Joint Optimization of Measurement and Modeling Strategies With Application to Radial Flow in Stratified Aquifers. Issue 7 (29th June 2020)

• Record Type: Journal Article
• Authors: Maier, R.; Gonzalez‐Nicolas, A.; Leven, C.; Nowak, W.; Cirpka, O. A.
• Is Part Of: Water resources research. Volume 56:Issue 7(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-29
• Subjects: model selection -- optimal design of observation networks -- stratified aquifer -- pumping tests; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2019WR026872
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: When applying environmental models, the choice of model complexity and the design of field campaigns depend on each other and on the modeling/prediction goal. We propose jointly optimizing model complexity and data collection (design) by maximizing the expected performance for the modeling goal. We use ensembles of highly resolved virtual realities and of less complex modeling variants that differ in their degrees of upscaling and simplified parameterization. For each design under consideration, we simulate hypothetical measurement data (subject to noise) with all realizations of all models. To mimic model calibration with hypothetical data, we identify pairs of best fitting realizations between virtual reality and each model variant for each design. Then, we emulate model choice by selecting (across the model variants, for each design and for each virtual reality) the pair that shows the best predictive match in the modeling goal. Finally, we identify the model/design combination that offers, on average over all virtual realities, the best predictive match. As a test application, we consider a heterogeneous, stratified aquifer, in which the stratification enhances hydraulic anisotropy on the macroscale. We define two different modeling goals: (a) estimating the hydraulic conductivity tensor upscaled to the full aquifer thickness and (b) predicting the pumping rate needed to dewater a construction pit. Our results indicate that jointly optimizing observation networks and model selection can reduce the prediction uncertainty of parameters at lower experimental costs. We also show that the involved trade‐offs between model complexity and required design depend on the target quantity. Key Points: The best choice of model complexity and data collection depends on the modeling goals and on each other We propose a joint optimization concept for model complexity and data collection campaigns mimicking best fit calibration We apply the approach to identify hydraulic conductivity in a stratified aquifer by pumping tests with partially penetrating wells