the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluating the quality of the E-OBS meteorological forcing data in EStreams for large-sample hydrology studies in Europe
Abstract. To conduct large-sample hydrological studies over large spatial domains, standardized meteorological forcing data are often desired. For large-sample studies across Europe, the EStreams dataset and catalogue satisfies this demand. In EStreams, the meteorological time series are obtained from the Ensemble Observation (E-OBS) product which is available for all of Europe. Due to the large spatial extent of this dataset, limitations of data quality have to be expected when the dataset is compared to smaller-scale datasets, e.g., national level. In this study, we compare the meteorological time series included for 3423 catchments in EStreams to nine smaller datasets (mostly CAMELS datasets). We assess how the different meteorological data impact the performance of a bucket-type hydrological model. For most catchments, the precipitation amounts derived from E-OBS are lower than the ones from the CAMELS datasets, while the temperature and the potential evapotranspiration values are higher. Model performances tend to be (slightly) lower when the E-OBS data are used than when the CAMELS datasets are used for calibration. Exceptions arise when the CAMELS data were derived from global datasets rather than national products, as well as when the station density in the E-OBS data is high. This study provides the first assessment of the E-OBS data at a continental scale for hydrological applications and shows that, despite some limitations, the dataset offers a reasonable basis for large-sample hydrological modelling across Europe.
- Preprint
(12779 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 20 Oct 2025)
- RC1: 'Comment on egusphere-2025-3710', Alexander Dolich, 23 Sep 2025 reply
-
CC1: 'Comment from EGU peer review training', Adriaan J. (Ryan) Teuling, 29 Sep 2025
reply
Dear authors, editor,
As editor of HESS, I have participated in the recent EGU peer review training for which I selected the current manuscript as one of the possible manuscripts to review for the participants. One participant asked me to share the contents of the review in the online discussion in the hope it might help to improve the manuscript, which I hereby do. I have left out the introduction to the review, and only copied the (specific) comments.
best regards
Ryan Teuling
Main points:
Regarding the methods, the authors have included all catchments in their studies, regardless of being impacted by human activities or not (L94-97). This can be questionable, as only the climate forcings are used as the hydrological model inputs. This modeling approach may only be applicable to the natural sites without human intervention. Among the 3423 catchments, these include much noise in the modeling results. Importantly, this approach makes it hard to differentiate if one type of meteorological data is better than the other one because of natural condition or human intervention or the quality (or station density) of the meteorological data itself. What about other possible governing factors, such as climate types, topography, land use and land cover, and geology? These are all not addressed or analyzed by coming to the conclusion due to spatial resolution and station density. Simply saying one is better than the other without analyzing the possible governing factors could limit the applicability and generalization of the research outputs. Therefore, more analysis on process-based understanding and transferable knowledge is needed to make robust conclusions supported by the evidence.
The authors adopted the potential evapotranspiration data derived from different approaches: it is calculated with the simplest approach (only temperature based) in E-OBS, but with different varieties of methods in the CAMELS. If the authors want to do a comparison, it should be “apple” to “apple”. It is recommended that the potential evapotranspiration should be calculated with the same methods for both types of datasets.
Regarding the results, it would be more useful to state the governing factors (climatology, topography, land use, etc.) why E-OBS has over- or underestimations compared with CAMELS, besides simply stating which countries or regions have higher or lower meteorological values. More exactly, why one dataset is better than the other one in some countries yes while some countries not?
Another key aspect is that the authors calibrate the models individually with different climate datasets. Therefore, not only the climate data are different, but the model parameters are different. Therefore, the model performance lower or higher is not only due to climate data quality but also the model parameters.
Specific comments:
L10: Maybe mentioned the spatial resolution of the meteorological data from the E-OBS?
L16: Model performance is SLIGHTLY lower when E-OBS data are used compared with CAMELS data: is this difference statistically significant?
L48-53: the authors actually come to the same conclusion as the referred literature, and mentioned the same thing in the abstract. So what is the added value of evaluating E-OBS vs. CAMELS? Just because of a larger scale of detailed dataset?
L84: Why exclude the catchments with area more than 2000 km2? What is the impact or relation between the catchment area and the meteorological data?
L123-132: Why are the annual differences of precipitation and evapotranspiration between the datasets compared but not the seasonal differences? While for temperature, you compared the daily differences?
Figure 4: What are the reasons for the different model performance among the countries? What are the governing factors? Simply stating the KGE is higher here or lower there without providing further reasons sounds not helpful.
Figure 6: The important thing is not the exact number of catchments in a country where E-OBS dataset is better or worse than the CAMELS datasets, but why E-OBS is better/worse than the CAMELS in these catchments?
L275-278: Why is the model performance lower in Great Britain which shows opposite behavior? Please explain.
Figure 7: Simply stating the station density plays the key role seems not convincing, as the author stated that other factors may also play a role. It would be more interesting to analyze other factors as well? Are the relationships between the station numbers and the KGE statistically significant?
Figure 8: What about a trend assessment on the data? Is there a significant relationship between model performance and aridity index?
L366-369: it is too assertive and not supported by evidence. It is a very simple method to calculate the potential evapotranspiration which does not consider solar radiation impact. It is also too assertive to say different calculation approaches of potential evapotranspiration will not change the results.
Citation: https://doi.org/10.5194/egusphere-2025-3710-CC1 -
EC1: 'Reply on CC1', Albrecht Weerts, 29 Sep 2025
reply
Citation: https://doi.org/
10.5194/egusphere-2025-3710-EC1
-
EC1: 'Reply on CC1', Albrecht Weerts, 29 Sep 2025
reply
Viewed
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
2,084 | 33 | 8 | 2,125 | 10 | 11 |
- HTML: 2,084
- PDF: 33
- XML: 8
- Total: 2,125
- BibTeX: 10
- EndNote: 11
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
Thank you to the authors for this study, which shows that E-OBS offers high-quality meteorological data that can be used for large-sample hydrology studies in Europe, while also highlighting the limitations of E-OBS clearly. The manuscript is easy to follow and of overall high quality. There are some minor comments that should be addressed, the overall quality of the manuscript is already good.
Minor specific comments:
It would also be interesting to see how the different CAMELS precipitation data was collected / processed (maybe not so easy to find out). I only know about CAMELS-DE, but HYRAS is also based on interpolated station data (I guess mostly the same stations as used for E-OBS), which would explain the relative similarities, but it is still interesting to see that there are differences (maybe due to different interpolation / processing methods or the coarser resolution of E-OBS)
So in general, we now have a great (still growing) coverage of streamflow data in Europe through different national CAMELS datasets now. With the addition of, especially compared to ERA5, high quality E-OBS forcing data, readily available in EStreams, there is a great data basis for LSH studies covering the entirety of Europe.
Technical Corrections