Monitoring and modeling are closely related research fields and as such integral parts of hydrological studies. Hydro(geo)logical models help us to better understand hydrological processes and to make predictions. Thus, they became essential tools for water resources assessment and rehabilitation, but also for the energy sector, civil engineering, ecology, and socio-economy. However, the reliability of models strongly depends on the information on which they are based. Therefore, profound monitoring and exploration constitute fundamental prerequisites for a good modeling practice.
Within our working group groundwater models are applied for different scales and for different purposes. Currently, we are running models for groundwater resources assessment on the Arabian Peninsula, in Israel, in Ethiopia, and in Greece. Furthermore, we are simulating seawater intrusion in Bangladesh and the influence of mining activities on wetlands in Estonia.
The most common modeling software we are using in our group is MODFLOW, which we are operating with the graphical user interfaces ModelMuse and PMWIN. Moreover, we are a development partner of the open source software tool FREEWAT, which combines a geographical information system (QGIS) and MODFLOW. Another open source software we are using is OpenGeoSys, which is an extensive software package for the simulation of thermo-hydro-mechanical-chemical (THMC) processes. In order to meet the specific needs for our group, we developed own pre- and post-processing tools for OpenGeoSys. For model calibration we are using PEST as well as UCODE.
Furthermore, we are interested in the interaction between groundwater and surface water, e.g. groundwater recharge and evaporation from shallow groundwater. Thus, we also make use of coupled surface water and groundwater flow models (GSFLOW) and conceptual hydrological models like SWAT and J2000 (JAMS). We apply those models for case studies, e.g. in Germany, Jordan, Israel, Ethiopia, and Pakistan.
Working in remote areas and in developing countries is often associated with limited availability of ground data. To bridge this gap, within a number of studies we employ satellite remote sensing techniques, e.g. for model parameterization.
Contact: Dr. Stephan Schulz