Systematic hydrogeological conceptual model testing using remote sensing and geophysical data

Conceptual uncertainty is considered one of the major sources of uncertainty in groundwater flow modelling. In this regard, hypothesis testing is essential to increase system understanding by analysing and refuting alternative conceptual models. We present a systematic approach to conceptual model development and testing, which involves defining alternative models and then attempting to refute the alternative understandings using independent data. The method aims at finding an ensemble of conceptual understandings that are consistent with prior knowledge and observational data, rather than tuning the parameters of a single conceptual model to conform with the data through inversion.

The alternative understandings we test relate to the hydrological functioning of enclosed depressions in the landscape of the Wildman River Area, Northern Territory, Australia. These depressions provide potential for time-dependent surface water-groundwater interactions. Alternative models are developed representing the process structure and physical structure of the conceptual model of the depressions. Remote sensing data is used to test the process structure, while geophysical data is used to test the physical structure of the conceptual models.

The remote sensing and geophysical data are used twice in the applied workflow. First in a model rejection step, where models whose priors are inconsistent with the observations are rejected and removed from the ensemble. Then the data are used to update the probability of the accepted alternative conceptual models.

The updated conceptual model probabilities of the combined physical and process structures revealed the data indicated that the depressions act as preferential groundwater recharge features for three out of five depressions used as test case. For the fourth depression, the data is indecisive, and more testing would be needed to discriminate between model structures. For the fifth depression, all physical structures were rejected indicating that the model structure is still an unknown unknown.

This insight into system functioning gained from testing alternative conceptual models can be used in future modelling exercises. With more confidence in the conceptual model, confidence in the predictions of future modelling exercise increase, which can that underpin environmental management decisions.