the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
DebDab: A database of supraglacial debris thickness and physical properties
Abstract. Rocky debris covers around 7.3 % of the global glacier area, influencing ice melt rates and the surface mass balance of glaciers, making the dynamics and hydrology of debris-covered glaciers distinct from those of clean-ice glaciers. Accurate representation of debris in models is challenging, as measurements of the physical properties of supraglacial debris are scarce. Here, we compile a database of measured and reported physical properties and thickness of supraglacial debris that we call DebDab and that is open to community submissions. The majority of the database (90 %) is compiled from 172 sources in the literature, and the remaining 10 % has not been published before. DebDab contains 8,737 data entries for supraglacial debris thickness, of which 1,941 entries also include sub-debris ablation rates, 177 data entries of thermal conductivity of debris, 160 of aerodynamic surface roughness length, 79 of debris albedo, 59 of debris emissivity and 37 of debris porosity. The data are distributed over 83 glaciers in 13 regions in the Global Terrestrial Network for Glaciers. We show regional differences in the distribution of debris thickness measurements in DebDab, and fit Østrem curves for the 19 glaciers with sufficient debris thickness and ablation data. DebDab can be used for energy balance, melt, and surface mass balance studies by incorporating site-specific debris properties, or to evaluate remote sensing estimates of debris thickness and surface roughness. It can also help future field campaigns on debris-covered glaciers by identifying observation gaps. DebDab’s uneven spatial coverage points to sampling biases in community efforts to observe debris-covered glaciers, with some regions (e.g. Central Europe and South Asia) well-sampled, but gaps in other regions with prevalent debris (e.g. Andes and Alaska). Debris thickness measurements are mostly concentrated at lower elevations, leaving higher-elevation debris-covered areas under-sampled, suggesting that our knowledge of debris properties might not be representative of the entire manifestations of debris across elevations. DebDab is an openly available dataset that aims at evolving and being updated with community submissions as new data of supra-glacial properties become available. Data described in this manuscript can be accessed at Zenodo under https://doi.org/10.5281/zenodo.14224835 (Groeneveld et al., 2024).
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Status: final response (author comments only)
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RC1: 'Comment on essd-2024-559', Morgan Jones, 24 Jan 2025
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2024-559/essd-2024-559-RC1-supplement.pdf
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AC1: 'Reply on RC1', Adrià Fontrodona-Bach, 26 Mar 2025
Dear Dr. Morgan Jones,
We thank you for your time and appreciate your review of our manuscript. We are happy you find the dataset an excellent resource and valuable contribution to debris-covered glacier research, and we thank you for the comments to improve the manuscript. We essentially agree with all your suggestions for improvement, which mostly regard technical errors and textual or figure suggestions, and we will revise our manuscript to include them all.Regarding the suggestion to rename the dataset title to “DebDB” instead of “DebDab”, we would like to suggest an intermediate name: “DebDaB”. We acknowledge that DB is a more common acronym for database, but we also find “debdab” an appealing and pronounceable acronym, which we believe will make communication and visibility of the dataset name easier.
We also agree with you that the analysis in Lines 205-210 and Figure A2 do not add much to the manuscript and the presentation of the dataset. We tried to find links between debris properties but this is out of the scope for this paper and anyway no links were found with the data presented. We will remove it in the revised manuscript. We will instead add a sentence in section 5.1 (potential applications) indicating that when more fully populated, the database could form the basis for investigating links between debris parameters.
We find the suggestion to revise all instances of “the debris” or “debris properties” to incorporate the terms “layer” or “bulk” very appropriate, as we agree that most data in DebDaB concern the entire supraglacial debris layer or the bulk properties of the layer, instead of individual clasts.
Regarding the rest of textual suggestions and corrections, we will incorporate these all upon submission of the revised manuscript.
Citation: https://doi.org/10.5194/essd-2024-559-AC1
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AC1: 'Reply on RC1', Adrià Fontrodona-Bach, 26 Mar 2025
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CC1: 'Incorporation of thermal conductivity values available for other glacier(s) in Zanskar, Ladakh Himalaya (Region 14)', Basharat Nabi, 08 Mar 2025
The paper provides detailed and valuable information on supraglacial debris. However, the authors should include available thermal conductivity values for glaciers in the Zanskar region of the Himalaya. A relevant reference for this data is the study by Romshoo et al., 2024 titled "Influence of Debris Cover on Glacier Melting in the Himalaya", published in Cold Regions Science and Technology (2024). The authors may find it useful to incorporate insights from this paper (DOI: 10.1016/j.coldregions.2024.104204) to strengthen their discussion on the thermal properties of supraglacial debris.
Citation: https://doi.org/10.5194/essd-2024-559-CC1 -
AC2: 'Reply on CC1', Adrià Fontrodona-Bach, 26 Mar 2025
Dear Dr. Basharat Nabi,
We highly appreciate your feedback on the paper and your suggestion to include thermal conductivity values from the study by Romshoo et al. (2024) in the Zanskar region in the Himalaya, and we thank you for making those data available through your manuscript. We will incorporate the thermal conductivity values, as well as the debris thickness measurements, into DebDaB. We will reach out to you as we incorporate these data into our database. Indeed, we find the discussion in Romshoo et al. (2024) about the estimated thermal conductivities useful, and we find it interesting that the calculated values of 0.9 and 1.1 W/m/K fall exactly within the mode range of thermal conductivity values in DebDaB (see Figure 5a).
This is exactly the type of feedback we would expect from the community to expand the database and make it a living, evolving tool, and we thank you for starting this already during the review process.
Citation: https://doi.org/10.5194/essd-2024-559-AC2 -
CC2: 'Reply on AC2', Basharat Nabi, 27 Mar 2025
Thank you for your response. In addition to considering the thermal conductivity values in Fig. 5a, please include this value in Fig. 3 as well, specifically for region 14.
Citation: https://doi.org/10.5194/essd-2024-559-CC2
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CC2: 'Reply on AC2', Basharat Nabi, 27 Mar 2025
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AC2: 'Reply on CC1', Adrià Fontrodona-Bach, 26 Mar 2025
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RC2: 'Comment on essd-2024-559', Anonymous Referee #2, 03 Apr 2025
General Comments
Modelling the surface melt rate and mass balance response of debris-covered glaciers to a changing climate is an ongoing challenge in glaciology, with particular issue being the limited knowledge of physical and thermal properties of debris and their variability in space and time. The open publication of the DebDab database is therefore extremely welcome and timely, and will provide a valuable resource for cryospheric scientists, and water managers and users in glacierised mountain regions. The high level of usage DebDab will receive is evidenced by >250 downloads and addition of new datasets already since its publication on Zenodo late in 2024.
Although, 90% of the dataset has been published before (10% is new data, previously unavailable) it has been compiled from 172 publications, representing a considerable research effort. Certain sources may be inaccessible to some scientists due to paywalls, and in cases data and clarifications were obtained through personal communications to the authors. As the authors note, some of the key debris parameter values used in model studies tend to come from the same 1 or 2 publications, and DebDab will provide scientists with a better understanding of their variability and what may be a suitable parameter value for a specific glacier environment.
DebDab is easy to access and download (as an Excel workbook), and I found it very easy to navigate and use. The amount of supporting information is impressive and adds value. In addition to the value of each debris property, for each entry there are up to 29 further columns of information, such as the geographical co-ordinates, collection method, descriptive statistics, source reference, and notes. The database contains additional supporting metadata ‘read me’ files, a list of publications and templates for uploading new datasets.
The debris physical parameters included in DebDab (thickness, thermal conductivity, surface roughness, albedo, emissivity and porosity) are the key ones needed, since debris extent in space is relatively well known having been mapped from satellite. DebDab is also likely to be of value in improving the delineation of areas of debris-covered ice in debris cover products. The dataset is complete in terms of being compiled from an exhaustive search of available published (and some non-published) sources but will be added to over time as a central repository for results from future field and remote sensing investigations. Most of the published sources do not give extensive consideration to errors but often this is because the measurement technique is not sophisticated. For example, the majority of debris thickness measurements are from physical excavation and a measuring stick. The greater uncertainty is due to the spatially limited, and spatially biased, sampling of debris properties which are nicely highlighted and discussed in the article. I should make clear this is not the fault of the authors, or a limitation to the database. Indeed, DebDab should serve as an inspiration to the community to agree on measurement standards and encourage targeted campaigns using direct, geophysical and remote sensing techniques to improve knowledge of supraglacial debris properties and their variability.
The article is well written and structured, and of appropriate length and detail to support the database. The language is concise and clear, with accurate and high-quality figures and tables. Mathematical formulae, symbols, abbreviations, and units are generally correctly defined and used. The presentation of the data is consistent and some helpful general analysis and visualization is presented as an overview of the data. I particularly like Figure 6, and similarly, Figures A4 and A5. There are a small number of presentation issues to address as detailed in the “Technical corrections and typographical errors” section below.
In summary, DebDab is a substantial, unique and high-quality dataset, and the authors must be thanked for their considerable effort and dedication to bring this information together in an intuitive, easy to use and ‘living’ database. It will provide the community with a valuable resource for decades to come and I am sure it will get plenty of use.
Specific comments
- The only area where the authors may wish to consider some additional discussion in their manuscript is in the consistency of the data. Debris thickness, thermal conductivity and surface roughness values were obtained through different methods by different workers. While this is acknowledged in DebDab, identification of issues with individual methods, such as potential errors or issues of quality control, would be of benefit to users. Are there examples of more than one method being applied and achieving similar values at the same site, which would lead to confidence in the result?
- Line 174-177, the dataset is too small to make inferences about debris thickness differences between regions. Rarely are measurements of sufficient spatial coverage and density to derive statistical descriptors for a single glacier, let alone a region. Make the point that the regional differences more likely reflect biases in sampling rather than actual differences.
- Lines 179-186, Figure 4, Table A1. The fitted curves are not true “Østrem curves” as they omit the rising limb of ablation rate for very shallow debris up to an “effective thickness” (Adhikary et al., 1997) which was identified by Østrem (1959) and most subsequent studies of ice melt rate dependency on debris thickness. Fitting curves to very thin debris is clearly difficult with limited measurements, but I think it is important to identify this omission from Figure 4 and the ablation rate-debris thickness equation, as its application could lead to overestimation of ablation in very thin debris areas.
- Section 5.1. Another application could be to detect changes in debris properties over time particularly in the 21st century as glaciers rapidly shrink. Repeated measurements at glaciers with large numbers of measurements could be important in determining trends in debris properties to inform models of glacier evolution at multi-decadal and longer timescales.
Technical corrections, typographical errors
Line 33, clarify that in the term ‘glaciers’ you are excluding the Antarctic and Greenland Ice Sheets (while glaciologists are familiar with this distinction, the definition will help scientists from outside this community who may wish to use the dataset).
Line 154, “thicknesses” or “thickness measurements”.
Line 196, “…the 0.016 m, also from Brock et al. (2010).”
Line 209-210, also point out that in some cases linear fits have been applied to non-linear relationships, e.g. panel A2(c).
Figure A2 – check the consistency in terminology, e.g. debris thickness is hd on panels a, b and c, but DT on panel d. On panel (f) the vertical axis units should be Emissivity (dimensionless). By convention in bivariate relationships the y-axis variable depends on the x-axis variable and should be the first property in the sub-panel title. This is done correctly for panel (j) “ Porosity vs Emissivity” but incorrectly for the other panels.
Line 271, “…the number of measurements is much lower.”
Line 295, replace “gran” with “grain”.
Line 298, I don’t think “meteodata” is a commonly used term, if you mean “meteorological data” please state this instead.
Lines 312-314. I think the correct reference here is Brock et al. (2010). This study’s debris thermal conductivity value was calculated as the mean of 25 distributed measurements so perhaps it is not so coincidental that it is similar to the mean thermal conductivity of all measurements in DebDab.
Lines 332-333, please state again that this difference could be due to sample bias.
Reference
Adhikary, S., K. Seko,M. Nakawo,Y. Ageta and N. Miyazaki. Effect of surface dust on snow melt. Bulletin of Glacier Research, 15, 85-92, 1997.
Citation: https://doi.org/10.5194/essd-2024-559-RC2
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DebDab: A database of supraglacial debris thickness and physical properties Lars Groeneveld et al. https://doi.org/10.5281/zenodo.14224835
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