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Swedish long-term soil fertility experiments were used to investigate the effect of texture and fertilization regime on soil electrical conductivity. In one geophysical approach, fields were mapped to characterize the horizontal variability in apparent electrical conductivity down to 1.5 m soil depth using an electromagnetic induction meter (EM38 device). The data obtained were geo-referenced by dGPS. The other approach consisted of measuring the vertical variability in electrical conductivity along transects using a multi-electrode apparatus for electrical resistivity tomography (GeoTom RES/IP device) down to 2 m depth. Geophysical field work was complemented by soil analyses. The results showed that despite 40 years of different fertilization regimes, treatments had no significant effects on the apparent electrical conductivity. Instead, the comparison of sites revealed high and low conductivity soils, with gradual differences explained by soil texture. A significant, linear relationship found between apparent electrical conductivity and soil clay content explained 80% of the variability measured. In terms of soil depth, both low and high electrical conductivity values were measured. Abrupt changes in electrical conductivity within a field revealed the presence of 'deviating areas'. Higher values corresponded well with layers with a high clay content, while local inclusions of coarse-textured materials caused a high variability in conductivity in some fields. The geophysical methods tested provided useful information on the variability in soil texture at the experimental sites. The use of spatial EC variability as a co-variable in statistical analysis could be a complementary tool in the evaluation of experimental results.
It is commonly recognized that soil moisture exhibits spatial heterogeneities occurring in a wide range of scales. These heterogeneities are caused by different factors ranging from soil structure at the plot scale to land use at the landscape scale. There is an urgent need for effi-cient approaches to deal with soil moisture heterogeneity at large scales, where manage-ment decisions are usually made. The aim of this dissertation was to test innovative ap-proaches for making efficient use of standard soil hydrological data in order to assess seep-age rates and main controls on observed hydrological behavior, including the role of soil het-erogeneities.
As a first step, the applicability of a simplified Buckingham-Darcy method to estimate deep seepage fluxes from point information of soil moisture dynamics was assessed. This was done in a numerical experiment considering a broad range of soil textures and textural het-erogeneities. The method performed well for most soil texture classes. However, in pure sand where seepage fluxes were dominated by heterogeneous flow fields it turned out to be not applicable, because it simply neglects the effect of water flow heterogeneity. In this study a need for new efficient approaches to handle heterogeneities in one-dimensional water flux models was identified.
As a further step, an approach to turn the problem of soil moisture heterogeneity into a solu-tion was presented: Principal component analysis was applied to make use of the variability among soil moisture time series for analyzing apparently complex soil hydrological systems. It can be used for identifying the main controls on the hydrological behavior, quantifying their relevance, and describing their particular effects by functional averaged time series. The ap-proach was firstly tested with soil moisture time series simulated for different texture classes in homogeneous and heterogeneous model domains. Afterwards, it was applied to 57 mois-ture time series measured in a multifactorial long term field experiment in Northeast Germa-ny.
The dimensionality of both data sets was rather low, because more than 85 % of the total moisture variance could already be explained by the hydrological input signal and by signal transformation with soil depth. The perspective of signal transformation, i.e. analyzing how hydrological input signals (e.g., rainfall, snow melt) propagate through the vadose zone, turned out to be a valuable supplement to the common mass flux considerations. Neither different textures nor spatial heterogeneities affected the general kind of signal transfor-mation showing that complex spatial structures do not necessarily evoke a complex hydro-logical behavior. In case of the field measured data another 3.6% of the total variance was unambiguously explained by different cropping systems. Additionally, it was shown that dif-ferent soil tillage practices did not affect the soil moisture dynamics at all.
The presented approach does not require a priori assumptions about the nature of physical processes, and it is not restricted to specific scales. Thus, it opens various possibilities to in-corporate the key information from monitoring data sets into the modeling exercise and thereby reduce model uncertainties.