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

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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.
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
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
Download Permission:OnlyTR32
General Access and Use Conditions:For internal use only
Access Limitations:For internal use only
Licence:TR32DB Data policy agreement
Measurement Region:Ellebach
Measurement Location:Selhausen
Specific Informations - Publication
Article Type:Journal
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
Funding Phase:1
Metadata Language:English
Metadata Version:V40
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