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.

中文翻译：

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测量和建模策略的联合优化及其在层状含水层径向流中的应用

在应用环境模型时，模型复杂性的选择和野战活动的设计取决于彼此以及建模/预测目标。我们建议通过最大化建模目标的预期性能来共同优化模型复杂性和数据收集（设计）。我们使用高度解析的虚拟现实和不太复杂的建模变体的合奏，它们的升级程度和简化的参数化程度不同。对于正在考虑的每个设计，我们使用所有模型的所有实现来模拟假设的测量数据（受噪声影响）。为了用假设数据模拟模型校准，我们为每个设计在虚拟现实和每个模型变体之间找出最合适的实现对。然后，我们通过选择（跨模型变体，（对于每个设计和每个虚拟现实），一对在建模目标中显示出最佳预测匹配。最后，我们确定在所有虚拟现实中平均提供最佳预测匹配的模型/设计组合。作为测试应用程序，我们考虑了一种非均质的分层含水层，其中分层提高了宏观上的水力各向异性。我们定义了两个不同的建模目标：（a）估算放大到整个含水层厚度的水力传导率张量，以及（b）预测对施工坑进行脱水所需的抽水速率。我们的结果表明，联合优化观测网络和模型选择可以以较低的实验成本降低参数的预测不确定性。