Multiobjective design of aquifer monitoring networks for optimal spatial prediction and geostatistical parameter estimation

Abstract

[1] Effective sampling of hydrogeological systems is essential in guiding groundwater management practices. Optimal sampling of groundwater systems has previously been formulated based on the assumption that heterogeneous subsurface properties can be modeled using a geostatistical approach. Therefore, the monitoring schemes have been developed to concurrently minimize the uncertainty in the spatial distribution of systems' states and parameters, such as the hydraulic conductivity K and the hydraulic head H, and the uncertainty in the geostatistical model of system parameters using a single objective function that aggregates all objectives. However, it has been shown that the aggregation of possibly conflicting objective functions is sensitive to the adopted aggregation scheme and may lead to distorted results. In addition, the uncertainties in geostatistical parameters affect the uncertainty in the spatial prediction of K and H according to a complex nonlinear relationship, which has often been ineffectively evaluated using a first-order approximation. In this study, we propose a multiobjective optimization framework to assist the design of monitoring networks of K and H with the goal of optimizing their spatial predictions and estimating the geostatistical parameters of the K field. The framework stems from the combination of a data assimilation (DA) algorithm and a multiobjective evolutionary algorithm (MOEA). The DA algorithm is based on the ensemble Kalman filter, a MonteCarlo-based Bayesian update scheme for nonlinear systems, which is employed to approximate the posterior uncertainty in K, H, and the geostatistical parameters of K obtained by collecting new measurements. Multiple MOEA experiments are used to investigate the trade-off among design objectives and identify the corresponding monitoring schemes. The methodology is applied to design a sampling network for a shallow unconfined groundwater system located in Rocky Ford, Colorado. Results indicate that the effect of uncertainties associated with the geostatistical parameters on the spatial prediction might be significantly alleviated (by up to 80% of the prior uncertainty in K and by 90% of the prior uncertainty in H) by sampling evenly distributed measurements with a spatial measurement density of more than 1 observation per 60 m  60 m grid block. In addition, exploration of the interaction of objective functions indicates that the ability of head measurements to reduce the uncertainty associated with the correlation scale is comparable to the effect of hydraulic conductivity measurements.Citation: Alzraiee, A. H., D. A. Bau, and L. A. Garcia (2013), Multiobjective design of aquifer monitoring networks for optimal spatial prediction and geostatistical parameter estimation, Water Resour.

Design of optimal groundwater monitoring well network using stochastic modeling and reduced‐rank spatial prediction

“…Cost is often one of the largest constraints preventing extensive groundwater quality monitoring. A number of studies have also looked at the cost‐effective design of monitoring networks [ Loaiciga et al ., ; James and Gorelick , ; Sreekanth and Datta , ; Alzraiee et al ., ].…”

Optimal observation network design for conceptual model discrimination and uncertainty reduction

“…Readers are referred to several in‐depth review articles [ Loaiciga et al ., ; Hassan , ; Minsker , ; Kollat et al ., ]. Predominant studies are focused on improving parameter identification [ Hsu and Yeh , ; Cleveland and Yeh , ; Altmann‐Dieses et al ., ; Sciortino et al ., ; Chang et al ., ; Herrera and Pinder , ; Sun and Yeh , ], minimizing prediction uncertainty [ McKinney and Loucks , ; Wagner , ; Chadalavada and Datta , ; Janssen et al ., ; Nowak et al ., ], detecting plumes [ Meyer and Brill , ; Storck et al ., ; Dhar and Datta , ; Kim and Lee , ; Dokou and Pinder , ], and combinations of these in multiobjective formulations [ Knopman and Voss , ; Dhar and Datta , ; Kollat et al ., ; Alzraiee et al ., ]. Few studies have focused on model discrimination to identify a most favored model [ Knopman and Voss , ; Usunoff et al ., ; Yakirevich et al ., ; Kikuchi et al ., ].…”

Optimum experimental design of a monitoring network for parameter identification at riverbank well fields

“…Nevertheless, the interpretation of the monitoring data, which targets the understanding of surface-groundwater interactions under induced filtration (e.g., riverbed clogging processes), remains a challenge due to the non-steady-state flow conditions under RBF. A reliable estimation of hydraulic parameters is highly dependent on the design of the monitoring network (Alzraiee et al, 2013), which includes two important components: the network density and the sampling frequency (Zhou, 1996). Conversely, such interpretation should also serve as a guide for optimising the existing monitoring network for further changes in hydrological and other conditions after a long period of RBF (Hudak and Loaiciga, 1993;Shestakov, 1993).…”