Methods for Using Ground-Water Model Predictions to Guide Hydrogeologic Data Collection, with Applications to the Death Valley Regional Ground-Water Flow System

Tiedeman, Claire R. ; Hill, M. C. ; D' ; Agnese, F. A. ; Faunt, C. C.

United States. Department of Energy. Yucca Mountain Project Office.

关键词: Data Acquisition;    Hydrology;    Sensitivity Analysis;    Ground Water;    Valleys;   

DOI  :  10.2172/805570
RP-ID  :  NONE
RP-ID  :  NONE
RP-ID  :  805570

美国|英语

来源: UNT Digital Library

【 摘 要 】

Calibrated models of ground-water systems can provide substantial information for guiding data collection. This work considers using such models to guide hydrogeologic data collection for improving model predictions, by identifying model parameters that are most important to the predictions. Identification of these important parameters can help guide collection of field data about parameter values and associated flow-system features that can lead to improved predictions. Methods for identifying parameters important to predictions include prediction scaled sensitivities (PSS), which account for uncertainty on individual parameters as well as prediction sensitivity to parameters, and a new ''value of improved information'' (VOII) method, which includes the effects of parameter correlation in addition to individual parameter uncertainty and prediction sensitivity. The PSS and VOII methods are demonstrated using a model of the Death Valley regional ground-water flow system. The predictions of interest are advective-transport paths originating at sites of past underground nuclear testing. Results show that for two paths evaluated, the most important parameters include a subset of five or six of the 23 defined model parameters. Some of the parameters identified as most important are associated with flow-system attributes that do not lie in the immediate vicinity of the paths. Results also indicate that the PSS and VOII methods can identify different important parameters. Because the methods emphasize somewhat different criteria for parameter importance, it is suggested that parameters identified by both methods be carefully considered in subsequent data collection efforts aimed at improving model predictions.