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Many 3D hydrostratigraphic models of the subsurface are interpreted as deterministic models, where an experienced modeler combines relevant geophysical and geological information with background geological knowledge. Depending on the quality of the information from the input data, the interpretation phase will typically be accompanied by an estimated qualitative interpretation uncertainty. Given the qualitative and subjective nature of uncertainty, it is difficult to propagate the uncertainty to groundwater models. In this study, a stochastic simulation-based methodology to characterize interpretation uncertainty within a manual interpretation-based layer model is applied in a groundwater modeling setting. Three levels of interpretation uncertainty scenarios are generated and three locations in the models representing different geological structures are analyzed. The impact of interpretation uncertainty on predictions of capture zone area and median travel time is compared to the impact of uncertainty on parameters in the groundwater model. The main result is that in areas with thick and large aquifers and low geological uncertainty, the impact of interpretation uncertainty is negligible compared to the hydrogeological parameterization, while it may introduce a significant contribution in areas with thinner and smaller aquifers with high geologic uncertainty. The influence of the interpretation uncertainties is thus dependent on the geological setting as well as the confidence of the interpreter. In areas with thick aquifers, this study confirms existing evidence that if the conceptual model is well-defined, interpretation uncertainties within the conceptual model have limited impact on groundwater model predictions.

For more than a century, the strong advice in geology has been to rely on multiple working hypotheses. However, in groundwater research, as supported by modelling, often a stepwise approach with respect to complexity is promoted and preferred by many. Defining a hypothesis, let alone multiple hypotheses, and testing these via groundwater models is rarely applied. The so-called ‘conceptual model’ is generally considered the starting point of our beloved modelling method. A conceptual model summarises our current knowledge about a groundwater system, describing the hydrogeology and the dominating processes. Conceptual model development should involve formulating hypotheses and leading to choices in the modelling that steer the model predictions. As many conceptual models can explain the available data, multiple hypotheses allow assessing the conceptual or structural uncertainty.This presentation aims to review some of the key ideas of 125 years of research on (not) handling conceptual hydrogeological uncertainty, identify current approaches, unify scattered insights, and develop a systematic methodology of hydrogeological conceptual model development and testing. We advocate for a systematic model development approach based on mutually exclusive, collectively exhaustive range of hypotheses, although this is not fully achievable. We provide examples of this approach and the consequential model testing. It is argued that following this scientific recipe of refuting alternative models; we will increase the learnings of our research, reduce the risk of conceptual surprises and improve the robustness of the groundwater assessments. We conclude that acknowledging and explicitly accounting for conceptual uncertainty goes a long way in producing more reproducible groundwater research. Hypothesis testing is essential to increase system understanding by analyzing and refuting alternative conceptual models. It also provides more confidence in groundwater model predictions leading to improved groundwater management, which is more important than ever.

Reliable groundwater model predictions are dependent on representative models of the geological environment, which can be modelled using several different techniques. In order to inform the choice of the geological modelling technique, the differences between a layer modelling approach and a voxel modelling approach were analyzed. The layer model consists of stratigraphically ordered surfaces, while the voxel model consists of a structured mesh of volumetric pixels. Groundwater models based on the two models were developed to investigate their impact on groundwater model predictions. The study was conducted in the relatively data-dense area Egebjerg, Denmark, where both a layer model and a voxel model have been developed based on the same data and geological conceptualization. The characteristics of the two methodologies for developing the geological models were shown to have a direct impact on the resulting models. The differences between the layer and the voxel models were, however, shown to be diverse and not related to larger conceptual elements, with few exceptions. The analysis showed that the geological modelling approaches had an influence on preferred parameter values and thereby groundwater model predictions of hydraulic head, groundwater budget terms and particle tracking results. A significance test taking into account the predictive distributions showed, that for many predictions, the differences between the models were significant. The results suggest that the geological modelling strategy has an influence on groundwater model predictions even if based on the same geological conceptualization.

Reliable groundwater model predictions are dependent on representative models of the geological environment, which can be modelled using several different techniques. In order to inform the choice of the geological modelling technique, the differences between a layer modelling approach and a voxel modelling approach were analyzed. The layer model consist of stratigraphically ordered surfaces, while the voxel model consist of a structured mesh of volumetric pixels. Groundwater models based on the two models were developed to investigate their impact on groundwater model predictions. The study was conducted in the relatively data-dense area Egebjerg, Denmark, where both a layer model and a voxel model has been developed based on the same data and geological conceptualization. The characteristics of the two methodologies for developing the geological models were shown to have a direct impact on the resulting models. The differences between the layer and the voxel models were however shown to be diverse and not related to larger conceptual elements with few exceptions. The analysis showed that the geological modelling approaches had an influence on preferred parameter values and thereby groundwater model predictions of hydraulic head, groundwater budget terms and particle tracking results. A significance test taking into account the predictive distributions showed that for many predictions the differences between the models were significant. The results suggest that the geological modelling strategy has an influence on groundwater model predictions even if based on the same geological conceptualization.

© 2018 Elsevier BV. This manuscript version is made available under the CC-BY-NC-ND 4.0 license: http://creativecommons.org/licenses/by-nc-nd/4.0/ This author accepted manuscript is made available following 24 month embargo from date of publication (December 2018) in accordance with the publisher’s archiving policy, Hydrogeological conceptual models are collections of hypotheses describing the understanding of groundwater systems and they are considered one of the major sources of uncertainty in groundwater flow and transport modelling. A common method for characterizing the conceptual uncertainty is the multi-model approach, where alternative plausible conceptual models are developed and evaluated. This review aims to give an overview of how multiple alternative models have been developed, tested and used for predictions in the multi-model approach in international literature and to identify the remaining challenges. The review shows that only a few guidelines for developing the multiple conceptual models exist, and these are rarely followed. The challenge of generating a mutually exclusive and collectively exhaustive range of plausible models is yet to be solved. Regarding conceptual model testing, the reviewed studies show that a challenge remains in finding data that is both suitable to discriminate between conceptual models and relevant to the model objective. We argue that there is a need for a systematic approach to conceptual model building where all aspects of conceptualization relevant to the study objective are covered. For each conceptual issue identified, alternative models representing hypotheses that are mutually exclusive should be defined. Using a systematic, hypothesis based approach increases the transparency in the modelling workflow and therefore the confidence in the final model predictions, while also anticipating conceptual surprises. While the focus of this review is on hydrogeological applications, the concepts and challenges concerning model building and testing are applicable to spatio-temporal dynamical environmental systems models in general.