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SCHRIFTENREIHE Institut für Pflanzenernährung und Bodenkunde ...

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32<br />

Ln(WDPT)=-1.45Ln(K)+0.047Silt+5.3 (r =-0.89**)<br />

Ln(K)=-3.07Ln(WDPT)-0.26SOC+4.68 (r = -0.88**)<br />

SS=-8.16silt+81.3 (r = -0.80**)<br />

SWC=0.04SOC+0.92BD-0.006Ln(K)-0.142 (r = 0.82**) (2)<br />

Combined with the correlation matrix in Table 9, multiple regression analysis<br />

improved the correlation coefficients between WDPT and K, and between SWC<br />

and SOC, which <strong>und</strong>erlined the strong interaction between these soil parameters.<br />

Partial correlation analysis also showed a pronounced correlation between K<br />

and WDPT (r = 0.84**), between SS and silt (r = -0.58**), and between SWC and<br />

SOC (r = 0.40*). The derived equations (2) presented here and former<br />

correlation analysis neglected the regional geochemistry process (e.g. cross<br />

correlation between adjacent observations with respect to the lag effect;<br />

Goovaerts, 1998) of the analyzed parameters, therefore it should be handled<br />

carefully when unmeasured parameters were predicted from other measured<br />

parameters. In this study, slight differences in topography have negligible effects<br />

on analyzed soil properties (data not shown). For a regional scale,<br />

cross-variogram analysis is better which, however, was beyond the scope of this<br />

paper but will be investigated more closely in further studies.<br />

Many variables in hydrology are to be considered as spatiotemporal<br />

parameters. SWC, SS, WDPT and K measurements and their interactions were<br />

identified in this study as complex spatiotemporal functions. We showed that<br />

geostatistical techniques, together with correlation analysis, were useful tools for<br />

(i) determining spatial variability characteristics of physical soil properties and (ii)<br />

recognizing significant interactions between soil properties. Although a large<br />

amount of data is required for this kind of analysis we deem it necessary<br />

because it is a prerequisite when matter fluxes on a regional scale is<br />

investigated. The spatial variation of soil physical properties, ideally expressed<br />

as maps, is valuable information not only for deriving a conceptual<br />

<strong>und</strong>erstanding of landscape fluxes (e. g. water) but also the basis for spatial<br />

discretization and parameter estimation in the modeled domain. Furthermore,<br />

the analysis of correlation length is very useful to determine appropriate model

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