Coupling SKS and SWMM to Solve the Inverse Problem Based on Artificial Tracer Tests in Karstic Aquifers

Sivelle, Vianney; Renard, Philippe; Labat, David

doi:10.3390/w12041139

Cited by 13 publications

(8 citation statements)

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“…Another possible bias is the definition of the conduit network geometry: it has been defined based on surface fieldwork observations, analysis of the 3D geology and comparison with similar systems, but there are some uncertainties regarding tortuosity and depths. Applying a more extensive study on the possible variations of the network using, for example, a generator based on a fracture network, like the Stochastic Simulator (SKS; Borghi et al 2016Borghi et al , 2012Sivelle et al 2020), might help to refine optimal configurations for the karst network. Finally, the calibration process has not yet been automated in this case and investigating multi-parameter calibration could also yield new possibilities.…”

Section: Discussionmentioning

confidence: 99%

An evaluation of semidistributed-pipe-network and distributed-finite-difference models to simulate karst systems

et al. 2020

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Several different approaches have been developed to model the specific characteristics of karst aquifers, taking account of their inherent complex spatial and temporal heterogeneities. This paper sets out the development of a semidistributed modelling approach for applications in an Irish karst context using urban drainage software. The models have proven to be very useful for different studies, with examples given for the ecohydrology of ephemeral karst lakes, extreme groundwater-flood alleviation, karst network investigation, submarine groundwater discharge, and quantification of different recharge and flow components. The limitations of the approach are also highlighted, in particular not being able to simulate diffuse infiltration and flow paths explicitly across the groundwater catchment. Hence, a more distributed, finite-difference modelling approach using MODFLOW Unstructured Grid (USG) with the newly developed Connected Linear Network (CLN) process is then compared against the semidistributed approach on the same karst catchment. Whilst it has proven difficult to achieve the same levels of model performance in simulating the spring flows in the distributed model compared to the semidistributed model, the ability to interrogate the flow paths at any point on the three-dimensional aquifer is demonstrated, which can give new insights into flows (and potential contaminant transport) through such complex systems. The influence of the proximity of highly transmissive conduits on the flow dynamics through the much-lower transmissive matrix cells in which the network is embedded has been particularly investigated.

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Section: Discussionmentioning

confidence: 99%

An evaluation of semidistributed-pipe-network and distributed-finite-difference models to simulate karst systems

et al. 2020

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“…This approach is sufficiently fast to allow the generation of many equiprobable, hydrogeologically plausible realizations (Borghi et al 2012). This tool has been combined with parameter estimation to identify properties of the conduit network such as hydraulic conductivity of the matrix, number of major conduits, and conduit radius, for a small number of synthetic and real systems (Borghi et al 2016, Sivelle et al 2020.…”

Section: Conduit Network Model: Sksmentioning

confidence: 99%

“…This is likely because solute transport is much more dependent on particle flow paths, whereas similar flow behaviors can result from many different flow paths. However, it is likely that multiple different structures will fit tracer data as well as flow data (Borghi et al 2016;Sivelle et al 2020). Although tracer test data will not identify a single "best" structure, integrating tracer test data will still reduce prediction uncertainty, particularly uncertainty in predictions of solute transport, by enabling the rejection of some structures in the ensemble.…”

Section: Legendmentioning

confidence: 99%

“…These networks can then be used as the basis for distributed flow and transport models (Borghi et al 2012). The ability to quickly generate many networks constrained by the same geologic context makes it possible to use this technique for multi-model approaches to simulating karst system behavior, though it has only been used in a limited number of studies to date (Borghi et al 2016;Sivelle et al 2020).…”

Section: Introductionmentioning

confidence: 99%

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A model ensemble generator to explore structural uncertainty in karst systems with unmapped conduits

Fandel

Ferré

Chen³

et al. 2020

Hydrogeol J

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Karst aquifers are characterized by high-conductivity conduits embedded in a low-conductivity fractured matrix, resulting in extreme heterogeneity and variable groundwater flow behavior. The conduit network controls groundwater flow, but is often unmapped, making it difficult to apply numerical models to predict system behavior. This paper presents a multi-model ensemble method to represent structural and conceptual uncertainty inherent in simulation of systems with limited spatial information, and to guide data collection. The study tests the new method by applying it to a well-mapped, geologically complex long-term study site: the Gottesacker alpine karst system (Austria/Germany). The ensemble generation process, linking existing tools, consists of three steps: creating 3D geologic models using GemPy (a Python package), generating multiple conduit networks constrained by the geology using the Stochastic Karst Simulator (a MATLAB script), and, finally, running multiple flow simulations through each network using the Storm Water Management Model (C-based software) to reject nonbehavioral models based on the fit of the simulated spring discharge to the observed discharge. This approach captures a diversity of plausible system configurations and behaviors using minimal initial data. The ensemble can then be used to explore the importance of hydraulic flow parameters, and to guide additional data collection. For the ensemble generated in this study, the network structure was more determinant of flow behavior than the hydraulic parameters, but multiple different structures yielded similar fits to the observed flow behavior. This suggests that while modeling multiple network structures is important, additional types of data are needed to discriminate between networks.

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“…Previous studies applied the SKS algorithm to small‐scale karst aquifer systems, investigating only one phase of karst development. Therefore, limited input data variation was implemented and the uncertainty in conduit network geometry was only partially investigated (Borghi et al., 2016; Fandel et al., 2021; Sivelle et al., 2020; Vuilleumier et al., 2013).…”

Section: Introductionmentioning

confidence: 99%

Stochastic Modeling Approach to Identify Uncertainties of Karst Conduit Networks in Carbonate Aquifers

Banusch

Somogyvári

Sauter

et al. 2022

Water Resources Research

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Karst aquifers exist in all climate zones and constitute crucial water resources. Analyses by Goldscheider et al. (2020) showed that approximately 15% of the global ice-free land surface consists of carbonate rock, and Stevanović (2019) estimated that around 9% of the world population consumes water from karst resources. Karst aquifers are characterized by highly permeable conduits embedded in a less permeable porous rock matrix. Most of the groundwater flow in karst aquifers is therefore controlled by the conduits, which form complex, hierarchically organized networks (Ford & Williams, 2007). Thus, the conduit network dominates the hydraulic properties of karst aquifers. To adequately represent these flow dynamics, it is vital to account for the spatial distribution of the conduits. However, this task is often associated with challenges and uncertainties due to the large scale and complexity of karst aquifer systems (Bakalowicz, 2005).Numerical groundwater models are essential tools for addressing groundwater-related problems, for example, water resources assessment, and have been widely applied to study karst aquifers (Anderson et al., 2015;Parise et al., 2018;Zanini et al., 2021). Global model approaches, that link recharge estimations to spring discharge time series, provide information on the hydraulic behavior of the entire karst system, but do not provide detailed information on the hydraulic parameter field and therefore lack predictive power for climatic and geo-hydraulic interactions (Eisenlohr et al., 1997;Kovács & Sauter, 2007). Advanced model approaches for conduit networks are based on the speleogenetic evolution of karst aquifers (Kaufmann et al., 2019;Liedl et al., 2003). These models solve complex coupled process equations of chemical carbonate dissolution and groundwater flow with major computational effort (Bauer et al., 2003). Incorporating stochastic approaches into the modeling process provides the opportunity to evaluate uncertainties related to the resulting spatial distribution and geometry of a conduit network. A stochastic model approach developed by Jaquet et al. (2004) focuses on integrating conservative and reactive transport processes that control the conduit evolution such as advection, dispersion, and dissolution, but does not account for actual field observations of the karst system investigated. Further approaches by Frantz et al. (2021) and Pardo-Igúzquiza et al. (2012) simulate the conduit's geometry stochastically, based on field data of the dimensions of known conduit segments. However, these data are normally laborious to obtain,

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Coupling SKS and SWMM to Solve the Inverse Problem Based on Artificial Tracer Tests in Karstic Aquifers

Cited by 13 publications

References 60 publications

An evaluation of semidistributed-pipe-network and distributed-finite-difference models to simulate karst systems

An evaluation of semidistributed-pipe-network and distributed-finite-difference models to simulate karst systems

A model ensemble generator to explore structural uncertainty in karst systems with unmapped conduits

Stochastic Modeling Approach to Identify Uncertainties of Karst Conduit Networks in Carbonate Aquifers

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