Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Zeitschriften |
| DOI | 10.5194/essd-18-5485-2026 |
Lizenz  |
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| Titel (primär) | EARLS: A runoff reconstruction dataset for Europe |
| Autor | Klotz, D.; Miersch, P.
; do Nascimento, T.V.M.; Fenicia, F.; Gauch, M.; Zscheischler, J.
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| Quelle | Earth System Science Data |
| Erscheinungsjahr | 2026 |
| Department | CER |
| Band/Volume | 18 |
| Heft | 7 |
| Seite von | 5485 |
| Seite bis | 5504 |
| Sprache | englisch |
| Topic | T5 Future Landscapes |
| Daten-/Softwarelinks | https://doi.org/10.5281/zenodo.13864842 |
| Supplements | Supplement 1 |
| 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 5000 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.13864842 (Klotz et al., 2026b). |
| Klotz, D., Miersch, P., do Nascimento, T.V.M., Fenicia, F., Gauch, M., Zscheischler, J. (2026): EARLS: A runoff reconstruction dataset for Europe Earth Syst. Sci. Data 18 (7), 5485 - 5504 10.5194/essd-18-5485-2026 |
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