SCHRIFTENREIHE Institut für Pflanzenernährung und Bodenkunde ...

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Ln(WDPT)=-1.45Ln(K)+0.047Silt+5.3 (r =-0.89**)
Ln(K)=-3.07Ln(WDPT)-0.26SOC+4.68 (r = -0.88**)
SS=-8.16silt+81.3 (r = -0.80**)
SWC=0.04SOC+0.92BD-0.006Ln(K)-0.142 (r = 0.82**) (2)
Combined with the correlation matrix in Table 9, multiple regression analysis
improved the correlation coefficients between WDPT and K, and between SWC
and SOC, which underlined the strong interaction between these soil parameters.
Partial correlation analysis also showed a pronounced correlation between K
and WDPT (r = 0.84**), between SS and silt (r = -0.58**), and between SWC and
SOC (r = 0.40*). The derived equations (2) presented here and former
correlation analysis neglected the regional geochemistry process (e.g. cross
correlation between adjacent observations with respect to the lag effect;
Goovaerts, 1998) of the analyzed parameters, therefore it should be handled
carefully when unmeasured parameters were predicted from other measured
parameters. In this study, slight differences in topography have negligible effects
on analyzed soil properties (data not shown). For a regional scale,
cross-variogram analysis is better which, however, was beyond the scope of this
paper but will be investigated more closely in further studies.
Many variables in hydrology are to be considered as spatiotemporal
parameters. SWC, SS, WDPT and K measurements and their interactions were
identified in this study as complex spatiotemporal functions. We showed that
geostatistical techniques, together with correlation analysis, were useful tools for
(i) determining spatial variability characteristics of physical soil properties and (ii)
recognizing significant interactions between soil properties. Although a large
amount of data is required for this kind of analysis we deem it necessary
because it is a prerequisite when matter fluxes on a regional scale is
investigated. The spatial variation of soil physical properties, ideally expressed
as maps, is valuable information not only for deriving a conceptual
understanding of landscape fluxes (e. g. water) but also the basis for spatial
discretization and parameter estimation in the modeled domain. Furthermore,
the analysis of correlation length is very useful to determine appropriate model