Articles | Volume 15, issue 7
https://doi.org/10.5194/tc-15-3159-2021
© Author(s) 2021. 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-15-3159-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The 2020 glacial lake outburst flood at Jinwuco, Tibet: causes, impacts, and implications for hazard and risk assessment
Guoxiong Zheng
CORRESPONDING AUTHOR
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute
of Ecology and Geography, Chinese Academy of Sciences, 830011 Ürümqi, China
Climatic Change Impacts and Risks in the Anthropocene (C-CIA),
Institute for Environmental Sciences, University of Geneva, 1205 Geneva,
Switzerland
University of Chinese Academy of Sciences, 100049 Beijing, China
Martin Mergili
Institute of Geography and Regional Science, University of Graz, 8010
Graz, Austria
Institute of Applied Geology, University of Natural Resources and Life
Sciences (BOKU), 1190 Vienna, Austria
Adam Emmer
Institute of Geography and Regional Science, University of Graz, 8010
Graz, Austria
Simon Allen
Climatic Change Impacts and Risks in the Anthropocene (C-CIA),
Institute for Environmental Sciences, University of Geneva, 1205 Geneva,
Switzerland
Department of Geography, University of Zurich, 8057 Zurich,
Switzerland
Anming Bao
CORRESPONDING AUTHOR
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute
of Ecology and Geography, Chinese Academy of Sciences, 830011 Ürümqi, China
China-Pakistan Joint Research Centre on Earth Sciences, CAS-HEC, 45320
Islamabad, Pakistan
School of Geography and Tourism, Qufu Normal University, 276800
Rizhao, China
Markus Stoffel
Climatic Change Impacts and Risks in the Anthropocene (C-CIA),
Institute for Environmental Sciences, University of Geneva, 1205 Geneva,
Switzerland
Dendrolab.ch, Department of Earth Sciences, University of Geneva, 1205
Geneva, Switzerland
Department F.-A. Forel for Environmental and Aquatic Sciences,
University of Geneva, 1205 Geneva, Switzerland
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Virginia Ruiz-Villanueva, Alexandre Badoux, Dieter Rickenmann, Martin Böckli, Salome Schläfli, Nicolas Steeb, Markus Stoffel, and Christian Rickli
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Ekrem Canli, Martin Mergili, Benni Thiebes, and Thomas Glade
Nat. Hazards Earth Syst. Sci., 18, 2183–2202, https://doi.org/10.5194/nhess-18-2183-2018, https://doi.org/10.5194/nhess-18-2183-2018, 2018
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Regional-scale landslide forecasting traditionally strongly relies on empirical approaches and landslide-triggering rainfall thresholds. Today, probabilistic methods utilizing ensemble predictions are frequently used for flood forecasting. In our study, we specify how such an approach could also be applied for landslide forecasts and for operational landslide forecasting and early warning systems. To this end, we implemented a physically based landslide model in a probabilistic framework.
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
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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.
Adam Emmer
Nat. Hazards Earth Syst. Sci., 18, 813–827, https://doi.org/10.5194/nhess-18-813-2018, https://doi.org/10.5194/nhess-18-813-2018, 2018
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Martin Beniston, Daniel Farinotti, Markus Stoffel, Liss M. Andreassen, Erika Coppola, Nicolas Eckert, Adriano Fantini, Florie Giacona, Christian Hauck, Matthias Huss, Hendrik Huwald, Michael Lehning, Juan-Ignacio López-Moreno, Jan Magnusson, Christoph Marty, Enrique Morán-Tejéda, Samuel Morin, Mohamed Naaim, Antonello Provenzale, Antoine Rabatel, Delphine Six, Johann Stötter, Ulrich Strasser, Silvia Terzago, and Christian Vincent
The Cryosphere, 12, 759–794, https://doi.org/10.5194/tc-12-759-2018, https://doi.org/10.5194/tc-12-759-2018, 2018
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This paper makes a rather exhaustive overview of current knowledge of past, current, and future aspects of cryospheric issues in continental Europe and makes a number of reflections of areas of uncertainty requiring more attention in both scientific and policy terms. The review paper is completed by a bibliography containing 350 recent references that will certainly be of value to scholars engaged in the fields of glacier, snow, and permafrost research.
Christine Moos, Luuk Dorren, and Markus Stoffel
Nat. Hazards Earth Syst. Sci., 17, 291–304, https://doi.org/10.5194/nhess-17-291-2017, https://doi.org/10.5194/nhess-17-291-2017, 2017
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The goal of this study was to quantify the effect of forests on the occurrence frequency and intensity of rockfalls. This was done based on 3-D rockfall simulations for different forest and non-forest scenarios on a virtual slope. The rockfall frequency and intensity below forested slopes is significantly reduced. Statistical models provide information on how specific forest and terrain parameters influence this reduction and they allow prediction and quantification of the forest effect.
Martin Mergili, Jan-Thomas Fischer, Julia Krenn, and Shiva P. Pudasaini
Geosci. Model Dev., 10, 553–569, https://doi.org/10.5194/gmd-10-553-2017, https://doi.org/10.5194/gmd-10-553-2017, 2017
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M. Mergili, J. Krenn, and H.-J. Chu
Geosci. Model Dev., 8, 4027–4043, https://doi.org/10.5194/gmd-8-4027-2015, https://doi.org/10.5194/gmd-8-4027-2015, 2015
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M. Mergili and H.-J. Chu
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhessd-3-5677-2015, https://doi.org/10.5194/nhessd-3-5677-2015, 2015
Revised manuscript not accepted
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We propose a procedure to compute an integrated spatial landslide probability, combining release and propagation. The zonal release probability is introduced to correct the pixel-based release probability for the size of the release zone relevant for a pixel. For a test area in Taiwan we observe that the model performs moderately well in predicting the observed landslides and that the size of the release zone influences the result to a much higher degree than the pixel-based release probability.
M. Jochner, J. M. Turowski, A. Badoux, M. Stoffel, and C. Rickli
Earth Surf. Dynam., 3, 311–320, https://doi.org/10.5194/esurf-3-311-2015, https://doi.org/10.5194/esurf-3-311-2015, 2015
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The export of coarse particulate organic matter (CPOM) from mountain catchments seems to be strongly linked to rising discharge, but the mechanism leading to this is unclear. We show that log jams in a steep headwater stream are an effective barrier for CPOM export. Exceptional discharge events play a dual role: First, they destroy existing jams, releasing stored material. Second, they intensify channel--hillslope coupling, thereby recruiting logs to the channel, around which new jams can form.
M. Mergili, I. Marchesini, M. Alvioli, M. Metz, B. Schneider-Muntau, M. Rossi, and F. Guzzetti
Geosci. Model Dev., 7, 2969–2982, https://doi.org/10.5194/gmd-7-2969-2014, https://doi.org/10.5194/gmd-7-2969-2014, 2014
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The article deals with strategies to (i) reduce computation time and to (ii) appropriately account for uncertain input parameters when applying an open source GIS sliding surface model to estimate landslide susceptibility for a 90km² study area in central Italy. For (i), the area is split into a large number of tiles, enabling the exploitation of multi-processor computing environments. For (ii), the model is run with various parameter combinations to compute the slope failure probability.
H. Frey, H. Machguth, M. Huss, C. Huggel, S. Bajracharya, T. Bolch, A. Kulkarni, A. Linsbauer, N. Salzmann, and M. Stoffel
The Cryosphere, 8, 2313–2333, https://doi.org/10.5194/tc-8-2313-2014, https://doi.org/10.5194/tc-8-2313-2014, 2014
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Existing methods (area–volume relations, a slope-dependent volume estimation method, and two ice-thickness distribution models) are used to estimate the ice reserves stored in Himalayan–Karakoram glaciers. Resulting volumes range from 2955–4737km³. Results from the ice-thickness distribution models agree well with local measurements; volume estimates from area-related relations exceed the estimates from the other approaches. Evidence on the effect of the selected method on results is provided.
A. Emmer and V. Vilímek
Hydrol. Earth Syst. Sci., 18, 3461–3479, https://doi.org/10.5194/hess-18-3461-2014, https://doi.org/10.5194/hess-18-3461-2014, 2014
F. E. Gruber and M. Mergili
Nat. Hazards Earth Syst. Sci., 13, 2779–2796, https://doi.org/10.5194/nhess-13-2779-2013, https://doi.org/10.5194/nhess-13-2779-2013, 2013
A. Emmer and V. Vilímek
Nat. Hazards Earth Syst. Sci., 13, 1551–1565, https://doi.org/10.5194/nhess-13-1551-2013, https://doi.org/10.5194/nhess-13-1551-2013, 2013
Related subject area
Discipline: Other | Subject: Natural Hazards
Brief communication: An ice-debris avalanche in the Nupchu Valley, Kanchenjunga Conservation Area, eastern Nepal
Convolutional neural network and long short-term memory models for ice-jam predictions
Alton C. Byers, Marcelo Somos-Valenzuela, Dan H. Shugar, Daniel McGrath, Mohan B. Chand, and Ram Avtar
The Cryosphere, 18, 711–717, https://doi.org/10.5194/tc-18-711-2024, https://doi.org/10.5194/tc-18-711-2024, 2024
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In spite of enhanced technologies, many large cryospheric events remain unreported because of their remoteness, inaccessibility, or poor communications. In this Brief communication, we report on a large ice-debris avalanche that occurred sometime between 16 and 21 August 2022 in the Kanchenjunga Conservation Area (KCA), eastern Nepal.
Fatemehalsadat Madaeni, Karem Chokmani, Rachid Lhissou, Saeid Homayouni, Yves Gauthier, and Simon Tolszczuk-Leclerc
The Cryosphere, 16, 1447–1468, https://doi.org/10.5194/tc-16-1447-2022, https://doi.org/10.5194/tc-16-1447-2022, 2022
Short summary
Short summary
We developed three deep learning models (CNN, LSTM, and combined CN-LSTM networks) to predict breakup ice-jam events to be used as an early warning system of possible flooding in rivers. In the models, we used hydro-meteorological data associated with breakup ice jams. The models show excellent performance, and the main finding is that the CN-LSTM model is superior to the CNN-only and LSTM-only networks in both training and generalization accuracy.
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Short summary
This paper reports on a recent glacial lake outburst flood (GLOF) event that occurred on 26 June 2020 in Tibet, China. We find that this event was triggered by a debris landslide from a steep lateral moraine. As the relationship between the long-term evolution of the lake and its likely landslide trigger revealed by a time series of satellite images, this case provides strong evidence that it can be plausibly linked to anthropogenic climate change.
This paper reports on a recent glacial lake outburst flood (GLOF) event that occurred on 26 June...