the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Sep 2026
ALPAKAS: Alpine hydrographs and catchment attributes for karst springs
Abstract. Approximately 40 % of the European Alps consist of karstifiable rock. Due to their distinct recharge, storage, and discharge properties, karst aquifers are key components of the hydrological cycle and water supply in the Alpine region. However, large-sample datasets enabling comparative and data-driven analyses of karst spring discharge are currently lacking. Here, we present ALPAKAS (ALPine hydrographs and catchment Attributes for KArst Springs), the first large-sample benchmark dataset specifically dedicated to karst springs. ALPAKAS comprises 242 daily and 183 hourly quality-controlled and consistently preprocessed discharge time series from karst spring stations distributed across the EU Strategy for the Alpine Region (EUSALP) area, with stations located in seven countries. The dataset is complemented by both existing catchment delineations and circular catchment approximations, catchment-aggregated meteorological time series at multiple spatial scales, and a comprehensive set of static catchment attributes describing topography, positioning, meteorology, land cover, soil properties, and hydrogeology. In addition, the dataset includes karst-specific indices directly derived from the karst spring hydrographs.
ALPAKAS is available via Zenodo at https://doi.org/10.5281/zenodo.19329694 (Joger et al., 2026) under an open-access licence and is accompanied by comprehensive documentation. By providing a harmonized, large-sample compilation of karst spring catchments across diverse Alpine settings, the dataset enables comparative analyses, model benchmarking, and robust assessments of hydrogeological processes and climate change impacts.
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Status: open (until 16 Oct 2026)
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RC1: 'Comment on essd-2026-707', Pierre-Yves Jeannin, 01 Oct 2026
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AC1: 'Reply on RC1', Felix Joger, 13 Oct 2026
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Dear Pierre-Yves Jeannin,
Thank you for your thoughtful review, which has helped us to further improve the quality and clarity of the manuscript.
Please find our detailed responses to all your comments in the attached PDF file.
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AC1: 'Reply on RC1', Felix Joger, 13 Oct 2026
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CC1: 'Comment on essd-2026-707', Ruben Carl Kubina, 08 Oct 2026
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Dear authors,
Thank you for the excellent dataset and for your work.
I have a few comments regarding the dataset and the preprint.
- The attribute `ALPAKAS_ID` is spelled differently in the files `ALPAKAS_station_meta.csv` and `catchment_approx.geojson`.
- The attribute `catch_expert_shp` is set to “false” for every spring, even though you provide the delineations of 151 expert delineated catchments.
- In the attribute `area_catch_expert` there are many zeros that should likely be replaced with “NA”.
- I found one discharge time series of a daily station in the hourly stations folder (FR-0001) with wrong labels of `temp_res`. For this measuring station, the hourly and daily values should be identical, but they differ from one another. Perhaps you could double-check whether the hourly monitoring stations actually have an hourly resolution.
- In appendix A, you could describe in more detail which variables were used for the water balance to estimate the recharge area. In your code you use the total precipitation, the total evaporation, surface runoff, snow depth water equivalent, and the soil water. However, it is not clear to me whether all of the variables are actually used for the provided approximate catchments.
Citation: https://doi.org/10.5194/essd-2026-707-CC1 -
AC2: 'Reply on CC1', Felix Joger, 13 Oct 2026
reply
Dear Ruben Carl Kubina,
Thank you for your detailed comments on the dataset and code. We find them very valuable and appreciate your contribution. We will address the points you raised as follows:
- Thank you for pointing this out. We will correct this in the GEOJSON files to consistently use “ALPAKAS_ID”.
- We will correct this metadata in the dataset.
- Thank you for highlighting this issue. It affects catchment delineations that could not be published due to licensing restrictions but for which we provide catchment attributes. We will correct this so that the actual catchment areas are displayed instead of zero values for those sites.
- Thank you for making us aware of this error. This time series is actually only available at daily resolution. The observations were incorrectly assigned a time of 00:00 and subsequently aggregated using time-weighted averaging, resulting in daily values that differed from the original observations. We will correct this by providing the original daily observations and updated static hydrological attributes in a revised dataset version on Zenodo. We will also update the corresponding metadata in the data and code repositories. In addition, we will correct the number of hourly time series to 182 in the revised manuscript.
- We agree that this could be made clearer and will specify in the appendix of the revised manuscript that the water balance estimate accounts for precipitation, evapotranspiration, and changes in soil water and snow storage. Please note that surface runoff is not included and is therefore disabled in the corresponding code. We will make this more explicit in the code.
Citation: https://doi.org/10.5194/essd-2026-707-AC2
Data sets
ALPAKAS: ALPine hydrographs and catchment Attributes for KArst Springs Joger et al. https://doi.org/10.5281/zenodo.19329694
Model code and software
ALPAKAS – Code Repository Joger and Thinius https://github.com/FelixJoger/ALPAKAS
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Nice effort! Thank you for this work and I believe this is a really interesting basis for future work.
I only have two concerns (beside a series a "little" remarks in the pdf):
1) In the data gathering, a strong effort was dedicated to provide parameters for catchment areas, which are mostly very uncertain. Furthermore, key-parameters controlling karst spring discharge in alpine context are not considered at all: I mean here snow cover and evapotranspiration. Therefore, I don't really understand why so much effort was dedicated to details on uncertain catchment areas and none on snow and evapotranspiration. This should be explained somehow in chapters 6 and 7, and possibly in chapter 1 as well. It could be mentioned that further parameters should be added in the future.
2) The overall uncertainty of the discharge data is poorly discussed (chapter 6). Two types of uncertainties should be discussed:
a) uncertainty about measuring the complete discharge of one karst hydrogeological system (how much water of the hydrogeological system does flow around the gaging station?), and
b) What is the uncertainty on the absolute values of the provided measurements? Are provided values measured within 10, 50, 100 or more % of the real value? Is this uncertainty the same for all values or larger for low- and high-water conditions? How significant are the differences?
This uncertainty assessment should be more developed in chapter 6, and given as a track to follow for future improvement of the data characterization.
You can find more details on these two point in Jeannin, Malard & Sinreich 2026.
As the gathered dataset is expected to be used for deep-learning techniques any bias on the data can become a major issue. I really believe that deep-learning modelling would be strongly improved by considering the two concerns above.
Therefore I recommend to accept the paper with minor revisions as it is a good paper. Revisions must include a few sentences on the two concerns mentioned above.