Journal Article

PreJuSER-18340

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Multivariate conditional stochastic simulation of soil heterotrophic respiration at plot scale

Herbst, M.FZJ* ; Prolingheuer, N.FZJ* ; Graf, A.FZJ* ; Huisman, J. A.FZJ* ; Weihermüller, L.FZJ* ; Vanderborght, J.FZJ* ; Vereecken, H.FZJ*

2010
Elsevier Science Amsterdam [u.a.]

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Please use a persistent id in citations: doi:10.1016/j.geoderma.2009.11.018

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 x 14 m(2) 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. (C) 2009 Elsevier B.V. All rights reserved.

Keyword(s): J ; CO2 (auto) ; Carbon dioxide (auto) ; Random field (auto) ; Bare soil (auto) ; Field scale (auto) ; Spatial variability (auto) ; External drift kriging (auto)

Note: Special thanks to L Bornemann and F.M. Mertens for providing the EM38 data. Many thanks to R. Harms for support to field experiments and to A. Papritz for the fruitful discussion at the Eurosoil conference. Further, we gratefully acknowledge financial support by the SFB/TR 32 "Pattern in Soil-Vegetation-Atmosphere Systems: Monitoring, Modelling and Data Assimilation" funded by the Deutsche Forschungsgemeinschaft (DEG).

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Contributing Institute(s):

1. Agrosphäre (IBG-3)

Research Program(s):

1. Terrestrische Umwelt (P24)

Appears in the scientific report 2010

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