THREE LARGE SCALE GROUNDWATER MODELS AT THE LOWER RHINE – COUPLING AND DATA MANAGEMENT

Three finite element groundwater models were developed to forecast the impact of draining measures from open pit mining in the Lower Rhine Brown Coal Mining Area on the groundwater balance. Different scenarios of resource management shall be simulated and evaluated. One major attitude is the evaluation of measures to protect wetlands, which are endangered by the mining drainage. The groundwater models represent the geological units of "Erftscholle", "Rurscholle", and "Venloer Scholle". In total, the three models cover an area of about 3070 square kilometers and take into account seven, eight, or nine aquifers and their separating aquitards. We introduce the models and show the challenges of large scale modeling: besides the multiple boundary conditions, drainage wells, infiltrations, and public and industrial withdrawal result in a large amount of different transient sources and sinks. Additionally, the modeling of the open pit mining process requires a transient behaviour of soil parameters. The complex groundwater exchange rates between the three units used to be calculated in an iterative process of boundary condition adjustment. To improve modeling results and to avoid the time-consuming process of generating boundary conditions, the groundwater interchange now is realized by numerical coupling: finite beam elements are inserted between the boundary nodes and replace the exchange boundary conditions. The coupled models now are treated as one model. From the model coupling also further information about the interaction of the geological units are expected. We describe the coupling concept and show how the numerical coupling improves the model results. The coupling induces various new data management tasks. Aspects of model handling and data management are presented: The combined usage of advanced visualisation tools, geographic information systems (GIS) and databases allows an efficient handling, updating and checking of the large amount of model input and output data.