Articles | Volume 16, issue 12
https://doi.org/10.5194/essd-16-5703-2024
© 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/essd-16-5703-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
17 Dec 2024
Data description paper |
| 17 Dec 2024
| 17 Dec 2024
TPRoGI: a comprehensive rock glacier inventory for the Tibetan Plateau using deep learning
Zhangyu Sun
Department of Earth and Environmental Sciences, Faculty of Science, The Chinese University of Hong Kong, Hong Kong, China
Department of Earth and Environmental Sciences, Faculty of Science, The Chinese University of Hong Kong, Hong Kong, China
Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong, China
Adina Racoviteanu
Université Grenoble Alpes, CNRS, IRD, IGE, Saint-Martin-d'Hères, France
Department of Earth and Environmental Sciences, Faculty of Science, The Chinese University of Hong Kong, Hong Kong, China
Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong, China
Stephan Harrison
Faculty of Environment, Science and Economy, University of Exeter, Exeter, United Kingdom
Xiaowen Wang
Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, China
Jiaxin Cai
Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, China
Xin Guo
Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, China
Yujun He
Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, China
Hailun Yuan
Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, China
Related authors
No articles found.
Kathrin Maier, Zhuoxuan Xia, Lin Liu, Mark J. Lara, Jurjen van der Sluijs, Philipp Bernhard, and Irena Hajnsek
EGUsphere, https://doi.org/10.5194/egusphere-2025-2187, https://doi.org/10.5194/egusphere-2025-2187, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Our study explores how thawing permafrost on the Qinghai-Tibet Plateau triggers landslides, mobilising stored carbon. Using satellite data from 2011 to 2020, we measured soil erosion, ice loss, and carbon mobilisation. While current impacts are modest, increasing landslide activity suggests future significance. This research underscores the need to understand permafrost thaw's role in carbon dynamics and climate change.
Stephan Harrison, Adina Racoviteanu, Sarah Shannon, Darren Jones, Karen Anderson, Neil Glasser, Jasper Knight, Anna Ranger, Arindan Mandal, Brahma Dutt Vishwakarma, Jeffrey Kargel, Dan Shugar, Umesh Haritishaya, Dongfeng Li, Aristeidis Koutroulis, Klaus Wyser, and Sam Inglis
EGUsphere, https://doi.org/10.5194/egusphere-2024-4033, https://doi.org/10.5194/egusphere-2024-4033, 2025
Short summary
Short summary
Climate change is leading to a global recession of mountain glaciers, and numerical modelling suggests that this will result in the eventual disappearance of many glaciers, impacting water supplies. However, an alternative scenario suggests that increased rock fall and debris flows to valley bottoms will cover glaciers with thick rock debris, slowing melting and transforming glaciers into rock-ice mixtures called rock glaciers. This paper explores these scenarios.
Yan Hu, Stephan Harrison, Lin Liu, and Joanne Laura Wood
The Cryosphere, 17, 2305–2321, https://doi.org/10.5194/tc-17-2305-2023, https://doi.org/10.5194/tc-17-2305-2023, 2023
Short summary
Short summary
Rock glaciers are considered to be important freshwater reservoirs in the future climate. However, the amount of ice stored in rock glaciers is poorly quantified. Here we developed an empirical model to estimate ice content in rock the glaciers in the Khumbu and Lhotse valleys, Nepal. The modelling results confirmed the hydrological importance of rock glaciers in the study area. The developed approach shows promise in being applied to permafrost regions to assess water storage of rock glaciers.
Sarah Shannon, Anthony Payne, Jim Freer, Gemma Coxon, Martina Kauzlaric, David Kriegel, and Stephan Harrison
Hydrol. Earth Syst. Sci., 27, 453–480, https://doi.org/10.5194/hess-27-453-2023, https://doi.org/10.5194/hess-27-453-2023, 2023
Short summary
Short summary
Climate change poses a potential threat to water supply in glaciated river catchments. In this study, we added a snowmelt and glacier melt model to the Dynamic fluxEs and ConnectIvity for Predictions of HydRology model (DECIPHeR). The model is applied to the Naryn River catchment in central Asia and is found to reproduce past change discharge and the spatial extent of seasonal snow cover well.
Zhuoxuan Xia, Lingcao Huang, Chengyan Fan, Shichao Jia, Zhanjun Lin, Lin Liu, Jing Luo, Fujun Niu, and Tingjun Zhang
Earth Syst. Sci. Data, 14, 3875–3887, https://doi.org/10.5194/essd-14-3875-2022, https://doi.org/10.5194/essd-14-3875-2022, 2022
Short summary
Short summary
Retrogressive thaw slumps are slope failures resulting from abrupt permafrost thaw, and are widely distributed along the Qinghai–Tibet Engineering Corridor. The potential damage to infrastructure and carbon emission of thaw slumps motivated us to obtain an inventory of thaw slumps. We used a semi-automatic method to map 875 thaw slumps, filling the knowledge gap of thaw slump locations and providing key benchmarks for analysing the distribution features and quantifying spatio-temporal changes.
Adina E. Racoviteanu, Lindsey Nicholson, and Neil F. Glasser
The Cryosphere, 15, 4557–4588, https://doi.org/10.5194/tc-15-4557-2021, https://doi.org/10.5194/tc-15-4557-2021, 2021
Short summary
Short summary
Supraglacial debris cover comprises ponds, exposed ice cliffs, debris material and vegetation. Understanding these features is important for glacier hydrology and related hazards. We use linear spectral unmixing of satellite data to assess the composition of map supraglacial debris across the Himalaya range in 2015. One of the highlights of this study is the automated mapping of supraglacial ponds, which complements and expands the existing supraglacial debris and lake databases.
Xiaowen Wang, Lin Liu, Yan Hu, Tonghua Wu, Lin Zhao, Qiao Liu, Rui Zhang, Bo Zhang, and Guoxiang Liu
Nat. Hazards Earth Syst. Sci., 21, 2791–2810, https://doi.org/10.5194/nhess-21-2791-2021, https://doi.org/10.5194/nhess-21-2791-2021, 2021
Short summary
Short summary
We characterized the multi-decadal geomorphic changes of a low-angle valley glacier in the East Kunlun Mountains and assessed the detachment hazard influence. The observations reveal a slow surge-like dynamic pattern of the glacier tongue. The maximum runout distances of two endmember avalanche scenarios were presented. This study provides a reference to evaluate the runout hazards of low-angle mountain glaciers prone to detachment.
Jiahua Zhang, Lin Liu, Lei Su, and Tao Che
The Cryosphere, 15, 3021–3033, https://doi.org/10.5194/tc-15-3021-2021, https://doi.org/10.5194/tc-15-3021-2021, 2021
Short summary
Short summary
We improve the commonly used GPS-IR algorithm for estimating surface soil moisture in permafrost areas, which does not consider the bias introduced by seasonal surface vertical movement. We propose a three-in-one framework to integrate the GPS-IR observations of surface elevation changes, soil moisture, and snow depth at one site and illustrate it by using a GPS site in the Qinghai–Tibet Plateau. This study is the first to use GPS-IR to measure environmental variables in the Tibetan Plateau.
Jiahua Zhang, Lin Liu, and Yufeng Hu
The Cryosphere, 14, 1875–1888, https://doi.org/10.5194/tc-14-1875-2020, https://doi.org/10.5194/tc-14-1875-2020, 2020
Short summary
Short summary
Ground surface in permafrost areas undergoes uplift and subsides seasonally due to freezing–thawing active layer. Surface elevation change serves as an indicator of frozen-ground dynamics. In this study, we identify 12 GPS stations across the Canadian Arctic, which are useful for measuring elevation changes by using reflected GPS signals. Measurements span from several years to over a decade and at daily intervals and help to reveal frozen ground dynamics at various temporal and spatial scales.
Enze Zhang, Lin Liu, and Lingcao Huang
The Cryosphere, 13, 1729–1741, https://doi.org/10.5194/tc-13-1729-2019, https://doi.org/10.5194/tc-13-1729-2019, 2019
Short summary
Short summary
Conventionally, calving front positions have been manually delineated from remote sensing images. We design a novel method to automatically delineate the calving front positions of Jakobshavn Isbræ based on deep learning, the first of this kind for Greenland outlet glaciers. We generate high-temporal-resolution (about two measurements every month) calving fronts, demonstrating our methodology can be applied to many other tidewater glaciers through this successful case study on Jakobshavn Isbræ.
Sarah Shannon, Robin Smith, Andy Wiltshire, Tony Payne, Matthias Huss, Richard Betts, John Caesar, Aris Koutroulis, Darren Jones, and Stephan Harrison
The Cryosphere, 13, 325–350, https://doi.org/10.5194/tc-13-325-2019, https://doi.org/10.5194/tc-13-325-2019, 2019
Short summary
Short summary
We present global glacier volume projections for the end of this century, under a range of high-end climate change scenarios, defined as exceeding 2 °C global average warming. The ice loss contribution to sea level rise for all glaciers excluding those on the peripheral of the Antarctic ice sheet is 215.2 ± 21.3 mm. Such large ice losses will have consequences for sea level rise and for water supply in glacier-fed river systems.
Jiangjun Ran, Miren Vizcaino, Pavel Ditmar, Michiel R. van den Broeke, Twila Moon, Christian R. Steger, Ellyn M. Enderlin, Bert Wouters, Brice Noël, Catharina H. Reijmer, Roland Klees, Min Zhong, Lin Liu, and Xavier Fettweis
The Cryosphere, 12, 2981–2999, https://doi.org/10.5194/tc-12-2981-2018, https://doi.org/10.5194/tc-12-2981-2018, 2018
Short summary
Short summary
To accurately predict future sea level rise, the mechanisms driving the observed mass loss must be better understood. Here, we combine data from the satellite gravimetry, surface mass balance, and ice discharge to analyze the mass budget of Greenland at various temporal scales. This study, for the first time, suggests the existence of a substantial meltwater storage during summer, with a peak value of 80–120 Gt in July. We highlight its importance for understanding ice sheet mass variability
Stephan Harrison, Jeffrey S. Kargel, Christian Huggel, John Reynolds, Dan H. Shugar, Richard A. Betts, Adam Emmer, Neil Glasser, Umesh K. Haritashya, Jan Klimeš, Liam Reinhardt, Yvonne Schaub, Andy Wiltshire, Dhananjay Regmi, and Vít Vilímek
The Cryosphere, 12, 1195–1209, https://doi.org/10.5194/tc-12-1195-2018, https://doi.org/10.5194/tc-12-1195-2018, 2018
Short summary
Short summary
Most mountain glaciers have receded throughout the last century in response to global climate change. This recession produces a range of natural hazards including glacial lake outburst floods (GLOFs). We have produced the first global inventory of GLOFs associated with the failure of moraine dams and show, counterintuitively, that these have reduced in frequency over recent decades. In this paper we explore the reasons for this pattern.
Lin Liu and Kristine M. Larson
The Cryosphere, 12, 477–489, https://doi.org/10.5194/tc-12-477-2018, https://doi.org/10.5194/tc-12-477-2018, 2018
Short summary
Short summary
We demonstrate the use of reflected GPS signals to measure elevation changes over a permafrost area in northern Alaska. For the first time, we construct a daily-sampled time series of elevation changes over 12 summers. Our results show regular thaw subsidence within each summer and a secular subsidence trend of 0.3 cm per year. This method promises a new way to utilize GPS data in cold regions for studying frozen ground consistently and sustainably over a long time.
Xiaowen Wang, Lin Liu, Lin Zhao, Tonghua Wu, Zhongqin Li, and Guoxiang Liu
The Cryosphere, 11, 997–1014, https://doi.org/10.5194/tc-11-997-2017, https://doi.org/10.5194/tc-11-997-2017, 2017
Short summary
Short summary
Rock glaciers are abundant in high mountains in western China but have been ignored for 20 years. We used a new remote-sensing-based method to map active rock glaciers in the Chinese part of the Tien Shan and compiled an inventory of 261 active rock glaciers and included quantitative information about their locations, geomorphic parameters, and downslope velocities. Our dataset suggests that the lower limit of permafrost there is 2500–2800 m.
A. E. Racoviteanu, Y. Arnaud, M. W. Williams, and W. F. Manley
The Cryosphere, 9, 505–523, https://doi.org/10.5194/tc-9-505-2015, https://doi.org/10.5194/tc-9-505-2015, 2015
Short summary
Short summary
An overall negative glacier surface area change of 0.5±0.2% yr-1 was observed for the eastern Himalaya since 1962 based on remote sensing data. There were higher rates of area loss for clean glaciers (-34%, or -0.7% yr-1) compared to debris-covered glaciers (-14.3% or -0.3 yr-1) on a glacier-by-glacier basis. Patterns of area change are heterogenous and depend on topographic and climatic factors, glacier altitude (maximum, median, altitudinal range), glacier size, slope and aspect.
L. Liu, K. Schaefer, A. Gusmeroli, G. Grosse, B. M. Jones, T. Zhang, A. D. Parsekian, and H. A. Zebker
The Cryosphere, 8, 815–826, https://doi.org/10.5194/tc-8-815-2014, https://doi.org/10.5194/tc-8-815-2014, 2014
L. Liu, C. I. Millar, R. D. Westfall, and H. A. Zebker
The Cryosphere, 7, 1109–1119, https://doi.org/10.5194/tc-7-1109-2013, https://doi.org/10.5194/tc-7-1109-2013, 2013
Related subject area
Domain: ESSD – Ice | Subject: Permafrost
Permafrost temperature baseline at 15 m depth on the Qinghai–Tibetan Plateau (2010–2019)
Compilation and Analysis of Thaw Settlement Test Results: Implications for Prediction Tools and Stress-Strain Characterization in Permafrost
Rock Glacier Inventories (RoGI) in 12 areas worldwide using a multi-operator consensus-based procedure
Very high resolution aerial image orthomosaics, point clouds, and elevation datasets of select permafrost landscapes in Alaska and northwestern Canada
Multisource Synthesized Inventory of CRitical Infrastructure and HUman-Impacted Areas in AlaSka (SIRIUS)
The first hillslope thermokarst inventory for the permafrost region of the Qilian Mountains
An observational network of ground surface temperature under different land-cover types on the northeastern Qinghai–Tibet Plateau
Modern air, englacial and permafrost temperatures at high altitude on Mt Ortles (3905 m a.s.l.), in the eastern European Alps
A new 2010 permafrost distribution map over the Qinghai–Tibet Plateau based on subregion survey maps: a benchmark for regional permafrost modeling
Defu Zou, Lin Zhao, Guojie Hu, Erji Du, Guangyue Liu, Chong Wang, and Wangping Li
Earth Syst. Sci. Data, 17, 1731–1742, https://doi.org/10.5194/essd-17-1731-2025, https://doi.org/10.5194/essd-17-1731-2025, 2025
Short summary
Short summary
This study provides baseline data of permafrost temperature at 15 m depth on the Qinghai–Tibetan Plateau (QTP) over the period 2010–2019 at a spatial resolution of nearly 1 km, using 231 borehole records and a machine learning method. The average MAGT15 m of the QTP permafrost was −1.85 °C, with 90 % of the values ranging from −5.1 to −0.1 °C and 51.2 % exceeding −1.5 °C. The data can serve as a crucial boundary condition for deeper permafrost assessments and a reference for model simulations.
Zakieh Mohammadi and Jocelyn L. Hayley
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-514, https://doi.org/10.5194/essd-2024-514, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
This study aims to enhance understanding of thaw settlement, a common issue caused by climate change that impacts infrastructures on permafrost. A comprehensive dataset of thaw settlement test results was compiled from existing literature. The dataset was used to compare thaw settlement properties across various soil types and to evaluate empirical tools commonly used for predicting thaw settlement, identifying the most accurate methods.
Line Rouyet, Tobias Bolch, Francesco Brardinoni, Rafael Caduff, Diego Cusicanqui, Margaret Darrow, Reynald Delaloye, Thomas Echelard, Christophe Lambiel, Lucas Ruiz, Lea Schmid, Flavius Sirbu, and Tazio Strozzi
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-598, https://doi.org/10.5194/essd-2024-598, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Rock glaciers are landforms generated by the creep of frozen ground (permafrost) in cold-climate mountains. Mapping rock glaciers contributes to document the distribution and the dynamics of mountain permafrost. We compiled inventories documenting the location, the characteristics, and the extent of rock glaciers in 12 mountain regions around the world. In each region, a team of operators performed the work following common rules and agreed on final solutions when discrepancies were identified.
Tabea Rettelbach, Ingmar Nitze, Inge Grünberg, Jennika Hammar, Simon Schäffler, Daniel Hein, Matthias Gessner, Tilman Bucher, Jörg Brauchle, Jörg Hartmann, Torsten Sachs, Julia Boike, and Guido Grosse
Earth Syst. Sci. Data, 16, 5767–5798, https://doi.org/10.5194/essd-16-5767-2024, https://doi.org/10.5194/essd-16-5767-2024, 2024
Short summary
Short summary
Permafrost landscapes in the Arctic are rapidly changing due to climate warming. Here, we publish aerial images and elevation models with very high spatial detail that help study these landscapes in northwestern Canada and Alaska. The images were collected using the Modular Aerial Camera System (MACS). This dataset has significant implications for understanding permafrost landscape dynamics in response to climate change. It is publicly available for further research.
Soraya Kaiser, Julia Boike, Guido Grosse, and Moritz Langer
Earth Syst. Sci. Data, 16, 3719–3753, https://doi.org/10.5194/essd-16-3719-2024, https://doi.org/10.5194/essd-16-3719-2024, 2024
Short summary
Short summary
Arctic warming, leading to permafrost degradation, poses primary threats to infrastructure and secondary ecological hazards from possible infrastructure failure. Our study created a comprehensive Alaska inventory combining various data sources with which we improved infrastructure classification and data on contaminated sites. This resource is presented as a GeoPackage allowing planning of infrastructure damage and possible implications for Arctic communities facing permafrost challenges.
Xiaoqing Peng, Guangshang Yang, Oliver W. Frauenfeld, Xuanjia Li, Weiwei Tian, Guanqun Chen, Yuan Huang, Gang Wei, Jing Luo, Cuicui Mu, and Fujun Niu
Earth Syst. Sci. Data, 16, 2033–2045, https://doi.org/10.5194/essd-16-2033-2024, https://doi.org/10.5194/essd-16-2033-2024, 2024
Short summary
Short summary
It is important to know about the distribution of thermokarst landscapes. However, most work has been done in the permafrost regions of the Qinghai–Tibetan Plateau, except for the Qilian Mountains in the northeast. Here we used satellite images and field work to investigate and analyze its potential driving factors. We found a total of 1064 hillslope thermokarst (HT) features in this area, and 82 % were initiated in the last 10 years. These findings will be significant for the next predictions.
Raul-David Şerban, Huijun Jin, Mihaela Şerban, Giacomo Bertoldi, Dongliang Luo, Qingfeng Wang, Qiang Ma, Ruixia He, Xiaoying Jin, Xinze Li, Jianjun Tang, and Hongwei Wang
Earth Syst. Sci. Data, 16, 1425–1446, https://doi.org/10.5194/essd-16-1425-2024, https://doi.org/10.5194/essd-16-1425-2024, 2024
Short summary
Short summary
A particular observational network for ground surface temperature (GST) has been established on the northeastern Qinghai–Tibet Plateau, covering various environmental conditions and scales. This analysis revealed the substantial influences of the land cover on the spatial variability in GST over short distances (<16 m). Improving the monitoring of GST is important for the biophysical processes at the land–atmosphere boundary and for understanding the climate change impacts on cold environments.
Luca Carturan, Fabrizio De Blasi, Roberto Dinale, Gianfranco Dragà, Paolo Gabrielli, Volkmar Mair, Roberto Seppi, David Tonidandel, Thomas Zanoner, Tiziana Lazzarina Zendrini, and Giancarlo Dalla Fontana
Earth Syst. Sci. Data, 15, 4661–4688, https://doi.org/10.5194/essd-15-4661-2023, https://doi.org/10.5194/essd-15-4661-2023, 2023
Short summary
Short summary
This paper presents a new dataset of air, englacial, soil surface and rock wall temperatures collected between 2010 and 2016 on Mt Ortles, which is the highest summit of South Tyrol, Italy. Details are provided on instrument type and characteristics, field methods, and data quality control and assessment. The obtained data series are available through an open data repository. This is a rare dataset from a summit area lacking observations on permafrost and glaciers and their climatic response.
Zetao Cao, Zhuotong Nan, Jianan Hu, Yuhong Chen, and Yaonan Zhang
Earth Syst. Sci. Data, 15, 3905–3930, https://doi.org/10.5194/essd-15-3905-2023, https://doi.org/10.5194/essd-15-3905-2023, 2023
Short summary
Short summary
This study provides a new 2010 permafrost distribution map of the Qinghai–Tibet Plateau (QTP), using an effective mapping approach based entirely on satellite temperature data, well constrained by survey-based subregion maps, and considering the effects of local factors. The map shows that permafrost underlies about 41 % of the total QTP. We evaluated it with borehole observations and other maps, and all evidence indicates that this map has excellent accuracy.
Cited articles
Arenson, L., Hoelzle, M., and Springman, S.: Borehole deformation measurements and internal structure of some rock glaciers in Switzerland, Permafrost Periglac., 13, 117–135, https://doi.org/10.1002/ppp.414, 2002.
Arenson, L. U. and Springman, S. M.: Mathematical descriptions for the behaviour of ice-rich frozen soils at temperatures close to 0 °C, Can. Geotech. J., 42, 431–442, https://doi.org/10.1139/t04-109, 2005.
Azócar, G. and Brenning, A.: Hydrological and geomorphological significance of rock glaciers in the dry Andes, Chile (27–33 S), Permafrost Periglac., 21, 42–53, https://doi.org/10.1002/ppp.669, 2010.
Barsch, D.: Rockglaciers: indicators for the present and former geoecology in high mountain environments, Springer-Verlag, Berlin, 331 pp., https://doi.org/10.1007/978-3-642-80093-1, 1996.
Berthling, I.: Beyond confusion: Rock glaciers as cryo-conditioned landforms, Geomorphology, 131, 98–106, https://doi.org/10.1016/j.geomorph.2011.05.002, 2011.
Boeckli, L., Brenning, A., Gruber, S., and Noetzli, J.: Permafrost distribution in the European Alps: calculation and evaluation of an index map and summary statistics, The Cryosphere, 6, 807–820, https://doi.org/10.5194/tc-6-807-2012, 2012.
Bolch, T., Shea, J. M., Liu, S., Azam, F. M., Gao, Y., Gruber, S., Immerzeel, W. W., Kulkarni, A., Li, H., Tahir, A. A., Zhang, G., and Zhang, Y.: Status and change of the cryosphere in the extended Hindu Kush Himalaya region, in: The Hindu Kush Himalaya Assessment, Mountains, Climate Change, Sustainability and People, edited by: Wester, P., Mishra, A., Mukherji, A., and Shrestha, A. B., Springer International Publishing, Cham, 209–255, https://doi.org//10.1007/978-3-319-92288-1_7, 2019.
Bolch, T., Yao, T., Bhattacharya, A., Hu, Y., King, O., Liu, L., Pronk, J. B., Rastner, P., and Zhang, G.: Earth observation to investigate occurrence, characteristics and changes of glaciers, glacial lakes and rock glaciers in the Poiqu river basin (central Himalaya), Remote Sens.-Basel, 14, 1927, https://doi.org/10.3390/rs14081927, 2022.
Brardinoni, F., Scotti, R., Sailer, R., and Mair, V.: Evaluating sources of uncertainty and variability in rock glacier inventories, Earth Surf. Proc. Land., 44, 2450–2466, https://doi.org/10.1002/esp.4674, 2019.
Cai, J., Wang, X., Liu, G., and Yu, B.: A comparative study of active rock glaciers mapped from geomorphic-and kinematic-based approaches in Daxue Shan, southeast Tibetan Plateau, Remote Sens.-Basel, 13, 4931, https://doi.org/10.3390/rs13234931, 2021.
Cao, B., Li, X., Feng, M., and Zheng, D.: Quantifying overestimated permafrost extent driven by rock glacier inventory, Geophys. Res. Lett., 48, e2021GL092476, https://doi.org/10.1029/2021GL092476, 2021.
Capps Jr., S. R.: Rock glaciers in Alaska, J. Geol., 18, 359–375, https://doi.org/10.1086/621746, 1910.
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation, in: Proceedings of the European conference on computer vision (ECCV), Munich, Germany, 8–14 September 2018, 801–818, https://doi.org/10.48550/arXiv.1802.02611, 2018.
Chollet, F.: Xception: Deep learning with depthwise separable convolutions, in: Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, Hawaii, USA, 21–26 July 2017, 1251–1258, https://doi.org/10.48550/arXiv.1610.02357, 2017.
Cicoira, A., Beutel, J., Faillettaz, J., Gärtner-Roer, I., and Vieli, A.: Resolving the influence of temperature forcing through heat conduction on rock glacier dynamics: a numerical modelling approach, The Cryosphere, 13, 927–942, https://doi.org/10.5194/tc-13-927-2019, 2019a.
Cicoira, A., Beutel, J., Faillettaz, J., and Vieli, A.: Water controls the seasonal rhythm of rock glacier flow, Earth Planet. Sc. Lett., 528, 115844, https://doi.org/10.1016/j.epsl.2019.115844, 2019b.
Corte, A.: The hydrological significance of rock glaciers, J. Glaciol., 17, 157–158, https://doi.org/10.3189/S0022143000030859, 1976.
Crippen, R., Buckley, S., Agram, P., Belz, E., Gurrola, E., Hensley, S., Kobrick, M., Lavalle, M., Martin, J., Neumann, M., Nguyen, Q., Rosen, P., Shimada, J., Simard, M., and Tung, W.: NASADEM global elevation model: Methods and progress, Int. Arch. Photogramm., 41, 125–128, https://doi.org/10.5194/isprs-archives-XLI-B4-125-2016, 2016.
Delaloye, R., Lambiel, C., and Gärtner-Roer, I.: Overview of rock glacier kinematics research in the Swiss Alps, Geogr. Helv., 65, 135–145, https://doi.org/10.5194/gh-65-135-2010, 2010.
Delaloye, R., Barboux, C., Bodin, X., Brenning, A., Hartl, L., Hu, Y., Ikeda, A., Kaufmann, V., Kellerer-Pirklbauer, A., Lambiel, C., Liu, L., Marcer, M., Rick, B., Scotti, R., Takadema, H., Trombotto, D., Vivero, S., and Winterberger, M.: Rock glacier inventories and kinematics: A new IPA Action Group, in: Proceedings of the 5th European Conference on Permafrost, Chamonix-Mont Blanc, France, 23 June–1 July 2018, vol. 23, 392–393, https://bigweb.unifr.ch/Science/Geosciences/Geomorphology/Pub/Website/IPA/180628_EUCOP5_IPA_Action_Group_Delaloye.pdf (last access: 3 December 2024), 2018.
Erharter, G. H., Wagner, T., Winkler, G., and Marcher, T.: Machine learning – An approach for consistent rock glacier mapping and inventorying – Example of Austria, Applied Computing and Geosciences, 16, 100093, https://doi.org/10.1016/j.acags.2022.100093, 2022.
Feng, M., Xu, J., Wang, J., Ran, Y., and Li, X.: Identifying rock glacier in western China using deep learning and satellite data, in: AGU Fall Meeting Abstracts, San Francisco, USA, 9–13 December 2019, vol. 2019, GC53G–1249, https://ui.adsabs.harvard.edu/abs/2019AGUFMGC53G1249F/abstrac (last access: 3 December 2024), 2019.
Geiger, S. T., Daniels, J. M., Miller, S. N., and Nicholas, J. W.: Influence of rock glaciers on stream hydrology in the La Sal Mountains, Utah, Arct. Antarct. Alp. Res., 46, 645–658, https://doi.org/10.1657/1938-4246-46.3.645, 2014.
Haeberli, W., Hallet, B., Arenson, L., Elconin, R., Humlum, O., Kääb, A., Kaufmann, V., Ladanyi, B., Matsuoka, N., Springman, S., and Mühll, D. V.: Permafrost creep and rock glacier dynamics, Permafrost Periglac., 17, 189–214, https://doi.org/10.1002/ppp.561, 2006.
Hassan, J., Chen, X., Muhammad, S., and Bazai, N. A.: Rock glacier inventory, permafrost probability distribution modeling and associated hazards in the Hunza River Basin, Western Karakoram, Pakistan, Sci. Total Environ., 782, 146833, https://doi.org/10.1016/j.scitotenv.2021.146833, 2021.
Hu, Y., Liu, L., Huang, L., Zhao, L., Wu, T., Wang, X., and Cai, J.: Mapping and Characterizing Rock Glaciers in the Arid Western Kunlun Mountains Supported by InSAR and Deep Learning, J. Geophys. Res.-Earth, 128, e2023JF007206, https://doi.org/10.1029/2023JF007206, 2023.
Hu, Z., Yan, D., Feng, M., Xu, J., Liang, S., and Sheng, Y.: Enhancing mountainous permafrost mapping by leveraging a rock glacier inventory in northeastern Tibetan Plateau, Int. J. Digit. Earth, 17, 2304077, https://doi.org/10.1080/17538947.2024.2304077, 2024.
Huang, L., Liu, L., Jiang, L., and Zhang, T.: Automatic mapping of thermokarst landforms from remote sensing images using deep learning: A case study in the Northeastern Tibetan Plateau, Remote Sens.-Basel, 10, 2067, https://doi.org/10.3390/rs10122067, 2018.
Huang, L., Luo, J., Lin, Z., Niu, F., and Liu, L.: Using deep learning to map retrogressive thaw slumps in the Beiluhe region (Tibetan Plateau) from CubeSat images, Remote Sens. Environ., 237, 111534, https://doi.org/10.1016/j.rse.2019.111534, 2020.
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J., Tan, J.: GPM IMERG Final Precipitation L3 1 month 0.1 degree x 0.1 degree V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/IMERG/3B-MONTH/07, 2023.
Hungr, O., Leroueil, S., and Picarelli, L.: The Varnes classification of landslide types, an update, Landslides, 11, 167–194, https://doi.org/10.1007/s10346-013-0436-y, 2014.
Janke, J. and Bolch, T.: Rock glaciers, Treatise on Geomorphology, 2nd edn., vol. 4, Elsevier, https://doi.org/10.1016/B978-0-12-818234-5.00187-5, 2021.
Jones, D., Harrison, S., Anderson, K., Selley, H., Wood, J., and Betts, R.: The distribution and hydrological significance of rock glaciers in the Nepalese Himalaya, Global Planet. Change, 160, 123–142, https://doi.org/10.1016/j.gloplacha.2017.11.005, 2018.
Jones, D. B., Harrison, S., and Anderson, K.: Mountain glacier-to-rock glacier transition, Global Planet. Change, 181, 102999, https://doi.org/10.1016/j.gloplacha.2019.102999, 2019a.
Jones, D. B., Harrison, S., Anderson, K., and Whalley, W. B.: Rock glaciers and mountain hydrology: A review, Earth-Sci. Rev., 193, 66–90, https://doi.org/10.1016/j.earscirev.2019.04.001, 2019b.
Jones, D. B., Harrison, S., Anderson, K., and Betts, R.: Author Correction: Mountain rock glaciers contain globally significant water stores, Sci. Rep.-UK, 11, 23536, https://doi.org/10.1038/s41598-021-02401-0, 2021a.
Jones, D. B., Harrison, S., Anderson, K., Shannon, S., and Betts, R.: Rock glaciers represent hidden water stores in the Himalaya, Sci. Total Environ., 793, 145368, https://doi.org/10.1016/j.scitotenv.2021.145368, 2021b.
Kääb, A., Strozzi, T., Bolch, T., Caduff, R., Trefall, H., Stoffel, M., and Kokarev, A.: Inventory and changes of rock glacier creep speeds in Ile Alatau and Kungöy Ala-Too, northern Tien Shan, since the 1950s, The Cryosphere, 15, 927–949, https://doi.org/10.5194/tc-15-927-2021, 2021.
Kumar, L., Skidmore, A. K., and Knowles, E.: Modelling topographic variation in solar radiation in a GIS environment, Int. J. Geogr. Inf. Sci., 11, 475–497, https://doi.org/10.1080/136588197242266, 1997.
LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature, 521, 436–444, https://doi.org/10.1038/nature14539, 2015.
Li, M., Yang, Y., Peng, Z., and Liu, G.: Assessment of rock glaciers and their water storage in Guokalariju, Tibetan Plateau, The Cryosphere, 18, 1–16, https://doi.org/10.5194/tc-18-1-2024, 2024.
Liu, L., Millar, C. I., Westfall, R. D., and Zebker, H. A.: Surface motion of active rock glaciers in the Sierra Nevada, California, USA: inventory and a case study using InSAR, The Cryosphere, 7, 1109–1119, https://doi.org/10.5194/tc-7-1109-2013, 2013.
Marcer, M.: Rock glaciers automatic mapping using optical imagery and convolutional neural networks, Permafrost Periglac., 31, 561–566, https://doi.org/10.1002/ppp.2076, 2020.
Marcer, M., Bodin, X., Brenning, A., Schoeneich, P., Charvet, R., and Gottardi, F.: Permafrost favorability index: spatial modeling in the French Alps using a rock glacier inventory, Front. Earth Sci., 5, 105, https://doi.org/10.3389/feart.2017.00105, 2017.
Marcer, M., Cicoira, A., Cusicanqui, D., Bodin, X., Echelard, T., Obregon, R., and Schoeneich, P.: Rock glaciers throughout the French Alps accelerated and destabilised since 1990 as air temperatures increased, Communications Earth & Environment, 2, 81, https://doi.org/10.1038/s43247-021-00150-6, 2021.
McNally, A. and NASA/GSFC/HSL: FLDAS Noah Land Surface Model L4 Global Monthly 0.1 x 0.1 degree (MERRA-2 and CHIRPS), Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/5NHC22T9375G, 2018.
Mu, C., Abbott, B. W., Norris, A. J., Mu, M., Fan, C., Chen, X., Jia, L., Yang, R., Zhang, T., Wang, K., Peng, X., Wu, Q., Guggenberger, G., and Wu, X.: The status and stability of permafrost carbon on the Tibetan Plateau, Earth-Sci. Rev., 211, 103433, https://doi.org/10.1016/j.earscirev.2020.103433, 2020.
Munroe, J. S.: Distribution, evidence for internal ice, and possible hydrologic significance of rock glaciers in the Uinta Mountains, Utah, USA, Quaternary Res., 90, 50–65, https://doi.org/10.1017/qua.2018.24, 2018.
Nass, A., Manaud, N., van Gasselt, S., and Hare, T. M.: Towards a new face for Planetary Maps: Design and web-based Implementation of Planetary Basemaps, Advances in Cartography and GIScience of the ICA, 1, 15, https://doi.org/10.5194/ica-adv-1-15-2019, 2019.
Obu, J., Westermann, S., Kääb, A., and Bartsch, A.: Ground Temperature Map, 2000–2016, Northern Hemisphere Permafrost, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, PANGAEA, https://doi.org/10.1594/PANGAEA.888600, 2018.
Pfeffer, W. T., Arendt, A. A., Bliss, A., Bolch, T., Cogley, J. G., Gardner, A. S., Hagen, J.-O., Hock, R., Kaser, G., Kienholz, C., Miles, E. S., Moholdt, G., Mölg, N., Paul, F., Radić, V., Rastner, P., Raup, B. H., Rich, J., Sharp, M. J., and The Randolph Consortium: The Randolph Glacier Inventory: a globally complete inventory of glaciers, J. Glaciol., 60, 537–552, https://doi.org/10.3189/2014JoG13J176, 2014.
Racoviteanu, A., Nicholson, L., Glasser, N., Miles, E., Harrison, S., and Reynolds, J.: Debris-covered glacier systems and associated glacial lake outburst flood hazards: challenges and prospects, J. Geol. Soc. London, 179, jgs2021–084, https://doi.org/10.1144/jgs2021-084, 2022.
Racoviteanu, A., Sun, Z., Hu, Y., Liu, L., and Harrison, S.: Rock glacier inventorying and validation across the Hindu Kush Himalaya from deep learning and high-resolution images, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16141, https://doi.org/10.5194/egusphere-egu24-16141, 2024.
Ran, Z. and Liu, G.: Rock glaciers in Daxue Shan, south-eastern Tibetan Plateau: an inventory, their distribution, and their environmental controls, The Cryosphere, 12, 2327–2340, https://doi.org/10.5194/tc-12-2327-2018, 2018.
Rangecroft, S., Harrison, S., Anderson, K., Magrath, J., Castel, A. P., and Pacheco, P.: Climate change and water resources in arid mountains: an example from the Bolivian Andes, Ambio, 42, 852–863, https://doi.org/10.1007/s13280-013-0430-6, 2013.
Rangecroft, S., Harrison, S., and Anderson, K.: Rock glaciers as water stores in the Bolivian Andes: an assessment of their hydrological importance, Arct. Antarct. Alp. Res., 47, 89–98, https://doi.org/10.1657/AAAR0014-029, 2015.
Reinosch, E., Gerke, M., Riedel, B., Schwalb, A., Ye, Q., and Buckel, J.: Rock glacier inventory of the western Nyainqêntanglha Range, Tibetan Plateau, supported by InSAR time series and automated classification, Permafrost Periglac., 32, 657–672, https://doi.org/10.1002/ppp.2117, 2021.
Ren, M., Zeng, W., Yang, B., and Urtasun, R.: Learning to reweight examples for robust deep learning, in: International Conference on Machine Learning, Stockholm, Sweden, 10–15 July 2018, PMLR, 4334–4343, https://proceedings.mlr.press/v80/ren18a.html (last access: 3 December 2024), 2018.
RGIK: Guidelines for inventorying rock glaciers: baseline and practical concepts (version 1.0), IPA Action Group Rock glacier inventories and kinematics, 25 pp., https://doi.org/10.51363/unifr.srr.2023.002, 2023.
Rice, L., Wong, E., and Kolter, Z.: Overfitting in adversarially robust deep learning, in: International Conference on Machine Learning, online, 12–18 July 2020, PMLR, 8093–8104, https://proceedings.mlr.press/v119/rice20a (last access: 3 December 2024), 2020.
Robson, B. A., Bolch, T., MacDonell, S., Hölbling, D., Rastner, P., and Schaffer, N.: Automated detection of rock glaciers using deep learning and object-based image analysis, Remote Sens. Environ., 250, 112033, https://doi.org/10.1016/j.rse.2020.112033, 2020.
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.
Schaffer, N., MacDonell, S., Réveillet, M., Yáñez, E., and Valois, R.: Rock glaciers as a water resource in a changing climate in the semiarid Chilean Andes, Reg. Environ. Change, 19, 1263–1279, https://doi.org/10.1007/s10113-018-01459-3, 2019.
Schmid, M.-O., Baral, P., Gruber, S., Shahi, S., Shrestha, T., Stumm, D., and Wester, P.: Assessment of permafrost distribution maps in the Hindu Kush Himalayan region using rock glaciers mapped in Google Earth, The Cryosphere, 9, 2089–2099, https://doi.org/10.5194/tc-9-2089-2015, 2015.
Spencer, A.: A peculiar form of talus, Science, 11, 188–189, 1900.
Sun, Z., Hu, Y., Racoviteanu, A., Liu, L., Harrison, S., Wang, X., Cai, J., Guo, X., He, Y., and Yuan, H.: TPRoGI: a comprehensive rock glacier inventory for the Tibetan Plateau using deep learning (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.10732042, 2024a.
Sun, Z., Liu, L., Hu, Y., and Fan, C.: Assessing rock glacier velocities on the Tibetan Plateau using satellite SAR interferometry, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3349, https://doi.org/10.5194/egusphere-egu24-3349, 2024b.
Vivero, S., Bodin, X., Farías-Barahona, D., MacDonell, S., Schaffer, N., Robson, B. A., and Lambiel, C.: Combination of aerial, satellite, and UAV photogrammetry for quantifying rock glacier kinematics in the Dry Andes of Chile (30 S) since the 1950s, Frontiers in Remote Sensing, 2, 784015, https://doi.org/10.3389/frsen.2021.784015, 2021.
Wagner, T., Pauritsch, M., and Winkler, G.: Impact of relict rock glaciers on spring and stream flow of alpine watersheds: Examples of the Niedere Tauern Range, Eastern Alps (Austria), Austrian J. Earth Sci., 109, 84–98, https://doi.org/10.17738/ajes.2016.0006, 2016.
Wagner, T., Brodacz, A., Krainer, K., and Winkler, G.: Active rock glaciers as shallow groundwater reservoirs, Austrian Alps, Grundwasser, 25, 215–230, https://doi.org/10.1007/s00767-020-00455-x, 2020.
Wagner, T., Kainz, S., Helfricht, K., Fischer, A., Avian, M., Krainer, K., and Winkler, G.: Assessment of liquid and solid water storage in rock glaciers versus glacier ice in the Austrian Alps, Sci. Total Environ., 800, 149593, https://doi.org/10.1016/j.scitotenv.2021.149593, 2021.
Xu, J., Feng, M., Wang, J., Ran, Y., Qi, Y., Yang, L., and Li, X.: Automatically Identifying Rock Glacier based on Gaofen Satellite Image and Deep Learning, Remote Sensing Technology and Application, 35, 1329–1336, 2021.
Yao, T., Thompson, L., Yang, W., Yu, W., Gao, Y., Guo, X., Yang, X., Duan, K., Zhao, H., Xu, B., Pu, J., Lu, A., Xiang, Y., Kattel, D. B., and Joswiak, D.: Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings, Nat. Clim. Change, 2, 663–667, https://doi.org/10.1038/nclimate1580, 2012.
Yu, L. and Gong, P.: Google Earth as a virtual globe tool for Earth science applications at the global scale: progress and perspectives, Int. J. Remote Sens., 33, 3966–3986, https://doi.org/10.1080/01431161.2011.636081, 2012.
Zemp, M., Chao, Q., Han Dolman, A. J., Herold, M., Krug, T., Speich, S., Suda, K., Thorne, P., and Yu, W.: GCOS 2022 Implementation Plan, Global Climate Observing System GCOS, 85, https://doi.org/10.5167/uzh-224271, 2022.
Zhang, G., Yao, T., Xie, H., Yang, K., Zhu, L., Shum, C., Bolch, T., Yi, S., Allen, S., Jiang, L., Chen, W., and Ke, C.: Response of Tibetan Plateau lakes to climate change: Trends, patterns, and mechanisms, Earth-Sci. Rev., 208, 103269, https://doi.org/10.1016/j.earscirev.2020.103269, 2020.
Zhang, Q., Jia, N., Xu, H., Yi, C., Wang, N., and Zhang, L.: Rock glaciers in the Gangdise Mountains, southern Tibetan Plateau: Morphology and controlling factors, CATENA, 218, 106561, https://doi.org/10.1016/j.catena.2022.106561, 2022.
Zhang, X., Feng, M., Zhang, H., Wang, C., Tang, Y., Xu, J., Yan, D., and Wang, C.: Detecting rock glacier displacement in the central Himalayas using multi-temporal InSAR, Remote Sens.-Basel, 13, 4738, https://doi.org/10.3390/rs13234738, 2021.
Zhang, X., Feng, M., Xu, J., Yan, D., Wang, J., Zhou, X., Li, T., and Zhang, X.: Kinematic inventory of rock glaciers in the NyainqêntanglhaRange using the MT-InSAR method, Int. J. Digit. Earth, 16, 3923–3948, https://doi.org/10.1080/17538947.2023.2260778, 2023.
Zhao, L. and Sheng, Y.: Permafrost in the Qinghai-Tibet plateau and its changes, Science Press, Beijing, 356 pp., ISBN 7030581334, 2019.
Zhao, L., Zou, D., Hu, G., Du, E., Pang, Q., Xiao, Y., Li, R., Sheng, Y., Wu, X., Sun, Z., Wang, L., Wang, C., Ma, L., Zhou, H., and Liu, S.: Changing climate and the permafrost environment on the Qinghai–Tibet (Xizang) plateau, Permafrost Periglac., 31, 396–405, https://doi.org/10.1002/ppp.2056, 2020.
Zou, D., Zhao, L., Sheng, Y., Chen, J., Hu, G., Wu, T., Wu, J., Xie, C., Wu, X., Pang, Q., Wang, W., Du, E., Li, W., Liu, G., Li, J., Qin, Y., Qiao, Y., Wang, Z., Shi, J., and Cheng, G.: A new map of permafrost distribution on the Tibetan Plateau, The Cryosphere, 11, 2527–2542, https://doi.org/10.5194/tc-11-2527-2017, 2017.
Short summary
We propose a new dataset, TPRoGI (v1.0), encompassing rock glaciers in the entire Tibetan Plateau. We used a neural network, DeepLabv3+, and images from Planet Basemaps. The inventory identified 44 273 rock glaciers, covering 6 000 km2, mainly at elevations of 4000 to 5500 m a.s.l. The dataset, with details on distribution and characteristics, aids in understanding permafrost distribution, mountain hydrology, and climate impacts in High Mountain Asia, filling a knowledge gap.
We propose a new dataset, TPRoGI (v1.0), encompassing rock glaciers in the entire Tibetan...
Altmetrics
Final-revised paper
Preprint