General comments:

First of all, congratulations on your effort for this manuscript. It is well written and the care put into the document is apparent. This manuscript’s main goal is to introduce the reader to a new dataset called LamaH-Ice. It goes into depth on the data was collected, the different attributes of the catchments, analysis of hydrologic signatures and the uncertainties within the data. It is through and rigorous in describing all aspects of the dataset. This could be very intriguing for Arctic hydrologists who do not have many long term datasets to work with. This paper gives confidence to the validity of the data collected and the various datasets utilized.  

Specific comments:

While well written, the manuscript does suffer from being excessively wordy. I would suggest shortening some sections, especially Catchment attributes (section 5).

Between lines 275-287: The authors stated that there is an underestimation of precipitation due to the use of ERA5-Land dataset, and that the decision to continue with the ERA5-Land dataset comes from consistency with other LSH datasets. If CARRA pr Rav-II datasets provide better estimations of data, should those not be used? Rather than less-accurate data even if it matches with other LSH datasets?

Technical corrections:

Suggestions are added into the pdf as comments.

This manuscript presents an LSH dataset for Iceland. I believe it is a valuable addition to the discipline – the presence of glaciers and snow cover in the basins makes it very valuable to evaluate hydrological processes in cold regions and related to glacier hydrology, a component often neglected in LHS due to their region of interest. I believe this dataset will be very helpful to the hydrology community.

I enjoyed reading the manuscript and thought it was well-organized and clearly laid-out the attributes and organization of the data. My main concern relates to the precipitation – after showcasing that the ERA-5 product is underestimating precipitation, and providing alternatives, the climate attributes using precipitation are still done using the ERA-5 data. While I understand the need for consistency with other LamaH datasets, I think providing basin attributes that are likely to be erroneous is problematic. Despite the disclaimer in the manuscript, I fear people will use the existing dataset anyway, which will result in flawed conclusions. I suggest providing the attributes that use precipitation using the more appropriate Carra or RAV-II datasets in the main attribute table and adding the ERA-5 one as a comparison with the existing LamaH dataset. I see that this is somewhat already done by adding P_RAV in the water balance attribute and in the meteorological timeseries, but I think it should also be done in the catchment attribute.

Another option would be to add a comparison of ERA-5 precipitation and CARRA/RAV-II to justify if climatic indices that are based on precipitation values (as shown in Table A4) are reasonable. For example, despite the total amount of precipitation of ERA5 being too low, is the seasonality preserved?

Overall, I look forward to using this dataset and appreciate the efforts put into compiling it in a consistent manner. Below are minor comment about the text and dataset.

Detailed about the dataset and text:

In the text, the specific labels (for example, “aridity” or “frac_snow”) of the attribute is added directly in the text for climate indices. It would be helpful to include those for the glacier attribute and topographical attributes as well. It makes finding the description and discussion of the attribute in the text when looking at the data easier. For example, the “fsca_full_watershed in section 4.3.

 L25: There are more up-to-date references about the importance of snow and ice water resources.

L51: About the application of machine learning in hydrology: could you add a citation here?

L102: Could you mention the average elevation of these glaciers? Potentially in the glacier attribute section?

L111: Could you add a citation about the categories of river classification in Iceland?

Fig1: Could you also show the delineation of the basins A in the map?

L181: 51 catchments out of how many are classification A?

Fig 2: The labelling of subfigure is inconsistent – here is it upper right, upper left, but the rest is (a) (b) (c). Could the letter system be implemented throughout the manuscript?

L224: The line looks green to me.

L313: The data has consistent gaps for Nov-Jan-Feb due to lack of light (I assume). This would be good to mention.

L329: outlier removal which causes some additional gaps in the dataset?

L441 “water bodies” is missing the attribute label (“wat_frac”)

Fig 11: the colour between no impact and B is hard to distinguish. If these colors are to be consistent with the Lamah dataset, I understand, but if not, I suggest changing it to a colour with more contrast.

About the dataset:

Attribute elev_ran is missing from table A3.

Attribute “degimpact” in Table A1 is also named “degree” in the catchment attribute table.

In the annual timeseries/glacier timeseries, the headers are missing the YYYY values “,annual_net_MB,summer_MB,winter_MB,glac_area_perc,glac_area_km2” instead of ” YYYY (or date_time) ,annual_net_MB,summer_MB,winter_MB,glac_area_perc,glac_area_km2).

In the attribute table in the dataset, I could not find g_lon, g_lat, g_frac_dyn and g_area_dyn.

Is there a reason why the columns in the manuscript tables and in the catchment attribute tables in the dataset are not in the same order? For example, in the Topographical attributes, in Table A3, it goes area_calc, elev_mean, elev_med, elev_std, slope_mean, m_vert_dist,  nvert_dist, nvert_ang, asp_mean, elon_ratio, strm_dens, but in the catchment attribute table : area_calc,elev_mean,elev_med,elev_ran,slope_mean,elev_std,asp_mean, strm_dens,mvert_dist, ,elon_ratio , mvert _ang – with geological attributed inserted between the columns.

Is there a reason to not group the attributes of specific categories together as presented in the manuscript tables? It would make navigating the datasets easier.