{"article_type":"original","publisher":"Cambridge University Press","year":"2021","day":"01","date_published":"2021-04-01T00:00:00Z","intvolume":"        67","language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.1017/jog.2020.111"}],"quality_controlled":"1","scopus_import":"1","title":"Using climate reanalysis data in conjunction with multi-temporal satellite thermal imagery to derive supraglacial debris thickness changes from energy-balance modelling","issue":"262","oa_version":"Published Version","article_processing_charge":"No","citation":{"apa":"Stewart, R. L., Westoby, M., Pellicciotti, F., Rowan, A., Swift, D., Brock, B., &#38; Woodward, J. (2021). Using climate reanalysis data in conjunction with multi-temporal satellite thermal imagery to derive supraglacial debris thickness changes from energy-balance modelling. <i>Journal of Glaciology</i>. Cambridge University Press. <a href=\"https://doi.org/10.1017/jog.2020.111\">https://doi.org/10.1017/jog.2020.111</a>","chicago":"Stewart, Rebecca L., Matthew Westoby, Francesca Pellicciotti, Ann Rowan, Darrel Swift, Benjamin Brock, and John Woodward. “Using Climate Reanalysis Data in Conjunction with Multi-Temporal Satellite Thermal Imagery to Derive Supraglacial Debris Thickness Changes from Energy-Balance Modelling.” <i>Journal of Glaciology</i>. Cambridge University Press, 2021. <a href=\"https://doi.org/10.1017/jog.2020.111\">https://doi.org/10.1017/jog.2020.111</a>.","ista":"Stewart RL, Westoby M, Pellicciotti F, Rowan A, Swift D, Brock B, Woodward J. 2021. Using climate reanalysis data in conjunction with multi-temporal satellite thermal imagery to derive supraglacial debris thickness changes from energy-balance modelling. Journal of Glaciology. 67(262), 366–384.","ieee":"R. L. Stewart <i>et al.</i>, “Using climate reanalysis data in conjunction with multi-temporal satellite thermal imagery to derive supraglacial debris thickness changes from energy-balance modelling,” <i>Journal of Glaciology</i>, vol. 67, no. 262. Cambridge University Press, pp. 366–384, 2021.","mla":"Stewart, Rebecca L., et al. “Using Climate Reanalysis Data in Conjunction with Multi-Temporal Satellite Thermal Imagery to Derive Supraglacial Debris Thickness Changes from Energy-Balance Modelling.” <i>Journal of Glaciology</i>, vol. 67, no. 262, Cambridge University Press, 2021, pp. 366–84, doi:<a href=\"https://doi.org/10.1017/jog.2020.111\">10.1017/jog.2020.111</a>.","ama":"Stewart RL, Westoby M, Pellicciotti F, et al. Using climate reanalysis data in conjunction with multi-temporal satellite thermal imagery to derive supraglacial debris thickness changes from energy-balance modelling. <i>Journal of Glaciology</i>. 2021;67(262):366-384. doi:<a href=\"https://doi.org/10.1017/jog.2020.111\">10.1017/jog.2020.111</a>","short":"R.L. Stewart, M. Westoby, F. Pellicciotti, A. Rowan, D. Swift, B. Brock, J. Woodward, Journal of Glaciology 67 (2021) 366–384."},"month":"04","date_created":"2023-02-20T08:11:42Z","status":"public","page":"366-384","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"date_updated":"2023-02-28T13:07:11Z","volume":67,"extern":"1","type":"journal_article","publication_identifier":{"issn":["0022-1430"],"eissn":["1727-5652"]},"_id":"12587","doi":"10.1017/jog.2020.111","author":[{"last_name":"Stewart","first_name":"Rebecca L.","full_name":"Stewart, Rebecca L."},{"full_name":"Westoby, Matthew","first_name":"Matthew","last_name":"Westoby"},{"full_name":"Pellicciotti, Francesca","first_name":"Francesca","id":"b28f055a-81ea-11ed-b70c-a9fe7f7b0e70","last_name":"Pellicciotti"},{"first_name":"Ann","full_name":"Rowan, Ann","last_name":"Rowan"},{"last_name":"Swift","full_name":"Swift, Darrel","first_name":"Darrel"},{"first_name":"Benjamin","full_name":"Brock, Benjamin","last_name":"Brock"},{"full_name":"Woodward, John","first_name":"John","last_name":"Woodward"}],"publication_status":"published","publication":"Journal of Glaciology","abstract":[{"text":"Surface energy-balance models are commonly used in conjunction with satellite thermal imagery to estimate supraglacial debris thickness. Removing the need for local meteorological data in the debris thickness estimation workflow could improve the versatility and spatiotemporal application of debris thickness estimation. We evaluate the use of regional reanalysis data to derive debris thickness for two mountain glaciers using a surface energy-balance model. Results forced using ERA-5 agree with AWS-derived estimates to within 0.01 ± 0.05 m for Miage Glacier, Italy, and 0.01 ± 0.02 m for Khumbu Glacier, Nepal. ERA-5 data were then used to estimate spatiotemporal changes in debris thickness over a ~20-year period for Miage Glacier, Khumbu Glacier and Haut Glacier d'Arolla, Switzerland. We observe significant increases in debris thickness at the terminus for Haut Glacier d'Arolla and at the margins of the expanding debris cover at all glaciers. While simulated debris thickness was underestimated compared to point measurements in areas of thick debris, our approach can reconstruct glacier-scale debris thickness distribution and its temporal evolution over multiple decades. We find significant changes in debris thickness over areas of thin debris, areas susceptible to high ablation rates, where current knowledge of debris evolution is limited.","lang":"eng"}]}