Conceptual uncertainty is considered one of the major sources of uncertainty in groundwater flow modeling. Hypothesis testing is essential to increase system understanding by analyzing and refuting alternative conceptual models. We present a systematic approach to conceptual model testing aimed at finding an ensemble of conceptual understandings consistent with prior knowledge and observational data. This differs from the traditional approach of tuning the parameters of a single conceptual model to conform with the data through inversion. We apply this approach to a simplified hydrogeological characterization of the Wildman River area (Northern Territory, Australia) and evaluate the connectivity of sinkhole‐type depressions to groundwater. Alternative models are developed representing the process structure (i.e., different fluxes representing interactions between surface water and groundwater) and physical structure (i.e., different lithologies underlying the depressions) of the conceptual model of the depressions. Remote sensing data are used to test the process structure, while geophysical data are used to test the physical structure. Both data types are used to remove inconsistent models from an ensemble of 16 models and to update the probability of the remaining alternative conceptual models. Three out of five depressions that are used as a test case are conditionally confirmed to act as conduits for recharge, while for the last two depressions, the data are inconclusive. Although the framework is not directly prediction oriented, the testing of plausible conceptual models will ultimately lead to increased confidence of any groundwater model based on accepted posterior conceptualizations.

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遥感与地球物理数据相结合的水文地质概念模型测试的系统方法

概念不确定性被认为是地下水流模拟中不确定性的主要来源之一。假设测试对于通过分析和反驳其他概念模型来增强系统理解至关重要。我们提出了一种用于概念模型测试的系统方法，旨在找到与先验知识和观测数据一致的概念理解的集合。这与调整单个概念模型的参数以通过反演与数据一致的传统方法不同。我们将这种方法用于简化的怀尔德曼河地区（澳大利亚北领地）的水文地质特征，并评估了下沉型凹陷与地下水的连通性。已开发出代表流程结构的替代模型（即，不同的通量代表了地表水与地下水之间的相互作用）和凹陷的概念模型的物理结构（即凹陷下方的不同岩性）。遥感数据用于测试过程结构，而地球物理数据用于测试物理结构。两种数据类型均用于从16个模型的集合中删除不一致的模型，并更新剩余替代概念模型的概率。在有条件的情况下，有五分之三的被用作测试用例的凹陷被确认可以作为补给的渠道，而对于最后两个凹陷，数据尚无定论。尽管该框架不是直接面向预测的，