INFER - Information fusion for impact predictions of hydrological extremes
InWas - Indicator-based water and energy strategy for irrigation and drainage
The Future Lab Water (ZLW) is a new element in the Centre for Digital Innovation Lower Saxony (ZDIN) with the principle that water management and the water landscape represent indispensable ecosystem services for our society. In order to guarantee the security of supply and quality of the water ressources in the future and to improve the handling of extreme situations, the need for application-oriented digital innovations has increased significantly. The ZLW's mission is to coordinate and promote innovation in digital solutions for all areas of water management.
The Department of Hydrology and River Basin Management at the Leichtweiß-Institute for Hydraulic Engineering and Water Resources of the Technische Universität Braunschweig participates in the ZLW with the work packages 3.1 "InWas- Indicator-based water and energy strategy for irrigation and drainage" and 4.4 "INFER - Information fusion for impact predictions of hydrological extremes".
Low-elevation coastal zones (LECZs) around the world are vulnerable to the effects of climate change, such as rising sea levels and increased winter precipitation. These trends increase the risk of flooding, which poses a significant threat to both social stability and economic functions in LECZs.
The effectiveness of water management in the lowlands along the German North Sea coast depends largely on the capacity of the drainage infrastructure, including canals, sluices and pumps. As drainage capacity varies according to technical and environmental conditions, drainage operations are under particular pressure when compound events such as inland flooding and storm surges occur simultaneously. In addition, the industrial water demand is expected to increase significantly in the near future due to the production of hydrogen, presenting an additional challenge for water management.
In our ZLW projects, model-based approaches for flood forecasting (INFER) and control optimisation of existing infrastructure (InWas) are developed to improve flood risk and water resource management in the German coastal lowlands.
Within the framework of INFER, digital solutions for a consistent analysis of heterogeneous data from different sources such as remote sensing, simulation models, monitoring systems and operational control are researched. For this purpose, AI methods, such as machine learning and deep learning, for the fusion, management and exploration of heterogeneous data are applied to the development of forecasting and warning systems. The research is focused on water level simulations in drainage systems of low evelevation coastal zones and will be evaluated and demonstrated using reanalyses of past events.
Within the InWas framework, a smart drainage control system is designed that integrates measurement and simulation data with hydrometeorological forecasts to mathematically optimize the operation of sluices and pumps. The research area is the drainage area of the primary water board Emden at the German North Sea coast. The system is optimised to meet water and energy objectives, with the aim of improving flood risk and water resource management, while enabling increased use of renewable energy in drainage operations.
The water level modelling system developed in INFER serves as a key input for the optimization algorithm and is used to evaluate the impact of the resulting control schedule on water levels.
Ministry of Science and Culture of Lower Saxony
Project duration 1.10.2023 – 30.09.2027