Articles | Volume 21, issue 1
https://doi.org/10.5194/nhess-21-147-2021
© 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/nhess-21-147-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Jan 2021
Research article |
| 18 Jan 2021
| 18 Jan 2021
Modelling landslide hazards under global changes: the case of a Pyrenean valley
Séverine Bernardie
CORRESPONDING AUTHOR
BRGM, 3 avenue Claude Guillemin, 45060 Orléans, France
Rosalie Vandromme
BRGM, 3 avenue Claude Guillemin, 45060 Orléans, France
Yannick Thiery
BRGM, 3 avenue Claude Guillemin, 45060 Orléans, France
Thomas Houet
LETG-Rennes UMR 6554 CNRS, Place du Recteur Henri Le Moal, 35043
Rennes CEDEX, France
Marine Grémont
SUEZ, 34078 Montpellier, France
Florian Masson
BRGM, 3 avenue Claude Guillemin, 45060 Orléans, France
Gilles Grandjean
BRGM, 3 avenue Claude Guillemin, 45060 Orléans, France
Isabelle Bouroullec
BRGM, 31520 Ramonville-Saint-Agne, France
Related authors
Sophie Barthelemy, Bertrand Bonan, Miquel Tomas-Burguera, Gilles Grandjean, Séverine Bernardie, Jean-Philippe Naulin, Patrick Le Moigne, Aaron Boone, and Jean-Christophe Calvet
Hydrol. Earth Syst. Sci., 29, 2321–2337, https://doi.org/10.5194/hess-29-2321-2025, https://doi.org/10.5194/hess-29-2321-2025, 2025
Short summary
Short summary
A drought index is developed that quantifies drought on an annual scale, making it applicable to monitoring clay shrinkage damage to buildings. A comparison with the number of insurance claims for subsidence shows that the presence of trees near individual houses must be taken into account. Significant soil moisture droughts occurred in France in 2003, 2018, 2019, 2020, and 2022. Particularly high index values are observed in 2022. It is found that droughts will become more severe in the future.
Sophie Barthelemy, Bertrand Bonan, Jean-Christophe Calvet, Gilles Grandjean, David Moncoulon, Dorothée Kapsambelis, and Séverine Bernardie
Nat. Hazards Earth Syst. Sci., 24, 999–1016, https://doi.org/10.5194/nhess-24-999-2024, https://doi.org/10.5194/nhess-24-999-2024, 2024
Short summary
Short summary
This work presents a drought index specifically adapted to subsidence, a seasonal phenomenon of soil shrinkage that occurs frequently in France and damages buildings. The index is computed from land surface model simulations and evaluated by a rank correlation test with insurance data. With its optimal configuration, the index is able to identify years of both zero and significant loss.
Sophie Barthelemy, Bertrand Bonan, Miquel Tomas-Burguera, Gilles Grandjean, Séverine Bernardie, Jean-Philippe Naulin, Patrick Le Moigne, Aaron Boone, and Jean-Christophe Calvet
Hydrol. Earth Syst. Sci., 29, 2321–2337, https://doi.org/10.5194/hess-29-2321-2025, https://doi.org/10.5194/hess-29-2321-2025, 2025
Short summary
Short summary
A drought index is developed that quantifies drought on an annual scale, making it applicable to monitoring clay shrinkage damage to buildings. A comparison with the number of insurance claims for subsidence shows that the presence of trees near individual houses must be taken into account. Significant soil moisture droughts occurred in France in 2003, 2018, 2019, 2020, and 2022. Particularly high index values are observed in 2022. It is found that droughts will become more severe in the future.
Sophie Barthelemy, Bertrand Bonan, Jean-Christophe Calvet, Gilles Grandjean, David Moncoulon, Dorothée Kapsambelis, and Séverine Bernardie
Nat. Hazards Earth Syst. Sci., 24, 999–1016, https://doi.org/10.5194/nhess-24-999-2024, https://doi.org/10.5194/nhess-24-999-2024, 2024
Short summary
Short summary
This work presents a drought index specifically adapted to subsidence, a seasonal phenomenon of soil shrinkage that occurs frequently in France and damages buildings. The index is computed from land surface model simulations and evaluated by a rank correlation test with insurance data. With its optimal configuration, the index is able to identify years of both zero and significant loss.
Marc Peruzzetto, Yoann Legendre, Aude Nachbaur, Thomas J. B. Dewez, Yannick Thiery, Clara Levy, and Benoit Vittecoq
Nat. Hazards Earth Syst. Sci., 22, 3973–3992, https://doi.org/10.5194/nhess-22-3973-2022, https://doi.org/10.5194/nhess-22-3973-2022, 2022
Short summary
Short summary
Volcanic edifices result from successive construction and dismantling phases. Thus, the geological units forming volcanoes display complex geometries. We show that such geometries can be reconstructed thanks to aerial views, topographic surveys and photogrammetric models. In our case study of the Samperre cliff (Martinique, Lesser Antilles), it allows us to link destabilizations from a rocky cliff to the existence of a filled paleo-valley and estimate a potentially unstable volume.
Laurence Jolivet, Florian Masson, and Sonia Saïd
Proc. Int. Cartogr. Assoc., 4, 51, https://doi.org/10.5194/ica-proc-4-51-2021, https://doi.org/10.5194/ica-proc-4-51-2021, 2021
Olivier Evrard, Caroline Chartin, J. Patrick Laceby, Yuichi Onda, Yoshifumi Wakiyama, Atsushi Nakao, Olivier Cerdan, Hugo Lepage, Hugo Jaegler, Rosalie Vandromme, Irène Lefèvre, and Philippe Bonté
Earth Syst. Sci. Data, 13, 2555–2560, https://doi.org/10.5194/essd-13-2555-2021, https://doi.org/10.5194/essd-13-2555-2021, 2021
Short summary
Short summary
This dataset provides an original compilation of radioactive dose rates and artificial radionuclide activities in sediment deposited after floods in the rivers draining the main radioactive pollution plume in Fukushuma, Japan, between November
2011 and November 2020. In total, 782 sediment samples collected from 27 to 71 locations during 16 fieldwork campaigns were analysed. This provides a unique post-accidental dataset to better understand the environmental fate of radionuclides.
Related subject area
Landslides and Debris Flows Hazards
Exploring implications of input parameter uncertainties in glacial lake outburst flood (GLOF) modelling results using the modelling code r.avaflow
From rockfall source area identification to susceptibility zonation: a proposed workflow tested on El Hierro (Canary Islands, Spain)
Brief communication: Visualizing uncertainties in landslide susceptibility modelling using bivariate mapping
Topographic controls on landslide mobility: modeling hurricane-induced landslide runout and debris-flow inundation in Puerto Rico
Characterizing the scale of regional landslide triggering from storm hydrometeorology
A participatory approach to determine the use of road cut slope design guidelines in Nepal to lessen landslides
An integrated method for assessing vulnerability of buildings caused by debris flows in mountainous areas
Identifying unrecognised risks to life from debris flows
Predicting the thickness of shallow landslides in Switzerland using machine learning
Unraveling landslide failure mechanisms with seismic signal analysis for enhanced pre-survey understanding
Comparison of conditioning factor classification criteria in large-scale statistically based landslide susceptibility models
Invited perspectives: Integrating hydrologic information into the next generation of landslide early warning systems
Predicting deep-seated landslide displacement on Taiwan's Lushan through the integration of convolutional neural networks and the Age of Exploration-Inspired Optimizer
Limit analysis of earthquake-induced landslides considering two strength envelopes
The vulnerability of buildings to a large-scale debris flow and outburst flood hazard cascade that occurred on 30 August 2020 in Ganluo, southwest China
Optimizing rainfall-triggered landslide thresholds for daily landslide hazard warning in the Three Gorges Reservoir area
Is higher resolution always better? Open-access DEM comparison for Slope Units delineation and regional landslide prediction
Brief communication: Monitoring impending slope failure with very high-resolution spaceborne synthetic aperture radar
Size scaling of large landslides from incomplete inventories
InSAR-informed in situ monitoring for deep-seated landslides: insights from El Forn (Andorra)
Brief Communication: AI-driven rapid landslides mapping following the 2024 Hualien City Earthquake in Taiwan
A coupled hydrological and hydrodynamic modeling approach for estimating rainfall thresholds of debris-flow occurrence
More than one landslide per road kilometer – surveying and modeling mass movements along the Rishikesh–Joshimath (NH-7) highway, Uttarakhand, India
Transformations in Exposure to Debris Flows in Post-Earthquake Sichuan, China
Large-scale assessment of rainfall-induced landslide hazard based on hydrometeorological information: application to Partenio Massif (Italy)
Temporal clustering of precipitation for detection of potential landslides
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
Comparative Analysis of μ (I) and Voellmy-Type Grain Flow Rheologies in Geophysical Mass Flows: Insights from Theoretical and Real Case Studies
Probabilistic assessment of postfire debris-flow inundation in response to forecast rainfall
Evaluating post-wildfire debris-flow rainfall thresholds and volume models at the 2020 Grizzly Creek Fire in Glenwood Canyon, Colorado, USA
Addressing class imbalance in soil movement predictions
Assessing the impact of climate change on landslides near Vejle, Denmark, using public data
A regional analysis of paraglacial landslide activation in southern coastal Alaska
Analysis of three-dimensional slope stability combined with rainfall and earthquake
Assessing landslide damming susceptibility in Central Asia
Assessing locations susceptible to shallow landslide initiation during prolonged intense rainfall in the Lares, Utuado, and Naranjito municipalities of Puerto Rico
Evaluation of debris-flow building damage forecasts
Characteristics of debris-flow-prone watersheds and debris-flow-triggering rainstorms following the Tadpole Fire, New Mexico, USA
Morphological characteristics and conditions of drainage basins contributing to the formation of debris flow fans: an examination of regions with different rock strength using decision tree analysis
Comparison of debris flow observations, including fine-sediment grain size and composition and runout model results, at Illgraben, Swiss Alps
Simulation analysis of 3D stability of a landslide with a locking segment: a case study of the Tizicao landslide in Maoxian County, southwest China
Brief Communication: Weak correlation between building damage and loss of life from landslides
Space–time landslide hazard modeling via Ensemble Neural Networks
Optimization strategy for flexible barrier structures: investigation and back analysis of a rockfall disaster case in southwestern China
Numerical-model-derived intensity–duration thresholds for early warning of rainfall-induced debris flows in a Himalayan catchment
Slope Unit Maker (SUMak): an efficient and parameter-free algorithm for delineating slope units to improve landslide modeling
Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling
A new analytical method for stability analysis of rock blocks with basal erosion in sub-horizontal strata by considering the eccentricity effect
Rockfall monitoring with a Doppler radar on an active rockslide complex in Brienz/Brinzauls (Switzerland)
Landslide initiation thresholds in data-sparse regions: application to landslide early warning criteria in Sitka, Alaska, USA
Sonam Rinzin, Stuart Dunning, Rachel Joanne Carr, Ashim Sattar, and Martin Mergili
Nat. Hazards Earth Syst. Sci., 25, 1841–1864, https://doi.org/10.5194/nhess-25-1841-2025, https://doi.org/10.5194/nhess-25-1841-2025, 2025
Short summary
Short summary
We modelled multiple glacial lake outburst flood (GLOF) scenarios (84 simulations) and tested the effect of nine key input parameters on the modelling results using r.avaflow. Our results highlight that GLOF modelling results are subject to uncertainty from the multiple input parameters. The variation in the volume of mass movement entering the lake causes the highest uncertainty in the modelled GLOF, followed by the DEM dataset and the origin of mass movement.
Roberto Sarro, Mauro Rossi, Paola Reichenbach, and Rosa María Mateos
Nat. Hazards Earth Syst. Sci., 25, 1459–1479, https://doi.org/10.5194/nhess-25-1459-2025, https://doi.org/10.5194/nhess-25-1459-2025, 2025
Short summary
Short summary
This study proposes a novel systematic workflow that integrates source area identification, deterministic runout modelling, the classification of runout outputs to derive susceptibility zonation, and robust procedures for validation and comparison. The proposed approach enables the integration and comparison of different modelling, introducing a robust and consistent workflow/methodology that allows us to derive and verify rockfall susceptibility zonation, considering different steps.
Matthias Schlögl, Anita Graser, Raphael Spiekermann, Jasmin Lampert, and Stefan Steger
Nat. Hazards Earth Syst. Sci., 25, 1425–1437, https://doi.org/10.5194/nhess-25-1425-2025, https://doi.org/10.5194/nhess-25-1425-2025, 2025
Short summary
Short summary
Communicating uncertainties is a crucial yet challenging aspect of spatial modelling – especially in applied research, where results inform decisions. In disaster risk reduction, susceptibility maps for natural hazards guide planning and risk assessment, yet their uncertainties are often overlooked. We present a new type of landslide susceptibility map that visualizes both susceptibility and associated uncertainty alongside guidelines for creating such maps using free and open-source software.
Dianne L. Brien, Mark E. Reid, Collin Cronkite-Ratcliff, and Jonathan P. Perkins
Nat. Hazards Earth Syst. Sci., 25, 1229–1253, https://doi.org/10.5194/nhess-25-1229-2025, https://doi.org/10.5194/nhess-25-1229-2025, 2025
Short summary
Short summary
Landslide runout zones are the areas downslope or downstream of landslide initiation. People often live and work in these areas, leading to property damage and deaths. Landslide runout may occur on hillslopes or in channels, requiring different modeling approaches. We develop methods to identify potential runout zones and apply these methods to identify susceptible areas for three municipalities in Puerto Rico.
Jonathan Perkins, Nina S. Oakley, Brian D. Collins, Skye C. Corbett, and W. Paul Burgess
Nat. Hazards Earth Syst. Sci., 25, 1037–1056, https://doi.org/10.5194/nhess-25-1037-2025, https://doi.org/10.5194/nhess-25-1037-2025, 2025
Short summary
Short summary
Rainfall-induced landslides result in deaths and economic losses annually across the globe. However, it is unclear how storm severity relates to landslide severity across large regions. Here we develop a method to dynamically map landslide-affected areas, and we compare this to meteorological estimates of storm severity. We find that preconditioning by earlier storms and the location of rainfall bursts, rather than atmospheric storm strength, dictate landslide magnitude and pattern.
Ellen B. Robson, Bhim Kumar Dahal, and David G. Toll
Nat. Hazards Earth Syst. Sci., 25, 949–973, https://doi.org/10.5194/nhess-25-949-2025, https://doi.org/10.5194/nhess-25-949-2025, 2025
Short summary
Short summary
Slopes excavated alongside roads in Nepal frequently fail (a landslide), resulting in substantial losses. Our participatory approach study with road engineers aimed to assess how road slope design guidelines in Nepal can be improved. Our study revealed inconsistent guideline adherence due to a lack of user-friendliness and inadequate training. We present general recommendations to enhance road slope management, as well as technical recommendations to improve the guidelines.
Chenchen Qiu and Xueyu Geng
Nat. Hazards Earth Syst. Sci., 25, 709–726, https://doi.org/10.5194/nhess-25-709-2025, https://doi.org/10.5194/nhess-25-709-2025, 2025
Short summary
Short summary
We propose an integrated method using a combination of a physical vulnerability matrix and a machine learning model to estimate the potential physical damage and associated economic loss caused by future debris flows based on collected historical data on the Qinghai–Tibet Plateau region.
Mark Bloomberg, Tim Davies, Elena Moltchanova, Tom Robinson, and David Palmer
Nat. Hazards Earth Syst. Sci., 25, 647–656, https://doi.org/10.5194/nhess-25-647-2025, https://doi.org/10.5194/nhess-25-647-2025, 2025
Short summary
Short summary
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 that 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.
Christoph Schaller, Luuk Dorren, Massimiliano Schwarz, Christine Moos, Arie C. Seijmonsbergen, and E. Emiel van Loon
Nat. Hazards Earth Syst. Sci., 25, 467–491, https://doi.org/10.5194/nhess-25-467-2025, https://doi.org/10.5194/nhess-25-467-2025, 2025
Short summary
Short summary
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 at least 20 % compared to previous models.
Jui-Ming Chang, Che-Ming Yang, Wei-An Chao, Chin-Shang Ku, Ming-Wan Huang, Tung-Chou Hsieh, and Chi-Yao Hung
Nat. Hazards Earth Syst. Sci., 25, 451–466, https://doi.org/10.5194/nhess-25-451-2025, https://doi.org/10.5194/nhess-25-451-2025, 2025
Short summary
Short summary
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 first event of CL consisted of four sliding failures accompanied by a gradual reduction in landslide volume. The second and third events were minor toppling and rockfalls. Then combining the seismological-based knowledge and field survey results, the spatiotemporal variation in landslide evolution is proposed.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Mahnoor Ahmed, Giacomo Titti, Sebastiano Trevisani, Lisa Borgatti, and Mirko Francioni
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-211, https://doi.org/10.5194/nhess-2024-211, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
Elevation models are compared with a true dataset for terrain characteristics which selects a better ranking model to test with different parameters for partitioning the terrain. The partitioning of the terrain is measured by how well a partitioned unit can support the mapped landslide area and number of landslides. The effect of this relationship is reflected with different metrics in the susceptibility maps.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Lorenzo Nava, Alessandro Novellino, Chengyong Fang, Kushanav Bhuyan, Kathryn Leeming, Itahisa Gonzalez Alvarez, Claire Dashwood, Sophie Doward, Rahul Chahel, Emma McAllister, Sansar Raj Meena, and Filippo Catani
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-146, https://doi.org/10.5194/nhess-2024-146, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
On April 2, 2024, a Mw 7.4 earthquake hit Taiwan’s eastern coast, causing extensive landslides and damage. We used automated methods combining Earth Observation (EO) data with Artificial Intelligence (AI) to quickly inventory the landslides. This approach identified 7,090 landslides over 75 km2 within 3 hours of acquiring the EO imagery. The study highlights AI’s role in improving landslide detection and understanding earthquake-landslide interactions for better hazard mitigation.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Isabelle Utley, Tristram Hales, Ekbal Hussain, and Xuanmei Fan
EGUsphere, https://doi.org/10.5194/egusphere-2024-2277, https://doi.org/10.5194/egusphere-2024-2277, 2024
Short summary
Short summary
We analysed debris flows in Sichuan, China, using satellite data and simulations to assess check dam efficacy. Our study found whilst check dams can mitigate smaller flows, they may increase exposure to extreme events, with up to 40 % of structures in some areas affected. Urban development and reliance on check dams can create a false sense of security, raising exposure during large debris flows and highlights the need for risk management and infrastructure planning in hazard-prone areas.
Daniel Camilo Roman Quintero, Pasquale Marino, Abdullah Abdullah, Giovanni Francesco Santonastaso, and Roberto Greco
EGUsphere, https://doi.org/10.5194/egusphere-2024-2329, https://doi.org/10.5194/egusphere-2024-2329, 2024
Short summary
Short summary
Local thresholds for landslide forecasting, combining hydrologic predisposing factors and rainfall features, are developed from a physically based model of a slope. To extend their application to a wide area, uncertainty due to spatial variability of geomorphological and hydrologic variables is introduced. The obtained hydrometeorological thresholds, integrating root zone soil moisture and aquifer water level with rainfall depth, outperform thresholds based on rain intensity and duration.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Yu Zhuang, Brian W. McArdell, and Perry Bartelt
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-87, https://doi.org/10.5194/nhess-2024-87, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
This study reformulates the μ(I) rheology into a Voellmy-type relationship to elucidate its physical implications. The μ(I) rheology, incorporating a dimensionless inertial number, mimics granular temperature effects, reflecting shear thinning behavior of mass flows. However, its constant Coulomb friction coefficient limits accuracy in modeling deposition. Comparing μ(I) with Voellmy-type rheologies reveals strengths and limitations, enhancing mass flow modeling and engineering applications.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Jane Walden, Mylène Jacquemart, Bretwood Higman, Romain Hugonnet, Andrea Manconi, and Daniel Farinotti
EGUsphere, https://doi.org/10.5194/egusphere-2024-1086, https://doi.org/10.5194/egusphere-2024-1086, 2024
Short summary
Short summary
In a study of eight landslides adjacent to glaciers in Alaska, we found that landslide movement increased as the glacier retreated past the landslide at four sites. Movement at other sites coincided with heavy precipitation or increased glacier thinning, and two sites showed little-to-no motion. We suggest that landslides next to water-terminating glaciers may be especially vulnerable to acceleration, which we guess is due to faster retreat rates and water replacing ice at the landslide edge.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Maximillian Van Wyk de Vries, Alexandre Dunant, Amy L. Johnson, Erin L. Harvey, Sihan Li, Katherine Arrell, Jeevan Baniya, Dipak Basnet, Gopi K. Basyal, Nyima Dorjee Bhotia, Simon J. Dadson, Alexander L. Densmore, Tek Bahadur Dong, Mark E. Kincey, Katie Oven, Anuradha Puri, and Nick J. Rosser
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-40, https://doi.org/10.5194/nhess-2024-40, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
Mapping exposure to landslides is necessary to mitigate risk and reduce vulnerability. In this study, we show that there is a poor correlation between building damage and deaths from landslides- such that the deadliest landslides do not always destroy the most buildings and vice versa. This has important implications for our management on landslide risk.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
ALADIN International Team: The Aladin project mesoscale modelling seen as
basic tool for weather forecasting and atmospheric research, WMO
Bull., 46, 317–324, 1997.
Alvioli, M., Melillo, M.,Guzzetti, F., Rossi, M., Palazzi, E., von
Hardenberg, J., Brunetti, M., and Peruccacci, S.: Implications of climate
change on landslide hazard in Central Italy, Sci. Total Environ., 630, 1528–1543, https://doi.org/10.1016/j.scitotenv.2018.02.315, 2018.
Baills, A., Vandromme, R., Desramaut, N., Sedan, O., and Grandjean, G.: Changing patterns in climate-driven landslide hazard: an alpine test site, The Second World Landslides Forum, October 2011, Rome, Italy, available at: https://hal-brgm.archives-ouvertes.fr/hal-00631008 (last access: 12 January 2021), 2011.
Barrère, P., Bois, J.-P., Soulé, J.-C., and Ternet, Y.: Carte
géologique de la France au 1:50 000: Argelès-Gazost, XVI-47, 1070, BRGM, Argelès-Gazost, 1980 (in French).
Bartelletti, C., Galve, J. P., Barsanti, M., Giannecchini, R., Avanzi,
G. D. A., Galanti, Y., Cevasco, A., Azañón, J. M., and Mateos, R. M.:
GIS-Based Deterministic and Statistical Modelling of Rainfall-Induced
Landslides: A Comparative Study, in: Advancing Culture of Living with Landslide, edited by: Mikos, M., Tiwari, B., Yin, Y., and Sassa, K.,
Springer, Cham, 749–757, https://doi.org/10.1007/978-3-319-53498-5_86, 2017.
Beguería, S.: Validation and evaluation of predictive models in hazard
assessment and risk management, Nat. Hazards 37, 315–329, https://doi.org/10.1007/s11069-005-5182-6, 2006.
Bernardie, S., Vandromme, R., Mariotti, A., Houet, T., Grémont, M., Grandjean, G., and Thiery Y.: Estimation of landslides activities evolution due to land–use and climate change in a Pyrenean valley, in: Advancing Culture of Living with Landslides, Ljubljana, Slovenia, 29 May–2 June 2017, Springer, Cham, 859–867, https://doi.org/10.1007/978-3-319-53498-5_98, 2017.
Bischetti, G. B., Chiaradia, E. A., Epis, T., and Morlotti, E.: Root cohesion of forest species in the Italian Alps, Plant Soil, 324, 71–89, 2009.
Bonnard, C., Tacher, L., and Beniston, M.: Prediction of landslide movements caused by climate change: Modelling the behaviour of a mean elevation large slide in the Alps and assessing its uncertainties, in: Landslides and Engineered Slopes. From the Past to the Future, edited by: Chen, Z., Zhang, J.-M., Ho, K., Wu, F.-Q., and Li, Z.-K., CRC Press, London,
https://doi.org/10.1201/9780203885284-17, 217–227, 2008.
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.
Buma, J. and Dehn, M.: A method for predicting the impact of climate change on slope stability, Environ. Geol., 35, 190–196,
https://doi.org/10.1007/s002540050305, 1998.
Buma, J. and Dehn, M.: Impact of climate change on a landslide in South East
France, simulated using different GCM scenarios and downscaling methods for
local precipitation, Clim. Res., 15, 69–81, https://doi.org/10.3354/cr015069, 2000.
Cervi, F., Berti, M., Borgatti, L., Ronchetti, F., Manenti, F., and Corsini,
A.: Comparing predictive capability of statistical and deterministic methods
for landslide susceptibility mapping: a case study in the northern Apennines
(Reggio Emilia Province, Italy), Landslides, 7, 433–444, 2010.
Chang, S.-H. and Chiang, K.-T.: The potential impact of climate change on
typhoon triggered landslides in Taiwan, 2010–2099, Geomorphology, 133,
143–151, https://doi.org/10.1016/j.geomorph.2010.12.028, 2011.
Chung, C. F. and Fabbri, A. G.: Validation of spatial prediction models for
landslide hazard mapping, Nat. Hazards,30, 451–472, https://doi.org/10.1023/B:NHAZ.0000007172.62651.2b, 2003.
Ciabatta, L., Camici, S., Brocca, L., Ponziani, F., Stelluti, F., Berni, N., and Moramarco, T.: Assessing the impact of climate-change scenarios on landslide occurrence in Umbria Region, Italy, J. Hydrol.,
https://doi.org/10.1016/j.jhydrol.2016.02.007, 2016.
Ciurleo, M., Cascini, L., and Calvello, M.: A comparison of statistical and
deterministic methods for shallow landslide susceptibility zoning in clayey
soils, Eng. Geol., 223, 71–81, 2017.
Coe, J. A.: Regional moisture balance control of landslide motion: implications for landslide forecasting in a changing climate, Geology, 40, 323–326, https://doi.org/10.1130/G32897.1, 2012.
Collison, A., Wade, S., Griffiths, J., and Dehn, M.: Modelling the impact of
predicted climate change on landslide frequency and magnitude in SE England,
Eng. Geol., 5, 205–218, https://doi.org/10.1016/s0013-7952(99)00121-0,
2000.
Comegna, L., Picarelli, L., Bucchignani, E., and Mercogliano, P.: Potential
effects of incoming climate changes on the behaviour of slow active
landslides in clay, Landslides, 10, 373–391,
https://doi.org/10.1007/s10346-012-0339-3, 2013.
Corominas, J., van Westen, C., Frattini, P., Cascini, L., Malet, J.-P., Fotopoulou, S., Catani, F., Van Den Eeckhaut, M., Mavrouli, O., Agliardi, F.,
Pitilakis, K., Winter, M. G., Pastor, M., Ferlisi, S., Tofani, V., Hervás, J., and Smith, J. T.: Recommendations for the quantitative analysis of landslide risk, B. Eng. Geol. Environ., 73, 209,
https://doi.org/10.1007/s10064-013-0538-8, 2014.
Crow, P.: The influence of soils and species on tree root depth, Forestry Commission, Edinburgh, Information Note FCINO78, 8 pp., 2005.
Crozier, M. J.: Deciphering the effect of climate
change on landslide activity: a review, Geomorphology 124, 260–267.
https://doi.org/10.1016/j.geomorph.2010.04.009, 2010.
Cruden, D. M. and Varnes, D. J.: Landslides: investigation and mitigation.
Chapter 3-Landslide types and processes, Transportation research board
special report, 247, 1996.
Davis, J. and Blesius, L.: A hybrid physical and maximum-entropy landslide
susceptibility model, Entropy, 17, 4271–4292, 2015.
De Crécy, L.: L'histoire de la RTM: quelques réflexions d'un
praticien, Revue géographique des Pyrénées et du Sud-Ouest, 59, 16–28, https://doi.org/10.3406/rgpso.1988.3103, 1988 (in French).
Dehn, M. and Buma, J.: Modelling future landslide activity based on general
circulation models, Geomorphology, 30, 175–187,
https://doi.org/10.1016/s0169-555x(99)00053-7, 1999.
DRIAS: Drias les futurs du climat, projections climatiques pour
l'adaptation de nos sociétés; Simulations de Jouzel-2014,
données Météo-France, CNRM-GAME, CERFACS, IPSL,
available at: http://www.drias-climat.fr/ (last access: 28 January 2015), 2014.
Fabre, R., Lebourg, T., and Clément, B.: Typologie et modèles de
glissements de terrain: exemples de sites des Pyrénées occidentales
et centrales, Rev. Fr. Geotech., 99, 35–48, https://doi.org/10.1051/geotech/2002099035, 2002 (in French).
Fan, J. C., Yang, C. H., Chang, S. C., Huang, H. Y., and Guo, J. J.: Effects of climate change on the potential of the landslides in the basin of Kaoping
stream, J. Chin. Soil Water Conserv., 44, 335–350, 2013.
Galve, J. P., Cevasco, A., Brandolini, P., and Soldati, M.: Assessment of
shallow landslide risk mitigation measures based on land use planning
through probabilistic modelling, Landslides, 12, 101–114, 2015.
Galve, J. P., Bartelletti, C., Notti, D., Fernández-Chacón, F.,
Barsanti, M., Azañón, J. M., Pérez-Peña, V., Giannecchini,
R., Avanzi, G. D. A., Galanti, Y., and Lamas, F. J.: Deterministic and
Probabilistic Slope Stability Models Forecast Performance at
1:5000-Scal, Workshop on World Landslide Forum, Springer, Cham, 741–774, 2017.
Gariano, S. L. and Guzzetti, F.: Landslides in a changing climate, Earth Sci.
Rev., 162, 227–252, 2016.
Gariano, S. L., Rianna, G., Petrucci, O., and Guzzetti, F.: Assessing future
changes in the occurrence of rainfall-induced landslides at a regional
scale, Sci. Total Environ., 596–597, 417–426,
https://doi.org/10.1016/j.scitotenv.2017.03.103, 2017.
Gariano, S. L., Petrucci, O., Rianna, G., Santini, M., and Guzzetti, F.:
Impacts of past and future land changes on landslides in southern Italy, Reg.
Environ. Change, 18, 437–449, https://doi.org/10.1007/s10113-017-1210-9,
2018.
Gassner, C., Promper, C., Begueria, S., and Glade, T.: Climate change
impact for spatial landslide susceptibility, in: Engineering Geology for Society and Territory Climate Change and Engineering Geology 1, edited by: Lollino, G., Manconi, A., Clague, J., Shan, W., and Chiarle, M., Springer
International Publishing, 429–433, 2015.
Genet, M., Stoke, A., Fourcaud, T., and Norris, J. E.: The influence of plant
diversity on slope stability in a moist evergreen deciduous forest, Ecol.
Eng., 36, 265–275, 2010.
Glade, T.: Landslide occurrence as a response to land use change: a review
of evidence from New Zealand, Catena, 51, 297–314, 2003.
Grandjean, G., Thomas, L., Bernardie, S., and the SAMCO Team: A Novel
Multi-Risk Assessment Web-Tool for Evaluating Future Impacts of Global
Change in Mountainous Areas, Climate, 6, 92,
https://doi.org/10.3390/cli6040092, 2018.
Gruber, M.: Les relations climat-végétation dans les
Pyrénées centrales françaises, Acta Botanica Malacitana, 16,
405–415, 1991 (in French).
Guzzetti, F., Reichenbach, P., Ardizzone, F., Cardinali, M., and Galli, M.:
Estimating the quality of landslide susceptibility models, Geomorphology, 81,
166–184, https://doi.org/10.1016/j.geomorph.2006.04.007, 2006.
Houet, T., Gremont, M., Vacquié, L., Forget, Y., Marriotti, A.,
Puissant, A., Bernardie, S., Thiery, Y., Vandromme, R., and Grandjean, G.:
Downscaling scenarios of future land use and land cover changes using a
participatory approach: an application to mountain risk assessment in the
Pyrenees (France), Reg. Environ. Change, 17, 2293–2307, https://doi.org/10.1007/s10113-017-1171-z, 2017.
Huber, U. M., Bugmann, H. K. M., and Reasoner, M. A.: Global
Change and Mountain Regions: An Overview of Current Knowledge, in: Advances
in Global Change Research, Springer, Berlin, 650 pp., 2005.
IPCC: Climate Change 2007: The Physical Science Basis. Contribution of
Working Group I to the Fourth Assessment Report of the Intergovernmental
Panel on Climate Change, edited by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M., and Miller, H. L., Cambridge University Press, Cambridge, UK and New York, USA, 2007.
IPCC: Intergovernmental Panel on Climate Change: Climate Change 2014:
Synthesis Report. Contribution of Working Groups I, II and III to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change, Geneva,
Switzerland, 151 pp., 2014.
Jakob, M. and Lambert, S.: Climate change effects on landslides along the
southwest coast of British Columbia, Geomorphology, 107, 275–284,
https://doi.org/10.1016/j.geomorph.2008.12.009, 2009.
Ji, J., Kokutse N. K., Genet M., Fourcaud T., and Zhang Z. Q.: Effect of spatial variation of tree root characteristics on slope stability. A case study on Black Locust (Robinia pseudoacacia) and Arborvitae (Platycladus orientalis) stands on the Loess Plateau, China, Catena, 92, 139–154, 2012.
Jomelli, V., Brunstein, D., Déqué, M., Vrac, M., and Grancher, D.:
Impacts of future climatic change (2070–2099) on the potential occurrence
of debris flows: a case study in the Massif des Ecrins (French Alps), Clim.
Change, 97, 171–191, https://doi.org/10.1007/s10584-009-9616-0, 2009.
Kim, K. G., Lee, D. K., Park, C., Kil, S., Son, Y., and Park, J. H.: Evaluating landslide hazards using RCP 4.5 and 8.5 scenarios, Environ. Earth Sci., 73, 1385–1400, https://doi.org/10.1007/s12665-014-3775-7, 2015.
Kirsten, H. A..: Significance of the probability of failure in slope
engineering, Civ. Eng., 25, 17–27, 1983.
Kohler, T. and Maselli, D.: Mountains and Climate Change – From Understanding to Action, published by Geographica Bernensia with the support of the Swiss Agency for Development and Cooperation (SDC), and an international team of contributors, Bern, 2009.
Kokutse, N. K., Temgoua, A. G. T., and Kavazović, Z.: Slope stability and vegetation: Conceptual and numerical investigation of mechanical effects, Ecol. Eng., 86, 146–153, https://doi.org/10.1016/j.ecoleng.2015.11.005, 2016.
Lebourg, T., Fabre, R., Clement, B., and Frappa, M.: High-mountain landslides in the Atlantic Pyrenees: their relationship with the geology and
geomorphology, B. Eng. Geol. Environ., 62, 221–223,
https://doi.org/10.1007/s10064-002-0180-3, 2003a.
Lebourg, T., Riss, J., Fabre, R., and Clément, B.: Morphological
characteristics of till formations in relation with mechanical parameters,
Math. Geol., 35, 835–852, 2003b.
Lee, M.-J., Song, W.-K., Won, J.-S., Park, I., and Lee, S.: Spatial and temporal change in landslide hazard by future climate change scenarios using
probabilistic-based frequency ratio model, Geocarto Int., 29,
639–662, https://doi.org/10.1080/10106049.2013.826739, 2014.
McInnes, R., Jakeways, J., Fairbank, H., and Mathie, E.: Landslides and
Climate Change: Challenges and Solutions, in: Proceedings of the International Conference on Landslides and Climate Change, Ventnor, Isle of Wight, UK, 21–24 May 2007, Taylor & Francis, London, https://doi.org/10.1201/noe0415443180, 2007.
Melchiorre, C. and Frattini, P.: Modelling probability of rainfall-induced
shallow landslides in a changing climate, Otta, Central Norway. Clim. Change,
113, 413–436. https://doi.org/10.1007/s10584-011-0325-0, 2012.
Morgenstern, N. R. and Price, V. E.: The analysis of the stability of general
slip surfaces, Geotechnique, 15, 79–93, 1965.
Morgenstern, R. and Price, V. E.: A numerical method for solving the equations of stability of general slip surfaces, Comput. J., 9, 388–393, 1967.
Moore, R., Carey, J. M., Mc Innes, R. G., and Houghton, J. E. M.: Climate change, so what? Implications for ground movement and landslide event frequency in the Ventor Undercliff, Isle of Wight, in: Landslides and
Climate Change: Challenges and Solutions, in: Proceedings of the International Conference on Landslides and Climate Change, Isle of Wight, UK, 21–24 May 2007, edited by: McInnes, R., Jakeways, J., Fairbank, H., Mathie, E., 335–345, 2007.
Norris J. E., Stokes A., Mickovski S. B., Cammeraat E., van Beek L. P. H, Nicoll B., and Achim, A.: Slope stability and erosion control: ecotechnological solutions, Springer, Dortmund, 2008.
Oliveira, S. C., Zêzere, J. L., Lajas, S., and Melo, R.: Combination of statistical and physically based methods to assess shallow slide susceptibility at the basin scale, Nat. Hazards Earth Syst. Sci., 17, 1091–1109, https://doi.org/10.5194/nhess-17-1091-2017, 2017.
OPCC-CTP: Climate change in the Pyrenees: impacts, vulnerability and adaptation Bases of knowledge for the future adaptation strategy of the Pyrenees, ISBN: 978-84-09-06268-3, 2018.
Pack, R. T.: Assessing Terrain Stability in a GIS using SINMAP, 15th Annual GIS Conference, GIS 2001, p. 9., 2001.
Pack, R. T., Tarboton, D. G., and Goodwin, C. N.: Assessing terrain stability in a GIS using SINMAP, 15th Annual GIS Conference, GIS 2001, Vancouver, p. 9., 2001.
Peres, D. J. and Cancelliere, A.: Modeling impacts of climate change on return period of landslide triggering, J. Hydrol., 567, 420–434,
https://doi.org/10.1016/j.jhydrol.2018.10.036, 2018.
Persichillo, M. G., Bordoni, M., and Meisina, C.: The role of land use changes in the distribution of shallow landslides, Sci. Total Environ., 574, 924–937, https://doi.org/10.1016/j.scitotenv.2016.09.125, 2017.
Pisano, L., Zumpano, V., Malek, Ž., Rosskopf, C. M., and Parise, M.:
Variations in the susceptibility to landslides, as a consequence of land
cover changes: A look to the past, and another towards the future, Sci. Total Environ., 601–602, 1147–1159, https://doi.org/10.1016/j.scitotenv.2017.05.231, 2017.
Promper, C., Puissant, A., Malet, J.-P., and Glade, T.: Analysis of land cover changes in the past and the future as contribution to landslide risk
scenarios, Appl. Geogr., 53, 11–19,
https://doi.org/10.1016/j.apgeog.2014.05.020, 2014.
Reichenbach, P., Mondini, A. C., and Rossi, M.: The influence of land use
change on landslide susceptibility zonation: the Briga catchment test site
(Messina, Italy), Environ. Manage., 54, 1372–1384,
https://doi.org/10.1007/s00267-014-0357-0, 2014.
Remondo, J., González-Díez, A., Soto, J., Díaz de Terán, J. R., and Cendrero, A.: Human impact on geomorphic processes and hazards in mountain areas, Geomorphology, 66, 69–84, 2005.
Rianna, G., Zollo, A. L., Tommasi, P., Paciucci, M., Comegna, L., and Mercogliano, P.: Evaluation of the effects of climate changes on landslide activity of Orvieto clayey slope, Proc. Earth Plan. Sci., 9, 54–63,
https://doi.org/10.1016/j.proeps.2014.06.017, 2014.
Rianna, G., Reder, A., Mercogliano, P., and Pagano, L.: Evaluation of
Variations in Frequency of Landslide Events Affecting Pyroclastic Covers in
Campania Region under the Effect of Climate Changes, Hydrology, 4, 34,
https://doi.org/10.3390/hydrology4030034, 2017.
Robinson, J. D., Vahedifard, F., and AghaKouchak, A.: Rainfall-triggered
slope instabilities under a changing climate:comparative study using
historical and projected precipitation extremes, Can. Geotech. J., 54,
117–127, https://doi.org/10.1139/cgj-2015-0602, 2017.
Sangelantoni, L., Gioia, E., and Marincioni, F.: Impact of climate change on
landslides frequency: the Esino river basin case study (Central Italy), Nat.
Hazards, 93, 849–884, https://doi.org/10.1007/s11069-018-3328-6, 2018.
Schiechtl, H. M.: Bioengineering for land reclamation and conservation, Univ. of Alberta Press, Edmonton, Canada, 1980.
Sedan, O., Desramaut, N., and Vandromme, R.: Logiciel ALICE version 7-Guide
d'utilisateur, BRGM, RP-60004, 2013 (in French).
Shou, K. J. and Yang, C. M.: Predictive analysis of landslide susceptibility
under climate change conditions – a study on the Chingshui River Watershed
of Taiwan, Eng. Geol., 192, 46–62,
https://doi.org/10.1016/j.enggeo.2015.03.012, 2015.
Stoffel, M., Tiranti, D., and Huggel, C.: Climate change impacts on mass
movements – case studies from the European Alps, Sci. Total Environ., 493,
1255–1266, https://doi.org/10.1016/j.scitotenv.2014.02.102, 2014.
Tacher, L. and Bonnard, C.: Hydromechanical modelling of a large landslide
considering climate change conditions, in: Landslides and Climate Change: Challenges and Solutions Proceedings of the International Conference on Landslides and Climate Change, Ventnor, Isle of Wight, UK, 21–24 May 2007, edited by: McInnes, R., Jakeways, J.,
Fairbank, H., and Mathie, E., Taylor & Francis, London, 131–141, 2007.
Thiéry, D.: Logiciel GARDÉNIA version 6.0 – Guide d'utilisation.
BRGM report, RP-52832-FR, 104 pp., 2003 (in French).
Thiery, Y., Malet, J.-P., Sterlacchini, S., Puissant, A., and Maquaire, O.:
Landslide susceptibility assessment by bivariate methods at large scales:
application to a complex mountainous environment, Geomorphology, 9,
38–59, 2007.
Thiery, Y., Vandromme R., Maquaire O., and Bernardie S.: Landslide
Susceptibility Assessment by EPBM (Expert Physically Based Model): Strategy
of Calibration in Complex Environment, in: Advancing Culture of Living with
Landslides, https://doi.org/10.1007/978-3-319-53498-5_104, 917–926, 2017.
Turkington, T., Remaître, A., Ettema, J., Hussin, H., and van Westen, C.: Assessing debris flow activity in a changing climate, Clim. Change, 137, 293–305, https://doi.org/10.1007/s10584-016-1657-6, 2016.
Vanacker, V., Vanderschaeghe, M., Govers, G., Willems, E., Poesen, J.,
Deckers, J., and De Bievre, B.: Linking hydrological, infinite slope
stability and land-use change models through GIS for assessing the impact of
deforestation on slope stability in high Andean watersheds, Geomorphology,
52, 299–315, 2003.
Van Beek, L. P. H. and Van Asch, T. W.: Regional assessment of the effects of
land-use change on landslide hazard by means of physically based modelling,
Nat. Hazards, 31, 289–304, 2004.
Vandromme, R., Desramaut, N., Garnier, C., and Bernardie, S.: A novel approach to integrate effects of vegetation changes on slope stability, in: Lollino, G., Giordan, D., Crosta, G., Corominas, J., Azzam, R., Wasowski, J., and Sciarra, N., Engineering geology for society and territory, Springer, 2, 975–978, 2015.
Van Westen, C. J., Van Asch, T. W. J., and Soeters, R.: Landslide hazard and risk zonation - Why is it still so difficult?, B. Eng. Geol. Environ., 65,
167–184, https://doi.org/10.1007/s10064-005-0023-0, 2006.
Van Westen, C. J., Castellanos, E., and Kuriakose, S. L.: Spatial data for
landslide susceptibility, hazard, and vulnerability assessment: An overview,
Eng. Geol., 102, 112–131, https://doi.org/10.1016/j.enggeo.2008.03.010,
2008.
Varnes, D. J.: Slope Movement Types and Processes, in Special Report 176:
LAndslides: Analysis and Control, edited by Schuster, R. L. and Krizek, R. J. National Research Council, Washington, D.C., USA, 11-33, 1978.
Varnes, D. J. and COMMISSION ON LANDSLIDES AND OTHER MASS MOVEMENTS:
Landslide hazard zonation: a review of principles and practise, in: B. Int. Assoc. Eng. Geol., 23, 13–14, https://doi.org/10.1007/BF02594720, 1984.
Vandromme, R., Thiery, Y., Bernardie, S., and Sedan O.: ALICE (Assessment of
Landslides induced by Climatic Events): a single tool to integrate shallow
and deep landslides for susceptibility and hazard assessment, Geomorphology,
367, 107307, https://doi.org/10.1016/j.geomorph.2020.107307, 2020.
Viers, G.: Climat Et Climats Des Pyrénées Orientales, Ann. Geogr., 96, 339–343, 1987.
Villani, V., Rianna, G., Mercogliano, P., and Zollo, A. L.: Statistical
approaches versus weather generator to downscale RCM outputs to slope scale
for stability assessment: a comparison of performances, Electron. J.
Geotech. Eng., 20, 1495–1515, 2015.
Wieczorek, G. F.: Preparing a detailed landslide-inventory map for hazard
evaluation and reduction, B. Assoc. Eng. Geol., 21, 337–342, 1984.
Winter, M. G. and Shearer, B.: Climate change and landslide hazard and risk in Scotland, in: Engineering Geology for Society and Territory Climate Change and Engineering Geology, edited by: Lollino, G., Manconi, A., Clague, J., Shan, W., and Chiarle, M., Springer International Publishing, 1, 411–414,
2015.
World Meteorological Organization (WMO): General Meteorological Standards
and Recommended Practices, Technical regulations, WMO-no. 49, Vol. 1, ISBN: 978-92-63-10049-8, 2018.
Wu, T. H., McKineel, W. P., and Swanston, D. N.: Strength of the tree roots and landslides on Prince of Wales Island, Alaska, Can. Geotech. J., 16, 19–33, 1979.
Wu, X., Chen, X., Zhan, F. B., and Hong, S.: Global research trends in landslides during 1991–2014: a bibliometric analysis, Landslides, 12, 1215–1226, https://doi.org/10.1007/s10346-015-0624-z, 2015.
Zizioli, D., Meisina, C., Valentino, R., and Montrasio, L.: Comparison between different approaches to modeling shallow landslide susceptibility: a case history in Oltrepo Pavese, Northern Italy, Nat. Hazards Earth Syst. Sci., 13, 559–573, https://doi.org/10.5194/nhess-13-559-2013, 2013.
Short summary
The present study evaluates the impacts of land use and climate change, based on scenarios, on landslide hazards in a Pyrenean valley from the present to 2100.
The results demonstrate the influence of land cover on slope stability through the presence and type of forest. Climate change may have a significant impact because of the increase of the soil water content. The results indicate that the occurrence of landslide hazards in the future is expected to increase.
The present study evaluates the impacts of land use and climate change, based on scenarios, on...
Altmetrics
Final-revised paper
Preprint