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In Situ Long-Term Chloride Transport through a Layered, Nonsaturated Subsoil. 1. Data Set, Interpolation Methodology, and Results

M. Javauxa,b,* and M. Vancloostera

a Department of Environmental Sciences and Land use Planning, Université Catholique de Louvain (UCL), Croix du Sud, 2 Bte. 2, B-1348 Louvain-la-Neuve, Belgium
b Currently, Agrosphere Inst., ICG-IV Forschungszentrum Juelich GmbH, D-52425 Juelich, Germany



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Fig. 1. Schematic of the observation well.

 


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Fig. 2. Soil profile in the unsaturated zone. Dark gray refers to clay layers, light gray to the first loam layer, and white to sand layers. (Left) Water content distribution and standard variation along the year 1990; (right) the position of porous cup samplers (PCS).

 


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Fig. 3. Chloride concentration time series measured with porous cups at seven depths within the observation well (the x axis is common for all the graphics).

 


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Fig. 4. Water flow boundary conditions. (Top) Lake water level; (bottom) aquifer water level. Level 0 corresponds to the lake floor.

 


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Fig. 5. Experimental and modeled cross-covariograms.

 


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Fig. 6. The 1000 co-conditioned stochastic simulations (in blue) representing 1000 equiprobable Cl concentrations time series. The red crosses stand for the real Cl concentration time series.

 


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Fig. 7. Contour plot of the logarithm of the objective function (OF) at Depth 4.

 


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Fig. 8. Histogram of optimized longitudinal velocity values for the seven depths, considering that porous cup sampler concentrations are resident. Red triangles represent parameters obtained with the least error among the 1000 simulations; green triangles represent parameters obtained with the simple kriged input time series.

 


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Fig. 9. Histogram of optimized longitudinal dispersivity values for the seven depths, considering that porous cup sampler concentrations are resident. Red triangles represent parameters obtained with the least error among the 1000 simulations; green triangles represent parameters obtained with the simple kriged input time series.

 


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Fig. 10. Correlogram of the optimized L and {lambda}L values.

 


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Fig. 11. Optimal interpolated input time series in green compared with the 1000 input time series generated by stochastic simulation.

 





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