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5H - Catchment area mgnt (WEP/218) Decision Support System for the Elbe River Water Quality Management. Matthies, Michael1, Reimer, Silke2, Graf, Neil1, Lautenbach, Sven1, Berlekamp, Juergen 1, 1 University of Osnabrueck, Institute of Environmental Systems Research, Osnabrueck, Niedersachsen2 Intevation GmbH, Osnabrueck, Niedersachsen ABSTRACT- A decision support system (DSS) for integrated river basin management of the German part of the Elbe river basin is currently under development which involves taking account of the chemical quality and ecological state of surface waters according to the EU Water Framework Directive. The DSS integrates models, spatial and non-spatial data and analysis tools under a user-friendly interface which confronts the decision maker with possible measures as well as multiple objectives. GREAT-ER (Geo-referenced Regional Exposure Assessment Tool for European Rivers) was selected for the integration into the DSS to evaluate measures to achieve a good chemical state. It combines a simulation model system of the waste water pathways (point-sources) and the aquatic fate assessment with a GIS-based discrete digitized river network of the Elbe. GREAT-ER was coupled to a distributed rainfall-runoff model of the Elbe catchment which has been divided into 115 sub-catchments. Diffuse discharges (non-point sources) were estimated to the river stretches by the relative area fraction related to each stretch. Long-term discharge time series from gauging stations were used to calibrate the hydrological discharges. A transport and elimination model describes the downstream fate of the chemical. Temporal concentration distributions of chemicals in each river stretch were calculated from variable and uncertain input data. With the examples of phosphate, boron and ammonium, it is demonstrated how the concentration pattern in the whole catchment evolves from various discharges into the river network. Monitoring data from routine and specific programs were used to assess the quality of the simulations. Spatial concentration patterns of the whole catchment give stochastic information on a regional basis for probabilistic risk assessments and decisions for the improvement of the water quality. Key words: water mangement, decision support , exposure assessment, river water quality |
