A Karst Probability Map for the Western Mountain Aquifer (Israel & West Bank) using a stochastic modeling approach 

Author(s):  
Sandra Banusch ◽  
Márk Somogyvári ◽  
Martin Sauter ◽  
Philippe Renard ◽  
Irina Engelhardt

<p>Investigating the structure of conduit networks in karst aquifers is a common challenge when working in these complex hydrogeological environments. The network geometry plays an important role in karst flow dynamics, but highly karstified areas are often difficult to characterize by field measurements. Here, we present a methodology that generates karst conduit network geometries reasonably quick without solving complex flow or dissolution equations, and that uses only little input information. The stochastic approach also enables the investigation of the uncertainty of generated networks in the form of a karst probability map.</p><p>The “Stochastic Karst Simulator” (SKS) is a stochastic modeling approach developed by Borghi et al. (2012) to generate a 3D karst conduit network by computing a minimum effort path between the given inlet and outlet points. This study uses such a modeling approach to characterize the karst network geometry of the Western Mountain Aquifer (WMA), a highly karstified and exploited carbonate aquifer located in Israel and the West Bank. The SKS simulations are based on a conceptual model of the aquifer’s karst genesis, to identify the position of karst springs and recharge zones over past geological ages.</p><p>Three different phases of karst formation are identified for the WMA. Phase 1: a paleo-discharge zone exists, located close to the present-day coastline of Israel, phase 2: a period of extreme low sea levels during the Messinian salinity crisis, when paleo-canyons were reactivated along this coastline, and phase 3: the modern-day outlets of the aquifer. The iterative approach of the SKS algorithm accounts for these different phases and creates new conduit pathways by building on ones formed in earlier phases. The algorithm also uses the hydrological model of the study site as soft information, providing knowledge about the internal heterogeneities of the karst formations (e.g. statistical properties of fractures). The resulting karst probability map is compared to the location of the most productive pumping wells in the region, assuming a high yield in groundwater abstraction indicating major karst conduits near the pumped well. </p><p>We demonstrate the method by showing a reconstruction of the karst conduit networks at the WMA model area, an otherwise not available spatial information. The simulations show that the changes in karst spring and recharge locations have a great impact on the geometry and connectivity of the conduit network. Overarching trends in the conduit orientation of the resulting probability map are in keeping with the proposed karst genesis model, resulting in the evolution of a hierarchical network. High karstification is indicated around modern-day springs, also in agreement with the location of numerous pumping wells in that region.</p><p>The SKS algorithm is a useful tool to test different hypotheses of karst genesis and to understand the evolution of karst network geometries. The methodology is numerically efficient, and its inputs can be easily adjusted. Soft information on karst development allows for the generation of a sound hydraulic parameter field, which can be implemented in hydrological models to better understand and manage these aquifer systems.</p>

2020 ◽  
Vol 54 (16) ◽  
pp. 10382-10382
Author(s):  
Jennifer D. Drummond ◽  
Robert J. Davies-Colley ◽  
Rebecca Stott ◽  
James P. Sukias ◽  
John W. Nagels ◽  
...  

2017 ◽  
Vol 21 (7) ◽  
pp. 3635-3653 ◽  
Author(s):  
Cybèle Cholet ◽  
Jean-Baptiste Charlier ◽  
Roger Moussa ◽  
Marc Steinmann ◽  
Sophie Denimal

Abstract. The aim of this study is to present a framework that provides new ways to characterize the spatio-temporal variability of lateral exchanges for water flow and solute transport in a karst conduit network during flood events, treating both the diffusive wave equation and the advection–diffusion equation with the same mathematical approach, assuming uniform lateral flow and solute transport. A solution to the inverse problem for the advection–diffusion equations is then applied to data from two successive gauging stations to simulate flows and solute exchange dynamics after recharge. The study site is the karst conduit network of the Fourbanne aquifer in the French Jura Mountains, which includes two reaches characterizing the network from sinkhole to cave stream to the spring. The model is applied, after separation of the base from the flood components, on discharge and total dissolved solids (TDSs) in order to assess lateral flows and solute concentrations and compare them to help identify water origin. The results showed various lateral contributions in space – between the two reaches located in the unsaturated zone (R1), and in the zone that is both unsaturated and saturated (R2) – as well as in time, according to hydrological conditions. Globally, the two reaches show a distinct response to flood routing, with important lateral inflows on R1 and large outflows on R2. By combining these results with solute exchanges and the analysis of flood routing parameters distribution, we showed that lateral inflows on R1 are the addition of diffuse infiltration (observed whatever the hydrological conditions) and localized infiltration in the secondary conduit network (tributaries) in the unsaturated zone, except in extreme dry periods. On R2, despite inflows on the base component, lateral outflows are observed during floods. This pattern was attributed to the concept of reversal flows of conduit–matrix exchanges, inducing a complex water mixing effect in the saturated zone. From our results we build the functional scheme of the karst system. It demonstrates the impact of the saturated zone on matrix–conduit exchanges in this shallow phreatic aquifer and highlights the important role of the unsaturated zone on storage and transfer functions of the system.


2002 ◽  
Vol 153 (3) ◽  
pp. 217-227 ◽  
Author(s):  
O.A. Anisimov ◽  
N.I. Shiklomanov ◽  
F.E. Nelson

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