Articles | Volume 18, issue 12
https://doi.org/10.5194/tc-18-5595-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-5595-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Linking glacier retreat with climate change on the Tibetan Plateau through satellite remote sensing
Fumeng Zhao
Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China
Faculty of Engineering, China University of Geosciences, Wuhan, 430074, China
Wenping Gong
CORRESPONDING AUTHOR
Faculty of Engineering, China University of Geosciences, Wuhan, 430074, China
Silvia Bianchini
Earth Sciences Department, University of Florence, 50121 Florence, Italy
Zhongkang Yang
PowerChina Chengdu Engineering Corporation Limited, Chengdu, 610072, China
Related authors
Fumeng Zhao, Wenping Gong, Tianhe Ren, Jun Chen, Huiming Tang, and Tianzheng Li
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-9, https://doi.org/10.5194/tc-2022-9, 2022
Revised manuscript not accepted
Short summary
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In this study, a new permafrost stability mapping is obtained by integrating time-series InSAR and machine learning method, this method provides another alternative for measuring permafrost degradation when the ground temperature is limited to the site-specific measurements. Also, the influences of topography and vegetation coverage on the ground deformations are studied to illustrate that the permafrost stability is high related to the environmental factors.
Fumeng Zhao, Wenping Gong, Tianhe Ren, Jun Chen, Huiming Tang, and Tianzheng Li
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-9, https://doi.org/10.5194/tc-2022-9, 2022
Revised manuscript not accepted
Short summary
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In this study, a new permafrost stability mapping is obtained by integrating time-series InSAR and machine learning method, this method provides another alternative for measuring permafrost degradation when the ground temperature is limited to the site-specific measurements. Also, the influences of topography and vegetation coverage on the ground deformations are studied to illustrate that the permafrost stability is high related to the environmental factors.
O. Monserrat, A. Barra, G. Herrera, S. Bianchini, C. Lopez, R. Onori, P. Reichenbach, R. Sarro, R. M. Mateos, L. Solari, S. Ligüérzana, and I. P. Carralero
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W4, 351–355, https://doi.org/10.5194/isprs-archives-XLII-3-W4-351-2018, https://doi.org/10.5194/isprs-archives-XLII-3-W4-351-2018, 2018
Related subject area
Discipline: Glaciers | Subject: Glaciers
Twenty-first century global glacier evolution under CMIP6 scenarios and the role of glacier-specific observations
A quasi-one-dimensional ice mélange flow model based on continuum descriptions of granular materials
Modelling the historical and future evolution of six ice masses in the Tien Shan, Central Asia, using a 3D ice-flow model
Thinning and surface mass balance patterns of two neighbouring debris-covered glaciers in the southeastern Tibetan Plateau
Everest South Col Glacier did not thin during the period 1984–2017
Meltwater runoff and glacier mass balance in the high Arctic: 1991–2022 simulations for Svalbard
Impact of tides on calving patterns at Kronebreen, Svalbard – insights from three-dimensional ice dynamical modelling
Brief communication: Glacier mapping and change estimation using very high-resolution declassified Hexagon KH-9 panoramic stereo imagery (1971–1984)
Brief communication: Estimating the ice thickness of the Müller Ice Cap to support selection of a drill site
Glacier geometry and flow speed determine how Arctic marine-terminating glaciers respond to lubricated beds
A regionally resolved inventory of High Mountain Asia surge-type glaciers, derived from a multi-factor remote sensing approach
Towards ice-thickness inversion: an evaluation of global digital elevation models (DEMs) in the glacierized Tibetan Plateau
Record summer rains in 2019 led to massive loss of surface and cave ice in SE Europe
Evolution of the firn pack of Kaskawulsh Glacier, Yukon: meltwater effects, densification, and the development of a perennial firn aquifer
Full crystallographic orientation (c and a axes) of warm, coarse-grained ice in a shear-dominated setting: a case study, Storglaciären, Sweden
Contribution of calving to frontal ablation quantified from seismic and hydroacoustic observations calibrated with lidar volume measurements
Brief communication: Updated GAMDAM glacier inventory over high-mountain Asia
Ice cliff contribution to the tongue-wide ablation of Changri Nup Glacier, Nepal, central Himalaya
Harry Zekollari, Matthias Huss, Lilian Schuster, Fabien Maussion, David R. Rounce, Rodrigo Aguayo, Nicolas Champollion, Loris Compagno, Romain Hugonnet, Ben Marzeion, Seyedhamidreza Mojtabavi, and Daniel Farinotti
The Cryosphere, 18, 5045–5066, https://doi.org/10.5194/tc-18-5045-2024, https://doi.org/10.5194/tc-18-5045-2024, 2024
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Glaciers are major contributors to sea-level rise and act as key water resources. Here, we model the global evolution of glaciers under the latest generation of climate scenarios. We show that the type of observations used for model calibration can strongly affect the projections at the local scale. Our newly projected 21st century global mass loss is higher than the current community estimate as reported in the latest Intergovernmental Panel on Climate Change (IPCC) report.
Jason M. Amundson, Alexander A. Robel, Justin C. Burton, and Kavinda Nissanka
EGUsphere, https://doi.org/10.5194/egusphere-2024-297, https://doi.org/10.5194/egusphere-2024-297, 2024
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Some fjords contain dense packs of icebergs referred to as ice mélange. Ice mélange can affect the stability of marine-terminating glaciers by resisting the calving of new icebergs and by modifying fjord currents and water properties. We have developed the first numerical model of ice mélange that captures its granular nature and that is suitable for long time-scale simulations. The model is capable of explaining why some glaciers are more strongly influenced by ice mélange than others.
Lander Van Tricht and Philippe Huybrechts
The Cryosphere, 17, 4463–4485, https://doi.org/10.5194/tc-17-4463-2023, https://doi.org/10.5194/tc-17-4463-2023, 2023
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We modelled the historical and future evolution of six ice masses in the Tien Shan, Central Asia, with a 3D ice-flow model under the newest climate scenarios. We show that in all scenarios the ice masses retreat significantly but with large differences. It is highlighted that, because the main precipitation occurs in spring and summer, the ice masses respond to climate change with an accelerating retreat. In all scenarios, the total runoff peaks before 2050, with a (drastic) decrease afterwards.
Chuanxi Zhao, Wei Yang, Evan Miles, Matthew Westoby, Marin Kneib, Yongjie Wang, Zhen He, and Francesca Pellicciotti
The Cryosphere, 17, 3895–3913, https://doi.org/10.5194/tc-17-3895-2023, https://doi.org/10.5194/tc-17-3895-2023, 2023
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This paper quantifies the thinning and surface mass balance of two neighbouring debris-covered glaciers in the southeastern Tibetan Plateau during different seasons, based on high spatio-temporal resolution UAV-derived (unpiloted aerial
vehicle) data and in situ observations. Through a comparison approach and high-precision results, we identify that the glacier dynamic and debris thickness are strongly related to the future fate of the debris-covered glaciers in this region.
Fanny Brun, Owen King, Marion Réveillet, Charles Amory, Anton Planchot, Etienne Berthier, Amaury Dehecq, Tobias Bolch, Kévin Fourteau, Julien Brondex, Marie Dumont, Christoph Mayer, Silvan Leinss, Romain Hugonnet, and Patrick Wagnon
The Cryosphere, 17, 3251–3268, https://doi.org/10.5194/tc-17-3251-2023, https://doi.org/10.5194/tc-17-3251-2023, 2023
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The South Col Glacier is a small body of ice and snow located on the southern ridge of Mt. Everest. A recent study proposed that South Col Glacier is rapidly losing mass. In this study, we examined the glacier thickness change for the period 1984–2017 and found no thickness change. To reconcile these results, we investigate wind erosion and surface energy and mass balance and find that melt is unlikely a dominant process, contrary to previous findings.
Louise Steffensen Schmidt, Thomas Vikhamar Schuler, Erin Emily Thomas, and Sebastian Westermann
The Cryosphere, 17, 2941–2963, https://doi.org/10.5194/tc-17-2941-2023, https://doi.org/10.5194/tc-17-2941-2023, 2023
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Here, we present high-resolution simulations of glacier mass balance (the gain and loss of ice over a year) and runoff on Svalbard from 1991–2022, one of the fastest warming regions in the Arctic. The simulations are created using the CryoGrid community model. We find a small overall loss of mass over the simulation period of −0.08 m yr−1 but with no statistically significant trend. The average runoff was found to be 41 Gt yr−1, with a significant increasing trend of 6.3 Gt per decade.
Felicity A. Holmes, Eef van Dongen, Riko Noormets, Michał Pętlicki, and Nina Kirchner
The Cryosphere, 17, 1853–1872, https://doi.org/10.5194/tc-17-1853-2023, https://doi.org/10.5194/tc-17-1853-2023, 2023
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Glaciers which end in bodies of water can lose mass through melting below the waterline, as well as by the breaking off of icebergs. We use a numerical model to simulate the breaking off of icebergs at Kronebreen, a glacier in Svalbard, and find that both melting below the waterline and tides are important for iceberg production. In addition, we compare the modelled glacier front to observations and show that melting below the waterline can lead to undercuts of up to around 25 m.
Sajid Ghuffar, Owen King, Grégoire Guillet, Ewelina Rupnik, and Tobias Bolch
The Cryosphere, 17, 1299–1306, https://doi.org/10.5194/tc-17-1299-2023, https://doi.org/10.5194/tc-17-1299-2023, 2023
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The panoramic cameras (PCs) on board Hexagon KH-9 satellite missions from 1971–1984 captured very high-resolution stereo imagery with up to 60 cm spatial resolution. This study explores the potential of this imagery for glacier mapping and change estimation. The high resolution of KH-9PC leads to higher-quality DEMs which better resolve the accumulation region of glaciers in comparison to the KH-9 mapping camera, and KH-9PC imagery can be useful in several Earth observation applications.
Ann-Sofie Priergaard Zinck and Aslak Grinsted
The Cryosphere, 16, 1399–1407, https://doi.org/10.5194/tc-16-1399-2022, https://doi.org/10.5194/tc-16-1399-2022, 2022
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The Müller Ice Cap will soon set the scene for a new drilling project. To obtain an ice core with stratified layers and a good time resolution, thickness estimates are necessary for the planning. Here we present a new and fast method of estimating ice thicknesses from sparse data and compare it to an existing ice flow model. We find that the new semi-empirical method is insensitive to mass balance, is computationally fast, and provides good fits when compared to radar measurements.
Whyjay Zheng
The Cryosphere, 16, 1431–1445, https://doi.org/10.5194/tc-16-1431-2022, https://doi.org/10.5194/tc-16-1431-2022, 2022
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A glacier can speed up when surface water reaches the glacier's bottom via crevasses and reduces sliding friction. This paper builds up a physical model and finds that thick and fast-flowing glaciers are sensitive to this friction disruption. The data from Greenland and Austfonna (Svalbard) glaciers over 20 years support the model prediction. To estimate the projected sea-level rise better, these sensitive glaciers should be frequently monitored for potential future instabilities.
Gregoire Guillet, Owen King, Mingyang Lv, Sajid Ghuffar, Douglas Benn, Duncan Quincey, and Tobias Bolch
The Cryosphere, 16, 603–623, https://doi.org/10.5194/tc-16-603-2022, https://doi.org/10.5194/tc-16-603-2022, 2022
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Surging glaciers show cyclical changes in flow behavior – between slow and fast flow – and can have drastic impacts on settlements in their vicinity.
One of the clusters of surging glaciers worldwide is High Mountain Asia (HMA).
We present an inventory of surging glaciers in HMA, identified from satellite imagery. We show that the number of surging glaciers was underestimated and that they represent 20 % of the area covered by glaciers in HMA, before discussing new physics for glacier surges.
Wenfeng Chen, Tandong Yao, Guoqing Zhang, Fei Li, Guoxiong Zheng, Yushan Zhou, and Fenglin Xu
The Cryosphere, 16, 197–218, https://doi.org/10.5194/tc-16-197-2022, https://doi.org/10.5194/tc-16-197-2022, 2022
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A digital elevation model (DEM) is a prerequisite for estimating regional glacier thickness. Our study first compared six widely used global DEMs over the glacierized Tibetan Plateau by using ICESat-2 (Ice, Cloud and land Elevation Satellite) laser altimetry data. Our results show that NASADEM had the best accuracy. We conclude that NASADEM would be the best choice for ice-thickness estimation over the Tibetan Plateau through an intercomparison of four ice-thickness inversion models.
Aurel Perşoiu, Nenad Buzjak, Alexandru Onaca, Christos Pennos, Yorgos Sotiriadis, Monica Ionita, Stavros Zachariadis, Michael Styllas, Jure Kosutnik, Alexandru Hegyi, and Valerija Butorac
The Cryosphere, 15, 2383–2399, https://doi.org/10.5194/tc-15-2383-2021, https://doi.org/10.5194/tc-15-2383-2021, 2021
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Extreme precipitation events in summer 2019 led to catastrophic loss of cave and surface ice in SE Europe at levels unprecedented during the last century. The projected continuous warming and increase in precipitation extremes could pose an additional threat to glaciers in southern Europe, resulting in a potentially ice-free SE Europe by the middle of the next decade (2035 CE).
Naomi E. Ochwat, Shawn J. Marshall, Brian J. Moorman, Alison S. Criscitiello, and Luke Copland
The Cryosphere, 15, 2021–2040, https://doi.org/10.5194/tc-15-2021-2021, https://doi.org/10.5194/tc-15-2021-2021, 2021
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In May 2018 we drilled into Kaskawulsh Glacier to study how it is being affected by climate warming and used models to investigate the evolution of the firn since the 1960s. We found that the accumulation zone has experienced increased melting that has refrozen as ice layers and has formed a perennial firn aquifer. These results better inform climate-induced changes on northern glaciers and variables to take into account when estimating glacier mass change using remote-sensing methods.
Morgan E. Monz, Peter J. Hudleston, David J. Prior, Zachary Michels, Sheng Fan, Marianne Negrini, Pat J. Langhorne, and Chao Qi
The Cryosphere, 15, 303–324, https://doi.org/10.5194/tc-15-303-2021, https://doi.org/10.5194/tc-15-303-2021, 2021
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We present full crystallographic orientations of warm, coarse-grained ice deformed in a shear setting, enabling better characterization of how crystals in glacial ice preferentially align as ice flows. A commonly noted c-axis pattern, with several favored orientations, may result from bias due to overcounting large crystals with complex 3D shapes. A new sample preparation method effectively increases the sample size and reduces bias, resulting in a simpler pattern consistent with the ice flow.
Andreas Köhler, Michał Pętlicki, Pierre-Marie Lefeuvre, Giuseppa Buscaino, Christopher Nuth, and Christian Weidle
The Cryosphere, 13, 3117–3137, https://doi.org/10.5194/tc-13-3117-2019, https://doi.org/10.5194/tc-13-3117-2019, 2019
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Ice loss at the front of glaciers can be observed with high temporal resolution using seismometers. We combine seismic and underwater sound measurements of iceberg calving at Kronebreen, a glacier in Svalbard, with laser scanning of the glacier front. We develop a method to determine calving ice loss directly from seismic and underwater calving signals. This allowed us to quantify the contribution of calving to the total ice loss at the glacier front, which also includes underwater melting.
Akiko Sakai
The Cryosphere, 13, 2043–2049, https://doi.org/10.5194/tc-13-2043-2019, https://doi.org/10.5194/tc-13-2043-2019, 2019
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The Glacier Area Mapping for Discharge from the Asian Mountains (GAMDAM) glacier inventory was updated to revise the underestimated glacier area in the first version. The total number and area of glaciers are 134 770 and 100 693 ± 11 790 km2 from 453 Landsat images, which were carefully selected for the period from 1990 to 2010, to avoid mountain shadow, cloud cover, and seasonal snow cover.
Fanny Brun, Patrick Wagnon, Etienne Berthier, Joseph M. Shea, Walter W. Immerzeel, Philip D. A. Kraaijenbrink, Christian Vincent, Camille Reverchon, Dibas Shrestha, and Yves Arnaud
The Cryosphere, 12, 3439–3457, https://doi.org/10.5194/tc-12-3439-2018, https://doi.org/10.5194/tc-12-3439-2018, 2018
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On debris-covered glaciers, steep ice cliffs experience dramatically enhanced melt compared with the surrounding debris-covered ice. Using field measurements, UAV data and submetre satellite imagery, we estimate the cliff contribution to 2 years of ablation on a debris-covered tongue in Nepal, carefully taking into account ice dynamics. While they occupy only 7 to 8 % of the tongue surface, ice cliffs contributed to 23 to 24 % of the total tongue ablation.
Cited articles
Afonso, J. M. D. S., Vila, D. A., Gan, M. A., Quispe, D. P., Barreto, N. D. J. D. C., Huamán Chinchay, J. H., and Palharini, R. S. A.: Precipitation diurnal cycle assessment of satellite-based estimates over Brazil, Remote Sens.-Basel, 12, 2339, https://doi.org/10.3390/rs12142339, 2020.
Beraud, L., Cusicanqui, D., Rabatel, A., Brun, F., Vincent, C., and Six, D.: Glacier-wide seasonal and annual geodetic mass balances from Pléiades stereo images: application to the Glacier d'Argentière, French Alps, J. Glaciol., 69, 525–537, https://doi.org/10.1017/jog.2022.79, 2023.
Bevington, A. R. and Menounos, B.: Accelerated change in the glaciated environments of western Canada revealed through trend analysis of optical satellite imagery, Remote Sens. Environ., 270, 112862, https://doi.org/10.1016/j.rse.2021.112862, 2022.
Bibi, S., Wang, L., Li, X., Zhou, J., Chen, D., and Yao, T.: Climatic and associated cryospheric, biospheric, and hydrological changes on the Tibetan Plateau: A review, Int. J. Climatol., 38, e1–e17, https://doi.org/10.1002/joc.5411, 2018.
Blewitt, G., Kreemer, C., Hammond, W. C., and Gazeaux, J.: MIDAS robust trend estimator for accurate GPS station velocities without step detection, J. Geophys. Res.-Sol. Ea., 121, 2054–2068, https://doi.org/10.1002/2015JB012552, 2016.
Bolch, T., Menounos, B., and Wheate, R.: Landsat-based inventory of glaciers in western Canada, 1985–2005, Remote Sens. Environ., 114, 127–137, https://doi.org/10.1016/j.rse.2009.08.015, 2010.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Brun, F., Berthier, E., Wagnon, P., Kääb, A., and Treichler, D.: A spatially resolved estimate of High Mountain Asia glacier mass balances from 2000 to 2016, Nat. Geosci., 10, 668–673, https://doi.org/10.1038/ngeo2999, 2017.
Burns, P. and Nolin, A.: Using atmospherically-corrected Landsat imagery to measure glacier area change in the Cordillera Blanca, Peru from 1987 to 2010, Remote Sens. Environ., 140, 165–178, https://doi.org/10.1016/j.rse.2013.08.026, 2014.
Chander, G., Markham, B. L., and Helder, D. L.: Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors, Remote Sens. Environ., 113, 893–903, https://doi.org/10.1016/j.rse.2009.01.007, 2009.
Che, Y., Wang, S., Yi, S., Wei, Y., and Cai, Y.: Summer mass balance and surface velocity derived by unmanned aerial vehicle on debris-covered region of Baishui River Glacier No. 1, Yulong Snow Mountain, Remote Sens.-Basel, 12, 3280, https://doi.org/10.3390/rs12203280, 2020.
Chen, F., Wang, J., Li, B., Yang, A., and Zhang, M.: Spatial variability in melting on Himalayan debris-covered glaciers from 2000 to 2013, Remote Sens. Environ., 291, 113560, https://doi.org/10.1016/j.rse.2023.113560, 2023.
CMIP: Coupled Model Intercomparison Project, CMIP [data set], https://wcrp-cmip.org, last access: 10 October 2024.
Copernicus Climate Change Service (C3S) Climate Data Store (CDS): A planetary-scale platform for Earth science data & analysis, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://earthengine.google.com, last access: 2 December 2024.
Crespi, A., Lussana, C., Brunetti, M., Dobler, A., Maugeri, M., and Tveito, O. E.: High-resolution monthly precipitation climatologies over Norway (1981–2010): Joining numerical model data sets and in situ observations, Int. J. Climatol., 39, 2057–2070, https://doi.org/10.1002/joc.5933, 2019.
Curio, J., Maussion, F., and Scherer, D.: A 12-year high-resolution climatology of atmospheric water transport over the Tibetan Plateau, Earth Syst. Dynam., 6, 109–124, https://doi.org/10.5194/esd-6-109-2015, 2015.
Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., and Li, X.: Water Bodies' Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band, Remote Sens.-Basel, 8, 354, https://doi.org/10.3390/rs8040354, 2016.
Ebrahimy, H., Aghighi, H., Azadbakht, M., Amani, M., Mahdavi, S., and Matkan, A. A.: Downscaling MODIS land surface temperature product using an adaptive random forest regression method and Google Earth Engine for a 19-years spatiotemporal trend analysis over Iran, IEEE J. Sel. Top. Appl., 14, 2103–2112, https://doi.org/10.1109/JSTARS.2021.3051422, 2021.
EROS Centre: USGS EROS Archive – Digital Elevation – Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global [DataSet], EROS Centre, https://doi.org/10.5066/F7PR7TFT, 2018.
Essou, G. R., Sabarly, F., Lucas-Picher, P., Brissette, F., and Poulin, A.: Can precipitation and temperature from meteorological reanalyses be used for hydrological modeling?, J. Hydrometeorol., 17, 1929–1950, https://doi.org/10.1175/JHM-D-15-0138.1, 2016.
Farinotti, D., Immerzeel, W. W., de Kok, R. J., Quincey, D. J., and Dehecq, A.: Manifestations and mechanisms of the Karakoram glacier Anomaly, Nat. Geosci., 13, 8–16, https://doi.org/10.1038/s41561-019-0513-5, 2020.
Gadedjisso-Tossou, A., Adjegan, K. I., and Kablan, A. K. M.: Rainfall and temperature trend analysis by Mann–Kendall test and significance for Rainfed Cereal Yields in Northern Togo, Sci, 3, 17, https://doi.org/10.3390/sci3010017, 2021.
Gocic, M. and Trajkovic, S.: Analysis of changes in meteorological variables using Mann–Kendall and Sen's slope estimator statistical tests in Serbia, Global Planet. Change, 100, 172–182, https://doi.org/10.1016/j.gloplacha.2012.10.014, 2013.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031, 2017.
Güçlü, Y. S.: Multiple Şen-innovative trend analyses and partial Mann–Kendall test, J. Hydrol., 566, 685–704, https://doi.org/10.1016/j.jhydrol.2018.09.034, 2018.
Harrison, W. D.: How do glaciers respond to climate? Perspectives from the simplest models., J. Glaciol., 59, 949–960, https://doi.org/10.3189/2013JoG13J048, 2013.
Holobâcă, I.-H, Tielidze, L. G., Ivan, K., Elizbarashvili, M., Alexe, M., Germain, D., Petrescu, S. H., Pop, O. T., and Gaprindashvili, G.: Multi-sensor remote sensing to map glacier debris cover in the Greater Caucasus, Georgia., J. Glaciol., 67, 685–696, https://doi.org/10.1017/jog.2021.47, 2021.
Hu, Z., Liu, S., Zhong, G., Lin, H., and Zhou, Z.: Modified Mann-Kendall trend test for hydrological time series under the scaling hypothesis and its application, Hydrol. Sci. J., 65, 2419–2438, https://doi.org/10.1080/02626667.2020.1810253, 2020.
Huang, L., Li, Z., Zhou, J. M., and Zhang, P.: An automatic method for clean glacier and nonseasonal snow area change estimation in High Mountain Asia from 1990 to 2018, Remote Sens. Environ., 258, 112376, https://doi.org/10.1016/j.rse.2021.112376, 2021.
Hutengs, C. and Vohland, M.: Downscaling land surface temperatures at regional scales with random forest regression, Remote Sens. Environ., 178, 127–141, https://doi.org/10.1016/j.rse.2016.03.006, 2016.
Jiang, Y., Yang, K., Shao, C., Zhou, X., Zhao, L., Chen, Y., and Wu, H.: A downscaling approach for constructing high-resolution precipitation dataset over the Tibetan Plateau from ERA5 reanalysis, Atmos. Res., 256, 105574, https://doi.org/10.1016/j.atmosres.2021.105574, 2021.
Johnson, E. and Rupper, S.: An examination of physical processes that trigger the albedo-feedback on glacier surfaces and implications for regional glacier mass balance across high mountain Asia, Front. Earth Sci., 8, 129, https://doi.org/10.3389/feart.2020.00129, 2020.
Kang, S., Chen, F., Gao, T., Zhang, Y., Yang, W., Yu, W., and Yao, T.: Early onset of rainy season suppresses glacier melt: a case study on Zhadang glacier, Tibetan Plateau., J. Glaciol., 55, 755–758, https://doi.org/10.3189/002214309789470978, 2009.
Karaman, Ç. H. and Akyürek, Z.: Evaluation of near-surface air temperature reanalysis datasets and downscaling with machine learning based Random Forest method for complex terrain of Turkey, Adv. Space Res., 71, 5256–5281, https://doi.org/10.1016/j.asr.2023.02.006, 2023.
Kaushik, S., Singh, T., Joshi, P. K., and Dietz, A. J.: Automated mapping of glacial lakes using multisource remote sensing data and deep convolutional neural network, Int. J. Appl. Earth Obs., 115, 103085, https://doi.org/10.1016/j.jag.2022.103085, 2022.
Khan, A. A., Jamil, A., Hussain, D., Taj, M., Jabeen, G., and Malik, M. K.: Machine-learning algorithms for mapping debris-covered glaciers: the Hunza Basin case study, IEEE Access, 8, 12725–12734, https://doi.org/10.1109/ACCESS.2020.2965768, 2020.
Kusch, E. and Davy, R.: KrigR-a tool for downloading and statistically downscaling climate reanalysis data, Environ. Res. Lett., 17, 024005, https://doi.org/10.1088/1748-9326/ac48b3, 2022.
Lamsal, D., Sawagaki, T., Watanabe, T., and Byers, A. C.: Assessment of glacial lake development and prospects of outburst susceptibility: Chamlang South Glacier, eastern Nepal Himalaya, Geomat. Nat. Haz. Risk, 7, 403–423, https://doi.org/10.1080/19475705.2014.931306, 2016.
Latif, A., Ilyas, S., Zhang, Y., Xin, Y., Zhou, L., and Zhou, Q.: Review on global change status and its impacts on the Tibetan Plateau environment, J. Plant Ecol., 12, 917–930, https://doi.org/10.1093/jpe/rtz038, 2019.
Lin, R., Mei, G., Liu, Z., Xi, N., and Zhang, X.: Susceptibility analysis of glacier debris flow by investigating the changes in glaciers based on remote sensing: A case study, Sustainability, 13, 7196, https://doi.org/10.3390/su13137196, 2021.
Liu, C., Li, W., Zhu, G., Zhou, H., Yan, H., and Xue, P.: Land use/land cover changes and their driving factors in the Northeastern Tibetan Plateau based on Geographical Detectors and Google Earth Engine: A case study in Gannan Prefecture, Remote Sens.-Basel, 12, 3139, https://doi.org/10.3390/rs12193139, 2020.
McFeeters, S. K.: The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features, Int. J. Remote Sens., 17, 1425–1432, https://doi.org/10.1080/01431169608948714, 1996.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Neckel, N., Kropáček, J., Bolch, T., and Hochschild, V.: Glacier mass changes on the Tibetan Plateau 2003-2009 derived from ICESat laser altimetry measurements, Environ. Res. Lett., 9, 014009, https://doi.org/10.1088/1748-9326/9/1/014009, 2014.
Neeti, N. and Eastman, J. R.: A contextual mann-kendall approach for the assessment of trend significance in image time series, T. GIS, 15, 599–611, https://doi.org/10.1111/j.1467-9671.2011.01280.x, 2011.
Ojha, S., Fujita, K., Sakai, A., Nagai, H., and Lamsal, D.: Topographic controls on the debris-cover extent of glaciers in the Eastern Himalayas: Regional analysis using a novel high-resolution glacier inventory, Quatern. Int., 455, 82–92, https://doi.org/10.1016/j.quaint.2017.08.007, 2017.
Pratap, B., Dobhal, D. P., Mehta, M., and Bhambri, R.: Influence of debris cover and altitude on glacier surface melting: a case study on Dokriani Glacier, central Himalaya, India, Ann. Glaciol., 56, 9–16, https://doi.org/10.3189/2015AoG70A971, 2015.
Prăvălie, R., Sirodoev, I., Nita, I. A., Patriche, C., Dumitraşcu, M., Roşca, B., Tişcovsch, A., Bando, G., Săvulescu, I., Mănoiu, V., and Birsan, M. V.: NDVI-based ecological dynamics of forest vegetation and its relationship to climate change in Romania during 1987–2018, Ecol. Indic., 136, 108629, https://doi.org/10.1016/j.ecolind.2022.108629, 2022.
Rashid, I. and Majeed, U.: Recent recession and potential future lake formation on Drang Drung glacier, Zanskar Himalaya, as assessed with earth observation data and glacier modelling, Environ. Earth Sci., 77, 429, https://doi.org/10.1007/s12665-018-7601-5, 2018.
RGI 7.0 Consortium: Randolph Glacier Inventory – A Dataset of Global Glacier Outlines, Version 7.0 [DataSet], NSIDC: National Snow and Ice Data Center, Boulder, Colorado, USA, https://doi.org/10.5067/f6jmovy5navz, 2023.
Robson, B. A., Nuth, C., Dahl, S. O., Hölbling, D., Strozzi, T., and Nielsen, P. R.: Automated classification of debris-covered glaciers combining optical, SAR and topographic data in an object-based environment, Remote Sens. Environ., 170, 372–387, https://doi.org/10.1016/j.rse.2015.10.001, 2015.
Rounce, D. R., King, O., McCarthy, M., Shean, D. E., and Salerno, F.: Quantifying debris thickness of debris-covered glaciers in the Everest Region of Nepal through inversion of a subdebris melt model, J. Geophys. Res.-Earth, 123, 1094–1115, https://doi.org/10.1029/2017JF004395, 2018.
Rounce, D. R., Hock, R. W., McNabb, R., Millan, C., Sommer, M., and Braun, P.: Global Glacier Debris Thickness Estimates and Sub-Debris Melt Factors, Version 1, National Snow and Ice Data Center [data set], https://doi.org/10.5067/8DQKWY03KJWT, 2021.
Rounce, D. R., Hock, R., Maussion, F., Hugonnet, R., Kochtitzky, W., Huss, M., and McNabb, R. W.: Global glacier change in the 21st century: Every increase in temperature matters, Science, 379, 78–83, https://doi.org/10.1126/science.abo1324, 2023.
Royden, L. H., Burchfiel, B. C., and van der Hilst, R. D.: The geological evolution of the Tibetan Plateau, Science, 321, 1054–1058, https://doi.org/10.1126/science.1155371, 2008.
Salerno, F., Guyennon, N., Yang, K., Shaw, T. E., Lin, C., and Colombo, N.: Local cooling and drying induced by Himalayan glaciers under global warming, Nat. Geosci., 16, 1120–1127, https://doi.org/10.1038/s41561-023-01331-y, 2023.
Scherler, D., Wulf, H., and Gorelick, N.: Global assessment of supraglacial debris-cover extents, Geophys. Res. Lett., 45, 11–798, https://doi.org/10.1029/2018GL080158, 2018a.
Scherler, D., Wulf, H., and Gorelick, N.: Supraglacial Debris Cover. V. 1.0, GFZ Data Services [data set], https://doi.org/10.5880/GFZ.3.3.2018.005, 2018b.
Shean, D. E., Bhushan, S., Montesano, P., Rounce, D. R., Arendt, A., and Osmanoglu, B.: A systematic, regional assessment of high mountain Asia glacier mass balance, Front. Earth Sci., 7, 363, https://doi.org/10.3389/feart.2019.00363, 2020.
Shugar, D. H., Burr, A., Haritashya, U. K., Kargel, J. S., Watson, C. S., and Kennedy, M. C.: Rapid worldwide growth of glacial lakes since 1990, Nat. Clim. Change, 10, 939–945, https://doi.org/10.1038/s41558-020-0855-4, 2020.
Some'e, B. S., Ezani, A., and Tabari, H.: Spatiotemporal trends and change point of precipitation in Iran, Atmos. Res., 113, 1–12, https://doi.org/10.1016/j.atmosres.2012.04.016, 2012.
Su, B., Xiao, C., Chen, D., Huang, Y., Che, Y., and Zhao, H.: Glacier change in China over past decades: Spatiotemporal patterns and influencing factors, Earth-Sci. Rev., 226, 103926, https://doi.org/10.1016/j.earscirev.2022.103926, 2022.
Sugiyama, S., Fukui, K., Fujita, K., Tone, K., and Yamaguchi, S.: Changes in ice thickness and flow velocity of Yala Glacier, Langtang Himal, Nepal, from 1982 to 2009, Ann. Glaciol., 54, 157–162, https://doi.org/10.3189/2013AoG64A111, 2013.
Sun, H., Yao, T. D., Su, F. G., Ou, T., He, Z., Tang, G., and Chen, D.: Increased glacier melt enhances future extreme floods in the southern Tibetan Plateau, Adv. Clim. Change Res., 15, 431–441, https://doi.org/10.1016/j.accre.2024.01.003, 2024.
Sun, J., Zhou, T., Liu, M., Chen, Y., Shang, H., and Zhu, L.: Linkages of the dynamics of glaciers and lakes with the climate elements over the Tibetan Plateau, Earth-Sci. Rev., 185, 308–324, https://doi.org/10.1016/j.earscirev.2018.06.012, 2018.
Wang, F., Shao, W., Yu, H., Kan, G., He, X., Zhang, D., and Wang, G.: Re-evaluation of the power of the Mann–Kendall test for detecting monotonic trends in hydrometeorological time series, Front. Earth Sci., 8, 14, https://doi.org/10.3389/feart.2020.00014, 2020.
Wang, N., Tian, J., Su, S., and Tian, Q.: A Downscaling Method Based on MODIS Product for Hourly ERA5 Reanalysis of Land Surface Temperature, Remote Sens.-Basel, 15, 4441, https://doi.org/10.3390/rs15184441, 2023.
Wang, X., Siegert, F., Zhou, A. G., and Franke, J.: Glacier and glacial lake changes and their relationship in the context of climate change, Central Tibetan Plateau 1972–2010, Global Planet. Change, 111, 246–257, https://doi.org/10.1016/j.gloplacha.2013.09.011, 2013.
Wang, X., Tolksdorf, V., Otto, M., and Scherer, D.: WRF-based dynamical downscaling of ERA5 reanalysis data for High Mountain Asia: Towards a new version of the High Asia Refined analysis, Int. J. Climatol., 41, 743–762, https://doi.org/10.1002/joc.6686, 2021.
Wu, X., Su, J., Ren, W., Lü, H., and Yuan, F.: Statistical comparison and hydrological utility evaluation of ERA5-Land and IMERG precipitation products on the Tibetan Plateau, J. Hydrol., 620, 129384, https://doi.org/10.1016/j.jhydrol.2023.129384, 2023.
Xiao, Y., Ke, C. Q., Cai, Y., Shen, X., Wang, Z., Nourani, V., and Lhakpa, D.: Glacier retreating analysis on the southeastern Tibetan Plateau via multisource remote sensing data, IEEE J. Sel. Top. Appl., 16, 2035–2049, https://doi.org/10.1109/JSTARS.2023.3243771, 2023.
Yao, T., Thompson, L., Yang, W., Yu, W., Gao, Y., and Guo, X.: Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings, Nat. Clim. Change, 2, 663–667, https://doi.org/10.1038/nclimate1580, 2012.
Ye, Q., Zong, J., Tian, L., Cogley, J. G., Song, C., and Guo, W.: Glacier changes on the Tibetan Plateau derived from Landsat imagery: mid-1970s-2000-13., J. Glaciol., 63, 273–287, https://doi.org/10.1017/jog.2016.137, 2017.
Yilmaz, M.: Accuracy assessment of temperature trends from ERA5 and ERA5-Land, Sci. Total Environ., 856, 159182, https://doi.org/10.1016/j.scitotenv.2022.159182, 2023.
Zemp, M., Huss, M., Thibert, E., Eckert, N., McNabb, R., and Huber, J.: Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016, Nature, 568, 382–386, https://doi.org/10.1038/s41586-019-1071-0, 2019.
Zhang, J., Fan, H., He, D., and Chen, J.: Integrating precipitation zoning with random forest regression for the spatial downscaling of satellite-based precipitation: A case study of the Lancang-Mekong River basin, Int. J. Climatol., 39, 3947–3961, https://doi.org/10.1002/joc.6050, 2019.
Zhang, Y.: Integration dataset of Tibet Plateau boundary [Dataset], National Tibetan Plateau/Third Pole Environment Data Center, https://doi.org/10.11888/Geogra.tpdc.270099, 2019.
Zhang, Y., Gao, T., Kang, S., Shangguan, D., and Luo, X.: Albedo reduction as an important driver for glacier melting in Tibetan Plateau and its surrounding areas, Earth-Sci. Rev., 220, 103735, https://doi.org/10.1016/j.earscirev.2021.103735, 2021.
Zhao, F., Long, D., Li, X., Huang, Q., and Han, P.: Rapid glacier mass loss in the Southeastern Tibetan Plateau since the year 2000 from satellite observations, Remote Sens. Environ., 270, 112853, https://doi.org/10.1016/j.rse.2021.112853, 2022.
Zhao, H., Chen, F., and Zhang, M.: A systematic extraction approach for mapping glacial lakes in high mountain regions of Asia, IEEE J. Sel. Top. Appl., 11, 2788–2799, https://doi.org/10.1109/JSTARS.2018.2846551, 2018.
Short summary
Glacier retreat patterns and climatic drivers on the Tibetan Plateau are uncertain at finer resolutions. This study introduces a new glacier-mapping method covering 1988 to 2022, using downscaled air temperature and precipitation data. It quantifies the impacts of annual and seasonal temperature and precipitation on retreat. Results show rapid and varied retreat: annual temperature and spring precipitation influence retreat in the west and northwest, respectively.
Glacier retreat patterns and climatic drivers on the Tibetan Plateau are uncertain at finer...