Integrated modelling and control optimization for adaptive drainage management in coastal lowlands

Water Management in low-lying coastal regions of Germany is characterized by controlled drainage of polder areas. Flood risk in these coastal polders depends on the storage and drainage capacity of the infrastructure and the effectiveness of drainage control. Current operations rely on on-site specialists who base their decisions on expertise, system status, and ad-hoc interpretation of weather and tidal forecasts to manage the system and meet variable target stages. Effective management requires the consideration of flood and tidal dynamics of the adjacent marine or fluvial systems as well as the flood dynamics within the polder. Climate change significantly impacts these factors, driving adaptation needs for drainage management for low-lying coastal regions.

To address these challenges, we develop a model-based approach for optimizing drainage operations in a German coastal polder, aligning water and energy objectives to enhance flood risk and water resource management through increased operational flexibility. The model system incorporates deep learning-based forecasts of drainage volumes and water levels, surrogate models of drainage processes and wind energy availability, operational status data, and meteorological and tidal forecasts to optimize short-term sluice and pump operations of the primary drainage infrastructure via mixed-integer linear programming. We show that this integrated optimization approach reschedules pumping operations to coincide with high energy availability periods, thus reducing costs and enhancing renewable energy utilization while meeting the drainage management objectives. This approach is also applicable for anticipatory drainage management, facilitating preemptive adjustments to drainage operations in response to impending flood events or prolonged drought conditions, thereby mitigating associated risks.