A Spatiotemporal Radar-Based Precipitation Model for Water Level Prediction and Flood Forecasting

2025-03-25

AI Summary

To address the low reliability and short lead time of flood warning systems in Goslar and Göttingen, Lower Saxony, Germany, this study proposes a radar-precipitation-driven end-to-end water-level forecasting model. The model integrates RADOLAN radar precipitation imagery with local hydrological sensor data, eliminating reliance on upstream hydrological inputs. We introduce a novel Residual-enhanced Spatio-Temporal Radar Precipitation Model (STRPMr), which employs a (2+1)D convolutional neural network to extract spatiotemporal precipitation features, an LSTM to capture temporal dynamics, and residual learning to correct the nonlinear precipitation–water-level mapping. Experimental results demonstrate substantial improvement in 20-minute nowcasting accuracy, successful detection of abrupt water-level surges during the 2017 extreme rainfall event, and strong generalizability and regional transferability across RADOLAN-covered areas. This work establishes a new paradigm for real-time flood forecasting in data-scarce catchments.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Study Region: Goslar and G""ottingen, Lower Saxony, Germany. Study Focus: In July 2017, the cities of Goslar and G""ottingen experienced severe flood events characterized by short warning time of only 20 minutes, resulting in extensive regional flooding and 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. Additionally, the study examines how precipitation influences water level forecasts in G""ottingen. 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 Spatiotemporal Radar-based Precipitation Model with residuals (STRPMr). Unlike traditional hydrological models, our approach does not rely on upstream data, making it independent of additional hydrological inputs. This independence enhances its adaptability and allows for broader applicability in other regions with RADOLAN precipitation. The deep learning architecture integrates (2+1)D convolutional neural networks for spatial and temporal feature extraction with LSTM for timeseries forecasting. The results demonstrate the potential of the STRPMr for capturing extreme events and more accurate flood forecasting.

Problem

• Develops radar-based model for flood forecasting with short warning times
• Integrates spatiotemporal precipitation data to improve water level predictions
• Uses deep learning to capture extreme events without upstream data

Innovation

• Uses residual-based modeling for non-linearity
• Integrates (2+1)D CNNs with LSTM
• Independent of upstream hydrological data