Estimation of Soil Hydraulic Properties of Basin Loukkos (Morocco) by Inverse Modeling

Mustapha, Hachimi; Maslouhi, Abdellatif; Tamoh, Karim; Qanza, H.

doi:10.1007/s12205-019-0628-7

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…DREAM (ZS) creates the jump vector in DREAM from the past states of the joint chains [19,20]. Instead of generating candidate samples ζ i based only on the samples in the current row of the matrix X, all the samples in the current and past state matrices Z of the chain are used, the equation for obtaining the c candidate sample ζ i in the i chain is Equation (12).…”

Section: Bayesian Inference With Dream (Zs)mentioning

confidence: 99%

“…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%

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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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Section: Bayesian Inference With Dream (Zs)mentioning

confidence: 99%

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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“…[ 23 ] Hachimi et al (2019) used a direct and inverse method on the values obtained from the field measurements of cumulative infiltration and water contents to obtain soil hydraulic parameters. The results of their study showed a high correlation between the simulated and measured values, and they stated that that HYDRUS is highly efficient at predicting soil hydraulic parameters.…”

Section: Introductionmentioning

confidence: 99%

Using Inverse Modeling by HYDRUS-1D to Predict Some Soil Hydraulic Parameters from Soil Water Evaporation

2022

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The objective of this research was to assess the use of unsaturated water flow in terms of soil water evaporation, which was determined by evaluating some soil hydraulic parameters in different soil textures. The results show that the predicted values of these parameters, which were obtained through inverse modeling with the HYDRUS-1D software and depend on the change of the volumetric water content, exhibited a significant agreement with the measured values from laboratory or field simulation data for soil water evaporation at 5. 10. 20. and 45 days of measurement. At the same time, inverse simulation was conducted on soil hydraulic parameters obtained from a 5-day laboratory soil evaporation period to predict field infiltration values and water retention curve, which showed a significant agreement with measured values for all soil textures.

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Unsaturated Soil Hydraulic Properties Identification using Numerical Inversion and In-Situ Experiments from Mnasra Area, Morocco

Qanza

Maslouhi

Abboudi

et al. 2019

KSCE Journal of Civil Engineering

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Estimation of Soil Hydraulic Properties of Basin Loukkos (Morocco) by Inverse Modeling

Cited by 6 publications

References 30 publications

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

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

Using Inverse Modeling by HYDRUS-1D to Predict Some Soil Hydraulic Parameters from Soil Water Evaporation

Unsaturated Soil Hydraulic Properties Identification using Numerical Inversion and In-Situ Experiments from Mnasra Area, Morocco

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