Dear authors,

please find my review of this manuscript and dataset in the attachment. There is a comment for every highlighted instance in the commented manuscript. I hope it is accessible to you, otherwise, feel free to reach out.

Best wishes,

Franziska Clerc-Schwarzenbach

Review of the manuscipt “CAMELS-FI: hydrometeorological time series and landscape properties for 320 catchments in Finland” by I. Seppä et al. at ESSD.

The manuscript and associated data are well written, well organized, and they offer a valuable contribution to hydrology. Please find my main concerns below.

Major comments

1. L 106. “A Minimum of five years (1826 days) of observations required. We considered this sufficient to calculate streamflow signatures.” Many CAMELS data sets, including the first one for the USA, require about 20 or 30 years of Q observations for inclusion. For long-term studies, WMO recommends at least 30 years of hydrometeorological observations. The reason for these stricter requirements is that hydrometeorological variables often have long-term persistence (e.g. O’Connell et al., 2022) and temporal clustering (e.g., Chagas et al., 2024; Lun et al., 2020), thus, 5 years of data may not be representative of long-term hydrological behavior. In order to include the hydrological indices of catchments with fewer than about 20 years of data, it would be interesting for the manuscript to assess whether these values are representative of long-term conditions, for example by comparing with neighboring catchments that have longer data.

2. L 211. “In addition to providing the aforementioned data, we combined the data into one convenience PET attribute from 1981, which was also used for calculating climatic signatures (see section 5.2). This was done by using snow evaporation when the snow depth of the catchment was over zero, and filling the snow-free days with FMI PET if it was available; the remainder was completed by ERA5-Land potential evaporation.” Combining the time series from two different sources can be problematic because each source is usually built from completely different data and models, particularly evapotranspiration variables which often have high uncertainties. In order to keep the combined PET time series, it would be interesting to include a more in-depth investigation of whether that is appropriate, including comparisons with other data sources, where possible, and assessing if it does not introduce step changes in PET and other spurious artifacts.

3. Section 4.1 Hydrologic time series. The manuscript could benefit from a more in-depth description of how the streamflow measurements are conducted by the data providers (SYKE and ELY), including how the measurements have changed over time (if such information is available), how the data were quality checked, and whether data quality flags are available and included. Some description is already present in Section 7, but I believe that it could be expanded. If the data providers have documentation describing how the measurements are or were conducted, it may be worth citing it in the manuscript.

4. I could not find geological indices. The manuscript mentions that hydrolithological data are not available. However, the manuscript could include other variables such as geological type, to align the data set more closely with the CAMELS data sets.

Minor comments

5. Fig. 1 and Section 5.6 (Human influence). Does the regulation referred to here indicate regulation through artificial dams? What are the criteria for defining the regulation classes (not regulated, minor and major regulation)? It is not clear in either the figure caption or the section.

6. Table 1. The difference between gauge_id and basin_id is not clear. The gauge_id is described as “catchment identifier”.

7. Table 3. How was the attribute “regulation_level” calculated?

8. L 128. “Kammonen, Luiro (1358)”. Is 1358 the gauge_id? It may be worth clarifying.

References

Chagas, V. B. P., Chaffe, P. L. B., & Blöschl, G. (2024). Drought-Rich Periods Are More Likely Than Flood-Rich Periods in Brazil. Water Resources Research, 60(10), e2023WR035851. https://doi.org/10.1029/2023WR035851

Lun, D., Fischer, S., Viglione, A., & Blöschl, G. (2020). Detecting Flood-Rich and Flood-Poor Periods in Annual Peak Discharges Across Europe. Water Resources Research, 56(7), e2019WR026575. https://doi.org/10.1029/2019WR026575

O’Connell, E., O’Donnell, G., & Koutsoyiannis, D. (2022). On the spatial scale dependence of long‐term persistence in global annual precipitation data and the Hurst Phenomenon. Water Resources Research, e2022WR033133.