Methods for using groundwater model predictions to guide hydrogeologic data collection, with application to the Death Valley regional groundwater flow system

Abstract

[1] Calibrated models of groundwater 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 and 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 presented here, which includes the effects of parameter correlation in addition to individual parameter uncertainty and prediction sensitivity. In this work, the PSS and VOII methods are demonstrated and evaluated using a model of the Death Valley regional groundwater 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.

Combined Estimation of Hydrogeologic Conceptual Model, Parameter, and Scenario Uncertainty with Application to Uranium Transport at the Hanford Site 300 Area

“…This is generally carried out through the implementation of sensitivity analysis (Saltelli et al 2000a(Saltelli et al , 2004Helton 1993). Sensitivity measures may also be obtained during the calibration procedure (Hill 1998;Tiedeman et al 2003;Hill and Tiedeman 2007). Global sensitivity methods (Borgonovo et al 2003;Saltelli et al 2000bSaltelli et al , 2004McKay 1995;Hill and Tiedeman 2007) partition the total prediction variance according to the contribution of each parameter and also determine the contribution to prediction variance due to interactions between parameters.…”

Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications

“…However, we also argue that SA is a useful perspective for conceptualizing and understanding hydrological models for several reasons. As indicated by Rakovec et al (2014), SA can be used to: (a) detect when increasing model complexity can no longer be supported by observations and whether it is likely to affect model predictions (e.g., Saltelli et al, 1999;van Werkhoven et al, 2008a;Doherty and Welter, 2010;Rosolem et al, 2012;Gupta et al, 2012;Foglia et al, 2013); (b) reduce the time required for model calibration by focusing estimation efforts on parameters that are important for calibration metrics and predictions (e.g., Anderman et al, 1996;Hamm et al, 2006;Zambrano-Bigiarini and Rojas, 2013); (c) determine the priorities for theoretical and site-specific model development (e.g., Hill and Tiedeman, 2007;Saltelli et al, 2008;Kavetski and Clark, 2010); and (d) identify the advantageous placement and timing of new measurements (e.g., Tiedeman et al, 2003Tiedeman et al, , 2004.…”

Multi-Scale Assessment of Prediction Uncertainty in Coupled Reactive Transport Models Conducted at the Florida State University

“…Parametric uncertainty is due 48 to imperfect knowledge of model parameters, initial and boundary conditions, and/or driving 49 forces. Model structure uncertainty may be manifested by different plausible 50 conceptualizations and/or mathematical descriptions of the transport processes (e.g., the 51 traditional advection-dispersion model versus other alternative models, as discussed in 52 Srinivasan et al [2007] and Tang et al [2009]) and the geochemical reaction processes (e.g., 53 different formulations of surface complexation models developed for simulating uranium 54 adsorption in batch experiments [Davis et al, 2004b], column experiments [Kohler et al,55 1996], and tracer experiments [Davis et al, 2004b;Curtis and Davis, 2006]). Quantification 56 of model uncertainty can be pursued using either model selection methods [e.g., Kohler et al,57 1996; Davis et al, 2004b;Matott and Rabideau, 2008a] or model averaging methods [e.g., 58 Neuman Ye et al, , 2008Ye et al, , 2010a.…”