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

TL;DR

This paper introduces a comprehensive, spatially resolved dataset for rainfall streamflow modeling across five central European river basins, enabling advanced neural network hydrological studies beyond traditional lumped models.

Contribution

It provides a harmonized, multi-decadal dataset with meteorological and ancillary data on a 9km grid, along with code for integrating river discharge data for end-to-end modeling.

Findings

Dataset covers five major European basins.

Includes meteorological, soil, land cover, and orography data.

Supports neural network-based hydrological modeling.

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.