We provide an overview of the Rur hydrological observatory, which is the main observational platform of the TERENO (TERrestrial ENvironmental Observatories) Eifel/Lower Rhine Valley Observatory. The Rur catchment area exhibits distinct gradients in altitude, climate, land use, soil properties, and geology. The Eifel National Park is situated in the southern part of the Rur catchment and serves as a reference site for the hydrological observatory. We present information on general physical characteristics of the Rur catchment and describe the main features of the multi-scale and multi-compartment monitoring framework. In addition, we also present some examples of the ongoing interdisciplinary research that aims to advance the understanding of complex hydrological processes and interactions within the Rur catchment.

Electromagnetic induction (EMI) systems measure the soil apparent electrical conductivity (ECa), which is related to the soil water content, texture, and salinity changes. Large-scale EMI measurements often show relevant areal ECa patterns, but only few researchers have attempted to resolve vertical changes in electrical conductivity that in principle can be obtained using multiconfiguration EMI devices. In this work, we show that EMI measurements can be used to determine the lateral and vertical distribution of the electrical conductivity at the field scale and beyond. Processed ECa data for six coil configurations measured at the Selhausen (Germany) test site were calibrated using inverted electrical resistivity tomography (ERT) data from a short transect with a high ECa range, and regridded using a nearest neighbor interpolation. The quantitative ECa data at each grid node were inverted using a novel three-layer inversion that uses the shuffled complex evolution (SCE) optimization and a Maxwell-based electromagnetic forward model. The obtained 1-D results were stitched together to form a 3-D subsurface electrical conductivity model that showed smoothly varying electrical conductivities and layer thicknesses, indicating the stability of the inversion. The obtained electrical conductivity distributions were validated with low-resolution grain size distribution maps and two 120 m long ERT transects that confirmed the obtained lateral and vertical large-scale electrical conductivity patterns. Observed differences in the EMI and ERT inversion results were attributed to differences in soil water content between acquisition days. These findings indicate that EMI inversions can be used to infer hydrologically active layers.

This version available http://nora.nerc.ac.uk/510335/ NERC has developed NORA to enable users to access research outputs wholly or partially funded by NERC. Copyright and other rights for material on this site are retained by the rights owners. Users should read the terms and conditions of use of this material at http://nora.nerc.ac.uk/policies.html#access NOTICE: this is the author's version of a work that was accepted for publication in Geoderma. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Geoderma, 241-242. 262-271. 10.1016/j.geoderma.2014.11.015 www.elsevier.com/ Contact CEH NORA team at noraceh@ceh.ac.ukThe NERC and CEH trademarks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. offset ECa data and the subsoil clay content and soil profile depth, implies that along this buried 29 paleo-river structure the subsoil is mainly responsible for better crop development in drought 30 periods. Furthermore, observed stagnant water in the subsoil indicates that this paleo-river 31 structure still plays an important role in subsurface hydrology. These insights should be 32 considered and implemented in local hydrological as well as crop models. 33

Reliable and high-resolution subsurface characterization beyond the field scale is of great interest for precision agriculture and agro-ecological modelling because the shallow soil (~1-2 m depth) is responsible for the storage of moisture and nutrients that are accessible to crops. This can potentially be achieved with a combination of direct sampling and Electromagnetic Induction (EMI) measurements, which have shown great potential for soil characterization due to their noninvasive nature and high mobility. However, only a few studies have used EMI beyond the field scale because of the challenges associated with a consistent interpretation of EMI data from multiple fields and acquisition days. In this study, we performed a detailed EMI survey of an area of 1 km 2 divided in 51 agricultural fields where previous studies showed a clear connection between crop performance and soil properties. In total, nine apparent electrical conductivity (ECa) values were measured at each location with a depth of investigation ranging between 0-0.2 to 0-2.7 m. Based on the combination of ECa maps and available soil maps, an a priori interpretation was performed and four sub-areas with characteristic sediments and ECa were identified. Then, a supervised classification methodology was used to divide the ECa maps into areas with similar soil properties. In a next step, soil profile descriptions to a depth of 2 m were obtained at 100 sampling locations and 552 samples were analyzed for textural characteristics. The combination of the classified map and ground truth data resulted in a 1 m resolution soil map with eighteen units with a typical soil profile and texture information. It was found that the soil profile descriptions and texture of the EMI-based soil classes were significantly different when compared using a two-tailed t-test. Moreover, the high-resolution soil map corresponded well with patterns in crop health obtained from satellite imagery. It was concluded that this novel EMI 3 data processing approach provides a reliable and cost-effective tool to obtain high-resolution soil maps to support precision agriculture and agro-ecological modelling.

For the first time, we combine depth‐specific soil information obtained from the quantitative inversion of ground‐based multicoil electromagnetic induction data with the airborne hyperspectral vegetation mapping of 1 × 1‐m pixels including Sun‐induced fluorescence (F) to understand how subsurface structures drive above‐surface plant performance. Hyperspectral data were processed to quantitative F and selected biophysical canopy maps, which are proxies for actual photosynthetic rates. These maps showed within‐field spatial patterns, which were attributed to paleo‐river channels buried at around 1‐m depth. The soil structures at specific depths were identified by quantitative electromagnetic induction data inversions and confirmed by soil samples. Whereas the upper plowing layer showed minor correlation to the plant data, the deeper subsoil carrying vital plant resources correlated substantially. Linking depth‐specific soil information with plant performance data may greatly improve our understanding and the modeling of soil‐vegetation‐atmosphere exchange processes.

Multi-coil electromagnetic induction (EMI) systems induce magnetic fields below and above the subsurface. The resulting magnetic field is measured at multiple coils increasingly separated from the transmitter in a rigid boom. This field relates to the subsurface apparent electrical conductivity (σa), and σa represents an average value for the depth range investigated with a specific coil separation and orientation. Multi-coil EMI data can be inverted to obtain layered bulk electrical conductivity models. However, above-ground stationary influences alter the signal and the inversion results can be unreliable. This study proposes an improved data processing chain, including EMI data calibration, conversion, and inversion. For the calibration of σa, three direct current resistivity techniques are compared: Electrical resistivity tomography with Dipole-Dipole and Schlumberger electrode arrays and vertical electrical soundings. All three methods obtained robust calibration results. The Dipole-Dipole-based calibration proved stable upon testing on different soil types. To further improve accuracy, we propose a non-linear exact EMI conversion to convert the magnetic field to σa. The complete processing workflow provides accurate and quantitative EMI data and the inversions reliable estimates of the intrinsic electrical conductivities. This improves the ability to combine EMI with, e.g., remote sensing, and the use of EMI for monitoring purposes.

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16Ecosystem carbon (C) fluxes in terrestrial ecosystems are affected by varying environmental 17 conditions (e.g. soil heterogeneity and the weather) and land management. However, the 18 interactions between soil respiration (R s ) and net ecosystem exchange (NEE) and their spatio-19 temporal dependence on environmental conditions and land management at field scale is not 20 well understood. We performed repeated C flux measurement at 21 sites during the 2013 21 growing season in a temperate upland grassland in Germany, which was fertilized and cut 22 three times according to the agricultural practice typical of the region. Repeated 23 measurements included determination of NEE, R s , leaf area index (LAI), meteorological 24 conditions as well as physical and chemical soil properties. Temporal variability of R s was 25 controlled by air temperature, while LAI influenced the temporal variability of NEE. The 26 three grass cuts reduced LAI and affected NEE markedly. More than 50% of NEE variability 27 was explained by defoliation at field scale. Additionally, soil heterogeneity affected NEE, but 28 to a lower extent (>30%), while R s remained unaffected. We conclude that grassland 29 management (i.e. repeated defoliation) and soil heterogeneity affects the spatio-temporal 30 variability of NEE at field scale. 31