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

摘要

Calibrated models of ground-water systems can provide substantial information for guiding data collection. This work considers using such models to guide hydrogelogic data collection for improving model predictions, by identifying model parameters that are most important to the predictions. Identification of these important parametes 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.