TR32-Database: Database of Transregio 32

[649] - Multivariate conditional stochastic simulation of soil heterotrophic respiration at plot scale

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

Features
Citation
Herbst, M., Prolingheuer, N., Graf, A., Huisman, J. A., Weihermüller, L., Vanderborght, J., Vereecken, H., 2010. Multivariate conditional stochastic simulation of soil heterotrophic respiration at plot scale. Geoderma, 160, 74 - 82. DOI: 10.1016/j.geoderma.2009.11.018.
Identification
Title(s):Main Title: Multivariate conditional stochastic simulation of soil heterotrophic respiration at plot scale
Description(s):Abstract: Soil heterotrophic respiration fluxes at plot scale exhibit substantial spatial and temporal variability. Within this study secondary information was used to spatially predict heterotrophic respiration. Chamber-based measurements of heterotrophic respiration fluxes were repeated for 15 measurement campaigns within a bare 13× ^ 14 m2 soil plot. Soil water contents and temperatures were measured simultaneously with the same spatial and temporal resolution. Further, we used measurements of soil organic carbon content and apparent electrical conductivity as well as the prior measurement of the target variable. The previous variables were used as co-variates in a stepwise multiple linear regression analysis to spatially predict bare soil respiration. In particular the prior measurement of the target variable, the soil water content and the apparent electrical conductivity, showed a certain, even though limited, predictive power. In the first step we applied external drift kriging and regression kriging to determine the improvement of using co-variates in an estimation procedure in comparison to ordinary kriging. The improvement using co-variates ranged between 40 and 1% for a single measurement campaign. The difference in improving the prediction of respiration fluxes between external drift kriging and regression kriging was marginal. In a second step we applied sequential Gaussian simulations conditioned with external drift kriging to generate more realistic spatial patterns of heterotrophic respiration at plot scale. Compared to the estimation approaches the conditional stochastic simulations revealed a significantly improved reproduction of the probability density function and the semi-variogram of the original point data.
Identifier(s):DOI: 10.1016/j.geoderma.2009.11.018
Responsible Party
Creator(s):Author: Michael Herbst
Author: Nils Prolingheuer
Author: Alexander Graf
Author: Johan A. Huisman
Author: Lutz Weihermüller
Author: Jan Vanderborght
Author: Harry Vereecken
Publisher:Elsevier
Topic
TR32 Topic:Soil
Subject(s):CRC/TR32 Keywords: CO2, Carbon, Field Scale, Spatial Variability, External Drift Kriging, Heterotrophic Respiration
File Details
File Name:2010_Herbst_Geoderma.pdf
Data Type:Text
Size(s):9 Pages
File Size:1404 kB (1.371 MB)
Date(s):Date Accepted: 2009-11-21
Available: 2009-10-16
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:50.8728681
East:6.4576513
South:50.8620343
West:6.4404852
Measurement Region:Ellebach
Measurement Location:Selhausen
Specific Informations - Publication
Status:Published
Review:PeerReview
Year:2010
Type:Article
Article Type:Journal
Source:Geoderma
Volume:160
Number Of Pages:9
Page Range:74 - 82
Metadata Details
Metadata Creator:Nils Prolingheuer
Metadata Created:2013-12-02
Metadata Last Updated:2013-12-02
Subproject:B1
Funding Phase:1
Metadata Language:English
Metadata Version:V40
Dataset Metrics
Page Visits:272
Metadata Downloads:0
Dataset Downloads:0
Dataset Activity
Features