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SFB 1253:  Catchments as Reactors: Metabolism of Pollutants on the Landscape Scale (CAMPOS)

Subject Area Geosciences
Agriculture, Forestry and Veterinary Medicine
Computer Science, Systems and Electrical Engineering
Term from 2017 to 2021
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 281741268
 
Final Report Year 2022

Final Report Abstract

The CRC CAMPOS was focusing on metabolism of pollutants on the landscape scale. CAMPOS has identified reactive landscape elements and quantified process dynamics with detailed field studies on biogeochemical pollutant transformations at high resolution. CAMPOS combined novel analytical and sensing techniques and developed stochastic modeling methods. Field work was predominantly performed within the Ammer catchment, SW Germany. Results show that, under baseflow conditions, the analyzed mixture of organic pollutants in the River (P1) is dominated by inputs from wastewater treatment plants and urban areas. Results, however, also demonstrate that organic chemicals in stormwater discharge from urban and agricultural areas may carry a much higher burden of toxicity than river water under baseflow conditions and may also be stored in river bed sediments. In first-order streams (Sub-Catchments, P2) stream-water chemistry at the CAMPOS test sites is governed by an intricate interplay between hydrologic and reactive processes. Dynamic, bidirectional water and solute exchange between groundwater and the stream replacing water lost from the stream with water gained from groundwater of different chemical composition (hydrological turnover) and the concomitant reactive turnover of nitrogen (denitrification and nitrification) in the streambeds and the shallow riparian aquifers jointly shape stream water composition. The intensity and spatiotemporal patterns of these exchange fluxes induce corresponding variability of nitrate concentration in the stream and aquifer. The (hydro)geology and biogeochemistry of the Floodplain (P3, P4) is characterized by strong biogeochemical gradients, possibly facilitating intensive transformation of redox-selective pollutants such as nitrate, while hampering the degradation of pollutants requiring oxygen for their elimination. We could show that the floodplain functioning is linked to the presence or absence of major hydrogeological features, indicated by a strong connection of the local floodplain groundwater systems to the hillslope and by an alluvial, sedimentary gravel body mapped in the Ammer valley by geoelectrical mapping techniques developed in P3. Floodplain margins were also found to be locations of strong redox cycling because the material properties of the floodplain sediments, such as the content of natural organic matter, significantly differ from those of the hillslopes and bedrocks. Electron-acceptor inputs and differences in natural organic matter content at the hillslope-floodplain transition drive geochemical gradients, yielding strong redox cycling and governing the fate of contaminants (nitrate and glyphosate) in the landscape. For Fractured Aquifers (P5) field observations as well as modeling results confirm that Fe(II) and reduced sulfur species are important electron donors for denitrification in the fractured aquifer. Molecular-biological data suggest that alternative electron donors (e.g., H2, or CH4) or other nitrogen cycling processes (e.g., anaerobic ammonium oxidation, anammox; dissimilatory nitrate reduction to ammonium, DNRA; chemodenitrification) might be of importance for nitrate removal. Results furthermore demonstrate that the Triassic carbonate rocks take up and store pesticides (e.g., atrazine), causing retardation but also contributing to long-term groundwater pollution due to back diffusion. The results on chemical, biological and physical controls of pesticide turnover in Soils (P6) suggest that their heterogeneous distribution, strong sorption and the interplay with soil physical parameters are key to understand their long-term fate in the environment. It turned out that glyphosate is an ideal model compound due to its physicochemical properties leading to a complex speciation and sorption behavior in soil. For Modeling Flow and Transport under Uncertainty (P7, P8) we have developed a stochastic modeling framework. It couples 1-D soil-crop models based on improved soil-hydraulic parameterizations and pedotransfer functions to 3-D models of subsurface flow, and generates large ensembles of plausible model realizations. Within this framework, we have developed techniques for machine-learning-supported preselection of plausible parameter combinations, global sensitivity analysis, and data assimilation. Subprojects have been supported by providing Bayesian model selection and averaging schemes, and model-based optimal design of experiments. The Information Infrastructure (INF) project has set up an extensive data management system to store, describe and manage CAMPOS relevant research data. This database facility also allows to handle external data (digital elevation, land-use, geological maps, soil maps, satellite images, hydrological time series, meteorological data, etc.) and to safely store and manage all data and data types (field data, lab data, modelling data, etc.) generated within CAMPOS. All data and their accompanying metadata are centrally stored. They are published in the University Research Data Repository (RDR), including persistent identifiers (PID) to reference published data. Central Services (S1, S2) provided technical support services, data sharing, and modeling support but also internal and external communication.

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