What question did this study set out to answer?

The aim is to develop a comprehensive dataset for understanding hydrological processes across Europe.

July 30, 2026Earth system science data3 citationsOpen Access

EARLS: a runoff reconstruction dataset for Europe

DKDaniel Klotz PMPeter Miersch TNThiago Victor Medeiros do Nascimento

Key Points

The aim is to develop a comprehensive dataset for understanding hydrological processes across Europe.
Developed the EARLS dataset using a Long Short-Term Memory (LSTM) based rainfall–runoff model trained on over 5,000 basins.
Performed quantitative evaluations on held-out basin sets and qualitative assessments against previous hydrological studies.
Covered streamflow data for more than 10,000 basins from 1953 to 2023.
The LSTM model demonstrated high accuracy in predicting streamflow with significant improvements in ungauged basins.
EARLS-based peak flows and flood timings aligned well with findings from earlier large-scale hydrological studies.
Uncertainty estimates offered insights into the reliability of predictions across the dataset.

Abstract

Abstract. Data drives our understanding of hydrological processes, supports model development, and enables anticipatory water management. This contribution introduces EARLS: European Aggregated Reconstructions for Large-sample Studies. EARLS offers daily streamflow reconstructions for more than 10,000 basins in Europe including uncertainty estimates, covering the period from 1953 to 2023. The reconstruction is derived from a single Long Short-Term Memory (LSTM) based rainfall–runoff model trained on more than 5,000 basins. LSTMs represent the state of the art in rainfall–runoff modeling and are well suited to provide predictions in ungauged basins. We evaluate the quality of the reconstruction through quantitative evaluation on two held-out sets of basins and by conducting a qualitative assessment that compares EARLS-based peak flows and flood timing to previous large-scale hydrological studies. EARLS represents a new generation of datasets that harness the capabilities of Deep Learning to obtain accurate and high-resolution data. EARLS is available at https://doi.org/10.5281/zenodo.13864843 (Klotz et al., 2024b)

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