Skip to content
DraconiticResearch
Search Sources API
194,161 studies31 sourcesUpdated 1 min ago
Peer-reviewedCC BYEarth system science data

EARLS: a runoff reconstruction dataset for Europe

  • Daniel Klotz1,2,3,4
  • Peter Miersch5
  • Thiago Victor Medeiros do Nascimento6,7
  • Fabrizio Fenicia7
  • Corinna Frank8,9,10,11
  • Martin Gauch12,13,3,14
  • Jakob Zscheischler A13,5,14
14 affiliations
  1. 1Helmholtz Centre for Environmental Research (Leipzig, Germany)
  2. 2Interdisciplinary Transformation University Austria (Linz, Austria)
  3. 3Department of Compound Environmental Risks, Helmholtz Centre for Environmental Research — UFZ, Leipzig, Germany
  4. 4Google Research, Munich, Germany
  5. 5Department of Compound Environmental Risks, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
  6. 6Department of Geography, University of Zurich, Zurich, Switzerland
  7. 7Eawag: Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
  8. 8Climate Change, Extremes and Natural Hazards in Alpine Regions Research Center CERC, Davos Dorf, Switzerland
  9. 9Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
  10. 10WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland
  11. 11Google Research, Zurich, Switzerland
  12. 12Google Research, Zürich, Switzerland
  13. 13Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden/Leipzig, Germany
  14. 14Department of Hydro Sciences, TUD Dresden University of Technology, Dresden, Germany
Open at sourceDOICite

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)

Related

  1. cGRHyMoLAP: A compact continuous state-space formulation of GRHyMoLAP for rainfall–runoff modelling
    Sianou Ezéckiel Houénafa, Kyriakos Kandris, Evina Katsou
    EGUspherePreprintCC BY 4.0General earth scienceEGUsphere
  2. An interpretable model for efficient and robust runoff forecasting via causal-linear attention and adaptive KAN decoding
    Wei-can Tian, Lin Cheng, Wen-chuan Wang
    Scientific ReportsPeer-reviewedCC BY-NC-ND 4.0Full textEnvironmental science & pollutionCrossref
  3. An integrated ArcGIS Python toolbox for SCS-CN event runoff estimation and flood screening in data-scarce Ethiopian basins
    Kalid Hassen Yasin, Beyan Ahmed Yuya, Freya Muir +1
    Scientific ReportsPeer-reviewedCC BY-NC-ND 4.0Full textHydrology & soilsCrossref
  4. Synergistic valorization of oil palm empty fruit bunch and pome sludge for balanced erosion control and runoff quality
    Naseem Akhtar, Yazan Khalaf Ali Almanasir, Muhammad Izzuddin Syakir +8
    Scientific ReportsPeer-reviewedCC BY-NC-ND 4.0Full textHydrology & soilsCrossref
  5. Wildfires diminish vegetation control on surface runoff
    Yuanyuan Peng, Kun Shi, Shun Bi +1
    Science AdvancesPeer-reviewedCC BY-NCClimate scienceCrossref

Cite

BibTeXRIS
BibTeX
@article{klotz2026earls,
  title = {{EARLS: a runoff reconstruction dataset for Europe}},
  author = {Daniel Klotz and Peter Miersch and Thiago Victor Medeiros do Nascimento and Fabrizio Fenicia and Corinna Frank and Martin Gauch and Jakob Zscheischler A},
  journal = {Earth system science data},
  year = {2026},
  month = jul,
  doi = {10.5194/essd-18-5485-2026},
  url = {https://doi.org/10.5194/essd-18-5485-2026},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - EARLS: a runoff reconstruction dataset for Europe
AU  - Daniel Klotz
AU  - Peter Miersch
AU  - Thiago Victor Medeiros do Nascimento
AU  - Fabrizio Fenicia
AU  - Corinna Frank
AU  - Martin Gauch
AU  - Jakob Zscheischler A
PY  - 2026
DA  - 2026/07/27
JO  - Earth system science data
DO  - 10.5194/essd-18-5485-2026
UR  - https://doi.org/10.5194/essd-18-5485-2026
AB  - 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)
SN  - 1866-3508
SN  - 1866-3516
ER  -