the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
EARLS: A runoff reconstruction dataset for Europe
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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Status: final response (author comments only)
- RC1: 'Comment on essd-2024-450', Wouter Berghuijs, 03 Jan 2025
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RC2: 'Comment on essd-2024-450', Juliane Mai, 28 Feb 2025
Dear Dr. Klotz and co-authors,
It was a pleasure to review your manuscript on “EARLS: A runoff reconstruction dataset for Europe”, submitted to the Earth System Science Data journal. I found your study to be well-structured, informative, and a very valuable contribution to the field.
I have provided a list of minor comments for your consideration. Most of these do not require urgent revisions, but I have highlighted in blue those that may benefit from some additional attention.
My main comments focus on the following aspects:
- Slight inconsistencies between datasets – While these are not major issues and likely do not require changes, I would appreciate your expert opinion on what potential impacts these inconsistencies might have on your findings.
- Figure clarity – Some suggestions are provided to improve comparisons and ease interpretation.
- Simulation data and uncertainties – Comments regarding the data shared to ensure transparency and clarity.
The detailed comments are provided in the attached PDF.
Overall, I am recommending minor revisions. I appreciate the effort you have put into this work and would be happy to have another look at the revised version.
Best regards,
Julie
Data sets
EARLS: European aggregated reconstruction for large-sample studies Daniel Klotz et al. https://doi.org/10.5281/zenodo.13864843
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