R2RNet: A deep spatiotemporal RaintoRiverNetwork for water level prediction and flood forecasting

Study Region:

Goslar and Göttingen, Lower Saxony, Germany.

Study Focus:

In July 2017, the cities of Goslar and Göttingen experienced severe regional flood events characterized by short warning time of only 20 min with a significant damage. This highlights the critical need for a more reliable and timely flood forecasting system. This paper presents a comprehensive study on the impact of radar-based precipitation data on forecasting river water levels in Goslar and Göttingen. The analysis integrates radar-derived spatiotemporal precipitation patterns with hydrological sensor data obtained from ground stations to evaluate the effectiveness of this approach in improving flood prediction capabilities.

New Hydrological Insights for the Region:

A key innovation in this paper is the use of residual-based modeling to address the non-linearity between precipitation images and water levels, leading to a Rain2RiverNetwork with residual-corrections (R2RNet). The deep learning architecture integrates (2+1)D convolutional neural networks for spatial and temporal feature extraction with LSTM for timeseries forecasting. The results of our evaluation demonstrate the potential of R2RNet for capturing extreme events with good prediction accuracy for 4-hours ahead as Nash–Sutcliffe efficiency=0.93, Bravais–Pearson=0.98 and Index of Agreement=0.99. These results are comparable to the conventional model using upstream data for predictions, with NSE=0.95, BP=0.97 and IoA=0.98. These results quantitatively underscore the enhanced predictive capability of R2RNet, making it independent of additional hydrological upstream measurements.