Comparison of Three Multiobjective Optimization Algorithms for Inverse Modeling of Vadose Zone Hydraulic Properties

Wöhling, Thomas; Vrugt, Jasper A.; Barkle, G. F.

doi:10.2136/sssaj2007.0176

Cited by 113 publications

(144 citation statements)

References 53 publications

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“…The uncertainty involved in inverse procedures has given incentive to develop uncertainty estimation procedures such as GLUE and SUFI (Section 4.1.4.) and multi-objective optimization algorithms (Wöhling et al, 2008). Although inverse techniques have become very helpful tools, their further improvements are needed for their objective use.…”

Section: Benefits and Limitations Of Inverse Methodsmentioning

confidence: 99%

A review of model applications for structured soils: a) Water flow and tracer transport

Köhne

Šimůnek

2009

Journal of Contaminant Hydrology

292

224

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Section: Benefits and Limitations Of Inverse Methodsmentioning

confidence: 99%

A review of model applications for structured soils: a) Water flow and tracer transport

Köhne

Šimůnek

2009

Journal of Contaminant Hydrology

292

224

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“…Local optimisers have been shown not to be powerful enough to handle topographic complexities of the objective function such as those emanating from lack of a well-defined global minimum or having several local minima in the parameter space Vrugt and Bouten, 2002). More computationally intensive and robust external global techniques have been developed and can be interlinked with HYDRUS (Vrugt et al, 2003;Wohling et al, 2008;Zhu et al, 2007). When using the Marquardt-Levenberg technique, it is recommended to identify a limited number of parameters and to solve the inverse problem repeatedly using different initial estimates of the optimised parameters, and then select those parameter values that minimised the objective function Simunek et al, 2012).…”

Section: S S W Mavimbela and L D Van Rensburg: Estimating Hydraumentioning

confidence: 99%

Estimating hydraulic conductivity of internal drainage for layered soils in situ

Mavimbela

Rensburg

2013

Hydrol. Earth Syst. Sci.

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Abstract. The soil hydraulic conductivity (K function) of three layered soils cultivated at Paradys Experimental Farm, near Bloemfontein (South Africa), was determined from in situ drainage experiments and analytical models. Pre-ponded monoliths, isolated from weather and lateral drainage, were prepared in triplicate on representative sites of the Tukulu, Sepane and Swartland soil forms. The first two soils are also referred to as Cutanic Luvisols and the third as Cutanic Cambisol. Soil water content (SWC) was measured during a 1200 h drainage experiment. In addition soil physical and textural data as well as saturated hydraulic conductivity (K s ) were derived. Undisturbed soil core samples of 105 mm with a height of 77 mm from soil horizons were used to measure soil water retention curves (SWRCs). Parameterization of SWRC was through the Brooks and Corey model. Kosugi and van Genuchten models were used to determine SWRC parameters and fitted with a RMSE of less 2 %. The SWRC was also used to estimate matric suctions for in situ drainage SWC following observations that laboratory and in situ SWRCs were similar at near saturation. In situ K function for horizons and the equivalent homogeneous profiles were determined. Model predictions based on SWRC overestimated horizons K function by more than three orders of magnitude. The van Genuchten-Mualem model was an exception for certain soil horizons. Overestimates were reduced by one or more orders of magnitude when inverse parameter estimation was applied directly to drainage SWC with HYDRUS-1D code. Best fits (R 2 ≥ 0.90) were from Brooks and Corey, and van Genuchten-Mualem models. The latter also predicted the profiles' effective K function for the three soils, and the in situ based function was fitted with R 2 ≥ 0.98 irrespective of soil type. It was concluded that the inverse parameter estimation with HYDRUS-1D improved models' K function estimates for the studied layered soils.

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“…This allows the processing of a large number of samples, which opens up the possibility to study the spatial variability of the soil hydraulic properties. However, soil hydraulic properties derived from laboratory experiments on small soil cores are typically inadequate to simulate soil water dynamics at larger spatial scales (Ritter et al, 2003;Mertens et al, 2005;Guber et al, 2006;Wöhling et al, 2008;Baroni et al, 2010). There are multiple reasons for this discrepancy, most notably that the sample volume analysed in the laboratory is not a representative elementary volume (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…To alleviate these problems, Ritter et al (2003) fixed some of the soil hydraulic parameters at values derived from laboratory experiments. Wöhling et al (2008) derived effective soil hydraulic properties of a three-layer soil profile using pressure head observations from 3 locations and 3 depths. They compared the efficiency of three multiobjective search algorithms in finding Pareto solutions of soil hydraulic parameters that characterize the trade-off in the fitting of pressure head data at different depths.…”

Section: Introductionmentioning

confidence: 99%

Inverse modelling of in situ soil water dynamics: investigating the effect of different prior distributions of the soil hydraulic parameters

Scharnagl

Vrugt

Vereecken³

et al. 2011

Hydrol. Earth Syst. Sci.

110

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Abstract. In situ observations of soil water state variables under natural boundary conditions are often used to estimate the soil hydraulic properties. However, many contributions to the soil hydrological literature have demonstrated that the information content of such data is insufficient to accurately and precisely estimate all the soil hydraulic parameters. In this case study, we explored to which degree prior information about the soil hydraulic parameters can help improve parameter identifiability in inverse modelling of in situ soil water dynamics under natural boundary conditions. We used percentages of sand, silt, and clay as input variables to the ROSETTA pedotransfer function that predicts the parameters in the van Genuchten-Mualem (VGM) model of the soil hydraulic functions. To derive additional information about the correlation structure of the predicted parameters, which is not readily provided by ROSETTA, we employed a Monte Carlo approach. We formulated three prior distributions that incorporate to different extents the prior information about the VGM parameters derived with ROSETTA. The inverse problem was posed in a formal Bayesian framework and solved using Markov chain Monte Carlo (MCMC) simulation with the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm. Synthetic and real-world soil water content data were used to illustrate the approach. The results of this study demonstrated that prior information about the soil hydraulic Correspondence to: B. Scharnagl (benedikt.scharnagl@tu-bs.de) parameters significantly improved parameter identifiability and that this approach was effective and robust, even in case of biased prior information. To be effective and robust, however, it was essential to use a prior distribution that incorporates information about parameter correlation.

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Comparison of Three Multiobjective Optimization Algorithms for Inverse Modeling of Vadose Zone Hydraulic Properties

Cited by 113 publications

References 53 publications

A review of model applications for structured soils: a) Water flow and tracer transport

A review of model applications for structured soils: a) Water flow and tracer transport

Estimating hydraulic conductivity of internal drainage for layered soils in situ

Inverse modelling of in situ soil water dynamics: investigating the effect of different prior distributions of the soil hydraulic parameters

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