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Physical soil properties feature high spatial variabilities which are known to affect geophysical measurements. However, these variations are not considered in most cases. The challenging task is to quantify the influence of soil heterogeneities on geophysical data. This question is analysed for DC resistivity and GPR measurements which are frequently used for near-surface explorations. To determine the pattern of electric soil properties in situ with the required high spatial resolution, geophysical measuring techniques are methodically enhanced. High-resolution dipole-dipole resistivity measurements are used to determine the electric conductivity distribution of the topsoil. Due to the small electrode separations, the actual electrode geometry has to be considered and an analytic expression for geometric factors is derived instead of assuming point electrodes. Two methods are used to determine soil permittivity with GPR:(i) the coefficient of reflection at the interface air-soil is measured with an air-launched horn antenna, (ii) the velocity of the groundwave is measured with a new setup using two receiver antennas enhancing the lateral resolution from in the best case 0.5 m for standard techniques to approximately 0.1 m with the new technique. With the optimised measuring techniques, the electric properties of sandy soils are determined in the field. Conductivity and permittivity show high spatial variability with correlation lengths of a few decimetres. Geostatistical simulation techniques are used to generate synthetic random media featuring the same statistical properties as in the field. FD calculations are carried out with this media to provide realistic synthetic data of resistivity and GPR measurements. Conductivity variations as determined in the field generate significant variations of simulated Schlumberger sounding curves resulting in uncertainties of the inverted models. Even in pedologically homogeneous sandy soil, moisture pattern and resulting permittivity variations cause strong GPR diffractions as demonstated by FD calculations. This influences the detectability of small objects such as e.g. landmines or of large reflectors as e.g. the groundwater table. Conductivity variations as typical for soils showed to have a minor effect on GPR measurements than variations of permittivity. In summary, geostatistical analysis and simulation provide a powerful tool to simulate geophysical measurements under field conditions including soil heterogeneity which can be used to quantify the uncertainty of field measurements by geologic noise.
Artificial drainage of agricultural land, for example with ditches or drainage tubes, is used to avoid water logging and to manage high groundwater tables. Among other impacts it influences the nutrient balances by increasing leaching losses and by decreasing denitrification. To simulate terrestrial transport of nitrogen on the global scale, a digital global map of artificially drained agricultural areas was developed. The map depicts the percentage of each 5’ by 5’ grid cell that is equipped for artificial drainage. Information on artificial drainage in countries or sub-national units was mainly derived from international inventories. Distribution to grid cells was based, for most countries, on the "Global Croplands Dataset" of Ramankutty et al. (1998) and the "Digital Global Map of Irrigation Areas" of Siebert et al. (2005). For some European countries the CORINE land cover dataset was used instead of the both datasets mentioned above. Maps with outlines of artificially drained areas were available for 6 countries. The global drainage area on the map is 167 Mio hectares. For only 11 out of the 116 countries with information on artificial drainage areas, sub-national information could be taken into account. Due to this coarse spatial resolution of the data sources, we recommended to use the map of artificially drained areas only for continental to global scale assessments. This documentation describes the dataset, the data sources and the map generation, and it discusses the data uncertainty.