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000018340 0247_ $$2DOI$$a10.1016/j.geoderma.2009.11.018
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000018340 084__ $$2WoS$$aSoil Science
000018340 1001_ $$0P:(DE-Juel1)129469$$aHerbst, M.$$b0$$uFZJ
000018340 245__ $$aMultivariate conditional stochastic simulation of soil heterotrophic respiration at plot scale
000018340 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2010
000018340 300__ $$a74 - 82
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000018340 440_0 $$08464$$aGeoderma$$v160$$x0016-7061$$y1
000018340 500__ $$aSpecial thanks to L Bornemann and F.M. Mertens for providing the EM38 data. Many thanks to R. Harms for support to field experiments and to A. Papritz for the fruitful discussion at the Eurosoil conference. Further, we gratefully acknowledge financial support by the SFB/TR 32 "Pattern in Soil-Vegetation-Atmosphere Systems: Monitoring, Modelling and Data Assimilation" funded by the Deutsche Forschungsgemeinschaft (DEG).
000018340 520__ $$aSoil 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.
000018340 536__ $$0G:(DE-Juel1)FUEK407$$2G:(DE-HGF)$$aTerrestrische Umwelt$$cP24$$x0
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000018340 65320 $$2Author$$aCO2
000018340 65320 $$2Author$$aCarbon dioxide
000018340 65320 $$2Author$$aRandom field
000018340 65320 $$2Author$$aBare soil
000018340 65320 $$2Author$$aField scale
000018340 65320 $$2Author$$aSpatial variability
000018340 65320 $$2Author$$aExternal drift kriging
000018340 650_7 $$2WoSType$$aJ
000018340 7001_ $$0P:(DE-Juel1)VDB72509$$aProlingheuer, N.$$b1$$uFZJ
000018340 7001_ $$0P:(DE-Juel1)129461$$aGraf, A.$$b2$$uFZJ
000018340 7001_ $$0P:(DE-Juel1)129472$$aHuisman, J.A.$$b3$$uFZJ
000018340 7001_ $$0P:(DE-Juel1)VDB17057$$aWeihermüller, L.$$b4$$uFZJ
000018340 7001_ $$0P:(DE-Juel1)129548$$aVanderborght, J.$$b5$$uFZJ
000018340 7001_ $$0P:(DE-Juel1)129549$$aVereecken, H.$$b6$$uFZJ
000018340 773__ $$0PERI:(DE-600)2001729-7$$a10.1016/j.geoderma.2009.11.018$$gVol. 160, p. 74 - 82$$p74 - 82$$q160<74 - 82$$tGeoderma$$v160$$x0016-7061$$y2010
000018340 8567_ $$uhttp://dx.doi.org/10.1016/j.geoderma.2009.11.018
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