Combination of hydro-geophysical methods for delineation of characteristic structures in subsoil

Abstract

All terrestrial environmental processes involve the soil including hydrological, geological, meteorological, ecological and anthropological factors. The stress on natural soils increases significantly with agricultural and urban land use as a consequence of the world‘s growing population. This leads among others to deforestation and soil sealing and consequently to the decrease of natural habitats and resources. Thus, protection and preservation of soil and the maintaining and avoiding of negative affects from land-use change marks is a challenge for the policy, agricultural economics, and the geosciences. A perspective for maintaining and avoiding extensive and destructive land use is given by the optimization of current land use. Therefore reliable information of soil and subsoil properties are needed. However, direct analysis of crucial soil properties, e. g. grain size or soil moisture is still time consuming and costly, and provides only single point information. But in particular soil data for medium and large-scale areas are needed for the assessment of soil development and future-oriented planning. Proximal soil sensing techniques (PSS) offers an opportunity for obtaining data from medium and large–scale areas time and cost efficient. However, all PSS methods response only indirectly to the relevant soil properties and could be affected by several soil properties. Hence, a recent challenge is the improvement of PSS data evaluation and interpretation. The presented PhD thesis addresses the improvement of data evaluation and interpretation of the PSS methods electromagnetic induction (EMI) and gamma spectrometry (GS) at three different test sites and three different problems. For each problem an individual adjusted approach was develop, applied and critical discussed. In Part I the study consider the moisture distribution at a land slide affected hill slope in Austria by means of EMI. The presented study monitored the temporal, spatial and vertical behavior of soil-moisture distribution at a previously identified dynamic slope area over a period of nine month. By the assumption of relative temporal stability of soil properties, seasonal changes in measured electric conductivity (EC) should originate from soil moisture content. This study also faces the challenge of shifts in absolute EC values resulting from different calibration situation or different EMI devices and provides an opportunity for comparability of different EC data. By this approach an delineation of soil moisture pattern was successful derived. Part I also explores the visualization of temporal changes in three-dimensional subsurface data. Part II focuses the problem of synthesis and simplification of multilayered input data of a floodplain in Central Germany towards a clear delineation of surface. This study tackle the problem by a K-means cluster algorithm in order to generate a 2dimensional map from the test site that includes the main characteristics from divergent input data. However do the generated cluster partitions really reflect the main characteristics of soil properties? Hence, this study also addressed on the reliability of such cluster maps by validation of independent soil properties such as grain-size, thickness of soil layers, and the color. The results show that not all partitions can be confirmed by independent soil samples; one of three clusters significantly differs from the others, the other two clusters could not confirmed by the considered parameters. Part III investigates a floodplain of a low-mountain river in Switzerland in order to detect the ancient active stream channels (AAC). This part is subdivided into two approaches; first a 3D subsurface model as a result from the iterative inversion of predicted EMI values was generated. Thereby various electric conductivity maps (EC) were generated by forward modeling and compared with the corresponding measured data. The study use the best fitted input data for the generation of the 3D model. In a second approach a K-means cluster map for the floodplain surface was generated that combines the main characteristics from multilayered subsurface data by synthesis and simplification analogue to Part II. The obtained cluster characterizes different soil conditions, which are indicative for the delineation of AAC. Although developed under specific site conditions all demonstrated approaches offers portability and should be applied in other applications.