Articles | Volume 15, issue 4
https://doi.org/10.5194/nhess-15-905-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/nhess-15-905-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Monitoring and prediction in early warning systems for rapid mass movements
M. Stähli
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
M. Sättele
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland
C. Huggel
Department of Geography, University of Zurich, Zurich, Switzerland
B. W. McArdell
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
P. Lehmann
Soil and Terrestrial Environmental Physics, ETH Zurich, Zurich, Switzerland
A. Van Herwijnen
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland
Environmental Remote Sensing Laboratory, EPF Lausanne, Lausanne, Switzerland
M. Schleiss
Environmental Remote Sensing Laboratory, EPF Lausanne, Lausanne, Switzerland
now at: Civil and Environmental Engineering, Princeton University, Princeton, USA
A. Ferrari
Soil Mechanics Laboratory, EPF Lausanne, Lausanne, Switzerland
A. Kos
Institute for Geotechnical Engineering, ETH Zurich, Zurich, Switzerland
now at: Terrasense Switzerland Ltd, Werdenberg, Switzerland
Soil and Terrestrial Environmental Physics, ETH Zurich, Zurich, Switzerland
S. M. Springman
Institute for Geotechnical Engineering, ETH Zurich, Zurich, Switzerland
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- Critical rainfall conditions for the initiation of torrential flows. Results from the Rebaixader catchment (Central Pyrenees) C. Abancó et al. 10.1016/j.jhydrol.2016.01.019
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- Forecasting the time of failure of landslides at slope-scale: A literature review E. Intrieri et al. 10.1016/j.earscirev.2019.03.019
- A data-driven complex systems approach to early prediction of landslides A. Tordesillas et al. 10.1016/j.mechrescom.2018.08.008
- A Fast Deploying Monitoring and Real-Time Early Warning System for the Baige Landslide in Tibet, China Y. Wu et al. 10.3390/s20226619
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- The field survey and deformation characteristics of exit slope of Qingshuigou tunnel in the southwest of China T. Wen et al. 10.1007/s12517-022-10327-8
- M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation L. Winiwarter et al. 10.1016/j.isprsjprs.2021.06.011
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- Predictive performance of rainfall thresholds for shallow landslides in Switzerland from gridded daily data E. Leonarduzzi et al. 10.1002/2017WR021044
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- The Norwegian forecasting and warning service for rainfall- and snowmelt-induced landslides I. Krøgli et al. 10.5194/nhess-18-1427-2018
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- Entering the Era of Earth Observation-Based Landslide Warning Systems: A Novel and Exciting Framework K. Dai et al. 10.1109/MGRS.2019.2954395
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- Recent rainfall- and excavation-induced bedding rockslide occurring on 22 October 2018 along the Jian-En expressway, Hubei, China H. Yu et al. 10.1007/s10346-020-01468-6
- Effects of hydromechanical loading history and antecedent soil mechanical damage on shallow landslide triggering L. Fan et al. 10.1002/2015JF003615
- Simulated or measured soil moisture: which one is adding more value to regional landslide early warning? A. Wicki et al. 10.5194/hess-25-4585-2021
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- Landslide activation behaviour illuminated by electrical resistance monitoring A. Merritt et al. 10.1002/esp.4316
- Geographical landslide early warning systems F. Guzzetti et al. 10.1016/j.earscirev.2019.102973
- Hyperspectral image-based analysis of weathering sensitivity for safety diagnosis of Seongsan Ilchulbong Peak S. Kim & H. Kim 10.1016/j.jag.2016.07.012
- Shallow-landslide stability evaluation in loess areas according to the Revised Infinite Slope Model: a case study of the 7.25 Tianshui sliding-flow landslide events of 2013 in the southwest of the Loess Plateau, China J. Zhuang et al. 10.5194/nhess-24-2615-2024
- Experimental Study on the Relationship between the Velocity of Surface Movements and Tilting Rate in Pre-Failure Stage of Rainfall-Induced Landslides J. Xie et al. 10.3390/s21185988
- Short Communication: Monitoring rockfalls with the Raspberry Shake A. Manconi et al. 10.5194/esurf-6-1219-2018
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- An integrated geological, geotechnical and geophysical approach to identify predisposing factors for flowslide occurrence R. Di Maio et al. 10.1016/j.enggeo.2019.105473
- Successful early warning and emergency response of a disastrous rockslide in Guizhou province, China X. Fan et al. 10.1007/s10346-019-01269-6
- An acoustic emission landslide early warning system for communities in low-income and middle-income countries N. Dixon et al. 10.1007/s10346-018-0977-1
- Assessing the performance of regional landslide early warning models: the EDuMaP method M. Calvello & L. Piciullo 10.5194/nhess-16-103-2016
- Pre-collapse motion of the February 2021 Chamoli rock–ice avalanche, Indian Himalaya M. Van Wyk de Vries et al. 10.5194/nhess-22-3309-2022
- Prediction model oriented for landslide displacement with step-like curve by applying ensemble empirical mode decomposition and the PSO-ELM method H. Du et al. 10.1016/j.jclepro.2020.122248
- Opportunities for improving conservation early warning and alert systems K. Tabor et al. 10.1002/rse2.163
- Identification of potential failure zones in large progressive landslides: a method to optimize the cost of landslide early warning S. Kumar et al. 10.1007/s11069-024-06685-z
- The 2009 Jiweishan rock avalanche, Wulong, China: Precursor conditions and factors leading to failure M. Zhang et al. 10.1016/j.enggeo.2017.12.010
- Machine Learning Improves Debris Flow Warning M. Chmiel et al. 10.1029/2020GL090874
- A case study on geotechnical asset remote monitoring in Network Rail D. Abrams & M. Brown 10.1680/jtran.16.00157
- Soil moisture information can improve shallow landslide forecasting using the hydrometeorological threshold approach P. Marino et al. 10.1007/s10346-020-01420-8
- Rainfall Induced Shallow Landslide Temporal Probability Modelling and Early Warning Research in Mountains Areas: A Case Study of Qin-Ba Mountains, Western China Y. Song et al. 10.3390/rs14235952
- Monitoring strategies for local landslide early warning systems G. Pecoraro et al. 10.1007/s10346-018-1068-z
- Spatiotemporal Evolution of a Landslide: A Transition to Explosive Percolation K. Singh & A. Tordesillas 10.3390/e22010067
- Combining Site Characterization, Monitoring and Hydromechanical Modeling for Assessing Slope Stability S. Moradi et al. 10.3390/land10040423
- Towards early warning of gravitational slope failure with co-detection of microseismic activity: the case of an active rock glacier J. Faillettaz et al. 10.5194/nhess-19-1399-2019
- Controls on the formation and size of potential landslide dams and dammed lakes in the Austrian Alps A. Argentin et al. 10.5194/nhess-21-1615-2021
- Rainfall-induced shallow landslides and soil wetness: comparison of physically based and probabilistic predictions E. Leonarduzzi et al. 10.5194/hess-25-5937-2021
- Laboratory tests with interferometric optical fibre sensors to monitor shallow landslides triggered by rainfalls L. Longoni et al. 10.1007/s10346-021-01803-5
- Falling in Place: Geoscience, Disaster, and Cultural Heritage at the Frank Slide, Canada’s Deadliest Rockslide M. Vallee 10.1177/1206331218795829
- Numerical-model-derived intensity–duration thresholds for early warning of rainfall-induced debris flows in a Himalayan catchment S. Dixit et al. 10.5194/nhess-24-465-2024
- A Proactive Self-Adaptation Approach Based on Ensemble Prediction for Service-Based Systems S. Xie et al. 10.1142/S0218126624502347
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Short summary
This review paper describes the state of the art in monitoring and predicting rapid mass movements for early warning. It further presents recent innovations in observation technologies and modelling to be used in future early warning systems (EWS). Finally, the paper proposes avenues towards successful implementation of next-generation EWS.
This review paper describes the state of the art in monitoring and predicting rapid mass...
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