Field-scale apparent hydraulic parameterisation obtained from TDR time series and inverse modelling

Wollschläger, Ute; Pfaff, Tobias; Roth, Kurt

doi:10.5194/hess-13-1953-2009

Cited by 45 publications

(89 citation statements)

References 38 publications

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“…Despite decades of research, direct identification of these properties is time-consuming and near to impossible at larger scales. Therefore, indirect identification methods, such as inversion (Hopmans et al, 2002;Vrugt et al, 2008a), have been successfully applied to evaluate experiments starting from lab-scale (e.g., Parker et al, 1985;Van Dam et al, 1994;Šimůnek et al, 1998;Schneider et al, 2006) up to field-scale studies (e.g., Wollschläger et al, 2009;Huisman et al, 2010). Due to the multiscale heterogeneity of the soil hydraulic material properties (Nielsen et al, 1973;Gelhar, 1986;Cushman, 1990;Vogel and Roth, 2003), effective material properties have to be identified directly at the scale of interest.…”

Section: Introductionmentioning

confidence: 99%

Effect of unrepresented model errors on estimated soil hydraulic material properties

Jaumann¹,

Roth²

2017

Hydrol. Earth Syst. Sci.

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Abstract. Unrepresented model errors influence the estimation of effective soil hydraulic material properties. As the required model complexity for a consistent description of the measurement data is application dependent and unknown a priori, we implemented a structural error analysis based on the inversion of increasingly complex models. We show that the method can indicate unrepresented model errors and quantify their effects on the resulting material properties. To this end, a complicated 2-D subsurface architecture (AS-SESS) was forced with a fluctuating groundwater table while time domain reflectometry (TDR) and hydraulic potential measurement devices monitored the hydraulic state. In this work, we analyze the quantitative effect of unrepresented (i) sensor position uncertainty, (ii) small scale-heterogeneity, and (iii) 2-D flow phenomena on estimated soil hydraulic material properties with a 1-D and a 2-D study. The results of these studies demonstrate three main points: (i) the fewer sensors are available per material, the larger is the effect of unrepresented model errors on the resulting material properties. (ii) The 1-D study yields biased parameters due to unrepresented lateral flow. (iii) Representing and estimating sensor positions as well as small-scale heterogeneity decreased the mean absolute error of the volumetric water content data by more than a factor of 2 to 0.004.

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Section: Introductionmentioning

confidence: 99%

Effect of unrepresented model errors on estimated soil hydraulic material properties

Jaumann¹,

Roth²

2017

Hydrol. Earth Syst. Sci.

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“…It has been demonstrated by a variety of methods, which involved artificial forcing (three of them compared by Simunek et al, 1998a) or the analysis of long time series with natural forcing (e.g. Wollschläger et al, 2009). A main aspect in both strategies is the requirement of a suitable parameterisation, which is capable of representing the soil of interest.…”

Section: Introductionmentioning

confidence: 99%

Identifying a parameterisation of the soil water retention curve from on-ground GPR measurements

Dagenbach

Buchner

Klenk

et al. 2013

Hydrol. Earth Syst. Sci.

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Abstract.We show the potential of on-ground GroundPenetrating Radar (GPR) to identify the parameterisation of the soil water retention curve, i.e. its functional form, with a semi-quantitative analysis based on numerical simulations of the radar signal. An imbibition and drainage experiment has been conducted at the ASSESS-GPR site to establish a fluctuating water table, while an on-ground GPR antenna recorded traces over time at a fixed location. These measurements allow to identify and track the capillary fringe in the soil. The typical dynamics of soil water content with a transient water table can be deduced from the recorded radargrams. The characteristic reflections from the capillary fringes in model soils that are described by commonly used hydraulic parameterisations are investigated by numerical simulations. The parameterisations used are (i) full van Genuchten, (ii) simplified van Genuchten with m = 1 − 1 n and (iii) Brooks-Corey. All three yield characteristically different reflections, which allows the identification of an appropriate parameterisation by comparing to the measured signals. We show that for the sand used here, these signals are not consistent with the commonly used simplified van Genuchten parameterisation with m = 1 − 1 n .

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“…As a result, several studies used field soil water content data from different depths for inverse parameter estimations of single 10 domain models (Le Bourgeois et al, 2016;Over et al, 2015;Schelle et al, 2013;Scharnagl et al, 2011;Wollschläger et al, 2009;Ritter et al, 2003). Remote sensing data were also widely used to retrieve soil hydraulic parameters as has been reviewed by Mohanty (2013).…”

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confidence: 99%

“…Over et al, 2015;Scharnagl et al, 2011;Wollschläger et al, 2009), and could not be avoided in the mesocosms as well, since more extreme soil moisture conditions 5 could have damaged the grasses. If a sufficiently long time series is available an estimate of the effective residual water content may be obtained by using the lowest measured value.…”

mentioning

confidence: 99%

“…An important limitation to the vertical equilibrium approach is that vertical water content profiles can be discontinous in layered soil profiles (Wollschläger et al, 2009) and therefore a meaningful interpolation of water content measurements may 10 be impossible to obtain. This could be circumvented by piecewise interpolation or use of a step function, if at least one water content time series is available for every (hydrologically) different layer.…”

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confidence: 99%

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Modeling macropore seepage fluxes from soil water content time series by inversion of a dual permeability model

Valle

Potthast

Meyer

et al. 2017

Preprint

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Abstract. Dual permeability models are widely used to simulate water fluxes and solute transport in structured soils. However, so far obtaining necessary data for model calibration is a problem due to the large set of unconstrained parameters. Therefore, this study presents a simplified 1D dual permeability model whose structure is similar to the MACRO model together with a calibration scheme that allows constraining the parameters using time series of soil water content. The inversion scheme consists of four consecutive steps: First, the parameters of three different water retention functions were assessed using vertical 5 soil water content profiles assuming hydraulic equilibrium. Second, the soil sorptivity and diffusivity functions were estimated from Boltzmann-transformed soil water content data of a drying period. Third, the parameters governing macropore flow were determined using the most dynamic part of the soil water content time series during the first 12 h after a precipitation event.The model was calibrated using data of artificial, homogeneous and shallow soils from mesocosms. The resulting retention functions predicted similar values as pedotransfer functions apart from for very dry conditions. The predicted soil water content 10 time series were in good agreement with measurements at 5 and 12 cm soil depth. Predicted macropore seepage fluxes exhibited high uncertainty and differed between water retention functions, but average predictions were close to measurements for two of the three water retention functions.The study demonstrates the feasibility of calibrating a 1D dual permeability model with soil water content time series.

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Field-scale apparent hydraulic parameterisation obtained from TDR time series and inverse modelling

Cited by 45 publications

References 38 publications

Effect of unrepresented model errors on estimated soil hydraulic material properties

Effect of unrepresented model errors on estimated soil hydraulic material properties

Identifying a parameterisation of the soil water retention curve from on-ground GPR measurements

Modeling macropore seepage fluxes from soil water content time series by inversion of a dual permeability model

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