Effect of unrepresented model errors on estimated soil hydraulic material properties
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...
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
1 September 2017
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| In: |
Hydrology and earth system sciences
Year: 2017, Volume: 21, Issue: 9, Pages: 4301-4322 |
| ISSN: | 1607-7938 |
| DOI: | 10.5194/hess-21-4301-2017 |
| Online Access: | Verlag, kostenfrei, Volltext: https://www.hydrol-earth-syst-sci.net/21/4301/2017/ Verlag, kostenfrei, Volltext: http://dx.doi.org/10.5194/hess-21-4301-2017 
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| Author Notes: | S. Jaumann and K. Roth |
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| 520 | |a 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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