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Soil Hydraulic Parameters Characterizing Preferential Water Flow: Estimability Analysis and Identification

Identifieur interne : 000285 ( Main/Exploration ); précédent : 000284; suivant : 000286

Soil Hydraulic Parameters Characterizing Preferential Water Flow: Estimability Analysis and Identification

Auteurs : Viet V. Ngo [France] ; Abderrazak Latifi [France] ; Marie-Odile Simonnot [France]

Source :

RBID : Hal:hal-01274182

Abstract

Preferential flow may significantly affect the acceleration of contaminant transport, which can be described by the use of nonequilibrium models such as dual-porosity and dual-permeability models. Its modeling requires many parameters that are generally difficult to measure. The determination of the parameters in these nonequilibrium models from experimental measurements is a challenging task and is usually carried out using parameter optimization methods. In this study, prior to the identification process, an estimability analysis was carried out in order to determine the set of the most estimable parameters in the dual-porosity model from the available experimental data collected from the field lysimeter (pressure heads, water contents, cumulative percolation, and evaporation). The most estimable parameters were then identified, and the less estimable ones were fixed from previous studies, literature, or additional measures. The results obtained from the estimability analysis showed that five out of the eight parameters were estimable from the combination of daily pressure heads and water contents. The five estimable parameters were then identified using the Levenberg-Marquardt method implemented within one-dimensional finite-element model software. Saturated water contents in the mobile and immobile regions were impossible to estimate simultaneously due to their high correlation. When the water transfer coefficient was included in the identification procedure, the optimization results worsened due to its low estimability from pressure heads and water contents. Removing saturated water content in the immobile region and saturated hydraulic conductivity, which had a strong correlation with more estimable parameters, led to the improvement of the optimization results. As a result, the water transfer parameter was estimated with reliability.

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DOI: 10.1061/(ASCE)HE.1943-5584.0000953


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<div type="abstract" xml:lang="en">Preferential flow may significantly affect the acceleration of contaminant transport, which can be described by the use of nonequilibrium models such as dual-porosity and dual-permeability models. Its modeling requires many parameters that are generally difficult to measure. The determination of the parameters in these nonequilibrium models from experimental measurements is a challenging task and is usually carried out using parameter optimization methods. In this study, prior to the identification process, an estimability analysis was carried out in order to determine the set of the most estimable parameters in the dual-porosity model from the available experimental data collected from the field lysimeter (pressure heads, water contents, cumulative percolation, and evaporation). The most estimable parameters were then identified, and the less estimable ones were fixed from previous studies, literature, or additional measures. The results obtained from the estimability analysis showed that five out of the eight parameters were estimable from the combination of daily pressure heads and water contents. The five estimable parameters were then identified using the Levenberg-Marquardt method implemented within one-dimensional finite-element model software. Saturated water contents in the mobile and immobile regions were impossible to estimate simultaneously due to their high correlation. When the water transfer coefficient was included in the identification procedure, the optimization results worsened due to its low estimability from pressure heads and water contents. Removing saturated water content in the immobile region and saturated hydraulic conductivity, which had a strong correlation with more estimable parameters, led to the improvement of the optimization results. As a result, the water transfer parameter was estimated with reliability.</div>
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