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| Titel |
Inverse modelling of in situ soil water dynamics: investigating the effect of different prior distributions of the soil hydraulic parameters |
| VerfasserIn |
B. Scharnagl, J. A. Vrugt, H. Vereecken, M. Herbst |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 15, no. 10 ; Nr. 15, no. 10 (2011-10-04), S.3043-3059 |
| Datensatznummer |
250012983
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| Publikation (Nr.) |
 copernicus.org/hess-15-3043-2011.pdf |
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| Zusammenfassung |
| 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
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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