Abstract:The intention of the presented study is to gain a better understanding of the mechanisms that caused the bimodal rainfall-runoff responses which occurred up to the mid-1970s regularly in the Schäfertal catchment and vanished after the onset of mining activities. Understanding this process is a first step to understanding the ongoing hydrological change in this area. It is hypothesized that either subsurface stormflow, or fast displacement of groundwater could cause the second delayed peak. A top-down analysis of rainfall-runoff data, field observations as well as process modelling are combined within a rejectionistic framework. A statistical analysis is used to test whether different predictors, which characterize the forcing, near surface water content and deeper subsurface store, allow the prediction of the type of rainfall-runoff response. Regression analysis is used with generalized linear models as they can deal with non-Gaussian error distributions as well as a non-stationary variance. The analysis reveals that the dominant predictors are the pre-event discharge (proxy of state of the groundwater store) and the precipitation amount. In the field campaign, the subsurface at a representative hillslope was investigated by means of electrical resistivity tomography in order to identify possible strata as flow paths for subsurface stormflow. A low resistivity in approximately 4 m depth-either due to a less permeable layer or the groundwater surface-was detected. The former could serve as a flow path for subsurface stormflow. Finally, the physical-based hydrological model CATFLOW and the groundwater model FEFLOW are compared with respect to their ability to reproduce the bimodal runoff responses. The groundwater model is able to reproduce the observations, although it uses only an abstract representation of the hillslopes. Process model analysis as well as statistical analysis strongly suggest that fast displacement of groundwater is the dominant process underlying the bimodal runoff reactions.

In precision agriculture geoelectrical methods have shown their capability to detect spatial variation of important physico-chemical soil parameters in an efficient way. Nevertheless, relationships between the electrical parameters (electrical conductivity or resistivity) and other soil properties are not always consistent over different fields. This can, to some extent, be due to the characteristics of instruments used for soil mapping. However, a limited amount of research has addressed this issue. In this study, seven instruments for mobile mapping (continuous geoelectrical measurements) available on the market were tested (ARP 03, CM-138, EM38, EM38-DD, EM38-MK2, OhmMapper and Veris 3100). Instruments were employed on a sandy site in north-east Germany. Measurements were compared to a profile, which has been investigated with a high accuracy reference. Additional investigations were conducted concerning the influences of temperature drift, seasonal variations and soil properties on soil EC. Marked differences between the instruments were found with respect to depth of investigation, accuracy and handling that have to be taken into account when geoelectrical surveys are planned or interpreted. Regarding depth of investigation and robustness of the measurements, ARP 03 and Veris 3100 seem to be the most suitable instruments for precision agriculture.

Precision farming overcomes the paradigm of uniform field treatment by site‐specific data acquisition and treatment to cope with within‐field variability. Precision farming heavily relies on spatially dense information about soil and crop status. While it is often difficult and expensive to obtain precise soil information by traditional soil sampling and laboratory analysis some geophysical methods offer means to obtain subsidiary data in an efficient way. In particular, geoelectrical soil mapping has become widely accepted in precision farming. At present it is the most successful geophysical method providing the spatial distribution of relevant agronomic information that enables us to determine management zones for precision farming. Much work has been done to test the applicability of existing geoelectrical methods and to develop measurement systems applicable in the context of precision farming. Therefore, the aim of this paper was to introduce the basic ideas of precision farming, to discuss current precision farming applied geoelectrical methods and instruments and to give an overview about our corresponding activities during recent years. Different experiments were performed both in the laboratory and in the field to estimate first, electrical conductivity affecting factors, second, relationships between direct push and surface measurements, third, the seasonal stability of electrical conductivity patterns and fourth, the relationship between plant yield and electrical conductivity. From the results of these experiments, we concluded that soil texture is a very dominant factor in electrical conductivity mapping. Soil moisture affects both the level and the dynamic range of electrical conductivity readings. Nevertheless, electrical conductivity measurements can be principally performed independent of season. However, electrical conductivity field mapping does not produce reliable maps of spatial particle size distribution of soils, e.g., necessary to generate input parameters for water and nutrient transport models. The missing step to achieve this aim may be to develop multi‐sensor systems that allow adjusting the electrical conductivity measurement from the influence of different soil water contents.

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Peatlands store large amounts of carbon. This storage function has been reduced through intensive drainage, which leads to the decomposition of peat, resulting in a loss of carbon. Measurements of the real ( ′ ) and imaginary part ( ′′ ) of electrical conductivity can deliver information on peat properties, such as the pore fluid conductivity ( w ), cation exchange capacity (CEC), bulk density ( b ), water content (WC) and soil organic matter (SOM) content. These properties change with the peat's degree of decomposition (DD). To explore the relationships between the peat properties, ′ , ′′ and DD, we focused on three different types of survey and scales. First, point measurements were made with a conductivity probe at various locations over a large area of northeast Germany to determine the degree of correlation between ′ and DD. Second, nine of these locations were selected for sampling to determine which of the properties w , CEC, b , WC and SOM predominantly influence ′ and ′′ . This multisite dataset includes the entire range of DD and was analysed in the laboratory. Third, one site was selected for a survey of ′ including sampling, to identify which properties mainly control ′ in a single-site approach. Statistical analysis revealed that for the multisite laboratory dataset, w has the strongest effect on ′ , followed by CEC, whereas ′′ is mainly determined by CEC. In a single-site approach, WC followed by CEC had a dominant effect on ′ . No clear correlation could be observed between (i) DD and peat properties and (ii) DD and ′ or ′′ . This is because of the complex changes in properties with increasing DD.