Forecasting of Streamflow and Water Levels for Urban Flood Protection in Vejle, Denmark

Urban areas are increasingly vulnerable to extreme rainfall events, which can cause severe flooding and significant infrastructure damage. This study focuses on predicting water levels and flows in a Danish stream to support urban flood mitigation using rainfall observations and numerical weather predictions. The city of Vejle, Denmark, is vulnerable to extreme rainfall due to flooding risks associated with the stream that traverses the city; hench, a pump and sluice facility have been installed as protective measures. However, effective flood mitigation depends on timely early warning and informed decision-making, highlighting the need for accurate and reliable stream water level forecasts.

The approach presented in this study integrates observed rainfall from rain gauges and numerical weather predictions as inputs to two distinct hydrological modeling frameworks: a linear reservoir model and a neural network model. Both models aim to predict streamflow and water levels in the critical stream through Vejle. Real-time flow and water level sensors installed in the stream provide continuous measurements for model calibration and validation.

Results from this study demonstrate that the proposed models achieve high accuracy in forecasting both flow and water levels. The neural network model shows particular promise in capturing nonlinear dynamics, while the linear reservoir model offers robustness and interpretability. These forecasts are operationally significant: they enable the local utility to optimize pump and sluice operations, reducing the risk of urban flooding and minimizing potential damage during extreme events.

This work highlights the value of combining rainfall observations and weather forecasts with hydrological runoff models to enhance urban flood resilience. The proposed methodologies are computationally efficient, scalable, and adaptable, making them highly valuable in real-time applications used by municipalities for flood mitigation.