
MOWAX - Monitoring- and modelling concepts as a basis
for water budget assessments in Saxony
Made available by the European Regional Development Fund (EFRE)
and by tax revenue on the basis of the budget approved by
the Saxon state parliament (funding code 100702604)
Team
Principal investigators:
- Martin Schrön, UFZ Dep. Monitoring and Exploration Technologies
- Daniel Altdorff, UFZ Dep. Computational Hydrosystems
- Andreas Marx, UFZ Dep. Computational Hydrosystems
- Luis Samaniego, UFZ Dep. Computational Hydrosystems
- Steffen Zacharias, UFZ Dep. Monitoring and Exploration Technologies
- Peter Dietrich, UFZ Dep. Monitoring and Exploration Technologies
- Jan Bumberger, UFZ Dep. Research and Data Management
Investigators:
- Friedrich Boeing, UFZ Dep. Computational Hydrosystems
- Solveig Landmark, UFZ Dep. Monitoring and Exploration Technologies
- Rebekka Lange, UFZ Dep. Research and Data Management
- Julian Schlaak, UFZ Dep. Computational Hydrosystems
- Felix Thomas, UFZ Dep. Monitoring and Exploration Technologies
External Partners:
- Falk Böttcher, DWD German Weather Service
- Jannis Ballmann, DWD German Weather Service
- Rainer Petzold, Sachsenforst
- Alexander Peters, Sachsenforst
- Kerstin Jäkel, Landesamt für Umwelt, Landwirtschaft und Geologie
- Matthias Mauder, Technical University Dresden
Goals
- Join forces to generate a solid data basis for model validation and calibration,
- Improvement of the mHM drought monitor in Saxony and beyond,
- Near-realtime online visualization of observations and modelling data,
- Provide a scientific basis for recommendations toward timely actions and management decisions.
Implementation
To achieve these goals, we plan to make use of the mesoscale hydrological model (mHM), extensive soil moisture monitoring with Cosmic-Ray Neutron Sensors (CRNS), and modern Research Data Management tools. We will cooperate with existing local authorities and observatories, use running railway-CRNS data, establish 10 new CRNS probes, set up interactive online data visualization platforms, and improve input data, resolution, and parameters of the mHM model.
