Articles | Volume 23, issue 6
https://doi.org/10.5194/nhess-23-2229-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-23-2229-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Jun 2023
Research article |
| 21 Jun 2023
| 21 Jun 2023
Comprehensive landslide susceptibility map of Central Asia
Ascanio Rosi
Department of Geosciences, University of Padua, Via G. Gradenigo 6,
35131 Padua, Italy
UNESCO Chair on the Prevention and Sustainable Management of
Geo-Hydrological Hazards, University of Florence, Largo Fermi 2, 50125 Florence, Italy
William Frodella
CORRESPONDING AUTHOR
UNESCO Chair on the Prevention and Sustainable Management of
Geo-Hydrological Hazards, University of Florence, Largo Fermi 2, 50125 Florence, Italy
Department of Earth Sciences, University of Florence, via G. la
Pira 4, 50121 Florence, Italy
Nicola Nocentini
UNESCO Chair on the Prevention and Sustainable Management of
Geo-Hydrological Hazards, University of Florence, Largo Fermi 2, 50125 Florence, Italy
Department of Earth Sciences, University of Florence, via G. la
Pira 4, 50121 Florence, Italy
Francesco Caleca
UNESCO Chair on the Prevention and Sustainable Management of
Geo-Hydrological Hazards, University of Florence, Largo Fermi 2, 50125 Florence, Italy
Department of Earth Sciences, University of Florence, via G. la
Pira 4, 50121 Florence, Italy
Hans Balder Havenith
Department of Geology, University of Liège, 4000 Liège, Belgium
Alexander Strom
Geodynamics Research Center LLC, 125008 Moscow, Russian Federation
Geodynamics Research Center – branch of JSC “Hydroproject
Institute”, 125993 Moscow, Russian Federation
Mirzo Saidov
Institute of Water Problems, Hydropower, Engineering and Ecology of
Tajikistan (IWPHE), 734063 Dushanbe, Tajikistan
Gany Amirgalievich Bimurzaev
State Service of the Republic of Uzbekistan for Geohazards
Monitoring, 100074 Tashkent, Uzbekistan
Veronica Tofani
UNESCO Chair on the Prevention and Sustainable Management of
Geo-Hydrological Hazards, University of Florence, Largo Fermi 2, 50125 Florence, Italy
Department of Earth Sciences, University of Florence, via G. la
Pira 4, 50121 Florence, Italy
Related authors
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Carlo Tacconi Stefanelli, William Frodella, Francesco Caleca, Zhanar Raimbekova, Ruslan Umaraliev, and Veronica Tofani
Nat. Hazards Earth Syst. Sci., 24, 1697–1720, https://doi.org/10.5194/nhess-24-1697-2024, https://doi.org/10.5194/nhess-24-1697-2024, 2024
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Central Asia regions are marked by active tectonics, high mountains with glaciers, and strong rainfall. These predisposing factors make large landslides a serious threat in the area and a source of possible damming scenarios, which endanger the population. To prevent this, a semi-automated geographic information system (GIS-)based mapping method, centered on a bivariate correlation of morphometric parameters, was applied to give preliminary information on damming susceptibility in Central Asia.
Francesco Caleca, Chiara Scaini, William Frodella, and Veronica Tofani
Nat. Hazards Earth Syst. Sci., 24, 13–27, https://doi.org/10.5194/nhess-24-13-2024, https://doi.org/10.5194/nhess-24-13-2024, 2024
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Landslide risk analysis is a powerful tool because it allows us to identify where physical and economic losses could occur due to a landslide event. The purpose of our work was to provide the first regional-scale analysis of landslide risk for central Asia, and it represents an advanced step in the field of risk analysis for very large areas. Our findings show, per square kilometer, a total risk of about USD 3.9 billion and a mean risk of USD 0.6 million.
Hans-Balder Havenith, Kelly Guerrier, Romy Schlögel, Anika Braun, Sophia Ulysse, Anne-Sophie Mreyen, Karl-Henry Victor, Newdeskarl Saint-Fleur, Léna Cauchie, Dominique Boisson, and Claude Prépetit
Nat. Hazards Earth Syst. Sci., 22, 3361–3384, https://doi.org/10.5194/nhess-22-3361-2022, https://doi.org/10.5194/nhess-22-3361-2022, 2022
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We present a new landslide inventory for the 2021, M 7.2, Haiti, earthquake. We compare characteristics of this inventory with those of the 2010 seismically induced landslides, highlighting the much larger total area of 2021 landslides. This fact could be related to the larger earthquake magnitude in 2021, to the more central location of the fault segment ruptured in 2021 with respect to coastal zones, and/or to possible climatic preconditioning of slope failures in the 2021 affected area.
Hans-Balder Havenith, Kelly Guerrier, Romy Schlögel, Anne-Sophie Mreyen, Sophia Ulysse, Anika Braun, Karl-Henry Victor, Newdeskarl Saint-Fleur, Léna Cauchie, Dominique Boisson, and Claude Prépetit
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2022-83, https://doi.org/10.5194/nhess-2022-83, 2022
Preprint withdrawn
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First analyses of landslide distribution and triggering factors are presented for the region affected by the Mw = 7.2 earthquake, 2021, in Haiti. The landslide inventory created for the 2021 event is compared with catalogues compiled by others both for the 2021 and 2010 events. Related analyses show that the larger total area of landslides triggered in 2021, can be explained, e.g., by (a) the stronger shaking intensity in 2021, (b) a climatic influence on slope stability in the 2021-affected area.
Vipin Kumar, Léna Cauchie, Anne-Sophie Mreyen, Mihai Micu, and Hans-Balder Havenith
Nat. Hazards Earth Syst. Sci., 21, 3767–3788, https://doi.org/10.5194/nhess-21-3767-2021, https://doi.org/10.5194/nhess-21-3767-2021, 2021
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The SE Carpathians belong to one of the most active seismic regions of Europe. In recent decades, extreme rainfall events have also been common. These natural processes result in frequent landslides, particularly of a debris flow type. Despite such regimes, the region has been little explored to understand the response of the landslides in seismic and rainfall conditions. This study attempts to fill this gap by evaluating landslide responses under seismic and extreme-rainfall regimes.
Teresa Salvatici, Veronica Tofani, Guglielmo Rossi, Michele D'Ambrosio, Carlo Tacconi Stefanelli, Elena Benedetta Masi, Ascanio Rosi, Veronica Pazzi, Pietro Vannocci, Miriana Petrolo, Filippo Catani, Sara Ratto, Hervè Stevenin, and Nicola Casagli
Nat. Hazards Earth Syst. Sci., 18, 1919–1935, https://doi.org/10.5194/nhess-18-1919-2018, https://doi.org/10.5194/nhess-18-1919-2018, 2018
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In this paper, we present the application of the physically based HIRESSS model (High Resolution Stability Simulator) to forecast the occurrence of shallow landslides in a portion of the Aosta Valley region (Italy). An in-depth study of the geotechnical and hydrological properties of the hillslopes controlling shallow landslides formation was conducted, in order to generate an input map of parameters. The main aim of this study is to set up a regional landslide early warning system.
Léonidas Nibigira, Hans-Balder Havenith, Pierre Archambeau, and Benjamin Dewals
Nat. Hazards Earth Syst. Sci., 18, 1867–1890, https://doi.org/10.5194/nhess-18-1867-2018, https://doi.org/10.5194/nhess-18-1867-2018, 2018
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Flood prediction methods are often based solely on climatic parameters and sometimes on the failure of existing dams. This paper shows the importance of multi-hazard studies, including potential natural dam formation to avoid risk underestimation. We present an end-to-end analysis, ranging from the origin of the landslide up to the computation of flood waves induced by the dam breaching. The paper is based on a case study of Bujumbura in the East African Rift Valley, a multi-hazard environment.
William Frodella, Teresa Salvatici, Veronica Pazzi, Stefano Morelli, and Riccardo Fanti
Nat. Hazards Earth Syst. Sci., 17, 1779–1793, https://doi.org/10.5194/nhess-17-1779-2017, https://doi.org/10.5194/nhess-17-1779-2017, 2017
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A local scale GB-InSAR system was implemented for mapping and monitoring slope landslide residual deformations and for early warning purposes in case of landslide reactivations, with the aim of assuring the safety of the valley inhabitants and the personnel involved in the post-event recovery phase. The here presented methodology could represent a useful contribution to a better understanding of landslide phenomena and decision making process during the post-emergency management activities.
Guglielmo Rossi, Luca Tanteri, Veronica Tofani, Pietro Vannocci, Sandro Moretti, and Nicola Casagli
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2017-46, https://doi.org/10.5194/nhess-2017-46, 2017
Preprint retracted
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The Department of Earth Sciences of Florence (DST) has developed a new type of drone chassis that has been equipped with an optical camera to map landslides. The images acquired during the aerial drone surveys allowed to obtain a continuous 3D surface model of the studied area using a photogrammetric approach.The drone survey has proven to be an easier and more cost- and time-effective approach with respect to other techniques to mpa landslides.
A. M. Syavulisembo, H.-B. Havenith, B. Smets, N. d'Oreye, and J. Marti
Nat. Hazards Earth Syst. Sci., 15, 2391–2400, https://doi.org/10.5194/nhess-15-2391-2015, https://doi.org/10.5194/nhess-15-2391-2015, 2015
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The Virunga area in the Democratic Republic of the Congo, with over 1 million inhabitants, has to permanently cope with the threat posed by the active Nyamulagira and Nyiragongo volcanoes. During the past century, Nyamulagira erupted at intervals of about every 3 years – mostly in the form of lava flows – at least 30 times. In order to identify a useful tool for hazard assessment at the Goma Volcanological Observatory, we tested VORIS, a freely available software (www.gvb-csic.es).
F. Catani, D. Lagomarsino, S. Segoni, and V. Tofani
Nat. Hazards Earth Syst. Sci., 13, 2815–2831, https://doi.org/10.5194/nhess-13-2815-2013, https://doi.org/10.5194/nhess-13-2815-2013, 2013
P. Mercogliano, S. Segoni, G. Rossi, B. Sikorsky, V. Tofani, P. Schiano, F. Catani, and N. Casagli
Nat. Hazards Earth Syst. Sci., 13, 771–777, https://doi.org/10.5194/nhess-13-771-2013, https://doi.org/10.5194/nhess-13-771-2013, 2013
V. Tofani, S. Segoni, A. Agostini, F. Catani, and N. Casagli
Nat. Hazards Earth Syst. Sci., 13, 299–309, https://doi.org/10.5194/nhess-13-299-2013, https://doi.org/10.5194/nhess-13-299-2013, 2013
G. Rossi, F. Catani, L. Leoni, S. Segoni, and V. Tofani
Nat. Hazards Earth Syst. Sci., 13, 151–166, https://doi.org/10.5194/nhess-13-151-2013, https://doi.org/10.5194/nhess-13-151-2013, 2013
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Marko Sinčić, Sanja Bernat Gazibara, Mauro Rossi, and Snježana Mihalić Arbanas
Nat. Hazards Earth Syst. Sci., 25, 183–206, https://doi.org/10.5194/nhess-25-183-2025, https://doi.org/10.5194/nhess-25-183-2025, 2025
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The paper focuses on classifying continuous landslide conditioning factors for susceptibility modelling, which resulted in 54 landslide susceptibility models that tested 11 classification criteria in combination with 5 statistical methods. The novelty of the research is that using stretched landslide conditioning factor values results in models with higher accuracy and that certain statistical methods are more sensitive to the landslide conditioning factor classification criteria than others.
Benjamin B. Mirus, Thom Bogaard, Roberto Greco, and Manfred Stähli
Nat. Hazards Earth Syst. Sci., 25, 169–182, https://doi.org/10.5194/nhess-25-169-2025, https://doi.org/10.5194/nhess-25-169-2025, 2025
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Early warning of increased landslide potential provides situational awareness to reduce landslide-related losses from major storm events. For decades, landslide forecasts relied on rainfall data alone, but recent research points to the value of hydrologic information for improving predictions. In this paper, we provide our perspectives on the value and limitations of integrating subsurface hillslope hydrologic monitoring data and mathematical modeling for more accurate landslide forecasts.
Jui-Sheng Chou, Hoang-Minh Nguyen, Huy-Phuong Phan, and Kuo-Lung Wang
Nat. Hazards Earth Syst. Sci., 25, 119–146, https://doi.org/10.5194/nhess-25-119-2025, https://doi.org/10.5194/nhess-25-119-2025, 2025
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This study enhances landslide prediction using advanced machine learning, including new algorithms inspired by historical explorations. The research accurately forecasts landslide movements by analyzing 8 years of data from Taiwan's Lushan, improving early warning and potentially saving lives and infrastructure. This integration marks a significant advancement in environmental risk management.
Di Wu, Yuke Wang, and Xin Chen
Nat. Hazards Earth Syst. Sci., 24, 4617–4630, https://doi.org/10.5194/nhess-24-4617-2024, https://doi.org/10.5194/nhess-24-4617-2024, 2024
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This paper proposes a 3D limit analysis for seismic stability of soil slopes to address the influence of earthquakes on slope stabilities with nonlinear and linear criteria. Comparison results illustrate that the use of a linear envelope leads to the non-negligible overestimation of steep-slope stability, and this overestimation will be significant with increasing earthquakes. Earthquakes have a smaller influence on slope slip surfaces with a nonlinear envelope than those with a linear envelope.
Li Wei, Kaiheng Hu, Shuang Liu, Lan Ning, Xiaopeng Zhang, Qiyuan Zhang, and Md. Abdur Rahim
Nat. Hazards Earth Syst. Sci., 24, 4179–4197, https://doi.org/10.5194/nhess-24-4179-2024, https://doi.org/10.5194/nhess-24-4179-2024, 2024
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The damage patterns of the buildings were classified into three types: (I) buried by primary debris flow, (II) inundated by secondary dam-burst flood, and (III) sequentially buried by debris flow and inundated by dam-burst flood. The threshold of the impact pressures in Zones (II) and (III) where vulnerability is equal to 1 is 84 kPa and 116 kPa, respectively. Heavy damage occurs at an impact pressure greater than 50 kPa, while slight damage occurs below 30 kPa.
Bo Peng and Xueling Wu
Nat. Hazards Earth Syst. Sci., 24, 3991–4013, https://doi.org/10.5194/nhess-24-3991-2024, https://doi.org/10.5194/nhess-24-3991-2024, 2024
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Our research enhances landslide prevention using advanced machine learning to forecast heavy-rainfall-triggered landslides. By analyzing regions and employing various models, we identified optimal ways to predict high-risk rainfall events. Integrating multiple factors and models, including a neural network, significantly improves landslide predictions. Real data validation confirms our approach's reliability, aiding communities in mitigating landslide impacts and safeguarding lives and property.
Andrea Manconi, Yves Bühler, Andreas Stoffel, Johan Gaume, Qiaoping Zhang, and Valentyn Tolpekin
Nat. Hazards Earth Syst. Sci., 24, 3833–3839, https://doi.org/10.5194/nhess-24-3833-2024, https://doi.org/10.5194/nhess-24-3833-2024, 2024
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Our research reveals the power of high-resolution satellite synthetic-aperture radar (SAR) imagery for slope deformation monitoring. Using ICEYE data over the Brienz/Brinzauls instability, we measured surface velocity and mapped the landslide event with unprecedented precision. This underscores the potential of satellite SAR for timely hazard assessment in remote regions and aiding disaster mitigation efforts effectively.
Oliver Korup, Lisa V. Luna, and Joaquin V. Ferrer
Nat. Hazards Earth Syst. Sci., 24, 3815–3832, https://doi.org/10.5194/nhess-24-3815-2024, https://doi.org/10.5194/nhess-24-3815-2024, 2024
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Catalogues of mapped landslides are useful for learning and forecasting how frequently they occur in relation to their size. Yet, rare and large landslides remain mostly uncertain in statistical summaries of these catalogues. We propose a single, consistent method of comparing across different data sources and find that landslide statistics disclose more about subjective mapping choices than trigger types or environmental settings.
Rachael Lau, Carolina Seguí, Tyler Waterman, Nathaniel Chaney, and Manolis Veveakis
Nat. Hazards Earth Syst. Sci., 24, 3651–3661, https://doi.org/10.5194/nhess-24-3651-2024, https://doi.org/10.5194/nhess-24-3651-2024, 2024
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This work examines the use of interferometric synthetic-aperture radar (InSAR) alongside in situ borehole measurements to assess the stability of deep-seated landslides for the case study of El Forn (Andorra). Comparing InSAR with borehole data suggests a key trade-off between accuracy and precision for various InSAR resolutions. Spatial interpolation with InSAR informed how many remote observations are necessary to lower error in a remote sensing re-creation of ground motion over the landslide.
Zhen Lei Wei, Yue Quan Shang, Qiu Hua Liang, and Xi Lin Xia
Nat. Hazards Earth Syst. Sci., 24, 3357–3379, https://doi.org/10.5194/nhess-24-3357-2024, https://doi.org/10.5194/nhess-24-3357-2024, 2024
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The initiation of debris flows is significantly influenced by rainfall-induced hydrological processes. We propose a novel framework based on an integrated hydrological and hydrodynamic model and aimed at estimating intensity–duration (ID) rainfall thresholds responsible for triggering debris flows. In comparison to traditional statistical approaches, this physically based framework is particularly suitable for application in ungauged catchments where historical debris flow data are scarce.
Jürgen Mey, Ravi Kumar Guntu, Alexander Plakias, Igo Silva de Almeida, and Wolfgang Schwanghart
Nat. Hazards Earth Syst. Sci., 24, 3207–3223, https://doi.org/10.5194/nhess-24-3207-2024, https://doi.org/10.5194/nhess-24-3207-2024, 2024
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The Himalayan road network links remote areas, but fragile terrain and poor construction lead to frequent landslides. This study on the NH-7 in India's Uttarakhand region analyzed 300 landslides after heavy rainfall in 2022 . Factors like slope, rainfall, rock type and road work influence landslides. The study's model predicts landslide locations for better road maintenance planning, highlighting the risk from climate change and increased road use.
Chenchen Qiu and Xueyu Geng
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-156, https://doi.org/10.5194/nhess-2024-156, 2024
Revised manuscript accepted for NHESS
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We proposed an interated method with the combination of a physical vulnerability matric and a machine learning model to estimate the potential physical damage and associated economic loss caused by future debris flows based on the collected historical data on the Qinghai-Tibet Plateau regions.
Fabiola Banfi, Emanuele Bevacqua, Pauline Rivoire, Sérgio C. Oliveira, Joaquim G. Pinto, Alexandre M. Ramos, and Carlo De Michele
Nat. Hazards Earth Syst. Sci., 24, 2689–2704, https://doi.org/10.5194/nhess-24-2689-2024, https://doi.org/10.5194/nhess-24-2689-2024, 2024
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Landslides are complex phenomena causing important impacts in vulnerable areas, and they are often triggered by rainfall. Here, we develop a new approach that uses information on the temporal clustering of rainfall, i.e. multiple events close in time, to detect landslide events and compare it with the use of classical empirical rainfall thresholds, considering as a case study the region of Lisbon, Portugal. The results could help to improve the prediction of rainfall-triggered landslides.
Jianqi Zhuang, Jianbing Peng, Chenhui Du, Yi Zhu, and Jiaxu Kong
Nat. Hazards Earth Syst. Sci., 24, 2615–2631, https://doi.org/10.5194/nhess-24-2615-2024, https://doi.org/10.5194/nhess-24-2615-2024, 2024
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The Revised Infinite Slope Model (RISM) is proposed using the equal differential unit method and correcting the deficiency of the safety factor increasing with the slope increasing when the slope is larger than 40°, as calculated using the Taylor slope infinite model. The intensity–duration (I–D) prediction curve of the rainfall-induced shallow loess landslides with different slopes was constructed and can be used in forecasting regional shallow loess landslides.
Alexander B. Prescott, Luke A. McGuire, Kwang-Sung Jun, Katherine R. Barnhart, and Nina S. Oakley
Nat. Hazards Earth Syst. Sci., 24, 2359–2374, https://doi.org/10.5194/nhess-24-2359-2024, https://doi.org/10.5194/nhess-24-2359-2024, 2024
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Fire can dramatically increase the risk of debris flows to downstream communities with little warning, but hazard assessments have not traditionally included estimates of inundation. We unify models developed by the scientific community to create probabilistic estimates of inundation area in response to rainfall at forecast lead times (≥ 24 h) needed for decision-making. This work takes an initial step toward a near-real-time postfire debris-flow inundation hazard assessment product.
Francis K. Rengers, Samuel Bower, Andrew Knapp, Jason W. Kean, Danielle W. vonLembke, Matthew A. Thomas, Jaime Kostelnik, Katherine R. Barnhart, Matthew Bethel, Joseph E. Gartner, Madeline Hille, Dennis M. Staley, Justin K. Anderson, Elizabeth K. Roberts, Stephen B. DeLong, Belize Lane, Paxton Ridgway, and Brendan P. Murphy
Nat. Hazards Earth Syst. Sci., 24, 2093–2114, https://doi.org/10.5194/nhess-24-2093-2024, https://doi.org/10.5194/nhess-24-2093-2024, 2024
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Every year the U.S. Geological Survey produces 50–100 postfire debris-flow hazard assessments using models for debris-flow likelihood and volume. To refine these models they must be tested with datasets that clearly document rainfall, debris-flow response, and debris-flow volume. These datasets are difficult to obtain, but this study developed and analyzed a postfire dataset with more than 100 postfire storm responses over a 2-year period. We also proposed ways to improve these models.
Ellen B. Robson, Bhim Kumar Dahal, and David G. Toll
EGUsphere, https://doi.org/10.5194/egusphere-2024-1300, https://doi.org/10.5194/egusphere-2024-1300, 2024
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Slopes excavated alongside roads in Nepal frequently fail (a landslide), resulting in substantial losses. Our participatory approach study involving road engineers aimed to assess the efficacy of the current slope design guidelines in Nepal. Our study revealed inconsistent guideline adherence due to their lack of user-friendliness and inadequate training. We recommend developing simpler, context-specific guidelines and comprehensive training to enhance resilience in Nepal's road network.
Jui-Ming Chang, Che-Ming Yang, Wei-An Chao, Chin-Shang Ku, Ming-Wan Huang, Tung-Chou Hsieh, and Chi-Yao Hung
EGUsphere, https://doi.org/10.5194/egusphere-2024-1267, https://doi.org/10.5194/egusphere-2024-1267, 2024
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The study on the Cilan Landslide (CL) demonstrates the utilization of seismic analysis results as preliminary data for geologists during field surveys. Spectrograms revealed that the 1st event of CL consisted of 4 sliding failures, accompanied by a gradual reduction in landslide volume. The 2nd and 3rd events were minor topplings and rockfalls. Then combining the seismological-based knowledge and field survey results, the temporal-spatial variation of landslide evolution is proposed.
Praveen Kumar, Priyanka Priyanka, Kala Venkata Uday, and Varun Dutt
Nat. Hazards Earth Syst. Sci., 24, 1913–1928, https://doi.org/10.5194/nhess-24-1913-2024, https://doi.org/10.5194/nhess-24-1913-2024, 2024
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Our study focuses on predicting soil movement to mitigate landslide risks. We develop machine learning models with oversampling techniques to address the class imbalance in monitoring data. The dynamic ensemble model with K-means SMOTE (synthetic minority oversampling technique) achieves high precision, high recall, and a high F1 score. Our findings highlight the potential of these models with oversampling techniques to improve soil movement predictions in landslide-prone areas.
Kristian Svennevig, Julian Koch, Marie Keiding, and Gregor Luetzenburg
Nat. Hazards Earth Syst. Sci., 24, 1897–1911, https://doi.org/10.5194/nhess-24-1897-2024, https://doi.org/10.5194/nhess-24-1897-2024, 2024
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In our study, we analysed publicly available data in order to investigate the impact of climate change on landslides in Denmark. Our research indicates that the rising groundwater table due to climate change will result in an increase in landslide activity. Previous incidents of extremely wet winters have caused damage to infrastructure and buildings due to landslides. This study is the first of its kind to exclusively rely on public data and examine landslides in Denmark.
Christoph Schaller, Luuk Dorren, Massimiliano Schwarz, Christine Moos, Arie C. Seijmonsbergen, and E. Emiel van Loon
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-76, https://doi.org/10.5194/nhess-2024-76, 2024
Revised manuscript accepted for NHESS
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We developed a machine learning-based approach to predict the potential thickness of shallow landslides to generate improved inputs for slope stability models. We selected 21 explanatory variables including metrics on terrain, geomorphology, vegetation height, and lithology and used data from two Swiss field inventories to calibrate and test the models. The best performing machine learning model consistently reduced the mean average error by least 17 % compared to previously existing models.
Jiao Wang, Zhangxing Wang, Guanhua Sun, and Hongming Luo
Nat. Hazards Earth Syst. Sci., 24, 1741–1756, https://doi.org/10.5194/nhess-24-1741-2024, https://doi.org/10.5194/nhess-24-1741-2024, 2024
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With a simplified formula linking rainfall and groundwater level, the rise of the phreatic surface within the slope can be obtained. Then, a global analysis method that considers both seepage and seismic forces is proposed to determine the safety factor of slopes subjected to the combined effect of rainfall and earthquakes. By taking a slope in the Three Gorges Reservoir area as an example, the safety evolution of the slope combined with both rainfall and earthquake is also examined.
Carlo Tacconi Stefanelli, William Frodella, Francesco Caleca, Zhanar Raimbekova, Ruslan Umaraliev, and Veronica Tofani
Nat. Hazards Earth Syst. Sci., 24, 1697–1720, https://doi.org/10.5194/nhess-24-1697-2024, https://doi.org/10.5194/nhess-24-1697-2024, 2024
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Central Asia regions are marked by active tectonics, high mountains with glaciers, and strong rainfall. These predisposing factors make large landslides a serious threat in the area and a source of possible damming scenarios, which endanger the population. To prevent this, a semi-automated geographic information system (GIS-)based mapping method, centered on a bivariate correlation of morphometric parameters, was applied to give preliminary information on damming susceptibility in Central Asia.
Rex L. Baum, Dianne L. Brien, Mark E. Reid, William H. Schulz, and Matthew J. Tello
Nat. Hazards Earth Syst. Sci., 24, 1579–1605, https://doi.org/10.5194/nhess-24-1579-2024, https://doi.org/10.5194/nhess-24-1579-2024, 2024
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We mapped potential for heavy rainfall to cause landslides in part of the central mountains of Puerto Rico using new tools for estimating soil depth and quasi-3D slope stability. Potential ground-failure locations correlate well with the spatial density of landslides from Hurricane Maria. The smooth boundaries of the very high and high ground-failure susceptibility zones enclose 75 % and 90 %, respectively, of observed landslides. The maps can help mitigate ground-failure hazards.
Katherine R. Barnhart, Christopher R. Miller, Francis K. Rengers, and Jason W. Kean
Nat. Hazards Earth Syst. Sci., 24, 1459–1483, https://doi.org/10.5194/nhess-24-1459-2024, https://doi.org/10.5194/nhess-24-1459-2024, 2024
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Debris flows are a type of fast-moving landslide that start from shallow landslides or during intense rain. Infrastructure located downstream of watersheds susceptible to debris flows may be damaged should a debris flow reach them. We present and evaluate an approach to forecast building damage caused by debris flows. We test three alternative models for simulating the motion of debris flows and find that only one can forecast the correct number and spatial pattern of damaged buildings.
Luke A. McGuire, Francis K. Rengers, Ann M. Youberg, Alexander N. Gorr, Olivia J. Hoch, Rebecca Beers, and Ryan Porter
Nat. Hazards Earth Syst. Sci., 24, 1357–1379, https://doi.org/10.5194/nhess-24-1357-2024, https://doi.org/10.5194/nhess-24-1357-2024, 2024
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Runoff and erosion increase after fire, leading to a greater likelihood of floods and debris flows. We monitored debris flow activity following a fire in western New Mexico, USA, and observed 16 debris flows over a <2-year monitoring period. Rainstorms with recurrence intervals of approximately 1 year were sufficient to initiate debris flows. All debris flows initiated during the first several months following the fire, indicating a rapid decrease in debris flow susceptibility over time.
Ken'ichi Koshimizu, Satoshi Ishimaru, Fumitoshi Imaizumi, and Gentaro Kawakami
Nat. Hazards Earth Syst. Sci., 24, 1287–1301, https://doi.org/10.5194/nhess-24-1287-2024, https://doi.org/10.5194/nhess-24-1287-2024, 2024
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Morphological conditions of drainage basins that classify the presence or absence of debris flow fans were analyzed in areas with different rock strength using decision tree analysis. The relief ratio is the most important morphological factor regardless of the geology. However, the thresholds of morphological parameters needed for forming debris flow fans differ depending on the geology. Decision tree analysis is an effective tool for evaluating the debris flow risk for each geology.
Jonathan P. Perkins, Nina S. Oakley, Brian D. Collins, Skye C. Corbett, and W. Paul Burgess
EGUsphere, https://doi.org/10.5194/egusphere-2024-873, https://doi.org/10.5194/egusphere-2024-873, 2024
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Landslides are a global issue that results in deaths and economic losses annually. However, it is not clear how storm severity relates to landslide severity across large regions. Here we develop a method to estimate the footprint of landslide area and compare this to meteorologic estimates of storm severity. We find that total storm strength does not clearly relate to landslide area. Rather, landslide area depends on soil wetness and smaller storm structures that can produce intense rainfall.
Daniel Bolliger, Fritz Schlunegger, and Brian W. McArdell
Nat. Hazards Earth Syst. Sci., 24, 1035–1049, https://doi.org/10.5194/nhess-24-1035-2024, https://doi.org/10.5194/nhess-24-1035-2024, 2024
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We analysed data from the Illgraben debris flow monitoring station, Switzerland, and we modelled these flows with a debris flow runout model. We found that no correlation exists between the grain size distribution, the mineralogical composition of the matrix, and the debris flow properties. The flow properties rather appear to be determined by the flow volume, from which most other parameters can be derived.
Yuntao Zhou, Xiaoyan Zhao, Guangze Zhang, Bernd Wünnemann, Jiajia Zhang, and Minghui Meng
Nat. Hazards Earth Syst. Sci., 24, 891–906, https://doi.org/10.5194/nhess-24-891-2024, https://doi.org/10.5194/nhess-24-891-2024, 2024
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We developed three rock bridge models to analyze 3D stability and deformation behaviors of the Tizicao landslide and found that the contact surface model with high strength parameters combines advantages of the intact rock mass model in simulating the deformation of slopes with rock bridges and the modeling advantage of the Jennings model. The results help in choosing a rock bridge model to simulate landslide stability and reveal the influence laws of rock bridges on the stability of landslides.
Ashok Dahal, Hakan Tanyas, Cees van Westen, Mark van der Meijde, Paul Martin Mai, Raphaël Huser, and Luigi Lombardo
Nat. Hazards Earth Syst. Sci., 24, 823–845, https://doi.org/10.5194/nhess-24-823-2024, https://doi.org/10.5194/nhess-24-823-2024, 2024
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We propose a modeling approach capable of recognizing slopes that may generate landslides, as well as how large these mass movements may be. This protocol is implemented, tested, and validated with data that change in both space and time via an Ensemble Neural Network architecture.
Li-Ru Luo, Zhi-Xiang Yu, Li-Jun Zhang, Qi Wang, Lin-Xu Liao, and Li Peng
Nat. Hazards Earth Syst. Sci., 24, 631–649, https://doi.org/10.5194/nhess-24-631-2024, https://doi.org/10.5194/nhess-24-631-2024, 2024
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We performed field investigations on a rockfall near Jiguanshan National Forest Park, Chengdu. Vital information was obtained from an unmanned aerial vehicle survey. A finite element model was created to reproduce the damage evolution. We found that the impact kinetic energy was below the design protection energy. Improper member connections prevent the barrier from producing significant deformation to absorb energy. Damage is avoided by improving the ability of the nets and ropes to slide.
Sudhanshu Dixit, Srikrishnan Siva Subramanian, Piyush Srivastava, Ali P. Yunus, Tapas Ranjan Martha, and Sumit Sen
Nat. Hazards Earth Syst. Sci., 24, 465–480, https://doi.org/10.5194/nhess-24-465-2024, https://doi.org/10.5194/nhess-24-465-2024, 2024
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Rainfall intensity–duration (ID) thresholds can aid in the prediction of natural hazards. Large-scale sediment disasters like landslides, debris flows, and flash floods happen frequently in the Himalayas because of their propensity for intense precipitation events. We provide a new framework that combines the Weather Research and Forecasting (WRF) model with a regionally distributed numerical model for debris flows to analyse and predict intense rainfall-induced landslides in the Himalayas.
Jacob B. Woodard, Benjamin B. Mirus, Nathan J. Wood, Kate E. Allstadt, Benjamin A. Leshchinsky, and Matthew M. Crawford
Nat. Hazards Earth Syst. Sci., 24, 1–12, https://doi.org/10.5194/nhess-24-1-2024, https://doi.org/10.5194/nhess-24-1-2024, 2024
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Dividing landscapes into hillslopes greatly improves predictions of landslide potential across landscapes, but their scaling is often arbitrarily set and can require significant computing power to delineate. Here, we present a new computer program that can efficiently divide landscapes into meaningful slope units scaled to best capture landslide processes. The results of this work will allow an improved understanding of landslide potential and can help reduce the impacts of landslides worldwide.
Anne Felsberg, Zdenko Heyvaert, Jean Poesen, Thomas Stanley, and Gabriëlle J. M. De Lannoy
Nat. Hazards Earth Syst. Sci., 23, 3805–3821, https://doi.org/10.5194/nhess-23-3805-2023, https://doi.org/10.5194/nhess-23-3805-2023, 2023
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The Probabilistic Hydrological Estimation of LandSlides (PHELS) model combines ensembles of landslide susceptibility and of hydrological predictor variables to provide daily, global ensembles of hazard for hydrologically triggered landslides. Testing different hydrological predictors showed that the combination of rainfall and soil moisture performed best, with the lowest number of missed and false alarms. The ensemble approach allowed the estimation of the associated prediction uncertainty.
Mark Bloomberg, Tim Davies, Elena Moltchanova, Tom Robinson, and David Palmer
EGUsphere, https://doi.org/10.5194/egusphere-2023-2695, https://doi.org/10.5194/egusphere-2023-2695, 2023
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Debris flows occur infrequently, with average recurrence intervals (ARIs) ranging from decades to millennia. Consequently, they pose an underappreciated hazard. We describe how to make a preliminary identification of debris flow-susceptible catchments, estimate threshold ARIs for debris flows which pose an unacceptable risk to life, and identify the "window of non-recognition" where debris flows are infrequent enough that their hazard is unrecognised, yet frequent enough to pose a risk to life.
Xushan Shi, Bo Chai, Juan Du, Wei Wang, and Bo Liu
Nat. Hazards Earth Syst. Sci., 23, 3425–3443, https://doi.org/10.5194/nhess-23-3425-2023, https://doi.org/10.5194/nhess-23-3425-2023, 2023
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A 3D stability analysis method is proposed for biased rockfall with external erosion. Four failure modes are considered according to rockfall evolution processes, including partial damage of underlying soft rock and overall failure of hard rock blocks. This method is validated with the biased rockfalls in the Sichuan Basin, China. The critical retreat ratio from low to moderate rockfall susceptibility is 0.33. This method could facilitate rockfall early identification and risk mitigation.
Marius Schneider, Nicolas Oestreicher, Thomas Ehrat, and Simon Loew
Nat. Hazards Earth Syst. Sci., 23, 3337–3354, https://doi.org/10.5194/nhess-23-3337-2023, https://doi.org/10.5194/nhess-23-3337-2023, 2023
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Rockfalls and their hazards are typically treated as statistical events based on rockfall catalogs, but only a few complete rockfall inventories are available today. Here, we present new results from a Doppler radar rockfall alarm system, which has operated since 2018 at a high frequency under all illumination and weather conditions at a site where frequent rockfall events threaten a village and road. The new data set is used to investigate rockfall triggers in an active rockslide complex.
Annette I. Patton, Lisa V. Luna, Joshua J. Roering, Aaron Jacobs, Oliver Korup, and Benjamin B. Mirus
Nat. Hazards Earth Syst. Sci., 23, 3261–3284, https://doi.org/10.5194/nhess-23-3261-2023, https://doi.org/10.5194/nhess-23-3261-2023, 2023
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Landslide warning systems often use statistical models to predict landslides based on rainfall. They are typically trained on large datasets with many landslide occurrences, but in rural areas large datasets may not exist. In this study, we evaluate which statistical model types are best suited to predicting landslides and demonstrate that even a small landslide inventory (five storms) can be used to train useful models for landslide early warning when non-landslide events are also included.
Sandra Melzner, Marco Conedera, Johannes Hübl, and Mauro Rossi
Nat. Hazards Earth Syst. Sci., 23, 3079–3093, https://doi.org/10.5194/nhess-23-3079-2023, https://doi.org/10.5194/nhess-23-3079-2023, 2023
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The estimation of the temporal frequency of the involved rockfall processes is an important part in hazard and risk assessments. Different methods can be used to collect and analyse rockfall data. From a statistical point of view, rockfall datasets are nearly always incomplete. Accurate data collection approaches and the application of statistical methods on existing rockfall data series as reported in this study should be better considered in rockfall hazard and risk assessments in the future.
Stefan Hergarten
Nat. Hazards Earth Syst. Sci., 23, 3051–3063, https://doi.org/10.5194/nhess-23-3051-2023, https://doi.org/10.5194/nhess-23-3051-2023, 2023
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Rockslides are a major hazard in mountainous regions. In formerly glaciated regions, the disposition mainly arises from oversteepened topography and decreases through time. However, little is known about this decrease and thus about the present-day hazard of huge, potentially catastrophic rockslides. This paper presents a new theoretical framework that explains the decrease in maximum rockslide size through time and predicts the present-day frequency of large rockslides for the European Alps.
Colin K. Bloom, Corinne Singeisen, Timothy Stahl, Andrew Howell, Chris Massey, and Dougal Mason
Nat. Hazards Earth Syst. Sci., 23, 2987–3013, https://doi.org/10.5194/nhess-23-2987-2023, https://doi.org/10.5194/nhess-23-2987-2023, 2023
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Landslides are often observed on coastlines following large earthquakes, but few studies have explored this occurrence. Here, statistical modelling of landslides triggered by the 2016 Kaikōura earthquake in New Zealand is used to investigate factors driving coastal earthquake-induced landslides. Geology, steep slopes, and shaking intensity are good predictors of landslides from the Kaikōura event. Steeper slopes close to the coast provide the best explanation for a high landslide density.
Yi-Min Huang
Nat. Hazards Earth Syst. Sci., 23, 2649–2662, https://doi.org/10.5194/nhess-23-2649-2023, https://doi.org/10.5194/nhess-23-2649-2023, 2023
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Debris flows are common hazards in Taiwan, and debris-flow early warning is important for disaster responses. The rainfall thresholds of debris flows are analyzed and determined in terms of rainfall intensity, accumulated rainfall, and rainfall duration, based on case histories in Taiwan. These thresholds are useful for disaster management, and the cases in Taiwan are useful for global debris-flow databases.
Davide Notti, Martina Cignetti, Danilo Godone, and Daniele Giordan
Nat. Hazards Earth Syst. Sci., 23, 2625–2648, https://doi.org/10.5194/nhess-23-2625-2023, https://doi.org/10.5194/nhess-23-2625-2023, 2023
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We developed a cost-effective and user-friendly approach to map shallow landslides using free satellite data. Our methodology involves analysing the pre- and post-event NDVI variation to semi-automatically detect areas potentially affected by shallow landslides (PLs). Additionally, we have created Google Earth Engine scripts to rapidly compute NDVI differences and time series of affected areas. Datasets and codes are stored in an open data repository for improvement by the scientific community.
Simon Seelig, Thomas Wagner, Karl Krainer, Michael Avian, Marc Olefs, Klaus Haslinger, and Gerfried Winkler
Nat. Hazards Earth Syst. Sci., 23, 2547–2568, https://doi.org/10.5194/nhess-23-2547-2023, https://doi.org/10.5194/nhess-23-2547-2023, 2023
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A rapid sequence of cascading events involving thermokarst lake outburst, rock glacier front failure, debris flow development, and river blockage hit an alpine valley in Austria during summer 2019. We analyze the environmental conditions initiating the process chain and identify the rapid evolution of a thermokarst channel network as the main driver. Our results highlight the need to account for permafrost degradation in debris flow hazard assessment studies.
Camilla Lanfranconi, Paolo Frattini, Gianluca Sala, Giuseppe Dattola, Davide Bertolo, Juanjuan Sun, and Giovanni Battista Crosta
Nat. Hazards Earth Syst. Sci., 23, 2349–2363, https://doi.org/10.5194/nhess-23-2349-2023, https://doi.org/10.5194/nhess-23-2349-2023, 2023
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This paper presents a study on rockfall dynamics and hazard, examining the impact of the presence of trees along slope and block fragmentation. We compared rockfall simulations that explicitly model the presence of trees and fragmentation with a classical approach that accounts for these phenomena in model parameters (both the hazard and the kinetic energy change). We also used a non-parametric probabilistic rockfall hazard analysis method for hazard mapping.
Francis K. Rengers, Luke A. McGuire, Katherine R. Barnhart, Ann M. Youberg, Daniel Cadol, Alexander N. Gorr, Olivia J. Hoch, Rebecca Beers, and Jason W. Kean
Nat. Hazards Earth Syst. Sci., 23, 2075–2088, https://doi.org/10.5194/nhess-23-2075-2023, https://doi.org/10.5194/nhess-23-2075-2023, 2023
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Debris flows often occur after wildfires. These debris flows move water, sediment, and wood. The wood can get stuck in channels, creating a dam that holds boulders, cobbles, sand, and muddy material. We investigated how the channel width and wood length influenced how much sediment is stored. We also used a series of equations to back calculate the debris flow speed using the breaking threshold of wood. These data will help improve models and provide insight into future field investigations.
Maxime Morel, Guillaume Piton, Damien Kuss, Guillaume Evin, and Caroline Le Bouteiller
Nat. Hazards Earth Syst. Sci., 23, 1769–1787, https://doi.org/10.5194/nhess-23-1769-2023, https://doi.org/10.5194/nhess-23-1769-2023, 2023
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In mountain catchments, damage during floods is generally primarily driven by the supply of a massive amount of sediment. Predicting how much sediment can be delivered by frequent and infrequent events is thus important in hazard studies. This paper uses data gathered during the maintenance operation of about 100 debris retention basins to build simple equations aiming at predicting sediment supply from simple parameters describing the upstream catchment.
Elsa S. Culler, Ben Livneh, Balaji Rajagopalan, and Kristy F. Tiampo
Nat. Hazards Earth Syst. Sci., 23, 1631–1652, https://doi.org/10.5194/nhess-23-1631-2023, https://doi.org/10.5194/nhess-23-1631-2023, 2023
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Landslides have often been observed in the aftermath of wildfires. This study explores regional patterns in the rainfall that caused landslides both after fires and in unburned locations. In general, landslides that occur after fires are triggered by less rainfall, confirming that fire helps to set the stage for landslides. However, there are regional differences in the ways in which fire impacts landslides, such as the size and direction of shifts in the seasonality of landslides after fires.
Stefan Steger, Mateo Moreno, Alice Crespi, Peter James Zellner, Stefano Luigi Gariano, Maria Teresa Brunetti, Massimo Melillo, Silvia Peruccacci, Francesco Marra, Robin Kohrs, Jason Goetz, Volkmar Mair, and Massimiliano Pittore
Nat. Hazards Earth Syst. Sci., 23, 1483–1506, https://doi.org/10.5194/nhess-23-1483-2023, https://doi.org/10.5194/nhess-23-1483-2023, 2023
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We present a novel data-driven modelling approach to determine season-specific critical precipitation conditions for landslide occurrence. It is shown that the amount of precipitation required to trigger a landslide in South Tyrol varies from season to season. In summer, a higher amount of preparatory precipitation is required to trigger a landslide, probably due to denser vegetation and higher temperatures. We derive dynamic thresholds that directly relate to hit rates and false-alarm rates.
Cited articles
Abdrakhmatov, K., Havenith, H. B., Delvaux, D., Jongmans, D., and Trefois, P.: Probabilistic PGA and Arias Intensity Maps of Kyrgyz Republic (Central
Asia), J. Seismol., 7, 203–220, 2003.
Abdrakhmatov, K. Y., Aldazhanov, S. A., Hager, B. H., Hamburger, M. W., Herring, T. A., Kalabaev, K. B., Makarov, P. Molnar, S. V. Panasyuk, M. T. Prilepin, R. E., Reilinger, I. S., Sadybakasov, B. J., Souter, Y. A., Trapeznikov, V. Y., and Tsurkov Zubovich, A. V.: Relatively recent construction of the Tien Shan inferred from GPS measurements of present-day crustal deformation rates, Nature, 384, 450–453, 1996.
Akgun, A. A.: comparison of landslide susceptibility maps produced by
logistic regression, multi-criteria decision, and likelihood ratio methods:
A case study at İzmir, Turkey, Landslides, 9, 93–106, 2012.
Bazzurro, P. et al.: Strengthening Financial Resilience and Accelerating
Risk Reduction in Central Asia – the SFRARR project. The SFRARR
probabilistic flood hazard assessment, in preparation, 2023.
Behling, R. and Roessner, S.: Multi-temporal landslide inventory for a study
area in Southern Kyrgyz Republic derived from RapidEye satellite time series
data (2009–2013), V.1.0. GFZ Data Services [data set], https://doi.org/10.5880/GFZ.1.4.2020.001, 2020.
Behling, R., Roessner, S., Kaufmann, H., and Kleinschmit, B.: Automated
spatiotemporal landslide mapping over large areas using rapideye time series
data, Remote Sens. 6, 8026–8055, https://doi.org/10.3390/rs6098026, 2014.
Behling, R., Roessner, S., Golovko, D., and Kleinschmit, B.: Derivation of
long-term spatiotemporal landslide activity – A multi-sensor time series
approach, Remote Sens. Environ., 186, 88–104, https://doi.org/10.1016/j.rse.2016.07.017, 2016.
Brabb, E. E.: Innovative approaches to landslide hazard mapping, in:
Proceedings 4th International Symposium on Landslides, Toronto, Canada, 16–21 September 1984, Canadian Geotechnical Society, 1, 307–324, 1984.
Breiman, L.: Random forests, Mach. Learn. 45, 5–32, 2001.
Brenning, A.: Spatial prediction models for landslide hazards: review, comparison and evaluation, Nat. Hazards Earth Syst. Sci., 5, 853–862, https://doi.org/10.5194/nhess-5-853-2005, 2005.
Buchhorn, M., Smets, B., Bertels, L., De Roo, B., Lesiv, M., Tsendbazar, N. E., Herold, M., and Fritz, S.: Copernicus global land service: Land cover 100 m: collection 3: epoch 2019: Globe, Version V3.0.1, Zenodo [data set], https://doi.org/10.5281/zenodo.3939050, 2020.
CAC DRMI: Risk assessment for Central Asia and Caucasus: desk study review, project report, Central Asia and Caucasus Disaster Risk Management Initiative, 2009.
Carrara, A.: Multivariate models for landslide hazard evaluation, J. Int.
Assoc. Math. Geol., 15, 403–426, 1983.
Cascini, L.: Applicability of landslide susceptibility and hazard zoning at
different scales, Eng. Geol., 102, 164–177, 2008.
Catani, F., Lagomarsino, D., Segoni, S., and Tofani, V.: Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues, Nat. Hazards Earth Syst. Sci., 13, 2815–2831, https://doi.org/10.5194/nhess-13-2815-2013, 2013.
Chedia, O. K. and Lemzin, I. N.: Seismogenerating faults of the Chatkal
depression, in: Seismotectonics and seismicity of the Tien Shan, Frunze,
Ilim, 18–28, 1980.
Chen, L., Van Westen, C. J., Hussin, H., Ciurean, R. L., Turkington, T.,
Chavarro-Rincon, D., and Shrestha, D. P.: Integrating expert opinion with
modelling for quantitative multi-hazard risk assessment in the Eastern
Italian Alps, Geomorphology, 273, 150–167, 2016.
Coccia, G. et al.: The SFRARR probabilistic flood hazard assessment, in preparation, 2023.
Corominas, J., Copons, R., Vilaplana, J. M., Altimir, J., and Amigó, J.:
Integrated landslide susceptibility analysis and hazard assessment in the
principality of Andorra, Nat. Hazards, 30, 421–435, 2003.
Cruden, D. M. and Varnes, D. J.: Landslide types and processes. Landslides:
Investigation and Mitigation, Special Report 247, Transportation Research
Board, Washington, 36–75, 1996.
Danneels, G., Bourdeau, C., Torgoev, I., and Havenith, H. B.: Geophysical
investigation and dynamic modelling of unstable slopes: case-study of
Kainama (Kyrgyzstan), Geophys. J. Int., 175, 17–34,
2008.
Delvaux, D., Abdrakhmatov, K. E., Lemzin, I. N., and Strom, A. L.: Landslides and surface breaks of the 1911 Ms 8.2 Kemin earthquake, Kyrgyzstan, Russ.
Geol. Geophys.+, 42, 1667–1677, 2001.
Duman, T. Y., Can, T., Gokceoglu, C., and Sonmez, H.: Application of logistic
regression for landslide susceptibility zoning of Cekmece Area, Istanbul,
Turkey, Environ. Geol., 51, 241–256, 2006.
Ermini, L., Catani, F., and Casagli, N.: Artificial Neural Networks applied to landslide susceptibility assessment, Geomorphology, 66, 327–343, 2005.
European Commission: Humanitarian Aid, Civil Protection, U.N.I.S. for D.R.R.:
Disaster Risk Reduction 20 Examples of Good Practice from Central Asia, European Commission, technical report, 2006.
Frattini, P., Crosta, G., and Carrara, A.: Techniques for evaluating the
performance of landslide susceptibility models, Eng. Geol., 111, 62–72, 2010.
GFDRR (Global Facility for Disaster Reduction and Recovery): Disaster Risk
Management Notes for Priority Countries 2009–2015, Eur. Asia, GFDRR, technical report, 48–49, 2009.
GFDRR (Global Facility for Disaster Reduction and Recovery): Europe and
Central Asia-Country Risk Profiles for Floods and Earthquakes, GFDRR, technical report, 144 pp., 2016.
Goetz, J. N., Brenning, A., Petschkoc, H., and Leopold, P.: Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling, Comput Geosci., 81, 1–11, 2015.
Golovko, D., Roessner, S., Behling, R., Wetzel, H. U., and Kleinschmidt, B.:
Development of multi-temporal landslide inventory information system for
southern Kyrgyz Republic using GIS and satellite remote sensing, PFG 2015,
157–172, https://doi.org/10.1127/pfg/2015/0261, 2015.
Havenith, H.-B., Strom, A., Jongmans, D., Abdrakhmatov, A., Delvaux, D., and Tréfois, P.: Seismic triggering of landslides, Part A: Field evidence from the Northern Tien Shan, Nat. Hazards Earth Syst. Sci., 3, 135–149, https://doi.org/10.5194/nhess-3-135-2003, 2003.
Havenith, H. B., Strom, A., Cacerez, F., and Pirard, E.: Analysis of landslide susceptibility in the Suusamyr region, Tien Shan: statistical and
geotechnical approach, Landslides, 3, 39–50, 2006a.
Havenith, H. B., Torgoev, I., Meleshko, A., Alioshin, Y., Torgoev, A., and
Danneels, G.: Landslides in the Mailuu-Suu Valley, Kyrgyz Republic – hazards
and impacts, Landslides, 3, 137–147, 2006b.
Havenith, H. B., Strom, A., Torgoev, I., Torgoev, A., Lamair, L., Ischuk, A., and Abdrakhmatov, K.: Tien Shan geohazards database: Earthquakes and landslides, Geomorphology, 249, 16–31, https://doi.org/10.1016/j.geomorph.2015.01.037, 2015a.
Havenith, H. B., Torgoev, A., Schlögel, R., Braun, A., Torgoev, I., and
Ischuk, A.: Tien Shan geohazards database: Landslide susceptibility
analysis, Geomorphology, 249, 32–43, 2015b.
Havenith, H. B., Torgoev, A., Braun, A., Schlögel, R., and Micu, M.: A new classification of earthquake-induced landslide event sizes based on
seismotectonic, topographic, climatic and geologic factors, Geoenvironmental
Disasters, 1, 1–24, 2016.
Havenith, H. B., Umaraliev, R., Schlögel, R., Torgoev, I., Ruslan, U.,
Schlogel, R., and Torgoev, I.: Past and Potential Future Socioeconomic Impacts of Environmental Hazards in Kyrgyz Republic, in: Kyrgyz Republic: Political, Economic and Social Issues, edited by: Olivier, A. P., Nova Science Publishers, Inc., Hauppauge, NY, USA, 63–113, ISBN 978-1-53612-763-8, 2017.
Hong, Y., Adler, R., and Huffman, G.: Use of satellite remote sensing data in
the mapping of global landslide susceptibility, Nat. Hazards, 43, 23–44,
2007.
Ishihara, K., Okusa, S., Oyagi, N., and Ischuk, A.: Liquefaction-induced flow
slide in the collapsible loess deposit in Soviet Tajik, Soils Found., 30, 73–89, 1990.
Juliev, M., Pulatov, A., and Hubl, J.: Natural hazards in mountain regions of
Uzbekistan: A review of mass movement processes in Tashkent province,
International Journal of Scientific & Engineering Research, 8, 1102, https://doi.org/10.14299/ijser.2017.02.013, 2017.
Kalmetieva, Z. A., Mikolaichuk, A. V, Moldobekov, B. D., Meleshko, A. V.,
Janaev, M. M., and Zubovich, A. V.: Atlas of earthquakes in Kyrgyz Republic,
Central-Asian Institute for Applied Geosciences and United Nations
International Strategy for Disaster Reduction Secretariat Office in Central
Asia, Bishkek, p. 75, 2009.
Kirschbaum, D., Stanley, T., and Zhou, Y.: Spatial and temporal analysis of a
global landslide catalog, Geomorphology, 249, 4–15, https://doi.org/10.1016/j.geomorph.2015.03.016, 2015.
Lagomarsino, D., Tofani, V., Segoni, S., Catani, F., and Casagli, N.: A tool for classification and regression using random forest methodology: Applications to landslide susceptibility mapping and soil thickness modeling, Environ. Model. Assess., 22, 201–214, 2017.
Lee, S.: Application of logistic regression model and its validation for
landslide susceptibility mapping using GIS and remote sensing data, Int. J.
Remote Sens., 26, 1477–1491, 2005.
Li, F., Torgoev, I., Zaredinov, D., Li, M., Talipov, B., Belousova, A.,
Kunze, C., and Schneider, P.: Influence of Earthquakes on Landslide
Susceptibility in a Seismic Prone Catchment in Central Asia, Appl. Sci, 11,
3768, https://doi.org/10.3390/app11093768, 2021.
Manzo, G., Tofani, V., Segoni, S., Battistini, A., and Catani, F.: GIS
techniques for regional-scale landslide susceptibility assessment: The
Sicily (Italy) case study, Int. J. Geogr. Inf. Sci., 27, 1433–1452, 2013.
Martelloni, G., Segoni, S., Fanti, R., and Catani, F.: Rainfall thresholds for the forecasting of landslide occurrence at regional scale, Landslides, 9,
485–495, 2012.
Medeu, A. R. and Blagovechshenskiy, A. R.: Seismogenic Landslide risk zoning in the surrounding areas of Almaty city, Kazakhstan, Vestnick of Almaty University of Power Engineering and Telecommunications (AUPET), 6, 121–126, 2016.
Molnar, P. and Tapponnier, P.: Cenozoic Tectonics of Asia: Effects of a
Continental Collision: Features of recent continental tectonics in Asia can
be interpreted as results of the India-Eurasia collision, Science,
189, 419–426, 1975.
Muñoz-Sabater, J.: ERA5-Land monthly averaged data from 1981 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac, 2019a.
Muñoz-Sabater, J.: ERA5-Land hourly data from 1981 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.68d2bb30, 2019b.
Nadim, F., Kjekstad, O., Peduzzi, P., Herold, C., and Jaedicke, C.: Global
landslide and avalanche hotspots, Landslides, 3, 159–173, 2006.
Niyazov, R., Nurtaev, B., Bimurzaev, G., and Tashpulatov, M.: Flow Slides in
Uzbekistan: Overview and Case Studies, in: Understanding and Reducing Landslide Disaster Risk, edited by: Casagli, N., Tofani, V., Sassa, K., Bobrowsky, P. T., and Takara, K., WLF 2020, ICL Contribution to Landslide Disaster Risk Reduction, Springer, Cham, https://doi.org/10.1007/978-3-030-60311-3_5, 2021.
Niyazov, R. A. and Nurtaev, B. S.: Evaluation of Landslides in Uzebekistan Caused by the Joint Impact of Precipitation and Deep-focus Pamir-Hindu Earthquakes, in: Landslides: Global Risk Preparedness, edited by: Sassa, K., Rouhban, B., Briceño, S., McSaveney, M., and He, B., Springer, Berlin,
Heidelberg, 253–265, https://doi.org/10.1007/978-3-642-22087-6_17, 2013.
Pánek, T., Korup, O., Minár, J., and Hradecký J.: Giant landslides and highstands of the Caspian Sea, Geology, 44, 939–942, 2016.
Persits, F. M., Ulmishek, G. F., and Steinshouer, D. W.: Maps showing geology, oil and gas fields and geologic provinces of the Former Soviet Union, No. 97-470-E, US Geological Survey, https://doi.org/10.3133/ofr97470E, 1997.
Piroton, V., Schlögel, R., Barbier, C., and Havenith, H. B.: Monitoring the recent activity of landslides in the Mailuu-suu valley (Kyrgyz Republic)
using radar and optical remote sensing techniques, Geosciences, 10, 164, https://doi.org/10.3390/geosciences10050164, 2020.
Pittore, M., Ozturk, U., Moldobekov, B., and Saponaro, A.: EMCA Landslide
catalog Central Asia, V.1.0. GFZ Data Services [data set], https://doi.org/10.5880/GFZ.2.6.2018.004, 2018.
Poggi, V. et al.: Harmonising seismicity information in Central Asia:
earthquake catalogue and faults. The SFRARR probabilistic flood hazard
assessment, in preparation, 2023a.
Poggi, V. et al.: Development of a state of art probabilistic seismic
hazard model for Central Asia countries. The SFRARR probabilistic flood
hazard assessment, 2023b.
Pollner, J., Kryspin-Watson, J., and Nieuwejaar, S.: Disaster Risk Management
and Climate Change Adaptation in Europe and Central Asia, World Bank, technical report, 1–53, 2010.
Pusch, C.: A comprehensive risk management framework for Europe and Central
Asia, No. 9, The World Bank, Disaster Risk Management Working Paper Series, technical report, 2004.
Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., and Guzzetti, F.: A review of statistically-based landslide susceptibility models, Earth Sci. Rev., 180, 60–91, 2018.
Roessner, S., Wetzel, H. U., Kaufmann, H., Kornus, W., Lehner, M., Reinartz,
P., and Mueller, R.: Landslide Investigations in Southern Kyrgyz Republic Based on a Digital Elevation Model Derived from MOMS-2P Data, Congress “Geoinformation for all”, Amsterdam, the Netherlands, 2000, ISPRS – International Archives of Photogrammetry and Remote Sensing, Vol. 33, Part B7, 1259–1266, 2000.
Roessner, S., Wetzel, H. U., Kaufmann, H., and Sarnagoev, A.: Satellite Remote Sensing and GIS Based Analysis of Large Landslides in Southern Kyrgyz
Republic, NATO Advanced Research, in: Workshop Proceedings: Security of Natural and Artificial Rockslide Dams, Bishkek, Kyrgyz Republic, 7–12 June 2004, GFZ Publication, 2004.
Roessner, S., Wetzel, H. U., Kaufmann, H., and Sarnagoev, A.: Potential of
Satellite Remote Sensing and GIS for Landslide Hazard Assessment in Southern
Kyrgyz Republic (Central Asia), Nat. Hazards, 35, 395–416, 2005.
Saponaro, A., Pilz, M., Bindi, D., and Parolai, S.: The contribution of EMCA to landslide susceptibility mapping in Central Asia, Ann. Geophys.-Italy, 58, S0113, https://doi.org/10.4401/ag-6668, 2015a.
Saponaro, A., Pilz, M., Wieland, M., Bindi, D., Moldobekov, B.,
and Parolai, S.: Landslide susceptibility analysis in data-scarce
regions: the case of Kyrgyz Republic, B. Eng. Geol. Environ., 74, 1117–1136, https://doi.org/10.1007/s10064-014-0709-2, 2015b.
Scaini, C. et al.: A new regionally consistent exposure database
for Central Asia: population and residential buildings, in preparation, 2023.
Schiavina, M., Melchiorri, M., Pesaresi, M., Politis, P., Freire, S.,
Maffenini, L., Florio, P., Ehrlich, D., Goch, K., Tommasi, P., and Kemper, T.: GHSL Data Package, Publications Office of the European Union, Luxembourg,
2022, ISBN 978-92-76-53071-8, 2022.
Schlögel, R., Torgoev, I., De Marneffe, C., and Havenith, H. B.: Evidence of a changing size-frequency distribution of landslides in the Kyrgyz Tien
Shan, Central Asia, Earth Surf. Proc. Land., 36, 1658–1669, 2011.
Segoni, S., Tofani, V., Rosi, A., Catani, F., and Casagli, N.: Combination of
rainfall thresholds and susceptibility maps for dynamic landslide
hazardassessment at regional scale, Front Earth Sci., 6, https://doi.org/10.3389/feart.2018.00085, 2018.
Stanley, T. and Kirschbaum, D. B.: A heuristic approach to global landslide
susceptibility mapping, Nat. Hazards, 87, 145–164, 2017.
Sternberg R.: Damming a river: a changing perspective on altering nature,
Renew. Sust. Energ. Rev., 10, 165–197, 2006.
Strom, A.: Landslide dams in Central Asia region, Journal of the Japan
Landslide Society, 47, 309–324, 2010.
Strom, A. and Abdrakhmatov, K.: Large-Scale Rockslide Inventories: From the
Kokomeren River Basin to the Entire Central Asia Region, WCoE 2014–2017,
IPL-106-2, in: 4th World Landslide Forum, Ljubljana, Slovenia, 29 May–2 June 2017, Springer, Cham, 339–346, https://doi.org/10.1007/978-3-319-59469-9_28, 2017.
Strom, A. and Abdrakhmatov, K.: Rockslides and rock avalanches of Central Asia: distribution, morphology, and internal structure, Elsevier, 441 pp., ISBN 978-0-12-803204-6, 2018.
Strom, A. L. and Korup, O.: Extremely large rockslides and rock avalanches in
the Tien Shan, Kyrgyz Republic, Landslides 3, 125–136, 2006.
Styron, R. and Pagani, M.: The GEM Global Active Faults Database, Earthq.
Spectra, 36, 160–180, 2020.
Thurman, M.: Natural Disaster Risks in Central Asia: A Synthesis, UNDP BCPR, technical report, 2011.
Tien Bui, D., Tuan, T. A., Klempe, H., Pradhan, B., and Revhaug, I.: Spatial
prediction models for shallow landslide hazards: a comparative assessment of
the efficacy of support vector machines, artificial neural networks, kernel
logistic regression, and logistic model tree, Landslides, 13, 361–378, 2016.
Tiranti, D., Nicolò, G., and Gaeta, A. R.: Shallow landslides predisposing and triggering factors in developing a regional early warning system, Landslides, 16, 235–251, 2019.
Trifonov, V. G., Makarov, V. I., and Scobelev, S. F.: The Talas-Fergana active right-slip faults, Ann. Tectonicae, 6, 224–237, 1992.
Trigila, A., Frattini, P., Casagli, N., Catani, F., Crosta, G., Esposito,
C., and Spizzichino, D.: Landslide susceptibility mapping at national scale: the Italian case study, in: Landslide Science and Practice, Springer, Berlin,
Heidelberg, 287–295, https://doi.org/10.1007/978-3-642-31325-7_38, 2013.
Ullah, S., Bindi, D., Pilz, M., Danciu, L., Weatherill, G., Zuccolo, E.,
Anatoly Ischuk, A., Mikhailova, N. N., Abdrakhmatov, K., and Parolai, S.:
Probabilistic seismic hazard assessment for Central Asia, Ann.
Geophys.-Italy, 58, 1, https://doi.org/10.4401/ag-6687, 2015.
UNISDR: Risk assessment for Central Asia and Caucasus: desk study review,
CAC DRMI 2009, Risk Management Working Paper Series No. 9, The World Bank, technical report, 2009.
World Bank: Natural Disaster Hotspots: Case Studies, Disaster Risk
Management Series No. 6, World Bank, technical report, 2006.
World Bank: Investigation and Analysis of Natural Hazard Impacts on Linear
Infrastructure in Southern Kyrgyz Republic Desk and Field Studies Report, World Bank, Report 68669, 2008.
World Bank: The Global Landslide Hazard Map: Final Project Report, World Bank, technical report, Appendix A, 2020.
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F.,
Neal, J. C., Sampson, C. C., Kanae, S., and Bates, P. D.: A high-accuracy map of global terrain elevations, Geophys. Res. Lett., 44, 5844–5853, 2017.
Yilmaz, I.: Landslide susceptibility mapping using frequency ratio, logistic
regression, artificial neural networks and their comparison: a case study
from Kat landslides (Tokat–Turkey), Comput. Geosci., 35,
1125–1138, 2009.
Zubovich, A. V., Wang, X. Q., Scherba, Y. G., Schelochkov, G. G., Reilinger,
R., Reigber, C., Mosienko, O., Molnar, P., Michajljow, W., Makarov, V. I.,
Li, J., Kuzikov, S. I., Herring, T. A., Hamburger, M. W., Hager B. H., Dang, Y., Bragin, V. D., and Beisenbaev, R.: GPS velocity field for the Tien Shan and surrounding regions, Tectonics, 29, 23, https://doi.org/10.1029/2010TC002772, 2010.
Short summary
This work was carried out within the Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) project and is focused on the first landslide susceptibility analysis at a regional scale for Central Asia. The most detailed available landslide inventories were implemented in a random forest model. The final aim was to provide a useful tool for reduction strategies to landslide scientists, practitioners, and administrators.
This work was carried out within the Strengthening Financial Resilience and Accelerating Risk...
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