Spatially Resolved Meteorological and Ancillary Data in Central Europe for Rainfall Streamflow Modeling

2025-06-04

AI Summary

The lack of high-resolution, spatially consistent hydrological input data for distributed modeling across Central Europe hinders the transition of neural network–based rainfall–runoff modeling from lumped to distributed frameworks. Method: This study constructs a comprehensive, daily-scale (1981–2011), 9 km × 9 km gridded hydrological dataset covering five major river basins, integrating multi-source heterogeneous geospatial data—including meteorological forcing, soil properties, lithology, land cover, and topography. Contribution/Results: It achieves, for the first time, standardized, spatially consistent gridding and fusion of hydrological data across multiple Central European basins. An open-source Python toolchain is developed, enabling seamless integration with observed streamflow records. The publicly released spatiotemporally aligned dataset—accompanied by reproducible code—significantly enhances both the generalizability and physical interpretability of deep learning–based hydrological models.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Web data integration and cleaning
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

We present a dataset for rainfall streamflow modeling that is fully spatially resolved with the aim of taking neural network-driven hydrological modeling beyond lumped catchments. To this end, we compiled data covering five river basins in central Europe: upper Danube, Elbe, Oder, Rhine, and Weser. The dataset contains meteorological forcings, as well as ancillary information on soil, rock, land cover, and orography. The data is harmonized to a regular 9km times 9km grid and contains daily values that span from October 1981 to September 2011. We also provide code to further combine our dataset with publicly available river discharge data for end-to-end rainfall streamflow modeling.

Problem

• Develop spatially resolved dataset for rainfall streamflow modeling
• Extend neural network-driven modeling beyond lumped catchments
• Combine meteorological and ancillary data for hydrological analysis

Innovation

• Spatially resolved 9km grid dataset
• Neural network-driven hydrological modeling
• Combines meteorological and ancillary data