Articles | Volume 15, issue 1
https://doi.org/10.5194/nhess-15-75-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/nhess-15-75-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
13 Jan 2015
Research article |
| 13 Jan 2015
Statistical modelling of rainfall-induced shallow landsliding using static predictors and numerical weather predictions: preliminary results
V. Capecchi
CORRESPONDING AUTHOR
Consorzio LaMMA, Via Madonna del piano 10, Sesto Fiorentino, 50019 Florence, Italy
Istituto di Biometeorologia, Consiglio Nazionale delle Ricerche, Via Madonna del piano 10, Sesto Fiorentino, 50019 Florence, Italy
M. Perna
Consorzio LaMMA, Via Madonna del piano 10, Sesto Fiorentino, 50019 Florence, Italy
Istituto di Biometeorologia, Consiglio Nazionale delle Ricerche, Via Madonna del piano 10, Sesto Fiorentino, 50019 Florence, Italy
A. Crisci
Istituto di Biometeorologia, Consiglio Nazionale delle Ricerche, Via Caproni 8, 50145, Florence, Italy
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Related subject area
Landslides and Debris Flows Hazards
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
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
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
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
InSAR-Informed In-Situ Monitoring for Deep-Seated Landslides: Insights from El Forn (Andorra)
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
A coupled hydrological and hydrodynamic modelling approach for estimating rainfall thresholds of debris-flow occurrence
Rockfall monitoring with a Doppler radar on an active rockslide complex in Brienz/Brinzauls (Switzerland)
More than one landslide per road kilometer – surveying and modeling mass movements along the Rishikesh-Joshimath (NH-7) highway, Uttarakhand, India
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
Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine
The role of thermokarst evolution in debris flow initiation (Hüttekar Rock Glacier, Austrian Alps)
Accounting for the effect of forest and fragmentation in probabilistic rockfall hazard
Comprehensive landslide susceptibility map of Central Asia
The influence of large woody debris on post-wildfire debris flow sediment storage
Statistical modeling of sediment supply in torrent catchments of the northern French Alps
A data-driven evaluation of post-fire landslide susceptibility
Deciphering seasonal effects of triggering and preparatory precipitation for improved shallow landslide prediction using generalized additive mixed models
Brief communication: The northwest Himalaya towns slipping towards potential disaster
Dynamic response and breakage of trees subject to a landslide-induced air blast
Debris-flow surges of a very active alpine torrent: a field database
Rainfall thresholds estimation for shallow landslides in Peru from gridded daily data
Instantaneous limit equilibrium back analyses of major rockslides triggered during the 2016–2017 central Italy seismic sequence
Deadly disasters in southeastern South America: flash floods and landslides of February 2022 in Petrópolis, Rio de Janeiro
Multi-event assessment of typhoon-triggered landslide susceptibility in the Philippines
Antecedent rainfall as a critical factor for the triggering of debris flows in arid regions
Sensitivity analysis of a built environment exposed to the synthetic monophasic viscous debris flow impacts with 3-D numerical simulations
Characteristics and causes of natural and human-induced landslides in a tropical mountainous region: the rift flank west of Lake Kivu (Democratic Republic of the Congo)
Spatio-temporal analysis of slope-type debris flow activity in Horlachtal, Austria, based on orthophotos and lidar data since 1947
Assessing the relationship between weather conditions and rockfall using terrestrial laser scanning to improve risk management
Using principal component analysis to incorporate multi-layer soil moisture information in hydrometeorological thresholds for landslide prediction: an investigation based on ERA5-Land reanalysis data
Fabiola Banfi, Emanuele Bevacqua, Pauline Rivoire, Sérgio C. Oliveira, Joaquim G. Pinto, Alexandre M. Ramos, and Carlo De Michele
Nat. Hazards Earth Syst. Sci., 24, 2689–2704, https://doi.org/10.5194/nhess-24-2689-2024, https://doi.org/10.5194/nhess-24-2689-2024, 2024
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Landslides are complex phenomena causing important impacts in vulnerable areas, and they are often triggered by rainfall. Here, we develop a new approach that uses information on the temporal clustering of rainfall, i.e. multiple events close in time, to detect landslide events and compare it with the use of classical empirical rainfall thresholds, considering as a case study the region of Lisbon, Portugal. The results could help to improve the prediction of rainfall-triggered landslides.
Jianqi Zhuang, Jianbing Peng, Chenhui Du, Yi Zhu, and Jiaxu Kong
Nat. Hazards Earth Syst. Sci., 24, 2615–2631, https://doi.org/10.5194/nhess-24-2615-2024, https://doi.org/10.5194/nhess-24-2615-2024, 2024
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The Revised Infinite Slope Model (RISM) is proposed using the equal differential unit method and correcting the deficiency of the safety factor increasing with the slope increasing when the slope is larger than 40°, as calculated using the Taylor slope infinite model. The intensity–duration (I–D) prediction curve of the rainfall-induced shallow loess landslides with different slopes was constructed and can be used in forecasting regional shallow loess landslides.
Alexander B. Prescott, Luke A. McGuire, Kwang-Sung Jun, Katherine R. Barnhart, and Nina S. Oakley
Nat. Hazards Earth Syst. Sci., 24, 2359–2374, https://doi.org/10.5194/nhess-24-2359-2024, https://doi.org/10.5194/nhess-24-2359-2024, 2024
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Fire can dramatically increase the risk of debris flows to downstream communities with little warning, but hazard assessments have not traditionally included estimates of inundation. We unify models developed by the scientific community to create probabilistic estimates of inundation area in response to rainfall at forecast lead times (≥ 24 h) needed for decision-making. This work takes an initial step toward a near-real-time postfire debris-flow inundation hazard assessment product.
Francis K. Rengers, Samuel Bower, Andrew Knapp, Jason W. Kean, Danielle W. vonLembke, Matthew A. Thomas, Jaime Kostelnik, Katherine R. Barnhart, Matthew Bethel, Joseph E. Gartner, Madeline Hille, Dennis M. Staley, Justin K. Anderson, Elizabeth K. Roberts, Stephen B. DeLong, Belize Lane, Paxton Ridgway, and Brendan P. Murphy
Nat. Hazards Earth Syst. Sci., 24, 2093–2114, https://doi.org/10.5194/nhess-24-2093-2024, https://doi.org/10.5194/nhess-24-2093-2024, 2024
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Every year the U.S. Geological Survey produces 50–100 postfire debris-flow hazard assessments using models for debris-flow likelihood and volume. To refine these models they must be tested with datasets that clearly document rainfall, debris-flow response, and debris-flow volume. These datasets are difficult to obtain, but this study developed and analyzed a postfire dataset with more than 100 postfire storm responses over a 2-year period. We also proposed ways to improve these models.
Praveen Kumar, Priyanka Priyanka, Kala Venkata Uday, and Varun Dutt
Nat. Hazards Earth Syst. Sci., 24, 1913–1928, https://doi.org/10.5194/nhess-24-1913-2024, https://doi.org/10.5194/nhess-24-1913-2024, 2024
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Our study focuses on predicting soil movement to mitigate landslide risks. We develop machine learning models with oversampling techniques to address the class imbalance in monitoring data. The dynamic ensemble model with K-means SMOTE (synthetic minority oversampling technique) achieves high precision, high recall, and a high F1 score. Our findings highlight the potential of these models with oversampling techniques to improve soil movement predictions in landslide-prone areas.
Kristian Svennevig, Julian Koch, Marie Keiding, and Gregor Luetzenburg
Nat. Hazards Earth Syst. Sci., 24, 1897–1911, https://doi.org/10.5194/nhess-24-1897-2024, https://doi.org/10.5194/nhess-24-1897-2024, 2024
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In our study, we analysed publicly available data in order to investigate the impact of climate change on landslides in Denmark. Our research indicates that the rising groundwater table due to climate change will result in an increase in landslide activity. Previous incidents of extremely wet winters have caused damage to infrastructure and buildings due to landslides. This study is the first of its kind to exclusively rely on public data and examine landslides in Denmark.
Jiao Wang, Zhangxing Wang, Guanhua Sun, and Hongming Luo
Nat. Hazards Earth Syst. Sci., 24, 1741–1756, https://doi.org/10.5194/nhess-24-1741-2024, https://doi.org/10.5194/nhess-24-1741-2024, 2024
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With a simplified formula linking rainfall and groundwater level, the rise of the phreatic surface within the slope can be obtained. Then, a global analysis method that considers both seepage and seismic forces is proposed to determine the safety factor of slopes subjected to the combined effect of rainfall and earthquakes. By taking a slope in the Three Gorges Reservoir area as an example, the safety evolution of the slope combined with both rainfall and earthquake is also examined.
Carlo Tacconi Stefanelli, William Frodella, Francesco Caleca, Zhanar Raimbekova, Ruslan Umaraliev, and Veronica Tofani
Nat. Hazards Earth Syst. Sci., 24, 1697–1720, https://doi.org/10.5194/nhess-24-1697-2024, https://doi.org/10.5194/nhess-24-1697-2024, 2024
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Central Asia regions are marked by active tectonics, high mountains with glaciers, and strong rainfall. These predisposing factors make large landslides a serious threat in the area and a source of possible damming scenarios, which endanger the population. To prevent this, a semi-automated geographic information system (GIS-)based mapping method, centered on a bivariate correlation of morphometric parameters, was applied to give preliminary information on damming susceptibility in Central Asia.
Rex L. Baum, Dianne L. Brien, Mark E. Reid, William H. Schulz, and Matthew J. Tello
Nat. Hazards Earth Syst. Sci., 24, 1579–1605, https://doi.org/10.5194/nhess-24-1579-2024, https://doi.org/10.5194/nhess-24-1579-2024, 2024
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We mapped potential for heavy rainfall to cause landslides in part of the central mountains of Puerto Rico using new tools for estimating soil depth and quasi-3D slope stability. Potential ground-failure locations correlate well with the spatial density of landslides from Hurricane Maria. The smooth boundaries of the very high and high ground-failure susceptibility zones enclose 75 % and 90 %, respectively, of observed landslides. The maps can help mitigate ground-failure hazards.
Katherine R. Barnhart, Christopher R. Miller, Francis K. Rengers, and Jason W. Kean
Nat. Hazards Earth Syst. Sci., 24, 1459–1483, https://doi.org/10.5194/nhess-24-1459-2024, https://doi.org/10.5194/nhess-24-1459-2024, 2024
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Debris flows are a type of fast-moving landslide that start from shallow landslides or during intense rain. Infrastructure located downstream of watersheds susceptible to debris flows may be damaged should a debris flow reach them. We present and evaluate an approach to forecast building damage caused by debris flows. We test three alternative models for simulating the motion of debris flows and find that only one can forecast the correct number and spatial pattern of damaged buildings.
Luke A. McGuire, Francis K. Rengers, Ann M. Youberg, Alexander N. Gorr, Olivia J. Hoch, Rebecca Beers, and Ryan Porter
Nat. Hazards Earth Syst. Sci., 24, 1357–1379, https://doi.org/10.5194/nhess-24-1357-2024, https://doi.org/10.5194/nhess-24-1357-2024, 2024
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Runoff and erosion increase after fire, leading to a greater likelihood of floods and debris flows. We monitored debris flow activity following a fire in western New Mexico, USA, and observed 16 debris flows over a <2-year monitoring period. Rainstorms with recurrence intervals of approximately 1 year were sufficient to initiate debris flows. All debris flows initiated during the first several months following the fire, indicating a rapid decrease in debris flow susceptibility over time.
Ken'ichi Koshimizu, Satoshi Ishimaru, Fumitoshi Imaizumi, and Gentaro Kawakami
Nat. Hazards Earth Syst. Sci., 24, 1287–1301, https://doi.org/10.5194/nhess-24-1287-2024, https://doi.org/10.5194/nhess-24-1287-2024, 2024
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Morphological conditions of drainage basins that classify the presence or absence of debris flow fans were analyzed in areas with different rock strength using decision tree analysis. The relief ratio is the most important morphological factor regardless of the geology. However, the thresholds of morphological parameters needed for forming debris flow fans differ depending on the geology. Decision tree analysis is an effective tool for evaluating the debris flow risk for each geology.
Daniel Bolliger, Fritz Schlunegger, and Brian W. McArdell
Nat. Hazards Earth Syst. Sci., 24, 1035–1049, https://doi.org/10.5194/nhess-24-1035-2024, https://doi.org/10.5194/nhess-24-1035-2024, 2024
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We analysed data from the Illgraben debris flow monitoring station, Switzerland, and we modelled these flows with a debris flow runout model. We found that no correlation exists between the grain size distribution, the mineralogical composition of the matrix, and the debris flow properties. The flow properties rather appear to be determined by the flow volume, from which most other parameters can be derived.
Yuntao Zhou, Xiaoyan Zhao, Guangze Zhang, Bernd Wünnemann, Jiajia Zhang, and Minghui Meng
Nat. Hazards Earth Syst. Sci., 24, 891–906, https://doi.org/10.5194/nhess-24-891-2024, https://doi.org/10.5194/nhess-24-891-2024, 2024
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We developed three rock bridge models to analyze 3D stability and deformation behaviors of the Tizicao landslide and found that the contact surface model with high strength parameters combines advantages of the intact rock mass model in simulating the deformation of slopes with rock bridges and the modeling advantage of the Jennings model. The results help in choosing a rock bridge model to simulate landslide stability and reveal the influence laws of rock bridges on the stability of landslides.
Ashok Dahal, Hakan Tanyas, Cees van Westen, Mark van der Meijde, Paul Martin Mai, Raphaël Huser, and Luigi Lombardo
Nat. Hazards Earth Syst. Sci., 24, 823–845, https://doi.org/10.5194/nhess-24-823-2024, https://doi.org/10.5194/nhess-24-823-2024, 2024
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We propose a modeling approach capable of recognizing slopes that may generate landslides, as well as how large these mass movements may be. This protocol is implemented, tested, and validated with data that change in both space and time via an Ensemble Neural Network architecture.
Li-Ru Luo, Zhi-Xiang Yu, Li-Jun Zhang, Qi Wang, Lin-Xu Liao, and Li Peng
Nat. Hazards Earth Syst. Sci., 24, 631–649, https://doi.org/10.5194/nhess-24-631-2024, https://doi.org/10.5194/nhess-24-631-2024, 2024
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We performed field investigations on a rockfall near Jiguanshan National Forest Park, Chengdu. Vital information was obtained from an unmanned aerial vehicle survey. A finite element model was created to reproduce the damage evolution. We found that the impact kinetic energy was below the design protection energy. Improper member connections prevent the barrier from producing significant deformation to absorb energy. Damage is avoided by improving the ability of the nets and ropes to slide.
Rachael Lau, Carolina Seguí, Tyler Waterman, Nathaniel Chaney, and Manolis Veveakis
EGUsphere, https://doi.org/10.48550/arXiv.2311.01564, https://doi.org/10.48550/arXiv.2311.01564, 2024
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This work examines the use of Interferometric Synthetic Aperture Radar (InSAR) alongside in-situ borehole measurements to assess the stability of deep-seated landslides for the case study of El Forn (Andorra). InSAR data compared with borehole data suggests a key tradeoff between accuracy and precision for various InSAR resolutions. Spatial interpolation with InSAR informed how many remote observations are necessary to lower error on remote-sensing recreation of ground motion over the landslide.
Sudhanshu Dixit, Srikrishnan Siva Subramanian, Piyush Srivastava, Ali P. Yunus, Tapas Ranjan Martha, and Sumit Sen
Nat. Hazards Earth Syst. Sci., 24, 465–480, https://doi.org/10.5194/nhess-24-465-2024, https://doi.org/10.5194/nhess-24-465-2024, 2024
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Rainfall intensity–duration (ID) thresholds can aid in the prediction of natural hazards. Large-scale sediment disasters like landslides, debris flows, and flash floods happen frequently in the Himalayas because of their propensity for intense precipitation events. We provide a new framework that combines the Weather Research and Forecasting (WRF) model with a regionally distributed numerical model for debris flows to analyse and predict intense rainfall-induced landslides in the Himalayas.
Jacob B. Woodard, Benjamin B. Mirus, Nathan J. Wood, Kate E. Allstadt, Benjamin A. Leshchinsky, and Matthew M. Crawford
Nat. Hazards Earth Syst. Sci., 24, 1–12, https://doi.org/10.5194/nhess-24-1-2024, https://doi.org/10.5194/nhess-24-1-2024, 2024
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Dividing landscapes into hillslopes greatly improves predictions of landslide potential across landscapes, but their scaling is often arbitrarily set and can require significant computing power to delineate. Here, we present a new computer program that can efficiently divide landscapes into meaningful slope units scaled to best capture landslide processes. The results of this work will allow an improved understanding of landslide potential and can help reduce the impacts of landslides worldwide.
Anne Felsberg, Zdenko Heyvaert, Jean Poesen, Thomas Stanley, and Gabriëlle J. M. De Lannoy
Nat. Hazards Earth Syst. Sci., 23, 3805–3821, https://doi.org/10.5194/nhess-23-3805-2023, https://doi.org/10.5194/nhess-23-3805-2023, 2023
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The Probabilistic Hydrological Estimation of LandSlides (PHELS) model combines ensembles of landslide susceptibility and of hydrological predictor variables to provide daily, global ensembles of hazard for hydrologically triggered landslides. Testing different hydrological predictors showed that the combination of rainfall and soil moisture performed best, with the lowest number of missed and false alarms. The ensemble approach allowed the estimation of the associated prediction uncertainty.
Xushan Shi, Bo Chai, Juan Du, Wei Wang, and Bo Liu
Nat. Hazards Earth Syst. Sci., 23, 3425–3443, https://doi.org/10.5194/nhess-23-3425-2023, https://doi.org/10.5194/nhess-23-3425-2023, 2023
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A 3D stability analysis method is proposed for biased rockfall with external erosion. Four failure modes are considered according to rockfall evolution processes, including partial damage of underlying soft rock and overall failure of hard rock blocks. This method is validated with the biased rockfalls in the Sichuan Basin, China. The critical retreat ratio from low to moderate rockfall susceptibility is 0.33. This method could facilitate rockfall early identification and risk mitigation.
Zhen Lei Wei, Yue Quan Shang, Qiu Hua Liang, and Xi Lin Xia
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-180, https://doi.org/10.5194/nhess-2023-180, 2023
Revised manuscript accepted for NHESS
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The initiation of debris flows is influenced significantly by rainfall-induced hydrological processes. We propose a novel framework, which is based on an integrated hydrological and hydrodynamic model, aimed at estimating Intensity-Duration (I-D) rainfall thresholds responsible for triggering debris flows. In comparison to traditional statistical approaches, this physically-based framework particularly suitable for application in ungauged catchments where historical debris flow data is scarce.
Marius Schneider, Nicolas Oestreicher, Thomas Ehrat, and Simon Loew
Nat. Hazards Earth Syst. Sci., 23, 3337–3354, https://doi.org/10.5194/nhess-23-3337-2023, https://doi.org/10.5194/nhess-23-3337-2023, 2023
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Rockfalls and their hazards are typically treated as statistical events based on rockfall catalogs, but only a few complete rockfall inventories are available today. Here, we present new results from a Doppler radar rockfall alarm system, which has operated since 2018 at a high frequency under all illumination and weather conditions at a site where frequent rockfall events threaten a village and road. The new data set is used to investigate rockfall triggers in an active rockslide complex.
Jürgen Mey, Ravi Kumar Guntu, Alexander Plakias, Igo Silva de Almeida, and Wolfgang Schwanghart
EGUsphere, https://doi.org/10.5194/egusphere-2023-1975, https://doi.org/10.5194/egusphere-2023-1975, 2023
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The Himalayan road network links remote areas, but fragile terrain and poor construction lead to frequent landslides. This study on NH-7 in India's Uttarakhand region analyzed 300 landslides after heavy 2022 rainfall. 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.
Annette I. Patton, Lisa V. Luna, Joshua J. Roering, Aaron Jacobs, Oliver Korup, and Benjamin B. Mirus
Nat. Hazards Earth Syst. Sci., 23, 3261–3284, https://doi.org/10.5194/nhess-23-3261-2023, https://doi.org/10.5194/nhess-23-3261-2023, 2023
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Landslide warning systems often use statistical models to predict landslides based on rainfall. They are typically trained on large datasets with many landslide occurrences, but in rural areas large datasets may not exist. In this study, we evaluate which statistical model types are best suited to predicting landslides and demonstrate that even a small landslide inventory (five storms) can be used to train useful models for landslide early warning when non-landslide events are also included.
Sandra Melzner, Marco Conedera, Johannes Hübl, and Mauro Rossi
Nat. Hazards Earth Syst. Sci., 23, 3079–3093, https://doi.org/10.5194/nhess-23-3079-2023, https://doi.org/10.5194/nhess-23-3079-2023, 2023
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The estimation of the temporal frequency of the involved rockfall processes is an important part in hazard and risk assessments. Different methods can be used to collect and analyse rockfall data. From a statistical point of view, rockfall datasets are nearly always incomplete. Accurate data collection approaches and the application of statistical methods on existing rockfall data series as reported in this study should be better considered in rockfall hazard and risk assessments in the future.
Stefan Hergarten
Nat. Hazards Earth Syst. Sci., 23, 3051–3063, https://doi.org/10.5194/nhess-23-3051-2023, https://doi.org/10.5194/nhess-23-3051-2023, 2023
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Rockslides are a major hazard in mountainous regions. In formerly glaciated regions, the disposition mainly arises from oversteepened topography and decreases through time. However, little is known about this decrease and thus about the present-day hazard of huge, potentially catastrophic rockslides. This paper presents a new theoretical framework that explains the decrease in maximum rockslide size through time and predicts the present-day frequency of large rockslides for the European Alps.
Colin K. Bloom, Corinne Singeisen, Timothy Stahl, Andrew Howell, Chris Massey, and Dougal Mason
Nat. Hazards Earth Syst. Sci., 23, 2987–3013, https://doi.org/10.5194/nhess-23-2987-2023, https://doi.org/10.5194/nhess-23-2987-2023, 2023
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Landslides are often observed on coastlines following large earthquakes, but few studies have explored this occurrence. Here, statistical modelling of landslides triggered by the 2016 Kaikōura earthquake in New Zealand is used to investigate factors driving coastal earthquake-induced landslides. Geology, steep slopes, and shaking intensity are good predictors of landslides from the Kaikōura event. Steeper slopes close to the coast provide the best explanation for a high landslide density.
Yi-Min Huang
Nat. Hazards Earth Syst. Sci., 23, 2649–2662, https://doi.org/10.5194/nhess-23-2649-2023, https://doi.org/10.5194/nhess-23-2649-2023, 2023
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Debris flows are common hazards in Taiwan, and debris-flow early warning is important for disaster responses. The rainfall thresholds of debris flows are analyzed and determined in terms of rainfall intensity, accumulated rainfall, and rainfall duration, based on case histories in Taiwan. These thresholds are useful for disaster management, and the cases in Taiwan are useful for global debris-flow databases.
Davide Notti, Martina Cignetti, Danilo Godone, and Daniele Giordan
Nat. Hazards Earth Syst. Sci., 23, 2625–2648, https://doi.org/10.5194/nhess-23-2625-2023, https://doi.org/10.5194/nhess-23-2625-2023, 2023
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We developed a cost-effective and user-friendly approach to map shallow landslides using free satellite data. Our methodology involves analysing the pre- and post-event NDVI variation to semi-automatically detect areas potentially affected by shallow landslides (PLs). Additionally, we have created Google Earth Engine scripts to rapidly compute NDVI differences and time series of affected areas. Datasets and codes are stored in an open data repository for improvement by the scientific community.
Simon Seelig, Thomas Wagner, Karl Krainer, Michael Avian, Marc Olefs, Klaus Haslinger, and Gerfried Winkler
Nat. Hazards Earth Syst. Sci., 23, 2547–2568, https://doi.org/10.5194/nhess-23-2547-2023, https://doi.org/10.5194/nhess-23-2547-2023, 2023
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A rapid sequence of cascading events involving thermokarst lake outburst, rock glacier front failure, debris flow development, and river blockage hit an alpine valley in Austria during summer 2019. We analyze the environmental conditions initiating the process chain and identify the rapid evolution of a thermokarst channel network as the main driver. Our results highlight the need to account for permafrost degradation in debris flow hazard assessment studies.
Camilla Lanfranconi, Paolo Frattini, Gianluca Sala, Giuseppe Dattola, Davide Bertolo, Juanjuan Sun, and Giovanni Battista Crosta
Nat. Hazards Earth Syst. Sci., 23, 2349–2363, https://doi.org/10.5194/nhess-23-2349-2023, https://doi.org/10.5194/nhess-23-2349-2023, 2023
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This paper presents a study on rockfall dynamics and hazard, examining the impact of the presence of trees along slope and block fragmentation. We compared rockfall simulations that explicitly model the presence of trees and fragmentation with a classical approach that accounts for these phenomena in model parameters (both the hazard and the kinetic energy change). We also used a non-parametric probabilistic rockfall hazard analysis method for hazard mapping.
Ascanio Rosi, William Frodella, Nicola Nocentini, Francesco Caleca, Hans Balder Havenith, Alexander Strom, Mirzo Saidov, Gany Amirgalievich Bimurzaev, and Veronica Tofani
Nat. Hazards Earth Syst. Sci., 23, 2229–2250, https://doi.org/10.5194/nhess-23-2229-2023, https://doi.org/10.5194/nhess-23-2229-2023, 2023
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This work was carried out within the Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) project and is focused on the first landslide susceptibility analysis at a regional scale for Central Asia. The most detailed available landslide inventories were implemented in a random forest model. The final aim was to provide a useful tool for reduction strategies to landslide scientists, practitioners, and administrators.
Francis K. Rengers, Luke A. McGuire, Katherine R. Barnhart, Ann M. Youberg, Daniel Cadol, Alexander N. Gorr, Olivia J. Hoch, Rebecca Beers, and Jason W. Kean
Nat. Hazards Earth Syst. Sci., 23, 2075–2088, https://doi.org/10.5194/nhess-23-2075-2023, https://doi.org/10.5194/nhess-23-2075-2023, 2023
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Debris flows often occur after wildfires. These debris flows move water, sediment, and wood. The wood can get stuck in channels, creating a dam that holds boulders, cobbles, sand, and muddy material. We investigated how the channel width and wood length influenced how much sediment is stored. We also used a series of equations to back calculate the debris flow speed using the breaking threshold of wood. These data will help improve models and provide insight into future field investigations.
Maxime Morel, Guillaume Piton, Damien Kuss, Guillaume Evin, and Caroline Le Bouteiller
Nat. Hazards Earth Syst. Sci., 23, 1769–1787, https://doi.org/10.5194/nhess-23-1769-2023, https://doi.org/10.5194/nhess-23-1769-2023, 2023
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In mountain catchments, damage during floods is generally primarily driven by the supply of a massive amount of sediment. Predicting how much sediment can be delivered by frequent and infrequent events is thus important in hazard studies. This paper uses data gathered during the maintenance operation of about 100 debris retention basins to build simple equations aiming at predicting sediment supply from simple parameters describing the upstream catchment.
Elsa S. Culler, Ben Livneh, Balaji Rajagopalan, and Kristy F. Tiampo
Nat. Hazards Earth Syst. Sci., 23, 1631–1652, https://doi.org/10.5194/nhess-23-1631-2023, https://doi.org/10.5194/nhess-23-1631-2023, 2023
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Landslides have often been observed in the aftermath of wildfires. This study explores regional patterns in the rainfall that caused landslides both after fires and in unburned locations. In general, landslides that occur after fires are triggered by less rainfall, confirming that fire helps to set the stage for landslides. However, there are regional differences in the ways in which fire impacts landslides, such as the size and direction of shifts in the seasonality of landslides after fires.
Stefan Steger, Mateo Moreno, Alice Crespi, Peter James Zellner, Stefano Luigi Gariano, Maria Teresa Brunetti, Massimo Melillo, Silvia Peruccacci, Francesco Marra, Robin Kohrs, Jason Goetz, Volkmar Mair, and Massimiliano Pittore
Nat. Hazards Earth Syst. Sci., 23, 1483–1506, https://doi.org/10.5194/nhess-23-1483-2023, https://doi.org/10.5194/nhess-23-1483-2023, 2023
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We present a novel data-driven modelling approach to determine season-specific critical precipitation conditions for landslide occurrence. It is shown that the amount of precipitation required to trigger a landslide in South Tyrol varies from season to season. In summer, a higher amount of preparatory precipitation is required to trigger a landslide, probably due to denser vegetation and higher temperatures. We derive dynamic thresholds that directly relate to hit rates and false-alarm rates.
Yaspal Sundriyal, Vipin Kumar, Neha Chauhan, Sameeksha Kaushik, Rahul Ranjan, and Mohit Kumar Punia
Nat. Hazards Earth Syst. Sci., 23, 1425–1431, https://doi.org/10.5194/nhess-23-1425-2023, https://doi.org/10.5194/nhess-23-1425-2023, 2023
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The NW Himalaya has been one of the most affected terrains of the Himalaya, subject to disastrous landslides. This article focuses on two towns (Joshimath and Bhatwari) of the NW Himalaya, which have been witnessing subsidence for decades. We used a slope stability simulation to determine the response of the hillslopes accommodating these towns under various loading conditions. We found that the maximum displacement in these hillslopes might reach up to 20–25 m.
Yu Zhuang, Aiguo Xing, Perry Bartelt, Muhammad Bilal, and Zhaowei Ding
Nat. Hazards Earth Syst. Sci., 23, 1257–1266, https://doi.org/10.5194/nhess-23-1257-2023, https://doi.org/10.5194/nhess-23-1257-2023, 2023
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Tree destruction is often used to back calculate the air blast impact region and to estimate the air blast power. Here we established a novel model to assess air blast power using tree destruction information. We find that the dynamic magnification effect makes the trees easier to damage by a landslide-induced air blast, but the large tree deformation would weaken the effect. Bending and overturning are two likely failure modes, which depend heavily on the properties of trees.
Suzanne Lapillonne, Firmin Fontaine, Frédéric Liebault, Vincent Richefeu, and Guillaume Piton
Nat. Hazards Earth Syst. Sci., 23, 1241–1256, https://doi.org/10.5194/nhess-23-1241-2023, https://doi.org/10.5194/nhess-23-1241-2023, 2023
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Debris flows are fast flows most often found in torrential watersheds. They are composed of two phases: a liquid phase which can be mud-like and a granular phase, including large boulders, transported along with the flow. Due to their destructive nature, accessing features of the flow, such as velocity and flow height, is difficult. We present a protocol to analyse debris flow data and results of the Réal torrent in France. These results will help experts in designing models.
Carlos Millán-Arancibia and Waldo Lavado-Casimiro
Nat. Hazards Earth Syst. Sci., 23, 1191–1206, https://doi.org/10.5194/nhess-23-1191-2023, https://doi.org/10.5194/nhess-23-1191-2023, 2023
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This study is the first approximation of regional rainfall thresholds for shallow landslide occurrence in Peru. This research was generated from a gridded precipitation data and landslide inventory. The analysis showed that the threshold based on the combination of mean daily intensity–duration variables gives the best results for separating rainfall events that generate landslides. Through this work the potential of thresholds for landslide monitoring at the regional scale is demonstrated.
Luca Verrucci, Giovanni Forte, Melania De Falco, Paolo Tommasi, Giuseppe Lanzo, Kevin W. Franke, and Antonio Santo
Nat. Hazards Earth Syst. Sci., 23, 1177–1190, https://doi.org/10.5194/nhess-23-1177-2023, https://doi.org/10.5194/nhess-23-1177-2023, 2023
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Stability analyses in static and seismic conditions were performed on four rockslides that occurred during the main shocks of the 2016–2017 central Italy seismic sequence. These results also indicate that specific structural features of the slope must carefully be accounted for in evaluating potential hazards on transportation infrastructures in mountainous regions.
Enner Alcântara, José A. Marengo, José Mantovani, Luciana R. Londe, Rachel Lau Yu San, Edward Park, Yunung Nina Lin, Jingyu Wang, Tatiana Mendes, Ana Paula Cunha, Luana Pampuch, Marcelo Seluchi, Silvio Simões, Luz Adriana Cuartas, Demerval Goncalves, Klécia Massi, Regina Alvalá, Osvaldo Moraes, Carlos Souza Filho, Rodolfo Mendes, and Carlos Nobre
Nat. Hazards Earth Syst. Sci., 23, 1157–1175, https://doi.org/10.5194/nhess-23-1157-2023, https://doi.org/10.5194/nhess-23-1157-2023, 2023
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The municipality of Petrópolis (approximately 305 687 inhabitants) is nestled in the mountains 68 km outside the city of Rio de Janeiro. On 15 February 2022, the city of Petrópolis in Rio de Janeiro, Brazil, received an unusually high volume of rain within 3 h (258 mm). This resulted in flash floods and subsequent landslides that caused 231 fatalities, the deadliest landslide disaster recorded in Petrópolis. This work shows how the disaster was triggered.
Joshua N. Jones, Georgina L. Bennett, Claudia Abancó, Mark A. M. Matera, and Fibor J. Tan
Nat. Hazards Earth Syst. Sci., 23, 1095–1115, https://doi.org/10.5194/nhess-23-1095-2023, https://doi.org/10.5194/nhess-23-1095-2023, 2023
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We modelled where landslides occur in the Philippines using landslide data from three typhoon events in 2009, 2018, and 2019. These models show where landslides occurred within the landscape. By comparing the different models, we found that the 2019 landslides were occurring all across the landscape, whereas the 2009 and 2018 landslides were mostly occurring at specific slope angles and aspects. This shows that landslide susceptibility must be considered variable through space and time.
Shalev Siman-Tov and Francesco Marra
Nat. Hazards Earth Syst. Sci., 23, 1079–1093, https://doi.org/10.5194/nhess-23-1079-2023, https://doi.org/10.5194/nhess-23-1079-2023, 2023
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Debris flows represent a threat to infrastructure and the population. In arid areas, they are observed when heavy rainfall hits steep slopes with sediments. Here, we use digital surface models and radar rainfall data to detect and characterize the triggering and non-triggering rainfall conditions. We find that rainfall intensity alone is insufficient to explain the triggering. We suggest that antecedent rainfall could represent a critical factor for debris flow triggering in arid regions.
Xun Huang, Zhijian Zhang, and Guoping Xiang
Nat. Hazards Earth Syst. Sci., 23, 871–889, https://doi.org/10.5194/nhess-23-871-2023, https://doi.org/10.5194/nhess-23-871-2023, 2023
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A sensitivity analysis on the building impact force resulting from the representative built environment parameters is executed through the FLOW-3D model. The surrounding buildings' properties, especially the azimuthal angle, have been confirmed to play significant roles in determining the peak impact forces. The single and combined effects of built environments are analyzed in detail. This will improve understanding of vulnerability assessment and migration design against debris flow hazards.
Jean-Claude Maki Mateso, Charles L. Bielders, Elise Monsieurs, Arthur Depicker, Benoît Smets, Théophile Tambala, Luc Bagalwa Mateso, and Olivier Dewitte
Nat. Hazards Earth Syst. Sci., 23, 643–666, https://doi.org/10.5194/nhess-23-643-2023, https://doi.org/10.5194/nhess-23-643-2023, 2023
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This research highlights the importance of human activities on the occurrence of landslides and the need to consider this context when studying hillslope instability patterns in regions under anthropogenic pressure. Also, this study highlights the importance of considering the timing of landslides and hence the added value of using historical information for compiling an inventory.
Jakob Rom, Florian Haas, Tobias Heckmann, Moritz Altmann, Fabian Fleischer, Camillo Ressl, Sarah Betz-Nutz, and Michael Becht
Nat. Hazards Earth Syst. Sci., 23, 601–622, https://doi.org/10.5194/nhess-23-601-2023, https://doi.org/10.5194/nhess-23-601-2023, 2023
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In this study, an area-wide slope-type debris flow record has been established for Horlachtal, Austria, since 1947 based on historical and recent remote sensing data. Spatial and temporal analyses show variations in debris flow activity in space and time in a high-alpine region. The results can contribute to a better understanding of past slope-type debris flow dynamics in the context of extreme precipitation events and their possible future development.
Tom Birien and Francis Gauthier
Nat. Hazards Earth Syst. Sci., 23, 343–360, https://doi.org/10.5194/nhess-23-343-2023, https://doi.org/10.5194/nhess-23-343-2023, 2023
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On highly fractured rockwalls such as those found in northern Gaspésie, most rockfalls are triggered by weather conditions. This study highlights that in winter, rockfall frequency is 12 times higher during a superficial thaw than during a cold period in which temperature remains below 0 °C. In summer, rockfall frequency is 22 times higher during a heavy rainfall event than during a mainly dry period. This knowledge could be used to implement a risk management strategy.
Nunziarita Palazzolo, David J. Peres, Enrico Creaco, and Antonino Cancelliere
Nat. Hazards Earth Syst. Sci., 23, 279–291, https://doi.org/10.5194/nhess-23-279-2023, https://doi.org/10.5194/nhess-23-279-2023, 2023
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We propose an approach exploiting PCA to derive hydrometeorological landslide-triggering thresholds using multi-layered soil moisture data from ERA5-Land reanalysis. Comparison of thresholds based on single- and multi-layered soil moisture information provides a means to identify the significance of multi-layered data for landslide triggering in a region. In Sicily, the proposed approach yields thresholds with a higher performance than traditional precipitation-based ones (TSS = 0.71 vs. 0.50).
Cited articles
Argnani, A., Barbacini, G., Bernini, M., Camurri, F., Ghielmi, M., Papani, G., Rizzini, F., Rogledi, S., and Torelli, L.: Gravity tectonics driven by Quaternary uplift in the Northern Apennines: insights from the La Spezia-Reggio Emilia geo-transect, Quatern. Int., 101, 13–26, 2003.
Atkinson, P. and Massari, R.: Generalised linear modelling of susceptibility to landsliding in the Central Apennines, Italy, Comp. Geosci., 24, 373–385, 1998.
Avanzi, G. D., Falaschi, F., Giannecchini, R., and Puccinelli, A.: Soil slip susceptibility assessment using mechanical–hydrological approach and GIS techniques: an application in the Apuan Alps (Italy), Nat. Hazards, 50, 591–603, 2009.
Avanzi, G. D., Galanti, Y., Giannecchini, R., and Puccinelli, A.: Fragility of Territory and Infrastructures Resulting from Rainstorms in Northern Tuscany (Italy), in: Landslide Science and Practice, Springer, Berlin, Heidelberg, 239–246, 2013.
Baldi, M., Ciardini, V., Dalu, J. D., De Filippis, T., Maracchi, G., and Dalu, G.: Hail occurrence in Italy: Towards a national database and climatology, Atmos. Res., 138, 268–277, 2014.
Borghuis, A., Chang, K., and Lee, H.: Comparison between automated and manual mapping of typhoon-triggered landslides from SPOT-5 imagery, Int. J. Remote Sens., 28, 1843–1856, 2007.
Bossard, M., Feranec, J., and Otahel, J.: CORINE land cover technical guide: Addendum, Copenhagen, the Netherlands, 105 pp., 2000.
Breiman, L.: Bagging predictors, Machine learning, 24, 123–140, 1996.
Breiman, L.: Random forests, Machine learning, 45, 5–32, 2001.
Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. A.: Classification and regression trees, CRC press, Belmont, CA, USA 1984.
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.
Brenning, A.: Benchmarking classifiers to optimally integrate terrain analysis and multispectral remote sensing in automatic rock glacier detection, Remote Sens. Environ., 113, 239–247, 2009.
Brunetti, M., Lentini, G., Maugeri, M., Nanni, T., Simolo, C., and Spinoni, J.: 1961–1990 high-resolution Northern and Central Italy monthly precipitation climatologies, Adv. Sci. Res., 3, 73–78, 2009.
Bryan, G. H. and Morrison, H.: Sensitivity of a Simulated Squall Line to Horizontal Resolution and Parameterization of Microphysics., Mon. Weather Rev., 140, 202–225, 2012.
Buzzi, A., Davolio, S., D'Isidoro, M., and Malguzzi, P.: The impact of resolution and of MAP reanalysis on the simulations of heavy precipitation during MAP cases, Meteorologische Zeitschrift, 13, 91–97, 2004.
Buzzi, A., Davolio, S., Malguzzi, P., Drofa, O., and Mastrangelo, D.: Heavy rainfall episodes over Liguria in autumn 2011: numerical forecasting experiments, Nat. Hazards Earth Syst. Sci., 14, 1325–1340, https://doi.org/10.5194/nhess-14-1325-2014, 2014.
Capecchi, V. and Brocca, L.: A simple assimilation method to ingest satellite soil moisture into a limited-area NWP model, Meteorologische Zeitschrift, 23, 105–121, 2014.
Carrara, A., Cardinali, M., Detti, R., Guzzetti, F., Pasqui, V., and Reichenbach, P.: GIS techniques and statistical models in evaluating landslide hazard, Earth Surf. Proc. Land., 16, 427–445, 1991.
Carrara, A., Guzzetti, F., Cardinali, M., and Reichenbach, P.: Use of GIS technology in the prediction and monitoring of landslide hazard, Nat. Hazards, 20, 117–135, 1999.
Carrara, A., Crosta, G., and Frattini, P.: Comparing models of debris-flow susceptibility in the alpine environment, Geomorphology, 94, 353–378, 2008.
Catani, F., Casagli, N., Ermini, L., Righini, G., and Menduni, G.: Landslide hazard and risk mapping at catchment scale in the Arno River basin, Landslides, 2, 329–342, 2005.
Catani, F., Lagomarsino, D., Segoni, S., and Tofani, V.: Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues, Nat. Hazards Earth Syst. Sci., 13, 2815–2831, https://doi.org/10.5194/nhess-13-2815-2013, 2013.
Chen, F., Mitchell, K., Schaake, J., Xue, Y., Pan, H.-L., Koren, V., Duan, Q. Y., Ek, M., and Betts, A.: Modeling of land surface evaporation by four schemes and comparison with FIFE observations, J. Geophys. Res., 101, 7251–7268, 1996.
Chen, S. and Dudhia, J.: Annual report: WRF physics, Air Force Weather Agency, 2000.
Chung, C.-J. F. and Fabbri, A. G.: Probabilistic prediction models for landslide hazard mapping, Photogramm. Eng. Rem. S., 65, 1389–1399, 1999.
Costanzo, D., Rotigliano, E., Irigaray, C., Jiménez-Perálvarez, J. D., and Chacón, J.: Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: application to the river Beiro basin (Spain), Nat. Hazards Earth Syst. Sci., 12, 327–340, https://doi.org/10.5194/nhess-12-327-2012, 2012.
Crozier, M. J.: Prediction of rainfall-triggered landslides: A test of the antecedent water status model, Earth Surf. Proc. Land., 24, 825–833, 1999.
Cruden, D. and Hu, X.: Landslides in the Rocky Mountains of Canada, in: Geomorphological Hazards in High Mountain Areas, Springer Netherlands, 133–148, 1998.
Cutler, D. R., Edwards Jr, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., and Lawler, J. J.: Random forests for classification in ecology, Ecology, 88, 2783–2792, 2007.
de Rosnay, P., Drusch, M., Vasiljevic, D., Balsamo, G., Albergel, C., and Isaksen, L.: A simplified Extended Kalman Filter for the global operational soil moisture analysis at ECMWF, Q. J. Roy. Meteor. Soc., 139, 1199–1213, 2013.
Demir, G., Aytekin, M., Akgün, A., \.Ikizler, S. B., and Tatar, O.: A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods, Nat. Hazards, 65, 1481–1506, 2013.
Devkota, K. C., Regmi, A. D., Pourghasemi, H. R., Yoshida, K., Pradhan, B., Ryu, I. C., Dhital, M. R., and Althuwaynee, O. F.: Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya, Nat. Hazards, 65, 135–165, 2013.
Dharssi, I., Bovis, K. J., Macpherson, B., and Jones, C. P.: Operational assimilation of ASCAT surface soil wetness at the Met Office, Hydrol. Earth Syst. Sci., 15, 2729–2746, https://doi.org/10.5194/hess-15-2729-2011, 2011.
Di Naccio, D., Boncio, P., Brozzetti, F., Pazzaglia, F. J., and Lavecchia, G.: Morphotectonic analysis of the Lunigiana and Garfagnana grabens (northern Apennines, Italy): Implications for active normal faulting, Geomorphology, 201, 293–311, 2013.
Díaz-Uriarte, R. and De Andres, S. A.: Gene selection and classification of microarray data using random forest, BMC bioinformatics, 7, 14 pp., 2006.
D'Odorico, P. and Fagherazzi, S.: A probabilistic model of rainfall-triggered shallow landslides in hollows: A long-term analysis, Water Resour. Res., 39, 1262–1275, 2003.
Donati, L. and Turrini, M.: An objective method to rank the importance of the factors predisposing to landslides with the GIS methodology: application to an area of the Apennines (Valnerina; Perugia, Italy), Eng. Geology, 63, 277–289, 2002.
Ermini, L., Catani, F., and Casagli, N.: Artificial neural networks applied to landslide susceptibility assessment, Geomorphology, 66, 327–343, 2005.
Falaschi, F., Giacomelli, F., Federici, P., Puccinelli, A., Avanzi, G., Pochini, A., and Ribolini, A.: Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley, Italy, Nat. Hazards, 50, 551–569, 2009.
Federici, P. R., Puccinelli, A., Cantarelli, E., Casarosa, N., D'Amato Avanzi, G., Falaschi, F., Giannecchini, R., Pochini, A., Ribolini, A., Bottai, M., Salvati, N., and Testi, C.: Multidisciplinary investigations in evaluating landslide susceptibility – an example in the Serchio River valley (Italy), Quatern. Int., 171, 52–63, 2007.
Felicísimo, \'A. M., Cuartero, A., Remondo, J., and Quirós, E.: Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study, Landslides, 10, 175–189, 2013.
Fiori, E., Comellas, A., Molini, L., Rebora, N., Siccardi, F., Gochis, D., Tanelli, S., and Parodi, A.: Analysis and hindcast simulations of an extreme rainfall event in the Mediterranean area: The Genoa 2011 case, Atmos. Res., 138, 13–29, 2014.
Floris, M., Iafelice, M., Squarzoni, C., Zorzi, L., De Agostini, A., and Genevois, R.: Using online databases for landslide susceptibility assessment: an example from the Veneto Region (northeastern Italy), Nat. Hazards Earth Syst. Sci., 11, 1915–1925, https://doi.org/10.5194/nhess-11-1915-2011, 2011.
Fowler, H., Blenkinsop, S., and Tebaldi, C.: Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling, Int. J. Climatol., 27, 1547–1578, 2007.
Frattini, P., Crosta, G., and Carrara, A.: Techniques for evaluating the performance of landslide susceptibility models, Eng. Geol., 111, 62–72, 2010.
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, Springer, Berlin, Heidelberg, 14 pp., 2014.
Gao, X., Huete, A. R., Ni, W., and Miura, T.: Optical–biophysical relationships of vegetation spectra without background contamination, Remote Sens. Environ., 74, 609–620, 2000.
Giannecchini, R.: Rainfall triggering soil slips in the southern Apuan Alps (Tuscany, Italy), Adv. Geosci., 2, 21–24, 2005.
Giannecchini, R.: Relationship between rainfall and shallow landslides in the southern Apuan Alps (Italy), Nat. Hazards Earth Syst. Sci., 6, 357–364, https://doi.org/10.5194/nhess-6-357-2006, 2006.
Giannecchini, R., Naldini, D., D'Amato Avanzi, G., and Puccinelli, A.: Modelling of the initiation of rainfall-induced debris flows in the Cardoso basin (Apuan Alps, Italy), Quatern. Int., 171, 108–117, 2007.
Giannecchini, R., Galanti, Y., and D'Amato Avanzi, G.: Critical rainfall thresholds for triggering shallow landslides in the Serchio River Valley (Tuscany, Italy), Nat. Hazards Earth Syst. Sci., 12, 829–842, https://doi.org/10.5194/nhess-12-829-2012, 2012.
Glade, T., Crozier, M., and Smith, P.: Applying probability determination to refine landslide-triggering rainfall thresholds using an empirical "Antecedent Daily Rainfall Model", Pure Appl. Geophys., 157, 1059–1079, 2000.
Goetz, J. N., Guthrie, R. H., and Brenning, A.: Integrating physical and empirical landslide susceptibility models using generalized additive models, Geomorphology, 129, 376–386, 2011.
Guzzetti, F., Carrara, A., Cardinali, M., and Reichenbach, P.: Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy, Geomorphology, 31, 181–216, 1999.
Guzzetti, F., Aleotti, P., Malamud, B., and Turcotte, D.: Comparison of three landslide event inventories in central and northern Italy, in: Proceedings of the 4th EGS Plinius Conference, Spain: Universitat de les Ilies Balears, 2002.
Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., and Ardizzone, F.: Landslide hazard assessment in the Staffora basin, northern Italian Apennines, Geomorphology, 72, 272–299, 2005a.
Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., and Ardizzone, F.: Probabilistic landslide hazard assessment at the basin scale, Geomorphology, 72, 272–299, 2005b.
Guzzetti, F., Peruccacci, S., Rossi, M., and Stark, C. P.: Rainfall thresholds for the initiation of landslides in central and southern Europe, Meteorol. Atmos. Phys., 98, 239–267, 2007.
Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., and Chang, K.-T.: Landslide inventory maps: New tools for an old problem, Earth-Sci. Rev., 112, 42–66, 2012.
Hong, S.-Y., Noh, Y., and Dudhia, J.: A new vertical diffusion package with an explicit treatment of entrainment processes., Mon. Weather Rev., 134, 2318–2341, 2006.
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., and Ferreira, L. G.: Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sens. Environ., 83, 195–213, 2002.
Kain, J. S., Weiss, S. J., Bright, D. R., Baldwin, M. E., Levit, J. J., Carbin, G. W., Schwartz, C. S., Weisman, M. L., Droegemeier, K. K., Weber, D. B., et al.: Some practical considerations regarding horizontal resolution in the first generation of operational convection-allowing NWP., Weather Forecast., 23, 931–952, 2008.
Klemp, J., Skamarock, W., and Dudhia, J.: Conservative split-explicit time integration methods for the compressible nonhydrostatic equations., Mon. Weather Rev., 135, 2897–2913, 2007.
Liaw, A. and Wiener, M.: Classification and Regression by randomForest, R news, 2, 18–22, 2002.
Malamud, B. D., Turcotte, D. L., Guzzetti, F., and Reichenbach, P.: Landslide inventories and their statistical properties, Earth Surf. Proc. Land., 29, 687–711, 2004.
Mancini, F., Ceppi, C., and Ritrovato, G.: GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy, Nat. Hazards Earth Syst. Sci., 10, 1851–1864, https://doi.org/10.5194/nhess-10-1851-2010, 2010.
Mărgărint, M. C., Grozavu, A., and Patriche, C. V.: Assessing the spatial variability of weights of landslide causal factors in different regions from Romania using logistic regression, Nat. Hazards Earth Syst. Sci. Discuss., 1, 1749–1774, https://doi.org/10.5194/nhessd-1-1749-2013, 2013.
Marjanovic, M., Kovacevic, M., Bajat, B., Mihalic, S., and Abolmasov, B.: Landslide assessment of the Starca basin (Croatia) using machine learning algorithms, Acta Geotechnica Slovenica, 8, 45–55, 2011.
Marmion, M., Hjort, J., Thuiller, W., and Luoto, M.: A comparison of predictive methods in modelling the distribution of periglacial landforms in Finnish Lapland, Earth Surf. Proc. Land., 33, 2241–2254, 2008.
McCullagh, P.: Generalized linear models, Eur. J. Oper. Res., 16, 285–292, 1984.
McCullagh, P. and Nelder, J.: General linear models, Chapman and Halls, London, UK, 532 pp., 1989.
Mercogliano, P., Casagli, N., Catani, F., Damiano, E., Olivares, L., Picarelli, L., Rossi, G., Schiano, P., Segoni, S., Sikorski, B., et al.: Short Term Weather Forecasting for Shallow Landslide Prediction, in: Landslide Science and Practice, Springer, Berline, Heidelberg, 121–129, 2013a.
Mercogliano, P., Segoni, S., Rossi, G., Sikorsky, B., Tofani, V., Schiano, P., Catani, F., and Casagli, N.: Brief communication "A prototype forecasting chain for rainfall induced shallow landslides", Nat. Hazards Earth Syst. Sci., 13, 771–777, https://doi.org/10.5194/nhess-13-771-2013, 2013b.
Mondini, A., Guzzetti, F., Reichenbach, P., Rossi, M., Cardinali, M., and Ardizzone, F.: Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images, Remote Sens. Environ., 115, 1743–1757, 2011.
Moore, I. D. and Wilson, J. P.: Length-slope factors for the Revised Universal Soil Loss Equation: Simplified method of estimation, J. Soil Water Conserv., 47, 423–428, 1992.
Mossa, S., Capolongo, D., Pennetta, L., and Wasowski, J.: A GIS-based assessment of landsliding in the Daunia Apennines, southern Italy, Polish Geological Institute Special Papers, 20, 86–91, 2005.
Nefeslioglu, H. A., Duman, T. Y., and Durmaz, S.: Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey), Geomorphology, 94, 401–418, 2008.
Nefeslioglu, H. A., Gokceoglu, C., Sonmez, H., and Gorum, T.: Medium-scale hazard mapping for shallow landslide initiation: the Buyukkoy catchment area (Cayeli, Rize, Turkey), Landslides, 8, 459–483, 2011.
Nefeslioglu, H. A., San, B., Gokceoglu, C., and Duman, T.: An assessment on the use of Terra ASTER L3A data in landslide susceptibility mapping, Int. J. Appl. Earth Obs., 14, 40–60, 2012.
Oberto, E., Milelli, M., Pasi, F., and Gozzini, B.: Intercomparison of two meteorological limited area models for quantitative precipitation forecast verification, Nat. Hazards Earth Syst. Sci., 12, 591–606, https://doi.org/10.5194/nhess-12-591-2012, 2012.
Orlanski, I.: A rational subdivision of scales for atmospheric processes, B. Am. Meteorol. Soc., 56, 527–530, 1975.
Pal, M.: Random forest classifier for remote sensing classification, Int. J. Remote Sens., 26, 217–222, 2005.
Parodi, A., Boni, G., Ferraris, L., Siccardi, F., Pagliara, P., Trovatore, E., Foufoula-Georgiou, E., and Kranzlmueller, D.: The "perfect storm": From across the Atlantic to the hills of Genoa, Eos, T. Am. Geophys. Union, 93, 225–226, 2012.
Perna, M., Crisci, A., Capecchi, V., Bartolini, G., Betti, G., Piani, F., Gozzini, B., Barsanti, B., Bigio, T., Bonciani, F., et al.: Sensitivity Analysis for Shallow Landsliding Susceptibility Assessment in Northern Tuscany, in: Engineering Geology for Society and Territory, 2, 197–200, 2015.
Ponziani, F., Pandolfo, C., Stelluti, M., Berni, N., Brocca, L., and Moramarco, T.: Assessment of rainfall thresholds and soil moisture modeling for operational hydrogeological risk prevention in the Umbria region (central Italy), Landslides, 9, 229–237, 2012.
Pradhan, B., Chaudhari, A., Adinarayana, J., and Buchroithner, M. F.: Soil erosion assessment and its correlation with landslide events using remote sensing data and GIS: a case study at Penang Island, Malaysia, Environ. Monit. Assess., 184, 715–727, 2012.
Ray, R. L., Jacobs, J. M., and Cosh, M. H.: Landslide susceptibility mapping using downscaled AMSR-E soil moisture: A case study from Cleveland Corral, California, US, Remote Sens. Environ., 114, 2624–2636, 2010.
Rebora, N., Molini, L., Casella, E., Comellas, A., Fiori, E., Pignone, F., Siccardi, F., Silvestro, F., Tanelli, S., and Parodi, A.: Extreme Rainfall in the Mediterranean: What Can We Learn from Observations?, J. Hydrometeorol., 14, 906–922, 2013.
Regione Toscana, G. R.: Indirizzi operativi per la gestione organizzativa e funzionale del sistema di allertamento nazionale, statale e regionale per il rischio idrogeologico e idraulico ai fini di protezione civile (in italian), Tech. rep., Regione Toscana, 2006.
Regione Toscana, S. I. R.: Report evento meteo-idrologico del giorno 25 Ottobre 2011 (in italian), Tech. rep., Centro Funzionale della Regione Toscana, 2011.
Regione Toscana, S. I. R.: Report eventi meteo-idrologici occorsi nel periodo 5-19 Marzo 2013 sul territorio regionale (in italian), Tech. rep., Centro Funzionale della Regione Toscana, 2013.
Rosi, A., Segoni, S., Catani, F., and Casagli, N.: Statistical and environmental analyses for the definition of a regional rainfall threshold system for landslide triggering in Tuscany (Italy), J. Geogr. Sci., 22, 617–629, 2012.
Sacchi, J. A.: Analisi meteorologica degli eventi alluvionali eccezionali tra l'Alta Toscana e l'estremo Levante ligure dal 2009 al 2011 (in italian), Atti della Societá Toscana di Scienze Naturali, Memorie serie A, 117–119, 75–88, 2012.
Schicker, R. and Moon, V.: Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at a regional scale, Geomorphology, 161, 40–57, 2012.
Schmidt, J., Turek, G., Clark, M. P., Uddstrom, M., and Dymond, J. R.: Probabilistic forecasting of shallow, rainfall-triggered landslides using real-time numerical weather predictions, Nat. Hazards Earth Syst. Sci., 8, 349–357, https://doi.org/10.5194/nhess-8-349-2008, 2008.
Schwartz, C. S.: Reproducing the September 2013 Record-Breaking Rainfall over the Colorado Front Range with High-Resolution WRF Forecasts, Weather Forecast., 29, 393–402, 2014.
Schwitalla, T. and Wulfmeyer, V.: Radar data assimilation experiments using the IPM WRF Rapid Update Cycle, Meteorologische Zeitschrift, 23, 79–102, 2014.
Segoni, S., Leoni, L., Benedetti, A. I., Catani, F., Righini, G., Falorni, G., Gabellani, S., Rudari, R., Silvestro, F., and Rebora, N.: Towards a definition of a real-time forecasting network for rainfall induced shallow landslides, Nat. Hazards Earth Syst. Sci., 9, 2119–2133, https://doi.org/10.5194/nhess-9-2119-2009, 2009.
Segoni, S., Battistini, A., Rossi, G., Rosi, A., Lagomarsino, D., Catani, F., Moretti, S., and Casagli, N.: Technical Note: An operational landslide early warning system at regional scale based on space-time variable rainfall thresholds, Nat. Hazards Earth Syst. Sci. Discuss., 2, 6599–6622, https://doi.org/10.5194/nhessd-2-6599-2014, 2014a.
Segoni, S., Rosi, A., Rossi, G., Catani, F., and Casagli, N.: Analysing the relationship between rainfalls and landslides to define a mosaic of triggering thresholds for regional scale warning systems, Nat. Hazards Earth Syst. Sci. Discuss., 2, 2185–2213, https://doi.org/10.5194/nhessd-2-2185-2014, 2014b.
Skamarock, W. C. and Klemp, J. B.: A time-split nonhydrostatic atmospheric model for weather research and forecasting applications, J. Comput. Phys., 227, 3465–3485, 2008.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Wang, W., and Powers, J. G.: A description of the advanced research WRF version 2, Tech. rep., DTIC Document, 2005.
Stumpf, A. and Kerle, N.: Combining Random Forests and object-oriented analysis for landslide mapping from very high resolution imagery, Procedia Environ. Sci., 3, 123–129, 2011a.
Stumpf, A. and Kerle, N.: Object-oriented mapping of landslides using Random Forests, Remote Sens. Environ., 115, 2564–2577, 2011b.
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization., Mont. Weather Rev., 136, 5095–5115, 2008.
Varnes, D. J., the International Association Engineering Geology Commission on Landslides, and on Slopes, O. M. M.: Landslide hazard zonation: a review of principles and practice, 1984.
Vorpahl, P., Elsenbeer, H., Märker, M., and Schröder, B.: How can statistical models help to determine driving factors of landslides?, Ecol. Modell., 239, 27–39, 2012.
Wilks, D. S.: Statistical methods in the atmospheric sciences, vol. 100, Academic press, Oxford, UK, 704 pp., 2011.
Yilmaz, I.: Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine, Environ. Earth Sci., 61, 821–836, 2010.
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