Hydrogeological conceptual model building and testing: A review

Enemark, Trine; Peeters, Luk; Mallants, Dirk; Batelaan, Okke

doi:10.1016/j.jhydrol.2018.12.007

Cited by 140 publications

(95 citation statements)

References 115 publications

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“…The framework presented by Brassington and Younger [17] was followed in order to further the aims of that study in standardising hydrogeological conceptual model development. It has been continually stated that hydrogeological conceptual model development should be systematic and comprehensive [18,48,49], though there have been few attempts to standardise an approach. Guidance exists within grey literature and textbooks essentially promoting the same steps of analysis of available data, field investigations and monitoring in order to evaluate aquifer properties, geometry and relationships, recharge and other fluxes, and water quality.…”

Section: Methodological Frameworkmentioning

confidence: 99%

“…The Ethiopian Agricultural Transformation Agency (ATA) have recognised the importance of shallow aquifers [16] and launched shallow groundwater mapping in 2013 across selected areas in Ethiopia. The mapping is based on acquiring extensive field data of shallow aquifers, remote sensing, and modelling to promote smallholder irrigation.Development of a hydrogeological conceptual model is key to better understanding of a hydrogeological system and is the key first step in any numerical modelling study [17,18]. A conceptual model is a set of simplifying assumptions describing the groundwater system produced by interpretation of all available information.…”

mentioning

confidence: 99%

“…Development of a hydrogeological conceptual model is key to better understanding of a hydrogeological system and is the key first step in any numerical modelling study [17,18]. A conceptual model is a set of simplifying assumptions describing the groundwater system produced by interpretation of all available information.…”

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confidence: 99%

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Development of a Hydrogeological Conceptual Model for Shallow Aquifers in the Data Scarce Upper Blue Nile Basin

et al. 2019

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Rural communities in sub-Saharan Africa commonly rely on shallow hand-dug wells and springs; consequently, shallow aquifers are an extremely important water source. Increased utilisation of shallow groundwater could help towards achieving multiple sustainable development goals (SDGs) by positively impacting poverty, hunger, and health. However, these shallow aquifers are little studied and poorly understood, partly due to a paucity of existing hydrogeological information in many regions of sub-Saharan Africa. This study develops a hydrogeological conceptual model for Dangila woreda (district) in Northwest Ethiopia, based on extensive field investigations and implementation of a citizen science programme. Geological and water point surveys revealed a thin (3-18 m) weathered volcanic regolith aquifer overlying very low permeability basalt. Hydrochemistry suggested that deep groundwater within fractured and scoriaceous zones of the basalt is not (or is poorly) connected to shallow groundwater. Isotope analysis and well monitoring indicated shallow groundwater flow paths that are not necessarily coincident with surface water flow paths. Characteristics of the prevalent seasonal floodplains are akin to "dambos" that are well-described in literature for Southern Africa. Pumping tests, recharge assessments, and hydrometeorological analysis indicated the regolith aquifer shows potential for increased utilisation. This research is transferrable to the shallow volcanic regolith aquifers that overlie a substantial proportion of Ethiopia and are prevalent throughout the East African Rift and in several areas elsewhere on the continent. Hydrology 2019, 6, 43 2 of 24Prior to promotion of groundwater irrigation in sub-Saharan Africa, we need to improve our knowledge of the aquifer systems; it is commonly expressed in the literature that the hydrogeology of the region is under-studied and poorly understood [11][12][13]. This view is shared by governments who could provide significant benefit to their populace in the form of interventions with such increased knowledge. For example, the Ethiopia's Ministry of Water Resources stated: "Ethiopia's hydrogeology is complex and at present only partly understood" [14]. The importance of shallow aquifers as locally important water sources is well reported; often simultaneously reported is the scarcity of observations of such groundwater systems, in particular sustained time-series data [11,12,15]. This scarcity of data contributes to poor understanding of these shallow hydrogeological systems. The Ethiopian Agricultural Transformation Agency (ATA) have recognised the importance of shallow aquifers [16] and launched shallow groundwater mapping in 2013 across selected areas in Ethiopia. The mapping is based on acquiring extensive field data of shallow aquifers, remote sensing, and modelling to promote smallholder irrigation.Development of a hydrogeological conceptual model is key to better understanding of a hydrogeological system and is the key first step in any numerical modelling study [...

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Section: Methodological Frameworkmentioning

confidence: 99%

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confidence: 99%

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Development of a Hydrogeological Conceptual Model for Shallow Aquifers in the Data Scarce Upper Blue Nile Basin

et al. 2019

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“…Include many models that are very similar to one another is equivalent to over-weighting one such model. This is a particularly difficult problem to address and it has been discussed in some detail by previous authors [87,88]. The best that we can do is to try to construct robust ensembles of models and recognize that our probabilities are, themselves, approximate and potentially subjective.…”

Section: Bayes From a Hydrologist's Perspective-the Next Levelmentioning

confidence: 99%

Being Bayesian: Discussions from the Perspectives of Stakeholders and Hydrologists

Ferré

2020

Water

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Bayes' Theorem is gaining acceptance in hydrology, but it is still far from standard practice to cast hydrologic analyses in a Bayesian context-especially in the realm of hydrologic practice. Three short discussions are presented to encourage more complete adoption of a Bayesian approach. The first, aimed at a stakeholder audience, seeks to explain that an informal Bayesian analysis is the default approach that we all take to any decision made under uncertainty. The second, aimed at a general hydrologist audience, seeks to establish multi-model approaches as the natural choice for Bayesian hydrologic analysis. The goal of this discussion is to provide a bridge from the stakeholder's natural approach to a more formal, quantitative Bayesian analysis. The third discussion is targeted to a more advanced hydrologist audience, suggesting that some elements of hydrologic practice do not yet reflect a Bayesian philosophy. In particular, an example is given that puts Bayes Theory to work to identify optimal observation sets before data are collected.

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“…The conceptualization of a groundwater flow problem is considered one of the major sources of uncertainty in groundwater flow modelling [1,2]. Conceptual uncertainty stems from the fact that the available data more often than not will fit more than one conceptual understanding [3].…”

Section: Introductionmentioning

confidence: 99%

Hydrogeological Bayesian Hypothesis Testing through Trans-Dimensional Sampling of a Stochastic Water Balance Model

Enemark

Peeters

Mallants

et al. 2019

Water

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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 refuting alternative conceptual models. Often a stepwise approach, with respect to complexity, is promoted but hypothesis testing of simple groundwater models is rarely applied. We present an approach to model-based Bayesian hypothesis testing in a simple groundwater balance model, which involves optimization of a model in function of both parameter values and conceptual model through trans-dimensional sampling. We apply the methodology to the Wildman River area, Northern Territory, Australia, where we set up 32 different conceptual models. A factorial approach to conceptual model development allows for direct attribution of differences in performance to individual uncertain components of the conceptual model. The method provides a screening tool for prioritizing research efforts while also giving more confidence to the predicted water balance compared to a deterministic water balance solution. We show that the testing of alternative conceptual models can be done efficiently with a simple additive and linear groundwater balance model and is best done relatively early in the groundwater modelling workflow.One branch of hypothesis testing is based on the Bayesian probability theory. In Bayesian hypothesis testing, a prior belief about the suitability of a conceptual model is updated to a posterior belief by evaluating the model performance against data. The performance of alternative models are then compared in order to quantitatively rank and potentially reject hypotheses based on the so-called Bayes factor [9,10]. Fields of application in hydrogeology include groundwater modelling [11,12], hydrogeophysics [13,14] and solute transport modelling [15,16]. In many applications, the data is not sufficient to allow for discrimination between the models, in which case Bayesian model averaging is often applied where model predictions are weighed according to their performance against data [17].In hydrogeology, conceptual models are often tested in mathematical models (e.g., [18,19]). Since a model comprises the description of a system as a whole, all assumptions and the interaction of assumptions are tested at once. Also, data that does not directly relate to the conceptually uncertain feature can be integrated because of the holistic testing of the system.A stepwise approach in regards to the complexity of groundwater flow modelling and hypothesis testing is often promoted [20,21]. In this paper the simplicity of a model is defined in terms of setup and run-time. In a stepwise approach, complexity is gradually built up, and involves testing the models in each step to better understand the relative importance of various assumptions. This is opposed to starting with a complex model where all known processes and structural aspects are incorporated "because they exist, not because they matter" [20].Although there seems to be a consensu...

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Hydrogeological conceptual model building and testing: A review

Cited by 140 publications

References 115 publications

Development of a Hydrogeological Conceptual Model for Shallow Aquifers in the Data Scarce Upper Blue Nile Basin

Development of a Hydrogeological Conceptual Model for Shallow Aquifers in the Data Scarce Upper Blue Nile Basin

Being Bayesian: Discussions from the Perspectives of Stakeholders and Hydrologists

Hydrogeological Bayesian Hypothesis Testing through Trans-Dimensional Sampling of a Stochastic Water Balance Model

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