TR32-Database: Database of Transregio 32

[675] - Bayesian inverse modelling of in situ soil water dynamics: using prior information about the soil hydraulic properties

All available metadata of the dataset are listed below. Some features are available, e.g. download of dataset or additional description file.

Features
Citation
Scharnagl, B., Vrugt, J. A., Vereecken, H., Herbst, M., 2011. Bayesian inverse modelling of in situ soil water dynamics: using prior information about the soil hydraulic properties. Hydrology and Earth System Sciences, 15, 3034 - 3059. DOI: 10.5194/hess-15-3043-2011.
Identification
Title(s):Main Title: Bayesian inverse modelling of in situ soil water dynamics: using prior information about the soil hydraulic properties
Description(s):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.
Identifier(s):DOI: 10.5194/hess-15-3043-2011
Responsible Party
Creator(s):Author: Benedikt Scharnagl
Author: Jasper A. Vrugt
Author: Harry Vereecken
Author: Michael Herbst
Publisher:European Geosciences Union
Topic
TR32 Topic:Soil
Subject(s):CRC/TR32 Keywords: Soil Water, Inverse Modelling, soil Hydraulic Properties
File Details
File Name:2011_Scharnagl_HESS.pdf
Data Type:Text
Size(s):17 Pages
File Size:1533 kB (1.497 MB)
Date(s):Date Accepted: 2011-09-20
Issued: 2011-10-04
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:Completed
Constraints
Download Permission:OnlyTR32
General Access and Use Conditions:For internal use only
Access Limitations:For internal use only
Licence:TR32DB Data policy agreement
Geographic
North:50.8731389
East:6.4577801
South:50.8623052
West:6.4406139
Measurement Region:Ellebach
Measurement Location:Selhausen
Specific Informations - Publication
Status:Published
Review:PeerReview
Year:2011
Type:Article
Article Type:Journal
Source:Hydrology and Earth System Sciences
Volume:15
Number Of Pages:17
Page Range:3034 - 3059
Metadata Details
Metadata Creator:Benedikt Scharnagl
Metadata Created:2013-12-05
Metadata Last Updated:2013-12-05
Subproject:B1
Funding Phase:2
Metadata Language:English
Metadata Version:V40
Dataset Metrics
Page Visits:182
Metadata Downloads:0
Dataset Downloads:4
Dataset Activity
Features