Stochastic discrete model of karstic networks

Jaquet, Olivier; Siegel, Paul S.; Klubertanz, George P.; Benabderrhamane, H.

doi:10.1016/j.advwatres.2004.03.007

Cited by 73 publications

(46 citation statements)

References 10 publications

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“…These models consider an average distribution for both the matrix and conduits. More recently, discrete models have taken into account an explicit conduit/matrix system identified through exploration and/or the physical process of conduit development (Mohrlok and Sauter, 1997;Jaquet et al, 2004;Kaufmann and Romanov, 2008). The models developed are thus specific to each aquifer and not immediately applicable to a karst aquifer.…”

Section: Karst Modelingmentioning

confidence: 99%

Complexity selection of a neural network model for karst flood forecasting: The case of the Lez Basin (southern France)

Siou

Johannet

Estupina

et al. 2011

Journal of Hydrology

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A neural network model is applied to simulate the rainfall-runoff relation of a karst spring. The input selection for such a model becomes a major issue when deriving a parsimonious and efficient model. The present study is focused on these input selection methods; it begins by proposing two such methods and combines them in a subsequent step. The methods introduced are assessed for both simulation and forecasting purposes. Since rainfall is very difficult to forecast, especially in the study area, we have chosen a forecasting mode that does not require any rainfall forecast assumptions. This application has been implemented on the Lez karst aquifer, a highly complex basin due to its structure and operating conditions. Our models yield very good results, and the forecasted discharge values at the Lez spring are acceptable up to a 1-day forecasting horizon. The combined input selection method ultimately proves to be promising, by reducing input selection time while taking into account: i) the model's ability to accommodate nonlinearity, and ii) the forecasting horizon.

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Section: Karst Modelingmentioning

confidence: 99%

Complexity selection of a neural network model for karst flood forecasting: The case of the Lez Basin (southern France)

Siou

Johannet

Estupina

et al. 2011

Journal of Hydrology

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“…The mechanical parameters and stress field are strictly connected to the hydraulic properties of karstic and hard rock (Macpherson 1983;Jaquet et al 2004).…”

Section: Fracture Density Analysismentioning

confidence: 99%

Anisotropy Modelling of the Fissure-Karstic Aquifer of the Opole-Zawadzkie Major Groundwater Basin (South-West Poland)

Wcisło

Olichwer

Staśko

2014

Acta Geologica Polonica

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ABSTRACT:Wcisło, M., Olichwer, T. and Staśko, S. 2014. Anisotropy modelling of the fissure-karstic aquifer of the OpoleZawadzkie Major Groundwater Basin (south-west Poland). Acta Geologica Polonica, 64 (3), 353-360. Warszawa.The problem of the anisotropy and heterogeneity of karstic aquifers has been previously described. The Opole-Zawadzkie Major Groundwater Body (south-west Poland) was chosen for this investigation. The parameters for anisotropy were analysed on the basis of well yield and observation of macro-fractures (field scale) compared with micro-fractures. Statistical tools were used to assess the directions and values of anisotropy. The estimated parameters were tested on two different models realized in Visual Modflow code. The anisotropy of hydraulic conductivity was recognized as an essential factor for groundwater flow direction and water table depletion prognosis as well as for water budget modification. The second model (M2), representing anisotropy flow conditions, gave an 11% lower value of safe yield in comparison with the first model (M1-isotropic). Additionally, anisotropy conditions caused water table lowering and limitation of aquifer recharge. The results of these studies indicate the need for more attention to be paid to the anisotropy problem in the area, where fracture-karstic aquifers are the main source of groundwater supply.

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“…However, they require large, complex, and highly non-linear sets of partial differential equations to be solved. Simplifications and approximations of these processes were also proposed to enable the generation of stochastic networks that resemble actual networks [5][6][7]. Even simpler models are based on the statistical resampling of existing data sets and allow stochastic networks to be generated which reproduce the main statistical characteristics of the training networks [8].…”

Section: Introductionmentioning

confidence: 99%

Subnetworks of Percolation Backbones to Model Karst Systems Around Tulum, Mexico

Hendrick

Renard

2016

Front. Phys.

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Karstic caves, which play a key role in groundwater transport, are often organized as complex connected networks resulting from the dissolution of carbonate rocks. In this work, we propose a new model to describe and study the structures of the two largest submersed karst networks in the world. Both of these networks are located in the area of Tulum (Quintana Roo, Mexico). In a previous work [1] we showed that these networks behave as self-similar structures exhibiting well-defined scaling behaviors. In this paper, we suggest that these networks can be modeled using substructures of percolation clusters (θ -subnetworks) having similar structural behavior (in terms of fractal dimension and conductivity exponent) to those observed in Tulum's karst networks. We show in addition that these θ -subnetworks correspond to structures that minimize a global function, where this global function includes energy dissipation by the viscous forces when water flows through the network, and the cost of network formation itself.

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Stochastic discrete model of karstic networks

Cited by 73 publications

References 10 publications

Complexity selection of a neural network model for karst flood forecasting: The case of the Lez Basin (southern France)

Complexity selection of a neural network model for karst flood forecasting: The case of the Lez Basin (southern France)

Anisotropy Modelling of the Fissure-Karstic Aquifer of the Opole-Zawadzkie Major Groundwater Basin (South-West Poland)

Subnetworks of Percolation Backbones to Model Karst Systems Around Tulum, Mexico

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