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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 21, issue 9
Hydrol. Earth Syst. Sci., 21, 4301–4322, 2017
https://doi.org/10.5194/hess-21-4301-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 21, 4301–4322, 2017
https://doi.org/10.5194/hess-21-4301-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 01 Sep 2017

Research article | 01 Sep 2017

Effect of unrepresented model errors on estimated soil hydraulic material properties

Stefan Jaumann1,2 and Kurt Roth1,3 Stefan Jaumann and Kurt Roth
  • 1Institute of Environmental Physics, Heidelberg University, Im Neuenheimer Feld 229, 69120 Heidelberg, Germany
  • 2HGSMathComp, Heidelberg University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
  • 3Interdisciplinary Center for Scientific Computing, Heidelberg University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany

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 (ASSESS) 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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We investigate the quantitative effect of neglected sensor position, small-scale heterogeneity, and lateral flow on soil hydraulic material properties. Thus, we analyze a fluctuating water table experiment in a 2-D architecture (ASSESS) with increasingly complex studies based on time domain reflectometry and hydraulic potential data. We found that 1-D studies may yield biased parameters and that estimating sensor positions as well as small-scale heterogeneity improves the model significantly.
We investigate the quantitative effect of neglected sensor position, small-scale heterogeneity,...
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