Inverse Estimation of the Hydrodispersive Properties of Unsaturated Soil Using Complex-Variable-Differentiation Method under Field Experiments Conditions

Qanza, H.; Maslouhi, Abdellatif; Hachimi, M.L. El; Hmimou, Abderrahim

doi:10.1134/s1064229318100101

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…Recently, the first author and co-authors have presented a modified LM algorithm for estimating hydro-dispersive parameters in Ref. [9], in which, the sensitivity coefficients are accurately determined by using the complex-variable-differentiation method (CVDM) [6][7][8][9]. In this approach the CVDM gives a very simple expression for estimating the first derivative.…”

Section: Inverse Modeling By Complex Variabledifferentiation Methodsmentioning

confidence: 99%

Simultaneous estimation of hydro-dipersive parameters using a new modified levenberg-marquardt algorithm

et al. 2019

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Determination of soil hydrodynamic parameters at field scale is of great importance for modeling soil water dynamics and for agricultural water management. The direct estimation of those parameters is time-consuming and afflicted with serious uncertainties. Inverse modeling is known to get efficient technique for solving non-linear problems in hydrology. Levenberg-Marquardt (LM) algorithm is a gradient-based method, which has been widely used for solving inverse soil water flow problems. In LM algorithm, sensitivity coefficients are mainly evaluated by numerical differentiation methods. However, sensitivity coefficients are difficult to be precisely calculated by numerical differentiation methods, if transient states and non-linearities are involved. In this paper, a new approach is proposed for sensitivity analysis using complex variabledifferentiation method (CVDM) to estimate simultaneously the hydraulic and dispersive properties of unsaturated soil from in-situ experiments. In this approach, the sensitivity coefficients can be determined in a more accurate way than the traditional finite difference method. The results show that the new inverse analysis method in the present work has the high accuracy, validity, uniqueness and higher inversion efficiency, compared with the previous least-squares method. The simulated and measured water contents and tracer concentration were generally close. Overall, it was concluded that the CVDM is promising method to estimate hydro-dispersive parameters in unsaturated zone.

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Section: Inverse Modeling By Complex Variabledifferentiation Methodsmentioning

confidence: 99%

Simultaneous estimation of hydro-dipersive parameters using a new modified levenberg-marquardt algorithm

et al. 2019

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“…Extracted from a depth of 0 to 20 cm, the soil boasted 1.41% organic matter and comprised 82.75% sand content. Preliminary investigations, preceding our research, were dedicated to scrutinizing the fundamental attributes of this sand [21,22]. The foundational physical properties of the sand were characterized by α G = 0.018 and Ks = 0.01 cm/s.…”

Section: Description Of the Study Domainmentioning

confidence: 99%

Exploring Capillary Fringe Flow: Quasilinear Modeling with Kirchhoff Transforms and Gardner Model

Karra,

Maslouhi

2024

FDMP

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Recent studies have underscored the significance of the capillary fringe in hydrological and biochemical processes. Moreover, its role in shallow waters is expected to be considerable. Traditionally, the study of groundwater flow has centered on unsaturated-saturated zones, often overlooking the impact of the capillary fringe. In this study, we introduce a steady-state two-dimensional model that integrates the capillary fringe into a 2-D numerical solution.Our novel approach employs the potential form of the Richards equation, facilitating the determination of boundaries, pressures, and velocities across different ground surface zones. We utilized a two-dimensional Freefem++ finite element model to compute the stationary solution. The validation of the model was conducted using experimental data. We employed the OFAT (One_Factor-At-Time) method to identify the most sensitive soil parameters and understand how changes in these parameters may affect the behavior and water dynamics of the capillary fringe. The results emphasize the role of hydraulic conductivity as a key parameter influencing capillary fringe shape and dynamics. Velocity values within the capillary fringe suggest the prevalence of horizontal flow. By variation of the water table level and the incoming flow q 0 , we have shown the correlation between water table elevation and the upper limit of the capillary fringe. KEYWORDSCapillary fringe; Freefem++; gardner model; modeling; porous media Nomenclature a G The Gardner model parameter depends on the size and distribution of pores ½L À1 b G Relative conductivity exponent ½À h s Volumetric water content scale parameter, which is called the volumetric water content at natural saturation ½À h r Residual water content ½À h e Air-entry pressure is defined as the suction pressure at which air begins to move water from the pores (appearance of first bubbles) ½L K h ð Þ Hydraulic conductivity ½LT À1 K s Saturated hydraulic conductivity ½LT À1

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“…Uncertainty in ground permeability can result in considerable alterations to the response of the porous medium [3,4]. And the simulation process is often timeconsuming and labor-intensive to obtain directly in field or laboratory experiments subject to scale constraints [5,6]. In recent years, numerical inversion methods based on field observations have been increasingly used to estimate the hydraulic conductivity of soils [7][8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Estimation of Soil Saturated Hydraulic Conductivity via Upscaling and Karhunen–Loève Expansion within DREAM(ZS)

Xia,

2024

Applied Sciences

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Quantification of the soil hydraulic conductivity is key to the study of water flow and solute transport in unsaturated soils. Rapid advances in measurement technology have provided a large number of observations at different scales, offering unprecedented opportunities and challenges for the estimation of hydraulic parameters. This paper proposes an inverse estimation method for downscaling of observations on coarse scales to estimate hydraulic parameters on high-resolution scales. Due to the significant spatial heterogeneity, the inversion faces the problems of dynamics-based integration of data at different scales, model uncertainty due to hundreds and thousands of parameters, and computational consumption due to the large number of forward simulations. To overcome these problems, this paper uses an efficient Bayesian optimization DREAM(ZS) as an inverse framework, and incorporates an analytical upscaling method and Karhunen–Loève (KL) expansion to infer finer-scale saturated hydraulic conductivity distribution conditioned on coarse-scale measurements. The efficient upscaling method is used to link measurements and hydraulic parameters at different scales, and Karhunen–Loève (KL) expansion is incorporated to greatly reduce the dimension of the parameter to be estimated. To further improve the efficiency of the inversion, a locally one-dimensional (LOD) algorithm is used to solve the multidimensional water flow model at coarse scales. The proposed inverse model is applied in a series of numerical experiments to demonstrate its applicability and effectiveness under different flow boundary conditions, different levels of ratio between coarse- and fine-scale grids, different densities of observation points, and different degrees of statistic heterogeneity of soil mediums.

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Inverse Estimation of the Hydrodispersive Properties of Unsaturated Soil Using Complex-Variable-Differentiation Method under Field Experiments Conditions

Cited by 6 publications

References 24 publications

Simultaneous estimation of hydro-dipersive parameters using a new modified levenberg-marquardt algorithm

Simultaneous estimation of hydro-dipersive parameters using a new modified levenberg-marquardt algorithm

Exploring Capillary Fringe Flow: Quasilinear Modeling with Kirchhoff Transforms and Gardner Model

High-Resolution Estimation of Soil Saturated Hydraulic Conductivity via Upscaling and Karhunen–Loève Expansion within DREAM(ZS)

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