Articles | Volume 25, issue 4
https://doi.org/10.5194/nhess-25-1459-2025
© Author(s) 2025. 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-25-1459-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Apr 2025
Research article |
| 16 Apr 2025
| 16 Apr 2025
From rockfall source area identification to susceptibility zonation: a proposed workflow tested on El Hierro (Canary Islands, Spain)
Department of Geohazards and Climate Change, Geological and Mining Institute of Spain (IGME-CSIC), Ríos Rosas 23, 28003 Madrid, Spain
Mauro Rossi
Research Institute for Geo-Hydrological Protection (IRPI-CNR), Via Madonna Alta 126, 06128 Perugia, Italy
Paola Reichenbach
Research Institute for Geo-Hydrological Protection (IRPI-CNR), Via Madonna Alta 126, 06128 Perugia, Italy
Rosa María Mateos
Department of Geohazards and Climate Change, Geological and Mining Institute of Spain (IGME-CSIC), Urb. Alcázar del Genil, Edificio Zulema, bajos, 18010 Granada, Spain
Related authors
Juan Antonio Luque-Espinar, Rosa María Mateos, Roberto Sarro, Eduardo Peña, Cristina Reyes-Carmona, and Mónica Martínez-Corbella
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2022-50, https://doi.org/10.5194/nhess-2022-50, 2022
Manuscript not accepted for further review
Short summary
Short summary
A spatio-temporal analysis of climate cycles on the island of Majorca (Spain) and their correlation with the landslide inventory is presented. Using spectral analysis techniques, the main climatic cycles that control the rainy periods on the island have been identified. Using geostatistical methods, the distribution of rainfall was mapped, as was the spatial representation of the statistical significance of the different natural cycles to define the areas of greatest danger from heavy rainfall.
Massimo Melillo, Stefano Luigi Gariano, Silvia Peruccacci, Roberto Sarro, Rosa Marìa Mateos, and Maria Teresa Brunetti
Nat. Hazards Earth Syst. Sci., 20, 2307–2317, https://doi.org/10.5194/nhess-20-2307-2020, https://doi.org/10.5194/nhess-20-2307-2020, 2020
Short summary
Short summary
In the Canary Islands, a link between rainfall and rockfall occurrence is found for most of the year, except for the warm season. Empirical rainfall thresholds for rockfalls are first proposed for Gran Canaria and Tenerife, and the dependence of the thresholds on the mean annual rainfall is discussed. The use of thresholds in early-warning systems might contribute to the mitigation of the rockfall hazard in the archipelago and reduce the associated risk.
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.
Oriol Monserrat, Anna Barra, Marta Béjar-Pizarro, Jonathan S. Rivera, Jorge Pedro Galve, Carolina Guardiola, Maria Cuevas-González, Rosa Maria Mateos, Pablo Ezquerro, Jose Miguel Azañon, Saeedeh Shahbazi, Jose Navarro, Michele Crosetto, and Guido Luzi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 351–356, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-351-2024, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-351-2024, 2024
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
Short summary
Short summary
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.
Luca Schilirò, Mauro Rossi, Federica Polpetta, Federica Fiorucci, Carolina Fortunato, and Paola Reichenbach
Nat. Hazards Earth Syst. Sci., 23, 1789–1804, https://doi.org/10.5194/nhess-23-1789-2023, https://doi.org/10.5194/nhess-23-1789-2023, 2023
Short summary
Short summary
We present a database of the main scientific articles published on earthquake-triggered landslides in the last 4 decades. To enhance data viewing, the articles were catalogued into a web-based GIS, which was specifically designed to show different types of information, such as bibliometric information, the relevant topic and sub-topic category (or categories), and earthquake(s) addressed. Such information can be useful to obtain a general overview of the topic, especially for a broad readership.
Mauro Rossi, Txomin Bornaetxea, and Paola Reichenbach
Geosci. Model Dev., 15, 5651–5666, https://doi.org/10.5194/gmd-15-5651-2022, https://doi.org/10.5194/gmd-15-5651-2022, 2022
Short summary
Short summary
LAND-SUITE is a software package designed to support landslide susceptibility zonation. The software integrates, extends, and completes LAND-SE (Rossi et al., 2010; Rossi and Reichenbach, 2016). The software is implemented in R, a free software environment for statistical computing and graphics, and gives expert users the possibility to perform easier, more flexible, and more informed statistically based landslide susceptibility applications and zonations.
Juan Antonio Luque-Espinar, Rosa María Mateos, Roberto Sarro, Eduardo Peña, Cristina Reyes-Carmona, and Mónica Martínez-Corbella
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2022-50, https://doi.org/10.5194/nhess-2022-50, 2022
Manuscript not accepted for further review
Short summary
Short summary
A spatio-temporal analysis of climate cycles on the island of Majorca (Spain) and their correlation with the landslide inventory is presented. Using spectral analysis techniques, the main climatic cycles that control the rainy periods on the island have been identified. Using geostatistical methods, the distribution of rainfall was mapped, as was the spatial representation of the statistical significance of the different natural cycles to define the areas of greatest danger from heavy rainfall.
Giuseppe Esposito, Ivan Marchesini, Alessandro Cesare Mondini, Paola Reichenbach, Mauro Rossi, and Simone Sterlacchini
Nat. Hazards Earth Syst. Sci., 20, 2379–2395, https://doi.org/10.5194/nhess-20-2379-2020, https://doi.org/10.5194/nhess-20-2379-2020, 2020
Short summary
Short summary
In this article, we present an automatic processing chain aimed to support the detection of landslides that induce sharp land cover changes. The chain exploits free software and spaceborne SAR data, allowing the systematic monitoring of wide mountainous regions exposed to mass movements. In the test site, we verified a general accordance between the spatial distribution of seismically induced landslides and the detected land cover changes, demonstrating its potential use in emergency management.
Massimo Melillo, Stefano Luigi Gariano, Silvia Peruccacci, Roberto Sarro, Rosa Marìa Mateos, and Maria Teresa Brunetti
Nat. Hazards Earth Syst. Sci., 20, 2307–2317, https://doi.org/10.5194/nhess-20-2307-2020, https://doi.org/10.5194/nhess-20-2307-2020, 2020
Short summary
Short summary
In the Canary Islands, a link between rainfall and rockfall occurrence is found for most of the year, except for the warm season. Empirical rainfall thresholds for rockfalls are first proposed for Gran Canaria and Tenerife, and the dependence of the thresholds on the mean annual rainfall is discussed. The use of thresholds in early-warning systems might contribute to the mitigation of the rockfall hazard in the archipelago and reduce the associated risk.
José Luis Pastor, Joaquín Mulas, Roberto Tomás, Gerardo Herrera, José Antonio Fernández-Merodo, Marta Béjar-Pizarro, Luis Jordá, Juan Carlos García López-Davalillos, Ramón Aragón, and Rosa María Mateos
Proc. IAHS, 382, 663–667, https://doi.org/10.5194/piahs-382-663-2020, https://doi.org/10.5194/piahs-382-663-2020, 2020
Michele Santangelo, Massimiliano Alvioli, Marco Baldo, Mauro Cardinali, Daniele Giordan, Fausto Guzzetti, Ivan Marchesini, and Paola Reichenbach
Nat. Hazards Earth Syst. Sci., 19, 325–335, https://doi.org/10.5194/nhess-19-325-2019, https://doi.org/10.5194/nhess-19-325-2019, 2019
Short summary
Short summary
The paper discusses the use of rockfall modelling software and photogrammetry applied to images acquired by RPAS to provide support to civil protection agencies during emergency response. The paper focuses on a procedure that was applied to define the residual rockfall risk for a road that was hit by an earthquake-triggered rockfall that occurred during the seismic sequence that hit central Italy on 24 August 2016. Road reopening conditions were decided based on the results of this study.
Txomin Bornaetxea, Mauro Rossi, Ivan Marchesini, and Massimiliano Alvioli
Nat. Hazards Earth Syst. Sci., 18, 2455–2469, https://doi.org/10.5194/nhess-18-2455-2018, https://doi.org/10.5194/nhess-18-2455-2018, 2018
Short summary
Short summary
While producing a landslide susceptibility map using a fieldwork-based landslide inventory and a logistic regression model, two crucial questions came to our minds. (i) Shall we consider unsurveyed regions of the study area, for which landslide absence is typically assumed? (ii) Which reference mapping unit should be used in our model? So we compared four maps and found that rejecting unsurveyed regions together with slope units as reference mapping unit should be the best option.
O. Monserrat, A. Barra, G. Herrera, S. Bianchini, C. Lopez, R. Onori, P. Reichenbach, R. Sarro, R. M. Mateos, L. Solari, S. Ligüérzana, and I. P. Carralero
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W4, 351–355, https://doi.org/10.5194/isprs-archives-XLII-3-W4-351-2018, https://doi.org/10.5194/isprs-archives-XLII-3-W4-351-2018, 2018
Federica Fiorucci, Daniele Giordan, Michele Santangelo, Furio Dutto, Mauro Rossi, and Fausto Guzzetti
Nat. Hazards Earth Syst. Sci., 18, 405–417, https://doi.org/10.5194/nhess-18-405-2018, https://doi.org/10.5194/nhess-18-405-2018, 2018
Short summary
Short summary
This paper describes the criteria for the optimal selection of remote sensing images to map event landslides, discussing the ability of monoscopic and stereoscopic VHR satellite images and ultra-high-resolution UAV images to resolve the landslide photographical and morphological signatures. The findings can be useful to decide on the optimal imagery and technique to be used when planning the production of a landslide inventory map.
Liesbet Jacobs, Olivier Dewitte, Jean Poesen, John Sekajugo, Adriano Nobile, Mauro Rossi, Wim Thiery, and Matthieu Kervyn
Nat. Hazards Earth Syst. Sci., 18, 105–124, https://doi.org/10.5194/nhess-18-105-2018, https://doi.org/10.5194/nhess-18-105-2018, 2018
Short summary
Short summary
While country-specific, continental and global susceptibility maps are increasingly available, local and regional susceptibility studies remain rare in remote and data-poor settings. Here, we provide a landslide susceptibility assessment for the inhabited region of the Rwenzori Mountains. We find that higher spatial resolutions do not necessarily lead to better models and that models built for local case studies perform better than aggregated susceptibility assessments on the regional scale.
Maria Elena Martinotti, Luca Pisano, Ivan Marchesini, Mauro Rossi, Silvia Peruccacci, Maria Teresa Brunetti, Massimo Melillo, Giuseppe Amoruso, Pierluigi Loiacono, Carmela Vennari, Giovanna Vessia, Maria Trabace, Mario Parise, and Fausto Guzzetti
Nat. Hazards Earth Syst. Sci., 17, 467–480, https://doi.org/10.5194/nhess-17-467-2017, https://doi.org/10.5194/nhess-17-467-2017, 2017
Short summary
Short summary
We studied a period of torrential rain between 1 and 6 September 2014 in the Gargano Promontory, Puglia, southern Italy, which caused a variety of geohydrological hazards, including landslides, flash floods, inundations and sinkholes. We used the rainfall and the landslide information available to us to design and test the new ensemble – non-exceedance probability (E-NEP) algorithm for the quantitative evaluation of the probability of the occurrence of rainfall-induced landslides.
Massimiliano Alvioli, Ivan Marchesini, Paola Reichenbach, Mauro Rossi, Francesca Ardizzone, Federica Fiorucci, and Fausto Guzzetti
Geosci. Model Dev., 9, 3975–3991, https://doi.org/10.5194/gmd-9-3975-2016, https://doi.org/10.5194/gmd-9-3975-2016, 2016
Short summary
Short summary
Slope units are morphological mapping units bounded by drainage and divide lines that maximize within-unit homogeneity and between-unit heterogeneity. We use r.slopeunits, a software for the automatic delination of slope units. We outline an objective procedure to optimize the software input parameters for landslide susceptibility (LS) zonation. Optimization is achieved by maximizing an objective function that simultaneously evaluates terrain aspect segmentation quality and LS model performance.
Mauro Rossi and Paola Reichenbach
Geosci. Model Dev., 9, 3533–3543, https://doi.org/10.5194/gmd-9-3533-2016, https://doi.org/10.5194/gmd-9-3533-2016, 2016
Short summary
Short summary
Landslide susceptibility maps show places where landslides may occur in the future. These maps are prepared using different approaches, information on past landslides distribution and a variety of geo-environmental data. The paper describes LAND-SE (LANDslide Susceptibility Evaluation), an open-source software coded in R for statistically based susceptibility zonation that provides estimates of model performances and uncertainty. A user guide and example data are distributed with the software.
R. Schlögel, J.-P. Malet, P. Reichenbach, A. Remaître, and C. Doubre
Nat. Hazards Earth Syst. Sci., 15, 2369–2389, https://doi.org/10.5194/nhess-15-2369-2015, https://doi.org/10.5194/nhess-15-2369-2015, 2015
Short summary
Short summary
The paper proposes an approach to prepare a multi-date landslide inventory for the Ubaye valley (French Alps), a complex mountainous area affected by several landslide types with different degrees of activity. The inventory covering the period 1956-2010 have been analysed in order to quantify the uncertainties associated to the mapping, to measure the evolution of morphological indicators and to estimate temporal occurrence. Evolution of landslide activity is compared to other inventory sources.
M. Mergili, I. Marchesini, M. Alvioli, M. Metz, B. Schneider-Muntau, M. Rossi, and F. Guzzetti
Geosci. Model Dev., 7, 2969–2982, https://doi.org/10.5194/gmd-7-2969-2014, https://doi.org/10.5194/gmd-7-2969-2014, 2014
Short summary
Short summary
The article deals with strategies to (i) reduce computation time and to (ii) appropriately account for uncertain input parameters when applying an open source GIS sliding surface model to estimate landslide susceptibility for a 90km² study area in central Italy. For (i), the area is split into a large number of tiles, enabling the exploitation of multi-processor computing environments. For (ii), the model is run with various parameter combinations to compute the slope failure probability.
G. Vessia, M. Parise, M. T. Brunetti, S. Peruccacci, M. Rossi, C. Vennari, and F. Guzzetti
Nat. Hazards Earth Syst. Sci., 14, 2399–2408, https://doi.org/10.5194/nhess-14-2399-2014, https://doi.org/10.5194/nhess-14-2399-2014, 2014
I. Marchesini, F. Ardizzone, M. Alvioli, M. Rossi, and F. Guzzetti
Nat. Hazards Earth Syst. Sci., 14, 2215–2231, https://doi.org/10.5194/nhess-14-2215-2014, https://doi.org/10.5194/nhess-14-2215-2014, 2014
S. Raia, M. Alvioli, M. Rossi, R. L. Baum, J. W. Godt, and F. Guzzetti
Geosci. Model Dev., 7, 495–514, https://doi.org/10.5194/gmd-7-495-2014, https://doi.org/10.5194/gmd-7-495-2014, 2014
Related subject area
Landslides and Debris Flows Hazards
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)
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
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
Exploring implications of input parameter uncertainties on GLOF modelling results using the state-of-the-art modelling code, r.avaflow
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
Lessons learnt from a rockfall time series analysis: data collection, statistical analysis, and applications
The concept of event-size-dependent exhaustion and its application to paraglacial rockslides
Coastal earthquake-induced landslide susceptibility during the 2016 Mw 7.8 Kaikōura earthquake, New Zealand
Characteristics of debris flows recorded in the Shenmu area of central Taiwan between 2004 and 2021
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.
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.
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.
Sonam Rinzin, Stuart Dunning, Rachel Carr, Ashim Sattar, and Martin Mergili
EGUsphere, https://doi.org/10.5194/egusphere-2024-1819, https://doi.org/10.5194/egusphere-2024-1819, 2024
Short summary
Short summary
We evaluated the sensitivity of model outputs to input parameter uncertainties by performing multiple GLOF simulations using the r.avaflow model. We found out that GLOF modelling outputs are highly sensitive to six parameters: volume of mass movements entering lakes, DEM datasets, origin of mass movements, mesh size, basal frictional angle, and entrainment coefficient. Future modelling should carefully consider the output uncertainty from these sensitive parameters.
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
Alqadhi, S., Mallick, J., Talukdar, S., Bindajam, A. A., Van Hong, N., and Saha, T. K.: Selecting optimal conditioning parameters for landslide susceptibility: an experimental research on Aqabat Al-Sulbat, Saudi Arabia, Environ. Sci. Pollut. Res., 29, 3743–3762, https://doi.org/10.1007/s11356-021-15886-z, 2022.
Alvioli, M., Santangelo, M., Fiorucci, F., Cardinali, M., Marchesini, I., Reichenbach, P., Rossi, M., Guzzetti, F., and Peruccacci, S.: Rockfall susceptibility and network-ranked susceptibility along the Italian railway, Eng. Geol., 293, 106301, https://doi.org/10.1016/j.enggeo.2021.106301, 2021.
Baeza, C., Lantada, N., and Amorim, S.: Statistical and spatial analysis of landslide susceptibility maps with different classification systems, Environ. Earth Sci., 75, 1318, https://doi.org/10.1007/s12665-016-6124-1, 2016.
BDMoves: http://info.igme.es/BD2DMoves/ (last access: 14 May 2024), 2024.
Borella, J., Quigley, M., Krauss, Z., Lincoln, K., Attanayake, J., Stamp, L., Lanman, H., Levine, S., Hampton, S., and Gravley, D.: Geologic and geomorphic controls on rockfall hazard: how well do past rockfalls predict future distributions?, Nat. Hazards Earth Syst. Sci., 19, 2249–2280, https://doi.org/10.5194/nhess-19-2249-2019, 2019.
Cantarino, I., Carrion, M. A., Goerlich, F., and Martinez Ibañez, V.: A ROC analysis-based classification method for landslide susceptibility maps, Landslides, 16, 265–282, https://doi.org/10.1007/s10346-018-1063-4, 2019.
Carlà, T., Nolesini, T., Solari, L., Rivolta, C., Dei Cas, L., and Casagli, N.: Rockfall forecasting and risk management along a major transportation corridor in the Alps through ground-based radar interferometry, Landslides, 16, 1425–1435, https://doi.org/10.1007/s10346-019-01190-y, 2019.
Centro de Descargas del CNIG (IGN): https://centrodedescargas.cnig.es/CentroDescargas/index.jsp (last access: 9 May 2024), 2024.
Crosta, G. B., Agliardi, F., Frattini, P., and Lari, S.: Key Issues in Rock Fall Modeling, Hazard and Risk Assessment for Rockfall Protection, in: Engineering Geology for Society and Territory – Volume 2, edited by: Lollino, G., Giordan, D., Crosta, G. B., Corominas, J., Azzam, R., Wasowski, J., and Sciarra, N., Springer International Publishing, Cham, 43–58, https://doi.org/10.1007/978-3-319-09057-3_4, 2015.
Dhiman, R. K. and Thakur, M.: Rockfall Hazard Assessment Using RAMMS for the SE Facing Escarpment of Manikaran, Himachal Pradesh, India, in: Recent Technologies for Disaster Management and Risk Reduction: Sustainable Community Resilience & Responses, edited by: Rai, P. K., Singh, P., and Mishra, V. N., Springer International Publishing, Cham, 57–74, https://doi.org/10.1007/978-3-030-76116-5_4, 2021.
Dinçer, İ., Orhan, A., Frattini, P., and Crosta, G. B.: Rockfall at the heritage site of the Tatlarin Underground City (Cappadocia, Turkey), Nat. Hazards, 82, 1075–1098, https://doi.org/10.1007/s11069-016-2234-z, 2016.
Ding, Y., Wu, Y., Zhu, Q., Zhang, L., Sun, Q., and Wang, W.: Virtual geographic environment-based integrated rockfall risk simulation method for canyon bridges, T. GIS, 27, 797–820, https://doi.org/10.1111/tgis.13046, 2023.
Dorren, L., Schaller, C., Erbach, A., and Moos, C.: Automated Delimitation of Rockfall Hazard Indication Zones Using High-Resolution Trajectory Modelling at Regional Scale, Geosciences, 13, 182, https://doi.org/10.3390/geosciences13060182, 2023.
Fanos, A. M. and Pradhan, B.: A Novel Hybrid Machine Learning-Based Model for Rockfall Source Identification in Presence of Other Landslide Types Using LiDAR and GIS, Earth Syst. Environ., 3, 491–506, https://doi.org/10.1007/s41748-019-00114-z, 2019.
Farvacque, M., Lopez-Saez, J., Corona, C., Toe, D., Bourrier, F., and Eckert, N.: How is rockfall risk impacted by land-use and land-cover changes? Insights from the French Alps, Global Planet. Change, 174, 138–152, https://doi.org/10.1016/j.gloplacha.2019.01.009, 2019.
Fernandez-Hernández, M., Paredes, C., Castedo, R., Llorente, M., and de la Vega-Panizo, R.: Rockfall detachment susceptibility map in El Hierro Island, Canary Islands, Spain, Nat. Hazards, 64, 1247–1271, https://doi.org/10.1007/s11069-012-0295-1, 2012.
Francioni, M., Antonaci, F., Sciarra, N., Robiati, C., Coggan, J., Stead, D., and Calamita, F.: Application of Unmanned Aerial Vehicle Data and Discrete Fracture Network Models for Improved Rockfall Simulations, Remote Sensing, 12, 2053, https://doi.org/10.3390/rs12122053, 2020.
Frattini, P., Crosta, G., Carrara, A., and Agliardi, F.: Assessment of rockfall susceptibility by integrating statistical and physically-based approaches, Geomorphology, 94, 419–437, https://doi.org/10.1016/j.geomorph.2006.10.037, 2008.
Frattini, P., Crosta, G. B., and Agliardi, F.: Rockfall characterization and modeling, in: Landslides, edited by: Clague, J. J. and Stead, D., Cambridge University Press, https://doi.org/10.1017/cbo9780511740367.023, 2013.
Fu, H., Chen, W., and Fu, J.: Rockfall mechanisms and block theoretical stability analysis, in: Rock Mechanics and Engineering, Elsevier, 89–125, https://doi.org/10.1016/B978-0-12-822424-3.00003-7, 2021.
Gallo, I. G., Martínez-Corbella, M., Sarro, R., Iovine, G., López-Vinielles, J., Hérnandez, M., Robustelli, G., Mateos, R. M., and García-Davalillo, J. C.: An Integration of UAV-Based Photogrammetry and 3D Modelling for Rockfall Hazard Assessment: The Cárcavos Case in 2018 (Spain), Remote Sensing, 13, 3450, https://doi.org/10.3390/rs13173450, 2021.
Gariano, S. L., Brunetti, M. T., Iovine, G., Melillo, M., Peruccacci, S., Terranova, O., Vennari, C., and Guzzetti, F.: Calibration and validation of rainfall thresholds for shallow landslide forecasting in Sicily, southern Italy, Geomorphology, 228, 653–665, https://doi.org/10.1016/j.geomorph.2014.10.019, 2015.
Giordan, D., Adams, M. S., Aicardi, I., Alicandro, M., Allasia, P., Baldo, M., De Berardinis, P., Dominici, D., Godone, D., Hobbs, P., Lechner, V., Niedzielski, T., Piras, M., Rotilio, M., Salvini, R., Segor, V., Sotier, B., and Troilo, F.: The use of unmanned aerial vehicles (UAVs) for engineering geology applications, B. Eng. Geol. Environ., 79, 3437–3481, https://doi.org/10.1007/s10064-020-01766-2, 2020.
Guzzetti, F., Crosta, G., Detti, R., and Agliardi, F.: STONE: a computer program for the three-dimensional simulation of rock-falls, Comput. Geosci., 28, 1079–1093, https://doi.org/10.1016/S0098-3004(02)00025-0, 2002.
Guzzetti, F., Reichenbach, P., and Wieczorek, G. F.: Rockfall hazard and risk assessment in the Yosemite Valley, California, USA, Nat. Hazards Earth Syst. Sci., 3, 491–503, https://doi.org/10.5194/nhess-3-491-2003, 2003.
Instituto Canario de Estadística (ISTAC): http://www.gobiernodecanarias.org/istac/ (last access: 8 December 2021), 2021.
Jaboyedoff, M. and Labiouse, V.: Technical Note: Preliminary estimation of rockfall runout zones, Nat. Hazards Earth Syst. Sci., 11, 819–828, https://doi.org/10.5194/nhess-11-819-2011, 2011.
Kakavas, M. P., Nikolakopoulos, K. G., Kyriou, A., and Koukouvelas, I.: The Influence of the DSM Spatial Resolution in Rockfall Simulation and Validation with In Situ Data, Geosciences, 13, 57, https://doi.org/10.3390/geosciences13020057, 2023.
Lanfranconi, C., Sala, G., Frattini, P., Crosta, G. B., and Valagussa, A.: Assessing the rockfall protection efficiency of forests at the regional scale, Landslides, 17, 2703–2721, https://doi.org/10.1007/s10346-020-01458-8, 2020.
Larcher, V., Simoni, S., Pasquazzo, R., Strada, C., Zampedrio, G., and Berger, F.: PARAmount: WP6 Guidelines, Rockfall and Forecast systems, 2012.
Lee, J.-J., Song, M.-S., Yun, H.-S., and Yum, S.-G.: Dynamic landslide susceptibility analysis that combines rainfall period, accumulated rainfall, and geospatial information, Scientific Reports, 12, 18429, https://doi.org/10.1038/s41598-022-21795-z, 2022.
Liu, H., Wang, X., Liao, X., Sun, J., and Zhang, S.: Rockfall Investigation and Hazard Assessment from Nang County to Jiacha County in Tibet, Applied Sciences, 10, 247, https://doi.org/10.3390/app10010247, 2020.
Loye, A., Pedrazzini, A., and Jaboyedoff, M.: Regional indicative rockfall map using LIDAR based slope frequency histogram and conefall modelling, in: Interdisciplinary Workshop on Rockfall Protection: Morschach, Switzerland, Proceedings, 64–66, 2008.
Loye, A., Jaboyedoff, M., and Pedrazzini, A.: Identification of potential rockfall source areas at a regional scale using a DEM-based geomorphometric analysis, Nat. Hazards Earth Syst. Sci., 9, 1643–1653, https://doi.org/10.5194/nhess-9-1643-2009, 2009.
Mateos, R. M., García-Moreno, I., Reichenbach, P., Herrera, G., Sarro, R., Rius, J., Aguiló, R., and Fiorucci, F.: Calibration and validation of rockfall modelling at regional scale: application along a roadway in Mallorca (Spain) and organization of its management, Landslides, 13, 751–763, https://doi.org/10.1007/s10346-015-0602-5, 2016.
Mateos, R. M., López-Vinielles, J., Poyiadji, E., Tsagkas, D., Sheehy, M., Hadjicharalambous, K., Liscák, P., Podolski, L., Laskowicz, I., Iadanza, C., Gauert, C., Todorović, S., Auflič, M. J., Maftei, R., Hermanns, R. L., Kociu, A., Sandić, C., Mauter, R., Sarro, R., Béjar, M., and Herrera, G.: Integration of landslide hazard into urban planning across Europe, Landscape Urban Plan., 196, 103740, https://doi.org/10.1016/j.landurbplan.2019.103740, 2020.
Michoud, C., Derron, M.-H., Horton, P., Jaboyedoff, M., Baillifard, F.-J., Loye, A., Nicolet, P., Pedrazzini, A., and Queyrel, A.: Rockfall hazard and risk assessments along roads at a regional scale: example in Swiss Alps, Nat. Hazards Earth Syst. Sci., 12, 615–629, https://doi.org/10.5194/nhess-12-615-2012, 2012.
Muzzillo, R., Losasso, L., and Sdao, F.: Rockfall Source Areas Assessment in an Area of the Pollino National Park (Southern Italy), in: Computational Science and Its Applications – ICCSA 2018, vol. 10962, edited by: Gervasi, O., Murgante, B., Misra, S., Stankova, E., Torre, C. M., Rocha, A. M. A. C., Taniar, D., Apduhan, B. O., Tarantino, E., and Ryu, Y., Springer International Publishing, Cham, 366–379, https://doi.org/10.1007/978-3-319-95168-3_25, 2018.
Nanehkaran, Y. A., Licai, Z., Chen, J., Azarafza, M., and Yimin, M.: Application of artificial neural networks and geographic information system to provide hazard susceptibility maps for rockfall failures, Environ. Earth Sci., 81, 475, https://doi.org/10.1007/s12665-022-10603-6, 2022.
Noël, F., Cloutier, C., Jaboyedoff, M., and Locat, J.: Impact-Detection Algorithm That Uses Point Clouds as Topographic Inputs for 3D Rockfall Simulations, Geosciences, 11, 188, https://doi.org/10.3390/geosciences11050188, 2021.
Noël, F., Nordang, S. F., Jaboyedoff, M., Digout, M., Guerin, A., Locat, J., and Matasci, B.: Comparing Flow-R, Rockyfor3D and RAMMS to Rockfalls from the Mel de la Niva Mountain: A Benchmarking Exercise, Geosciences, 13, 200, https://doi.org/10.3390/geosciences13070200, 2023.
Omran, A., Fahmida, K., Schröder, D., Arnous, M. O., El-Rayes, A. E., and Hochschild, V.: GIS-based rockfall hazard zones modeling along the coastal Gulf of Aqaba Region, Egypt, Earth Sci. Inform., 14, 691–709, https://doi.org/10.1007/s12145-021-00580-y, 2021.
Othman, A., Shaaban, F., Abotalib, A. Z., El-Saoud, W. A., Gabr, S. S., Habeebullah, T., and Hegazy, D.: Hazard Assessment of Rockfalls in Mountainous Urban Areas, Western Saudi Arabia, Arab. J. Sci. Eng., 46, 5717–5731, https://doi.org/10.1007/s13369-020-05098-x, 2021.
Paredes, C., Sarro, R., and Ramos, M.: Estimación preliminar de los alcances por caída de bloques en la sierra de La Cabrera, Madrid, España, Rev. Mex. Cienc. Geol., 32, 475–491, 2015.
Pérez-Rey, I., Riquelme, A., González-deSantos, L. M., Estévez-Ventosa, X., Tomás, R., and Alejano, L.: A multi-approach rockfall hazard assessment on a weathered granite natural rock slope, Landslides, 16, 2005–2015, https://doi.org/10.1007/s10346-019-01208-5, 2019.
Pfeiffer, T. J.: Application of Rockfall Simulation to Risk Analysis, 70th Highway Geology Symposium, Portland Oregon, United States, 21–24 October 2019, https://trid.trb.org/View/1692609 (last access: 15 April 2025), 2019.
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, https://doi.org/10.1016/j.earscirev.2018.03.001, 2018.
Robiati, C., Eyre, M., Vanneschi, C., Francioni, M., Venn, A., and Coggan, J.: Application of Remote Sensing Data for Evaluation of Rockfall Potential within a Quarry Slope, ISPRS Int. J. Geo-Inf., 8, 367, https://doi.org/10.3390/ijgi8090367, 2019.
Rossi, M. and Bornaetxea, T.: LAND-SUITE V1.0: a suite of tools for statistically-based landslide susceptibility zonation, Version 1.0, Zenodo [code], https://doi.org/10.5281/zenodo.5650810, 2021.
Rossi, M. and Reichenbach, P.: LAND-SE: a software for statistically based landslide susceptibility zonation, version 1.0, Geosci. Model Dev., 9, 3533–3543, https://doi.org/10.5194/gmd-9-3533-2016, 2016.
Rossi, M., Guzzetti, F., Reichenbach, P., Mondini, A. C., and Peruccacci, S.: Optimal landslide susceptibility zonation based on multiple forecasts, Geomorphology, 114, 129–142, https://doi.org/10.1016/j.geomorph.2009.06.020, 2010.
Rossi, M., Sarro, R., Reichenbach, P., and Mateos, R. M.: Probabilistic identification of rockfall source areas at regional scale in El Hierro (Canary Islands, Spain), Geomorphology, 381, 107661, https://doi.org/10.1016/j.geomorph.2021.107661, 2020.
Rossi, M., Bornaetxea, T., and Reichenbach, P.: LAND-SUITE V1.0: a suite of tools for statistically based landslide susceptibility zonation, Geosci. Model Dev., 15, 5651–5666, https://doi.org/10.5194/gmd-15-5651-2022, 2022.
Santangelo, M., Marchesini, I., Bucci, F., Cardinali, M., Cavalli, M., Crema, S., Marchi, L., Alvioli, M., and Guzzetti, F.: Exposure to landslides in rural areas in Central Italy, J. Maps, 17, 124–132, https://doi.org/10.1080/17445647.2020.1746699, 2020.
Santos, P. P., Reyes-Carmona, C., Pereira, S., Sarro, R., Martínez-Corbella, M., Coll-Ramis, M. À., Zêzere, J. L., and Mateos, R. M.: Seasonal rockfall risk analysis in a touristic island: Application to the Tramuntana Range (Mallorca, Spain), Int. J. Disast. Risk Re., 101, 104264, https://doi.org/10.1016/j.ijdrr.2024.104264, 2024.
Sarro, R., Mateos, R. M., García-Moreno, I., Herrera, G., Reichenbach, P., Laín, L., and Paredes, C.: The Son Poc rockfall (Mallorca, Spain) on the 6th of March 2013: 3D simulation, Landslides, 11, 493–503, https://doi.org/10.1007/s10346-014-0487-8, 2014.
Sarro, R., Riquelme, A., García-Davalillo, J. C., Mateos, R. M., Tomás, R., Pastor, J. L., Cano, M., and Herrera, G.: Rockfall simulation based on UAV photogrammetry data obtained during an emergency declaration: Application at a cultural heritage site, Remote Sens., 10, 1923, https://doi.org/10.3390/rs10121923, 2018.
Sarro, R., Mateos, R. M., Reichenbach, P., Aguilera, H., Riquelme, A., Hernández-Gutiérrez, L. E., Martín, A., Barra, A., Solari, L., Monserrat, O., Alvioli, M., Fernández-Merodo, J. A., López-Vinielles, J., and Herrera, G.: Geotechnics for rockfall assessment in the volcanic island of Gran Canaria (Canary Islands, Spain), J. Maps, 16, 605–613, https://doi.org/10.1080/17445647.2020.1806125, 2020.
Sarro, R., Pérez-Rey, I., Tomás, R., Alejano, L. R., Hernández-Gutiérrez, L. E., and Mateos, R. M.: Effects of Wildfire on Rockfall Occurrence: A Review through Actual Cases in Spain, Appl. Sci., 11, 2545, https://doi.org/10.3390/app11062545, 2021.
Scavia, C., Barbero, M., Castelli, M., Marchelli, M., Peila, D., Torsello, G., and Vallero, G.: Evaluating Rockfall Risk: Some Critical Aspects, Geosciences, 10, 98, https://doi.org/10.3390/geosciences10030098, 2020.
Straub, D. and Schubert, M.: Modeling and managing uncertainties in rock-fall hazards, Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 2, 1–15, https://doi.org/10.1080/17499510701835696, 2008.
Tehrani, F. S., Calvello, M., Liu, Z., Zhang, L., and Lacasse, S.: Machine learning and landslide studies: recent advances and applications, Nat. Hazards, 114, 1197–1245, https://doi.org/10.1007/s11069-022-05423-7, 2022.
Wang, L.-J., Guo, M., Sawada, K., Lin, J., and Zhang, J.: A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network, Geosci. J., 20, 117–136, https://doi.org/10.1007/s12303-015-0026-1, 2016.
Wang, X., Liu, H., and Sun, J.: A New Approach for Identification of Potential Rockfall Source Areas Controlled by Rock Mass Strength at a Regional Scale, Remote Sensing, 13, 938, https://doi.org/10.3390/rs13050938, 2021.
Wickham, H.: Data Analysis, in: ggplot2, Springer International Publishing, Cham, 189–201, https://doi.org/10.1007/978-3-319-24277-4_9, 2016.
Yan, J., Chen, J., Tan, C., Zhang, Y., Liu, Y., Zhao, X., and Wang, Q.: Rockfall source areas identification at local scale by integrating discontinuity-based threshold slope angle and rockfall trajectory analyses, Eng. Geol., 313, 106993, https://doi.org/10.1016/j.enggeo.2023.106993, 2023.
Yang, X., Zhang, G., Yu, Y., Yu, Q., Lei, M., and Ding, B.: Factors Influencing the Coefficient of Restitution in Rockfall Impacts, Nat. Hazards Rev., 22, 04021024, https://doi.org/10.1061/(ASCE)NH.1527-6996.0000454, 2021.
Žabota, B., Repe, B., and Kobal, M.: Influence of digital elevation model resolution on rockfall modelling, Geomorphology, 328, 183–195, https://doi.org/10.1016/j.geomorph.2018.12.029, 2019.
Zhan, J., Yu, Z., Lv, Y., Peng, J., Song, S., and Yao, Z.: Rockfall Hazard Assessment in the Taihang Grand Canyon Scenic Area Integrating Regional-Scale Identification of Potential Rockfall Sources, Remote Sensing, 14, 3021, https://doi.org/10.3390/rs14133021, 2022.
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.
This study proposes a novel systematic workflow that integrates source area identification,...
Altmetrics
Final-revised paper
Preprint