBMA). Evidence from groundwater model post-audits suggests that errors in the projected future hydrologic conditions of a site (hydrologic scenarios) are a significant source of model predictive errors. MLBMA can be extended to include hydrologic scenario uncertainty, along with conceptual model and parameter uncertainties, in a systematic and quantitative assessment of predictive uncertainty. Like conceptual model uncertainty, scenario uncertainty is represented by a discrete set of alternative scenarios. The effect of scenario uncertainty on model predictions is quantitatively assessed by conducting an MLBMA analysis under each scenario. We demonstrate that posterior model probability is a function of the scenario only through the possible dependence of prior model probabilities on the scenario. As a result, the model likelihoods (computed from calibration results), are not a function of the scenario and do not need to be recomputed under each scenario. MLBMA results for each scenario are weighted by the scenario probability and combined to render a joint assessment of scenario, conceptual model, and parameter uncertainty. Like model probability, scenario probability represents a subjective evaluation, in this case of the plausibility of the occurrence of the specific scenario. Because the scenarios describe future conditions, the scenario probabilities represent prior estimates and cannot be updated using the (past) system state data as is used to compute posterior model probabilities. Assessment of hydrologic scenario uncertainty is illustrated using a site-specific application considering future changes in land use, dam operations, and climate. Estimation of scenario probabilities and consideration of scenario characteristics (e.g., timing, magnitude) are discussed.
DE: 1816 Estimation and forecasting
DE: 1829 Groundwater hydrology
DE: 1832 Groundwater transport
DE: 1847 Modeling
SC: Hydrology [H]
MN: Fall Meeting 2005