Abstract

Transport and transformation processes of nitrogen in the soil are an essential part of understanding the relationship between agricultural input and nitrate (NO3−) concentrations in groundwater. The presented study describes these transformation processes around NO3− degradation at a water catchment in the Lower Rhine Embayment, Germany. Despite intensive agriculture, extracted groundwater at a depth of 21 to 22 m shows unexpectedly very low NO3− levels, below 3 mg/L NO3− for all wells. The local water supplier therefore carried out investigations in this area and generated soil data from 22 representative areas (142 soil samples from 82 drilling meters from the surface to a max. depth of 5.5 m) and groundwater analyses from 17 groundwater monitoring wells (from 3 to 5 m below ground surface). Soil types are predominantly luvisol and gleysol. The substrate in the topsoil is mainly clayey silt; underneath there are mostly medium-grained sands with partial silt intercalations which appear as a separate layer. Based on this dataset, the percolating water residence times and the NO3− leaching potential were calculated in this study. Together with the nitrogen surplus and with the help of reactive transport modelling, the denitrification potential in the vadose zone was simulated. The comparison of simulation results with laboratory-measured data shows a high correlation. Substantial NO3− reduction in the vadose zone was observed: dependent on soil type, reduction capacity and water residence time, up to 25% of the NO3− was reduced here. The applied modelling is considered an improvement in NO3− degradation potential assessment because it considers many relevant variables such as precipitation, soil parameters (grain size, field capacity, available water capacity, coarse fragments) and nitrogen input. Therefore, a transfer to other sites with comparable hydro(geo)logical conditions is possible, also due to relatively easily determinable input data. This assessment of nitrogen degradation in the vadose zone will be a useful tool for NO3− levels forecast in groundwater

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