[{"title":"DCG-MIP: The debris-covered glacier melt model intercomparison experiment","file":[{"success":1,"access_level":"open_access","date_created":"2026-05-18T06:07:53Z","creator":"dernst","file_id":"21886","date_updated":"2026-05-18T06:07:53Z","file_size":3168394,"checksum":"f15abad4ee360d41a3e8794f068711fc","content_type":"application/pdf","file_name":"2026_Cryosphere_Pellicciotti.pdf","relation":"main_file"}],"OA_type":"gold","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)"},"day":"02","DOAJ_listed":"1","year":"2026","citation":{"short":"F. Pellicciotti, A. Fontrodona-Bach, D.R. Rounce, C.L. Fyffe, L.S. Anderson, Á. Ayala, B.W. Brock, P. Buri, S. Fugger, K. Fujita, P. GANTAYAT, A.R. Groos, W. Immerzeel, M. Kneib, C. Mayer, S. MacDonell, M. McCarthy, J. McPhee, E. Miles, H. Purdie, E. Rets, A. Sakai, T. Shaw, J. Steiner, P. Wagnon, A. Winter-Billington, The Cryosphere 20 (2026) 1895–1928.","ieee":"F. Pellicciotti <i>et al.</i>, “DCG-MIP: The debris-covered glacier melt model intercomparison experiment,” <i>The Cryosphere</i>, vol. 20, no. 3. Copernicus Publications, pp. 1895–1928, 2026.","mla":"Pellicciotti, Francesca, et al. “DCG-MIP: The Debris-Covered Glacier Melt Model Intercomparison Experiment.” <i>The Cryosphere</i>, vol. 20, no. 3, Copernicus Publications, 2026, pp. 1895–928, doi:<a href=\"https://doi.org/10.5194/tc-20-1895-2026\">10.5194/tc-20-1895-2026</a>.","apa":"Pellicciotti, F., Fontrodona-Bach, A., Rounce, D. R., Fyffe, C. L., Anderson, L. S., Ayala, Á., … Winter-Billington, A. (2026). DCG-MIP: The debris-covered glacier melt model intercomparison experiment. <i>The Cryosphere</i>. Copernicus Publications. <a href=\"https://doi.org/10.5194/tc-20-1895-2026\">https://doi.org/10.5194/tc-20-1895-2026</a>","chicago":"Pellicciotti, Francesca, Adrià Fontrodona-Bach, David R. Rounce, Catriona Louise Fyffe, Leif S. Anderson, Álvaro Ayala, Ben W. Brock, et al. “DCG-MIP: The Debris-Covered Glacier Melt Model Intercomparison Experiment.” <i>The Cryosphere</i>. Copernicus Publications, 2026. <a href=\"https://doi.org/10.5194/tc-20-1895-2026\">https://doi.org/10.5194/tc-20-1895-2026</a>.","ama":"Pellicciotti F, Fontrodona-Bach A, Rounce DR, et al. DCG-MIP: The debris-covered glacier melt model intercomparison experiment. <i>The Cryosphere</i>. 2026;20(3):1895-1928. doi:<a href=\"https://doi.org/10.5194/tc-20-1895-2026\">10.5194/tc-20-1895-2026</a>","ista":"Pellicciotti F, Fontrodona-Bach A, Rounce DR, Fyffe CL, Anderson LS, Ayala Á, Brock BW, Buri P, Fugger S, Fujita K, GANTAYAT P, Groos AR, Immerzeel W, Kneib M, Mayer C, MacDonell S, McCarthy M, McPhee J, Miles E, Purdie H, Rets E, Sakai A, Shaw T, Steiner J, Wagnon P, Winter-Billington A. 2026. DCG-MIP: The debris-covered glacier melt model intercomparison experiment. The Cryosphere. 20(3), 1895–1928."},"intvolume":"        20","file_date_updated":"2026-05-18T06:07:53Z","publisher":"Copernicus Publications","article_processing_charge":"Yes","license":"https://creativecommons.org/licenses/by/4.0/","abstract":[{"text":"In a warming world of glacier changes, the scientific community has dedicated increasing attention to debris-covered glaciers and their response to climate. A variety of models with distinct complexity and data requirements have been developed and widely used to simulate melt under debris at different sites and scales, but their skills have never been compared. As part of the activities of the International Association of Cryospheric Sciences (IACS) Debris Covered Glacier Working Group, we present an intercomparison exercise aimed at advancing our understanding of model skills in simulating ice melt under a debris layer. We compare 15 models with different complexity at nine sites in the European Alps, Caucasus, Chilean Andes, Nepalese Himalaya and the Southern Alps of New Zealand, over one melt season. We run the models with measured meteorological data from automatic weather stations and estimated or measured debris properties. We consider four main model categories: (i) energy balance models that calculate melt by solving the physics of heat transfer to the debris layer, but require a high amount of input data; (ii) a simplified energy balance model; (iii) enhanced temperature-index models; and (iv) simple empirical temperature-index models that have been extensively used given their low data requirement but require calibration of their empirical parameters. Model performance is evaluated using on-site measurements of sub-debris melt (for all models) and surface temperature (for models based on the surface energy balance). Our results show that physically-based energy balance models and empirical temperature-index models perform in a distinct manner. At one end of the spectrum, simple temperature-index models are accurate when recalibrated or when using site-specific literature parameters, and show poor results when parameters are uncalibrated. At the other end, energy balance models show a range of performance: the most accurate energy balance models are those with the highest degree of complexity at the atmosphere-debris interface. An important data gap emerged from our experiment: the poor performance of all models at three sites was related to the poor knowledge of debris properties, and specifically of thermal conductivity. Future work should focus on both: (i) consistent data acquisition to evaluate existing models and support new model developments; (ii) advancing models by accounting for processes such as debris-snow interactions, moisture in the debris and refreezing. We suggest that a systematic effort of model development using a common model framework could be carried out in phase II of the Working Group.","lang":"eng"}],"publication_identifier":{"eissn":["1994-0424"]},"language":[{"iso":"eng"}],"page":"1895-1928","date_published":"2026-04-02T00:00:00Z","oa_version":"Published Version","status":"public","publication":"The Cryosphere","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","month":"04","issue":"3","oa":1,"type":"journal_article","has_accepted_license":"1","date_updated":"2026-05-18T06:12:56Z","volume":20,"date_created":"2026-05-07T08:48:38Z","PlanS_conform":"1","publication_status":"published","acknowledgement":"This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme grant agreement No\r\n772751, RAVEN, “Rapid mass losses of debris covered glaciers in\r\nHigh Mountain Asia”. It was also supported by the SNSF RENOIR\r\nproject “Resolving the thickness of debris on Earth’s glaciers and\r\nits rate of change (RENOIR)”, project number 204322.\r\nDavid Rounce received support from NASA-ROSES program\r\ngrants NNX17AB27G and 80NSSC17K0566. Walter Immerzeel\r\nand Jakob Steiner acknowledge support from the European Research Council (ERC) under the European Union’s Horizon 2020\r\nresearch and innovation program (grant agreement no. 676819).\r\nBen Brock acknowledges support from the EU/FP7 ACQWA\r\n(Assessing Climate impacts on the Quantity and quality of WAter) project, NERC grant NE/C514282/1, the British Council-Italian\r\nMinistry of University and Research Partnership programme and\r\nthe Carnegie Trust for the Universities of Scotland.\r\nThe authors acknowledge the International Association of\r\nCryospheric Sciences (IACS) for supporting the creation of the\r\nDebris-Covered Glaciers Working Group (DCG-WG) which enabled this model intercomparison experiment.\r\nThe authors thank Martin Heynen for producing Figs. 3 and 4.\r\nThe authors thank Duncan Quincey and Richard Essery for their\r\nconstructive feedback and comments.\r\n","department":[{"_id":"FrPe"}],"OA_place":"publisher","scopus_import":"1","quality_controlled":"1","doi":"10.5194/tc-20-1895-2026","author":[{"full_name":"Pellicciotti, Francesca","orcid":"0000-0002-5554-8087","first_name":"Francesca","id":"b28f055a-81ea-11ed-b70c-a9fe7f7b0e70","last_name":"Pellicciotti"},{"last_name":"Fontrodona-Bach","full_name":"Fontrodona-Bach, Adrià","first_name":"Adrià","id":"f06891fd-9f42-11ee-8632-a20971c43046"},{"full_name":"Rounce, David R.","first_name":"David R.","last_name":"Rounce"},{"last_name":"Fyffe","full_name":"Fyffe, Catriona Louise","first_name":"Catriona Louise","id":"001b0422-8d15-11ed-bc51-cab6c037a228"},{"full_name":"Anderson, Leif S.","first_name":"Leif S.","last_name":"Anderson"},{"last_name":"Ayala","first_name":"Álvaro","full_name":"Ayala, Álvaro"},{"last_name":"Brock","full_name":"Brock, Ben W.","first_name":"Ben W."},{"full_name":"Buri, Pascal","first_name":"Pascal","last_name":"Buri"},{"first_name":"Stefan","full_name":"Fugger, Stefan","last_name":"Fugger"},{"last_name":"Fujita","first_name":"Koji","full_name":"Fujita, Koji"},{"last_name":"GANTAYAT","id":"02734268-3e8d-11ef-80a1-cec4a088d004","full_name":"GANTAYAT, PRATEEK","first_name":"PRATEEK"},{"full_name":"Groos, Alexander R.","first_name":"Alexander R.","last_name":"Groos"},{"last_name":"Immerzeel","first_name":"Walter","full_name":"Immerzeel, Walter"},{"full_name":"Kneib, Marin","first_name":"Marin","last_name":"Kneib"},{"first_name":"Christoph","full_name":"Mayer, Christoph","last_name":"Mayer"},{"last_name":"MacDonell","full_name":"MacDonell, Shelley","first_name":"Shelley"},{"full_name":"McCarthy, Michael","first_name":"Michael","id":"22a2674a-61ce-11ee-94b5-d18813baf16f","last_name":"McCarthy"},{"first_name":"James","full_name":"McPhee, James","last_name":"McPhee"},{"last_name":"Miles","full_name":"Miles, Evan","first_name":"Evan"},{"full_name":"Purdie, Heather","first_name":"Heather","last_name":"Purdie"},{"first_name":"Ekaterina","full_name":"Rets, Ekaterina","last_name":"Rets"},{"last_name":"Sakai","full_name":"Sakai, Akiko","first_name":"Akiko"},{"last_name":"Shaw","id":"3caa3f91-1f03-11ee-96ce-e0e553054d6e","first_name":"Thomas","orcid":"0000-0001-7640-6152","full_name":"Shaw, Thomas"},{"first_name":"Jakob","full_name":"Steiner, Jakob","last_name":"Steiner"},{"last_name":"Wagnon","first_name":"Patrick","full_name":"Wagnon, Patrick"},{"last_name":"Winter-Billington","first_name":"Alex","full_name":"Winter-Billington, Alex"}],"corr_author":"1","_id":"21837","article_type":"original","ddc":["550"]},{"DOAJ_listed":"1","day":"04","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)"},"title":"Estimating robust melt factors and temperature thresholds for snow modelling across the Northern Hemisphere","OA_type":"gold","file":[{"success":1,"access_level":"open_access","date_created":"2026-06-02T09:22:26Z","file_id":"21940","checksum":"8bde4775545f9e049ea3806144b0d5f1","file_size":11250378,"date_updated":"2026-06-02T09:22:26Z","creator":"dernst","relation":"main_file","content_type":"application/pdf","file_name":"2026_HydrologyEarthSystemSciences_FontrodonaBach.pdf"}],"article_processing_charge":"Yes","file_date_updated":"2026-06-02T09:22:26Z","publisher":"Copernicus Publications","intvolume":"        30","citation":{"short":"A. Fontrodona-Bach, B. Schaefli, R. Woods, J.R. Larsen, Hydrology and Earth System Sciences 30 (2026) 2613–2636.","ieee":"A. Fontrodona-Bach, B. Schaefli, R. Woods, and J. R. Larsen, “Estimating robust melt factors and temperature thresholds for snow modelling across the Northern Hemisphere,” <i>Hydrology and Earth System Sciences</i>, vol. 30, no. 9. Copernicus Publications, pp. 2613–2636, 2026.","ama":"Fontrodona-Bach A, Schaefli B, Woods R, Larsen JR. Estimating robust melt factors and temperature thresholds for snow modelling across the Northern Hemisphere. <i>Hydrology and Earth System Sciences</i>. 2026;30(9):2613-2636. doi:<a href=\"https://doi.org/10.5194/hess-30-2613-2026\">10.5194/hess-30-2613-2026</a>","ista":"Fontrodona-Bach A, Schaefli B, Woods R, Larsen JR. 2026. Estimating robust melt factors and temperature thresholds for snow modelling across the Northern Hemisphere. Hydrology and Earth System Sciences. 30(9), 2613–2636.","chicago":"Fontrodona-Bach, Adrià, Bettina Schaefli, Ross Woods, and Joshua R. Larsen. “Estimating Robust Melt Factors and Temperature Thresholds for Snow Modelling across the Northern Hemisphere.” <i>Hydrology and Earth System Sciences</i>. Copernicus Publications, 2026. <a href=\"https://doi.org/10.5194/hess-30-2613-2026\">https://doi.org/10.5194/hess-30-2613-2026</a>.","apa":"Fontrodona-Bach, A., Schaefli, B., Woods, R., &#38; Larsen, J. R. (2026). Estimating robust melt factors and temperature thresholds for snow modelling across the Northern Hemisphere. <i>Hydrology and Earth System Sciences</i>. Copernicus Publications. <a href=\"https://doi.org/10.5194/hess-30-2613-2026\">https://doi.org/10.5194/hess-30-2613-2026</a>","mla":"Fontrodona-Bach, Adrià, et al. “Estimating Robust Melt Factors and Temperature Thresholds for Snow Modelling across the Northern Hemisphere.” <i>Hydrology and Earth System Sciences</i>, vol. 30, no. 9, Copernicus Publications, 2026, pp. 2613–36, doi:<a href=\"https://doi.org/10.5194/hess-30-2613-2026\">10.5194/hess-30-2613-2026</a>."},"year":"2026","abstract":[{"text":"Hydrological models commonly use very simple snow accumulation and melt models based on air temperature information, namely, a temperature threshold for snow accumulation as well as for snowmelt, and a melt factor. This utility emerges due to the simplicity, efficiency, and generally good performance of such models if sufficient calibration information is available. At scales beyond single gauged catchments, the estimation and evaluation of the temperature thresholds and the melt factor has been difficult due to a lack of observations on snow accumulation and melt. Using a recently published Northern Hemisphere snow water equivalent dataset (NH-SWE) and co-located climate station observations of temperature and precipitation (4736 stations across the Northern Hemisphere), this work estimates melt factors and temperature thresholds for snow modelling based on station observations and provides the first large-scale and long-term (1950–2023) evaluation of a simple temperature-index snow model and its parameters across a diverse range of snow climates. Our study reveals that the 0 °C as precipitation-phase threshold captures most snowfall days (89 %) and the 0 °C as snowmelt initiation threshold captures most snowmelt days (76 %). Adjusting large-scale uniform threshold values does not consistently improve performance across all snow accumulation and melt metrics. Estimated melt factors based on observations converge towards 3–5 mm (°C d)−1 for deeper snowpack climates (peak snow water equivalent >300 mm), but their estimation may be more challenging for colder climates with shallower snowpacks (<300 mm), conditions where the derived melt factors cover a wider range (1 to 12 mm (°C d)−1) and a much higher interannual and spatial variability. The temperature-index snow model performs consistently well, on average, across the available Northern Hemisphere data set for estimating long-term mean values of seasonal snow cover onset, snowmelt season onset, mean snow accumulation and snowmelt rates, but challenges may arise due to biases in temperature records or solid precipitation undercatch. Peak snow water equivalent is likely underestimated for deep or alpine snowpacks, while it is likely overestimated for shallow snowpacks in the coldest and continental climates. The best median performance of the temperature-index approach lies on relatively shallow snowpacks in temperate climates. This study provides valuable insights into temperature-threshold snowfall modelling and temperature-index melt modelling for applications across diverse climates and environments, and the results should help refine regional modelling approaches to enhance our understanding of snowpack responses to global warming.","lang":"eng"}],"date_published":"2026-05-04T00:00:00Z","page":"2613-2636","language":[{"iso":"eng"}],"publication_identifier":{"issn":["1027-5606"],"eissn":["1607-7938"]},"month":"05","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication":"Hydrology and Earth System Sciences","oa_version":"Published Version","status":"public","date_updated":"2026-06-02T09:24:00Z","volume":30,"type":"journal_article","has_accepted_license":"1","oa":1,"issue":"9","publication_status":"published","acknowledgement":"AFB acknowledges funding from the UK's Natural Environment Research Council (NERC) CENTA2 doctoral training program, grant number NE/S007350/1. AFB acknowledges support from the School of Geography, Earth and Environmental Science research fund. The computations described in this paper were performed using the University of Birmingham's BlueBEAR HPC service, which provides a High Performance Computing service to the University's research community. See http://www.birmingham.ac.uk/bear (last access: 15 December 2025) for more details. This research has been supported by the Natural Environment Research Council (grant no. CENTA2 NE/S007350/1).","PlanS_conform":"1","date_created":"2026-05-24T22:01:32Z","article_type":"original","ddc":["550"],"corr_author":"1","_id":"21915","author":[{"first_name":"Adrià","full_name":"Fontrodona-Bach, Adrià","id":"f06891fd-9f42-11ee-8632-a20971c43046","last_name":"Fontrodona-Bach"},{"first_name":"Bettina","full_name":"Schaefli, Bettina","last_name":"Schaefli"},{"full_name":"Woods, Ross","first_name":"Ross","last_name":"Woods"},{"last_name":"Larsen","full_name":"Larsen, Joshua R.","first_name":"Joshua R."}],"doi":"10.5194/hess-30-2613-2026","quality_controlled":"1","scopus_import":"1","OA_place":"publisher","department":[{"_id":"FrPe"}]},{"date_updated":"2026-07-02T06:42:37Z","has_accepted_license":"1","type":"conference_abstract","oa":1,"month":"07","status":"public","oa_version":"Published Version","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication":"EGU General Assembly 2026","author":[{"last_name":"Muñoz Hermosilla","first_name":"José M","orcid":"0000-0002-1990-8508","full_name":"Muñoz Hermosilla, José M","id":"e1037a6d-646e-11ef-b402-e0ed9ab0901e"},{"first_name":"Evan","full_name":"Miles, Evan","last_name":"Miles"},{"id":"22a2674a-61ce-11ee-94b5-d18813baf16f","full_name":"McCarthy, Michael","first_name":"Michael","last_name":"McCarthy"},{"first_name":"Juan Vicente","full_name":"Melo Velasco, Juan Vicente","id":"2611dec0-b9c6-11ed-9bea-a81c2b17a549","last_name":"Melo Velasco"},{"first_name":"Florian","full_name":"Hardmeier, Florian","last_name":"Hardmeier"},{"last_name":"GANTAYAT","id":"02734268-3e8d-11ef-80a1-cec4a088d004","first_name":"PRATEEK","full_name":"GANTAYAT, PRATEEK"},{"id":"f06891fd-9f42-11ee-8632-a20971c43046","first_name":"Adrià","full_name":"Fontrodona-Bach, Adrià","last_name":"Fontrodona-Bach"},{"first_name":"Guillaume","full_name":"Jouvet, Guillaume","last_name":"Jouvet"},{"id":"b28f055a-81ea-11ed-b70c-a9fe7f7b0e70","first_name":"Francesca","orcid":"0000-0002-5554-8087","full_name":"Pellicciotti, Francesca","last_name":"Pellicciotti"}],"ddc":["550"],"_id":"22119","corr_author":"1","OA_place":"publisher","department":[{"_id":"FrPe"},{"_id":"GradSch"}],"doi":"10.5194/egusphere-egu26-19367","conference":{"start_date":"2026-05-03","location":"Vienna, Austria & Virtual","name":"EGU General Assembly","end_date":"2026-05-08"},"publication_status":"published","date_created":"2026-06-22T12:16:50Z","article_processing_charge":"No","publisher":"European Geosciences Union","file_date_updated":"2026-07-02T06:22:50Z","year":"2026","citation":{"short":"J.M. Muñoz Hermosilla, E. Miles, M. McCarthy, J.V. Melo Velasco, F. Hardmeier, P. GANTAYAT, A. Fontrodona-Bach, G. Jouvet, F. Pellicciotti, in:, EGU General Assembly 2026, European Geosciences Union, 2026.","ieee":"J. M. Muñoz Hermosilla <i>et al.</i>, “Constraining debris input to Oberaletsch Glacier using ensemble-based Lagrangian modelling,” in <i>EGU General Assembly 2026</i>, Vienna, Austria &#38; Virtual, 2026.","mla":"Muñoz Hermosilla, José M., et al. “Constraining Debris Input to Oberaletsch Glacier Using Ensemble-Based Lagrangian Modelling.” <i>EGU General Assembly 2026</i>, EGU26-19367, European Geosciences Union, 2026, doi:<a href=\"https://doi.org/10.5194/egusphere-egu26-19367\">10.5194/egusphere-egu26-19367</a>.","apa":"Muñoz Hermosilla, J. M., Miles, E., McCarthy, M., Melo Velasco, J. V., Hardmeier, F., GANTAYAT, P., … Pellicciotti, F. (2026). Constraining debris input to Oberaletsch Glacier using ensemble-based Lagrangian modelling. In <i>EGU General Assembly 2026</i>. Vienna, Austria &#38; Virtual: European Geosciences Union. <a href=\"https://doi.org/10.5194/egusphere-egu26-19367\">https://doi.org/10.5194/egusphere-egu26-19367</a>","chicago":"Muñoz Hermosilla, José M, Evan Miles, Michael McCarthy, Juan Vicente Melo Velasco, Florian Hardmeier, PRATEEK GANTAYAT, Adrià Fontrodona-Bach, Guillaume Jouvet, and Francesca Pellicciotti. “Constraining Debris Input to Oberaletsch Glacier Using Ensemble-Based Lagrangian Modelling.” In <i>EGU General Assembly 2026</i>. European Geosciences Union, 2026. <a href=\"https://doi.org/10.5194/egusphere-egu26-19367\">https://doi.org/10.5194/egusphere-egu26-19367</a>.","ama":"Muñoz Hermosilla JM, Miles E, McCarthy M, et al. Constraining debris input to Oberaletsch Glacier using ensemble-based Lagrangian modelling. In: <i>EGU General Assembly 2026</i>. European Geosciences Union; 2026. doi:<a href=\"https://doi.org/10.5194/egusphere-egu26-19367\">10.5194/egusphere-egu26-19367</a>","ista":"Muñoz Hermosilla JM, Miles E, McCarthy M, Melo Velasco JV, Hardmeier F, GANTAYAT P, Fontrodona-Bach A, Jouvet G, Pellicciotti F. 2026. Constraining debris input to Oberaletsch Glacier using ensemble-based Lagrangian modelling. EGU General Assembly 2026. EGU General Assembly, EGU26-19367."},"day":"02","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)"},"article_number":"EGU26-19367","title":"Constraining debris input to Oberaletsch Glacier using ensemble-based Lagrangian modelling","OA_type":"gold","file":[{"success":1,"date_created":"2026-07-02T06:22:50Z","access_level":"open_access","checksum":"2ea3e691cfa53176d0e801b9172842d6","file_size":284023,"date_updated":"2026-07-02T06:22:50Z","file_id":"22233","creator":"dernst","relation":"main_file","content_type":"application/pdf","file_name":"2026_EGU26_MunozHermosilla.pdf"}],"date_published":"2026-07-02T00:00:00Z","language":[{"iso":"eng"}]},{"acknowledgement":"This project received funding from the Swiss National Science Foundation (Grant 204322, project “REsolving the thickNess Of debris on Earth's glacIers and its Rate of change,” RENOIR). We thank Lars Groeneveld, Diego Hernández, Alonso Mejías, Gabriela Reyes and Gabriela Tala for their support during fieldwork. Open access funding provided by Institute of Science and Technology Austria/KEMÖ.","publication_status":"published","date_created":"2025-06-23T13:54:01Z","author":[{"last_name":"Melo Velasco","id":"2611dec0-b9c6-11ed-9bea-a81c2b17a549","full_name":"Melo Velasco, Juan Vicente","first_name":"Juan Vicente"},{"last_name":"Miles","full_name":"Miles, Evan","first_name":"Evan"},{"last_name":"McCarthy","id":"22a2674a-61ce-11ee-94b5-d18813baf16f","full_name":"McCarthy, Michael","first_name":"Michael"},{"full_name":"Shaw, Thomas","orcid":"0000-0001-7640-6152","first_name":"Thomas","id":"3caa3f91-1f03-11ee-96ce-e0e553054d6e","last_name":"Shaw"},{"id":"001b0422-8d15-11ed-bc51-cab6c037a228","full_name":"Fyffe, Catriona Louise","first_name":"Catriona Louise","last_name":"Fyffe"},{"first_name":"Adrià","full_name":"Fontrodona-Bach, Adrià","id":"f06891fd-9f42-11ee-8632-a20971c43046","last_name":"Fontrodona-Bach"},{"id":"b28f055a-81ea-11ed-b70c-a9fe7f7b0e70","full_name":"Pellicciotti, Francesca","first_name":"Francesca","orcid":"0000-0002-5554-8087","last_name":"Pellicciotti"}],"article_type":"original","ddc":["550"],"corr_author":"1","_id":"19878","scopus_import":"1","OA_place":"publisher","department":[{"_id":"FrPe"}],"quality_controlled":"1","doi":"10.1029/2025jf008360","month":"06","status":"public","oa_version":"Published Version","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","publication":"Journal of Geophysical Research: Earth Surface","volume":130,"date_updated":"2025-09-30T13:42:28Z","issue":"6","type":"journal_article","has_accepted_license":"1","oa":1,"isi":1,"abstract":[{"lang":"eng","text":"Rock debris partially covers glaciers worldwide, with varying extents and distributions, and controls sub‐debris melt rates by modifying energy transfer from the atmosphere to the ice. Two key physical properties controlling this energy exchange are thermal conductivity (k) and aerodynamic roughness length (z0). Accurate representation of these properties in energy‐balance models is critical for understanding climate‐glacier interactions and predicting the behavior of debris‐covered glaciers. However, k and z0 have been derived at very few sites from limited local measurements, using different approaches, and most model applications rely on values reported from these few sites and studies. We derive k and z0 using established and modified approaches from data at three locations on Pirámide Glacier in the central Chilean Andes. By comparing methods and evaluating melt simulated with an energy‐balance model, we reveal substantial differences between approaches. These lead to discrepancies between ice melt from energy‐balance simulations and observed data, and highlight the impact of method choice on calculated ice melt. Optimizing k against measured melt appears a viable approach to constrain melt simulations. Determining z0 seems less critical, as it has a smaller impact on total melt. Profile aerodynamic method measurements for estimating z0, despite higher costs, are independent of ice melt calculations. The large, unexpected differences between methods indicate a substantial knowledge gap. The fact that field‐derived k and z0 fail to work well in energy‐balance models, suggests that model values represent bulk properties distinct from theoretical field measurements. Addressing this gap is essential for improving glacier melt predictions."}],"date_published":"2025-06-15T00:00:00Z","publication_identifier":{"issn":["2169-9003"],"eissn":["2169-9011"]},"language":[{"iso":"eng"}],"day":"15","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)"},"article_number":"e2025JF008360","external_id":{"isi":["001508794200001"]},"title":"Method dependence in thermal conductivity and aerodynamic roughness length estimates on a debris‐covered glacier","file":[{"file_id":"19886","checksum":"ca91541516c71d240321630ca42b4dc4","date_updated":"2025-06-24T06:27:34Z","file_size":3949928,"creator":"dernst","relation":"main_file","file_name":"2025_JGREarthSurface_MeloVelasco.pdf","content_type":"application/pdf","success":1,"access_level":"open_access","date_created":"2025-06-24T06:27:34Z"}],"OA_type":"hybrid","intvolume":"       130","article_processing_charge":"Yes (via OA deal)","publisher":"Wiley","file_date_updated":"2025-06-24T06:27:34Z","year":"2025","citation":{"short":"J.V. Melo Velasco, E. Miles, M. McCarthy, T. Shaw, C.L. Fyffe, A. Fontrodona-Bach, F. Pellicciotti, Journal of Geophysical Research: Earth Surface 130 (2025).","ieee":"J. V. Melo Velasco <i>et al.</i>, “Method dependence in thermal conductivity and aerodynamic roughness length estimates on a debris‐covered glacier,” <i>Journal of Geophysical Research: Earth Surface</i>, vol. 130, no. 6. Wiley, 2025.","ama":"Melo Velasco JV, Miles E, McCarthy M, et al. Method dependence in thermal conductivity and aerodynamic roughness length estimates on a debris‐covered glacier. <i>Journal of Geophysical Research: Earth Surface</i>. 2025;130(6). doi:<a href=\"https://doi.org/10.1029/2025jf008360\">10.1029/2025jf008360</a>","ista":"Melo Velasco JV, Miles E, McCarthy M, Shaw T, Fyffe CL, Fontrodona-Bach A, Pellicciotti F. 2025. Method dependence in thermal conductivity and aerodynamic roughness length estimates on a debris‐covered glacier. Journal of Geophysical Research: Earth Surface. 130(6), e2025JF008360.","chicago":"Melo Velasco, Juan Vicente, Evan Miles, Michael McCarthy, Thomas Shaw, Catriona Louise Fyffe, Adrià Fontrodona-Bach, and Francesca Pellicciotti. “Method Dependence in Thermal Conductivity and Aerodynamic Roughness Length Estimates on a Debris‐covered Glacier.” <i>Journal of Geophysical Research: Earth Surface</i>. Wiley, 2025. <a href=\"https://doi.org/10.1029/2025jf008360\">https://doi.org/10.1029/2025jf008360</a>.","apa":"Melo Velasco, J. V., Miles, E., McCarthy, M., Shaw, T., Fyffe, C. L., Fontrodona-Bach, A., &#38; Pellicciotti, F. (2025). Method dependence in thermal conductivity and aerodynamic roughness length estimates on a debris‐covered glacier. <i>Journal of Geophysical Research: Earth Surface</i>. Wiley. <a href=\"https://doi.org/10.1029/2025jf008360\">https://doi.org/10.1029/2025jf008360</a>","mla":"Melo Velasco, Juan Vicente, et al. “Method Dependence in Thermal Conductivity and Aerodynamic Roughness Length Estimates on a Debris‐covered Glacier.” <i>Journal of Geophysical Research: Earth Surface</i>, vol. 130, no. 6, e2025JF008360, Wiley, 2025, doi:<a href=\"https://doi.org/10.1029/2025jf008360\">10.1029/2025jf008360</a>."}},{"file":[{"success":1,"access_level":"open_access","date_created":"2025-10-27T08:38:40Z","file_id":"20548","checksum":"f77ebb9825f374134a89e0e6311fe188","file_size":3842196,"date_updated":"2025-10-27T08:38:40Z","creator":"dernst","relation":"main_file","content_type":"application/pdf","file_name":"2025_EarthSystemScienceData_FontrodonaBach.pdf"}],"OA_type":"gold","title":"DebDaB: A database of supraglacial debris  thickness and physical properties","external_id":{"isi":["001560847000001"]},"DOAJ_listed":"1","day":"29","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)"},"citation":{"short":"A. Fontrodona-Bach, L. Groeneveld, E. Miles, M. McCarthy, T. Shaw, J.V. Melo Velasco, F. Pellicciotti, Earth System Science Data 17 (2025) 4213–4234.","ieee":"A. Fontrodona-Bach <i>et al.</i>, “DebDaB: A database of supraglacial debris  thickness and physical properties,” <i>Earth System Science Data</i>, vol. 17, no. 8. Copernicus Publications, pp. 4213–4234, 2025.","ista":"Fontrodona-Bach A, Groeneveld L, Miles E, McCarthy M, Shaw T, Melo Velasco JV, Pellicciotti F. 2025. DebDaB: A database of supraglacial debris  thickness and physical properties. Earth System Science Data. 17(8), 4213–4234.","ama":"Fontrodona-Bach A, Groeneveld L, Miles E, et al. DebDaB: A database of supraglacial debris  thickness and physical properties. <i>Earth System Science Data</i>. 2025;17(8):4213-4234. doi:<a href=\"https://doi.org/10.5194/essd-17-4213-2025\">10.5194/essd-17-4213-2025</a>","chicago":"Fontrodona-Bach, Adrià, Lars Groeneveld, Evan Miles, Michael McCarthy, Thomas Shaw, Juan Vicente Melo Velasco, and Francesca Pellicciotti. “DebDaB: A Database of Supraglacial Debris  Thickness and Physical Properties.” <i>Earth System Science Data</i>. Copernicus Publications, 2025. <a href=\"https://doi.org/10.5194/essd-17-4213-2025\">https://doi.org/10.5194/essd-17-4213-2025</a>.","apa":"Fontrodona-Bach, A., Groeneveld, L., Miles, E., McCarthy, M., Shaw, T., Melo Velasco, J. V., &#38; Pellicciotti, F. (2025). DebDaB: A database of supraglacial debris  thickness and physical properties. <i>Earth System Science Data</i>. Copernicus Publications. <a href=\"https://doi.org/10.5194/essd-17-4213-2025\">https://doi.org/10.5194/essd-17-4213-2025</a>","mla":"Fontrodona-Bach, Adrià, et al. “DebDaB: A Database of Supraglacial Debris  Thickness and Physical Properties.” <i>Earth System Science Data</i>, vol. 17, no. 8, Copernicus Publications, 2025, pp. 4213–34, doi:<a href=\"https://doi.org/10.5194/essd-17-4213-2025\">10.5194/essd-17-4213-2025</a>."},"year":"2025","article_processing_charge":"Yes","publisher":"Copernicus Publications","file_date_updated":"2025-10-27T08:38:40Z","intvolume":"        17","abstract":[{"text":"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 and thickness of the supraglacial debris layer are scarce. Here, we compile a database of measured and reported bulk physical properties and layer thicknesses of supraglacial debris that we call the supraglacial Debris Database (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 % was previously unpublished. DebDaB contains 8741 data entries for supraglacial debris layer thickness, of which 1770 entries also include sub-debris ablation rates, 179 thermal conductivity of debris, 160 aerodynamic surface roughness length, 79 debris albedo, 59 debris emissivity, and 37 debris porosity. The data are distributed over 84 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 simplified Østrem curves to 19 glaciers with sufficient debris thickness and ablation data. The data in DebDaB can be used for energy balance, melt, and surface mass balance studies by incorporating site-specific debris properties or for evaluation of remote sensing estimates of debris thickness and surface roughness. They 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 others having gaps with prevalent debris (e.g. the Andes and Alaska). Debris thickness measurements are mostly concentrated at lower elevations, leaving higher-elevation debris-covered areas undersampled and suggesting that our knowledge of debris properties might not be representative of all elevations. The aims of DebDaB, as an openly available dataset, are to evolve over time, to be updated, and to add to community submissions as new data on supraglacial properties become available. The data described in this paper can be accessed from Zenodo at https://doi.org/10.5281/zenodo.14224835 (Groeneveld et al., 2025).","lang":"eng"}],"isi":1,"language":[{"iso":"eng"}],"publication_identifier":{"issn":["1866-3516"]},"date_published":"2025-08-29T00:00:00Z","page":"4213-4234","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication":"Earth System Science Data","status":"public","oa_version":"Published Version","month":"08","has_accepted_license":"1","type":"journal_article","oa":1,"issue":"8","date_updated":"2025-12-01T15:05:58Z","volume":17,"PlanS_conform":"1","date_created":"2025-10-27T08:21:22Z","acknowledgement":"This work was supported by SNF project RENOIR (“Resolving the thickness of debris on Earth’s glaciers and its rate of change”; grant no. 204322). This project received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and\r\ninnovation programme (grant no. 772751; RAVEN: “Rapid mass losses of debris covered glaciers in High Mountain Asia”). The authors acknowledge DCGWG of IACS for setting the stage and bringing together the debris-covered glacier community to focus on broader needs transcending a specific research topic and for starting the Zenodo community on debris-covered glaciers, where this database is hosted. The authors thank Achim A. Beylich (topical editor), Ken\r\nMankoff (chief editor), Morgan Jones (reviewer), and an anonymous reviewer for their  constructive feedback, comments, and discussions on the database and paper.","publication_status":"published","doi":"10.5194/essd-17-4213-2025","quality_controlled":"1","scopus_import":"1","department":[{"_id":"FrPe"}],"OA_place":"publisher","ddc":["550"],"article_type":"original","_id":"20546","corr_author":"1","author":[{"id":"f06891fd-9f42-11ee-8632-a20971c43046","full_name":"Fontrodona-Bach, Adrià","first_name":"Adrià","last_name":"Fontrodona-Bach"},{"last_name":"Groeneveld","full_name":"Groeneveld, Lars","first_name":"Lars"},{"last_name":"Miles","first_name":"Evan","full_name":"Miles, Evan"},{"last_name":"McCarthy","id":"22a2674a-61ce-11ee-94b5-d18813baf16f","first_name":"Michael","full_name":"McCarthy, Michael"},{"last_name":"Shaw","id":"3caa3f91-1f03-11ee-96ce-e0e553054d6e","orcid":"0000-0001-7640-6152","first_name":"Thomas","full_name":"Shaw, Thomas"},{"last_name":"Melo Velasco","first_name":"Juan Vicente","full_name":"Melo Velasco, Juan Vicente","id":"2611dec0-b9c6-11ed-9bea-a81c2b17a549"},{"last_name":"Pellicciotti","first_name":"Francesca","orcid":"0000-0002-5554-8087","full_name":"Pellicciotti, Francesca","id":"b28f055a-81ea-11ed-b70c-a9fe7f7b0e70"}],"related_material":{"record":[{"status":"public","relation":"research_data","id":"20547"}]}},{"abstract":[{"lang":"eng","text":"DebdaB is a database of measured and reported physical properties and thickness of supraglacial debris that is openly available and open to community submissions.\r\n\r\nThe majority of the database (90%) is compiled from 172 sources in the literature, and the remaining 10% has not been published before. DebDaB contains 8,286 data entries for supraglacial debris thickness, of which 1,852 entries also include sub-debris ablation rates, 167 data entries of thermal conductivity of debris, 157 of aerodynamic surface roughness length, 77 of debris albedo, 56 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. "}],"date_created":"2025-10-27T08:42:09Z","ddc":["550"],"date_published":"2025-05-16T00:00:00Z","_id":"20547","author":[{"last_name":"Groeneveld","first_name":"Lars","full_name":"Groeneveld, Lars"},{"last_name":"Fontrodona-Bach","id":"f06891fd-9f42-11ee-8632-a20971c43046","first_name":"Adrià","full_name":"Fontrodona-Bach, Adrià"},{"last_name":"Miles","first_name":"Evan","full_name":"Miles, Evan"},{"id":"22a2674a-61ce-11ee-94b5-d18813baf16f","first_name":"Michael","full_name":"McCarthy, Michael","last_name":"McCarthy"},{"id":"2611dec0-b9c6-11ed-9bea-a81c2b17a549","full_name":"Melo Velasco, Juan Vicente","first_name":"Juan Vicente","last_name":"Melo Velasco"},{"id":"3caa3f91-1f03-11ee-96ce-e0e553054d6e","full_name":"Shaw, Thomas","first_name":"Thomas","orcid":"0000-0001-7640-6152","last_name":"Shaw"},{"last_name":"Pellicciotti","id":"b28f055a-81ea-11ed-b70c-a9fe7f7b0e70","full_name":"Pellicciotti, Francesca","orcid":"0000-0002-5554-8087","first_name":"Francesca"},{"last_name":"Bauder","full_name":"Bauder, Andreas","first_name":"Andreas"},{"last_name":"Buri","full_name":"Buri, Pascal","first_name":"Pascal"},{"full_name":"Kneib, Marin","first_name":"Marin","last_name":"Kneib"},{"last_name":"Kumar","first_name":"Amit","full_name":"Kumar, Amit"},{"last_name":"Mishra","first_name":"Aditya","full_name":"Mishra, Aditya"},{"last_name":"Petersen","full_name":"Petersen, lene","first_name":"lene"},{"first_name":"Roman","full_name":"Renner, Roman","last_name":"Renner"},{"last_name":"Schmid","first_name":"Sandro","full_name":"Schmid, Sandro"}],"related_material":{"record":[{"id":"20546","relation":"used_in_publication","status":"public"}]},"doi":"10.5281/ZENODO.14224835","OA_place":"repository","department":[{"_id":"FrPe"}],"day":"16","month":"05","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","OA_type":"gold","title":"DebDaB: A database of supraglacial debris thickness and physical properties","status":"public","oa_version":"Published Version","article_processing_charge":"No","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5281/zenodo.15441000"}],"publisher":"Zenodo","date_updated":"2025-12-01T15:05:58Z","type":"research_data_reference","citation":{"ieee":"L. Groeneveld <i>et al.</i>, “DebDaB: A database of supraglacial debris thickness and physical properties.” Zenodo, 2025.","short":"L. Groeneveld, A. Fontrodona-Bach, E. Miles, M. McCarthy, J.V. Melo Velasco, T. Shaw, F. Pellicciotti, A. Bauder, P. Buri, M. Kneib, A. Kumar, A. Mishra,  lene Petersen, R. Renner, S. Schmid, (2025).","mla":"Groeneveld, Lars, et al. <i>DebDaB: A Database of Supraglacial Debris Thickness and Physical Properties</i>. Zenodo, 2025, doi:<a href=\"https://doi.org/10.5281/ZENODO.14224835\">10.5281/ZENODO.14224835</a>.","apa":"Groeneveld, L., Fontrodona-Bach, A., Miles, E., McCarthy, M., Melo Velasco, J. V., Shaw, T., … Schmid, S. (2025). DebDaB: A database of supraglacial debris thickness and physical properties. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.14224835\">https://doi.org/10.5281/ZENODO.14224835</a>","chicago":"Groeneveld, Lars, Adrià Fontrodona-Bach, Evan Miles, Michael McCarthy, Juan Vicente Melo Velasco, Thomas Shaw, Francesca Pellicciotti, et al. “DebDaB: A Database of Supraglacial Debris Thickness and Physical Properties.” Zenodo, 2025. <a href=\"https://doi.org/10.5281/ZENODO.14224835\">https://doi.org/10.5281/ZENODO.14224835</a>.","ama":"Groeneveld L, Fontrodona-Bach A, Miles E, et al. DebDaB: A database of supraglacial debris thickness and physical properties. 2025. doi:<a href=\"https://doi.org/10.5281/ZENODO.14224835\">10.5281/ZENODO.14224835</a>","ista":"Groeneveld L, Fontrodona-Bach A, Miles E, McCarthy M, Melo Velasco JV, Shaw T, Pellicciotti F, Bauder A, Buri P, Kneib M, Kumar A, Mishra A, Petersen  lene, Renner R, Schmid S. 2025. DebDaB: A database of supraglacial debris thickness and physical properties, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.14224835\">10.5281/ZENODO.14224835</a>."},"oa":1,"year":"2025"}]