Articles | Volume 18, issue 6
https://doi.org/10.5194/nhess-18-1735-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-18-1735-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Jun 2018
Research article |
| 22 Jun 2018
| 22 Jun 2018
Estimation of the susceptibility of a road network to shallow landslides with the integration of the sediment connectivity
Massimiliano Bordoni
CORRESPONDING AUTHOR
Department of Earth and Environmental Sciences, University of Pavia,
Pavia, 27100, Italy
M. Giuseppina Persichillo
Department of Earth and Environmental Sciences, University of Pavia,
Pavia, 27100, Italy
Claudia Meisina
Department of Earth and Environmental Sciences, University of Pavia,
Pavia, 27100, Italy
Stefano Crema
Research Institute for Geo-Hydrological Protection, National Research
Council, Padova, 35127, Italy
Marco Cavalli
Research Institute for Geo-Hydrological Protection, National Research
Council, Padova, 35127, Italy
Carlotta Bartelletti
Department of Earth Sciences, University of Pisa, Pisa, 56126, Italy
Yuri Galanti
Department of Earth Sciences, University of Pisa, Pisa, 56126, Italy
Michele Barsanti
Department of Civil and Industrial Engineering, University of Pisa,
Pisa, 56126, Italy
Roberto Giannecchini
Department of Earth Sciences, University of Pisa, Pisa, 56126, Italy
Giacomo D'Amato Avanzi
Department of Earth Sciences, University of Pisa, Pisa, 56126, Italy
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This manuscript defines a reliable methodology for shallow landslides triggering zones assessment at site-specific or local scales by using a well-established physically based model (TRIGRS-unsaturated) based on the calibration of the model at slope scale using soil hydrological behavior analyzed through a continuous field monitoring. Moreover, the paper analyzes the effects on triggering zones assessment of taking into account for different unit mapping of the slope soils of a studied area.
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Jui-Sheng Chou, Hoang-Minh Nguyen, Huy-Phuong Phan, and Kuo-Lung Wang
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Di Wu, Yuke Wang, and Xin Chen
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Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-213, https://doi.org/10.5194/nhess-2024-213, 2024
Revised manuscript accepted for NHESS
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Li Wei, Kaiheng Hu, Shuang Liu, Lan Ning, Xiaopeng Zhang, Qiyuan Zhang, and Md. Abdur Rahim
Nat. Hazards Earth Syst. Sci., 24, 4179–4197, https://doi.org/10.5194/nhess-24-4179-2024, https://doi.org/10.5194/nhess-24-4179-2024, 2024
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The damage patterns of the buildings were classified into three types: (I) buried by primary debris flow, (II) inundated by secondary dam-burst flood, and (III) sequentially buried by debris flow and inundated by dam-burst flood. The threshold of the impact pressures in Zones (II) and (III) where vulnerability is equal to 1 is 84 kPa and 116 kPa, respectively. Heavy damage occurs at an impact pressure greater than 50 kPa, while slight damage occurs below 30 kPa.
Bo Peng and Xueling Wu
Nat. Hazards Earth Syst. Sci., 24, 3991–4013, https://doi.org/10.5194/nhess-24-3991-2024, https://doi.org/10.5194/nhess-24-3991-2024, 2024
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Our research enhances landslide prevention using advanced machine learning to forecast heavy-rainfall-triggered landslides. By analyzing regions and employing various models, we identified optimal ways to predict high-risk rainfall events. Integrating multiple factors and models, including a neural network, significantly improves landslide predictions. Real data validation confirms our approach's reliability, aiding communities in mitigating landslide impacts and safeguarding lives and property.
Andrea Manconi, Yves Bühler, Andreas Stoffel, Johan Gaume, Qiaoping Zhang, and Valentyn Tolpekin
Nat. Hazards Earth Syst. Sci., 24, 3833–3839, https://doi.org/10.5194/nhess-24-3833-2024, https://doi.org/10.5194/nhess-24-3833-2024, 2024
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Our research reveals the power of high-resolution satellite synthetic-aperture radar (SAR) imagery for slope deformation monitoring. Using ICEYE data over the Brienz/Brinzauls instability, we measured surface velocity and mapped the landslide event with unprecedented precision. This underscores the potential of satellite SAR for timely hazard assessment in remote regions and aiding disaster mitigation efforts effectively.
Oliver Korup, Lisa V. Luna, and Joaquin V. Ferrer
Nat. Hazards Earth Syst. Sci., 24, 3815–3832, https://doi.org/10.5194/nhess-24-3815-2024, https://doi.org/10.5194/nhess-24-3815-2024, 2024
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Catalogues of mapped landslides are useful for learning and forecasting how frequently they occur in relation to their size. Yet, rare and large landslides remain mostly uncertain in statistical summaries of these catalogues. We propose a single, consistent method of comparing across different data sources and find that landslide statistics disclose more about subjective mapping choices than trigger types or environmental settings.
Rachael Lau, Carolina Seguí, Tyler Waterman, Nathaniel Chaney, and Manolis Veveakis
Nat. Hazards Earth Syst. Sci., 24, 3651–3661, https://doi.org/10.5194/nhess-24-3651-2024, https://doi.org/10.5194/nhess-24-3651-2024, 2024
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This work examines the use of interferometric synthetic-aperture radar (InSAR) alongside in situ borehole measurements to assess the stability of deep-seated landslides for the case study of El Forn (Andorra). Comparing InSAR with borehole data suggests a key trade-off between accuracy and precision for various InSAR resolutions. Spatial interpolation with InSAR informed how many remote observations are necessary to lower error in a remote sensing re-creation of ground motion over the landslide.
Zhen Lei Wei, Yue Quan Shang, Qiu Hua Liang, and Xi Lin Xia
Nat. Hazards Earth Syst. Sci., 24, 3357–3379, https://doi.org/10.5194/nhess-24-3357-2024, https://doi.org/10.5194/nhess-24-3357-2024, 2024
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The initiation of debris flows is significantly influenced by rainfall-induced hydrological processes. We propose a novel framework based on an integrated hydrological and hydrodynamic model and aimed at estimating intensity–duration (ID) rainfall thresholds responsible for triggering debris flows. In comparison to traditional statistical approaches, this physically based framework is particularly suitable for application in ungauged catchments where historical debris flow data are scarce.
Dianne L. Brien, Mark E. Reid, Collin Cronkite-Ratcliff, and Jonathan P. Perkins
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-141, https://doi.org/10.5194/nhess-2024-141, 2024
Revised manuscript accepted for NHESS
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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. We develop methods to identify potential runout zones from landslides. We apply our methods to create susceptibility maps for three study areas in Puerto Rico and assess the success of our methods based on mapped landslides from Hurricane Maria.
Jürgen Mey, Ravi Kumar Guntu, Alexander Plakias, Igo Silva de Almeida, and Wolfgang Schwanghart
Nat. Hazards Earth Syst. Sci., 24, 3207–3223, https://doi.org/10.5194/nhess-24-3207-2024, https://doi.org/10.5194/nhess-24-3207-2024, 2024
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The Himalayan road network links remote areas, but fragile terrain and poor construction lead to frequent landslides. This study on the NH-7 in India's Uttarakhand region analyzed 300 landslides after heavy rainfall in 2022 . Factors like slope, rainfall, rock type and road work influence landslides. The study's model predicts landslide locations for better road maintenance planning, highlighting the risk from climate change and increased road use.
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.
Roberto Sarro, Mauro Rossi, Paola Reichenbach, and Rosa María Mateos
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-85, https://doi.org/10.5194/nhess-2024-85, 2024
Revised manuscript accepted for NHESS
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This study proposes a novel workflow to precisely model rockfalls. It compares three methods for defining source areas to enhance model accuracy. Identified areas are inputted into a runout model to identify vulnerable zones. A new approach generates probabilistic susceptibility maps using ECDFs. Validation strategies employing various inventory types are included. Comparing six susceptibility maps highlights the impact of source area definition on model precision.
Alexander B. Prescott, Luke A. McGuire, Kwang-Sung Jun, Katherine R. Barnhart, and Nina S. Oakley
Nat. Hazards Earth Syst. Sci., 24, 2359–2374, https://doi.org/10.5194/nhess-24-2359-2024, https://doi.org/10.5194/nhess-24-2359-2024, 2024
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Fire can dramatically increase the risk of debris flows to downstream communities with little warning, but hazard assessments have not traditionally included estimates of inundation. We unify models developed by the scientific community to create probabilistic estimates of inundation area in response to rainfall at forecast lead times (≥ 24 h) needed for decision-making. This work takes an initial step toward a near-real-time postfire debris-flow inundation hazard assessment product.
Francis K. Rengers, Samuel Bower, Andrew Knapp, Jason W. Kean, Danielle W. vonLembke, Matthew A. Thomas, Jaime Kostelnik, Katherine R. Barnhart, Matthew Bethel, Joseph E. Gartner, Madeline Hille, Dennis M. Staley, Justin K. Anderson, Elizabeth K. Roberts, Stephen B. DeLong, Belize Lane, Paxton Ridgway, and Brendan P. Murphy
Nat. Hazards Earth Syst. Sci., 24, 2093–2114, https://doi.org/10.5194/nhess-24-2093-2024, https://doi.org/10.5194/nhess-24-2093-2024, 2024
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Every year the U.S. Geological Survey produces 50–100 postfire debris-flow hazard assessments using models for debris-flow likelihood and volume. To refine these models they must be tested with datasets that clearly document rainfall, debris-flow response, and debris-flow volume. These datasets are difficult to obtain, but this study developed and analyzed a postfire dataset with more than 100 postfire storm responses over a 2-year period. We also proposed ways to improve these models.
Ellen B. Robson, Bhim Kumar Dahal, and David G. Toll
EGUsphere, https://doi.org/10.5194/egusphere-2024-1300, https://doi.org/10.5194/egusphere-2024-1300, 2024
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Slopes excavated alongside roads in Nepal frequently fail (a landslide), resulting in substantial losses. Our participatory approach study involving road engineers aimed to assess the efficacy of the current slope design guidelines in Nepal. Our study revealed inconsistent guideline adherence due to their lack of user-friendliness and inadequate training. We recommend developing simpler, context-specific guidelines and comprehensive training to enhance resilience in Nepal's road network.
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.
Jonathan P. Perkins, Nina S. Oakley, Brian D. Collins, Skye C. Corbett, and W. Paul Burgess
EGUsphere, https://doi.org/10.5194/egusphere-2024-873, https://doi.org/10.5194/egusphere-2024-873, 2024
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Landslides are a global issue that results in deaths and economic losses annually. However, it is not clear how storm severity relates to landslide severity across large regions. Here we develop a method to estimate the footprint of landslide area and compare this to meteorologic estimates of storm severity. We find that total storm strength does not clearly relate to landslide area. Rather, landslide area depends on soil wetness and smaller storm structures that can produce intense rainfall.
Daniel Bolliger, Fritz Schlunegger, and Brian W. McArdell
Nat. Hazards Earth Syst. Sci., 24, 1035–1049, https://doi.org/10.5194/nhess-24-1035-2024, https://doi.org/10.5194/nhess-24-1035-2024, 2024
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We analysed data from the Illgraben debris flow monitoring station, Switzerland, and we modelled these flows with a debris flow runout model. We found that no correlation exists between the grain size distribution, the mineralogical composition of the matrix, and the debris flow properties. The flow properties rather appear to be determined by the flow volume, from which most other parameters can be derived.
Yuntao Zhou, Xiaoyan Zhao, Guangze Zhang, Bernd Wünnemann, Jiajia Zhang, and Minghui Meng
Nat. Hazards Earth Syst. Sci., 24, 891–906, https://doi.org/10.5194/nhess-24-891-2024, https://doi.org/10.5194/nhess-24-891-2024, 2024
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We developed three rock bridge models to analyze 3D stability and deformation behaviors of the Tizicao landslide and found that the contact surface model with high strength parameters combines advantages of the intact rock mass model in simulating the deformation of slopes with rock bridges and the modeling advantage of the Jennings model. The results help in choosing a rock bridge model to simulate landslide stability and reveal the influence laws of rock bridges on the stability of landslides.
Ashok Dahal, Hakan Tanyas, Cees van Westen, Mark van der Meijde, Paul Martin Mai, Raphaël Huser, and Luigi Lombardo
Nat. Hazards Earth Syst. Sci., 24, 823–845, https://doi.org/10.5194/nhess-24-823-2024, https://doi.org/10.5194/nhess-24-823-2024, 2024
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We propose a modeling approach capable of recognizing slopes that may generate landslides, as well as how large these mass movements may be. This protocol is implemented, tested, and validated with data that change in both space and time via an Ensemble Neural Network architecture.
Li-Ru Luo, Zhi-Xiang Yu, Li-Jun Zhang, Qi Wang, Lin-Xu Liao, and Li Peng
Nat. Hazards Earth Syst. Sci., 24, 631–649, https://doi.org/10.5194/nhess-24-631-2024, https://doi.org/10.5194/nhess-24-631-2024, 2024
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We performed field investigations on a rockfall near Jiguanshan National Forest Park, Chengdu. Vital information was obtained from an unmanned aerial vehicle survey. A finite element model was created to reproduce the damage evolution. We found that the impact kinetic energy was below the design protection energy. Improper member connections prevent the barrier from producing significant deformation to absorb energy. Damage is avoided by improving the ability of the nets and ropes to slide.
Sudhanshu Dixit, Srikrishnan Siva Subramanian, Piyush Srivastava, Ali P. Yunus, Tapas Ranjan Martha, and Sumit Sen
Nat. Hazards Earth Syst. Sci., 24, 465–480, https://doi.org/10.5194/nhess-24-465-2024, https://doi.org/10.5194/nhess-24-465-2024, 2024
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Rainfall intensity–duration (ID) thresholds can aid in the prediction of natural hazards. Large-scale sediment disasters like landslides, debris flows, and flash floods happen frequently in the Himalayas because of their propensity for intense precipitation events. We provide a new framework that combines the Weather Research and Forecasting (WRF) model with a regionally distributed numerical model for debris flows to analyse and predict intense rainfall-induced landslides in the Himalayas.
Jacob B. Woodard, Benjamin B. Mirus, Nathan J. Wood, Kate E. Allstadt, Benjamin A. Leshchinsky, and Matthew M. Crawford
Nat. Hazards Earth Syst. Sci., 24, 1–12, https://doi.org/10.5194/nhess-24-1-2024, https://doi.org/10.5194/nhess-24-1-2024, 2024
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Dividing landscapes into hillslopes greatly improves predictions of landslide potential across landscapes, but their scaling is often arbitrarily set and can require significant computing power to delineate. Here, we present a new computer program that can efficiently divide landscapes into meaningful slope units scaled to best capture landslide processes. The results of this work will allow an improved understanding of landslide potential and can help reduce the impacts of landslides worldwide.
Anne Felsberg, Zdenko Heyvaert, Jean Poesen, Thomas Stanley, and Gabriëlle J. M. De Lannoy
Nat. Hazards Earth Syst. Sci., 23, 3805–3821, https://doi.org/10.5194/nhess-23-3805-2023, https://doi.org/10.5194/nhess-23-3805-2023, 2023
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The Probabilistic Hydrological Estimation of LandSlides (PHELS) model combines ensembles of landslide susceptibility and of hydrological predictor variables to provide daily, global ensembles of hazard for hydrologically triggered landslides. Testing different hydrological predictors showed that the combination of rainfall and soil moisture performed best, with the lowest number of missed and false alarms. The ensemble approach allowed the estimation of the associated prediction uncertainty.
Xushan Shi, Bo Chai, Juan Du, Wei Wang, and Bo Liu
Nat. Hazards Earth Syst. Sci., 23, 3425–3443, https://doi.org/10.5194/nhess-23-3425-2023, https://doi.org/10.5194/nhess-23-3425-2023, 2023
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A 3D stability analysis method is proposed for biased rockfall with external erosion. Four failure modes are considered according to rockfall evolution processes, including partial damage of underlying soft rock and overall failure of hard rock blocks. This method is validated with the biased rockfalls in the Sichuan Basin, China. The critical retreat ratio from low to moderate rockfall susceptibility is 0.33. This method could facilitate rockfall early identification and risk mitigation.
Marius Schneider, Nicolas Oestreicher, Thomas Ehrat, and Simon Loew
Nat. Hazards Earth Syst. Sci., 23, 3337–3354, https://doi.org/10.5194/nhess-23-3337-2023, https://doi.org/10.5194/nhess-23-3337-2023, 2023
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Rockfalls and their hazards are typically treated as statistical events based on rockfall catalogs, but only a few complete rockfall inventories are available today. Here, we present new results from a Doppler radar rockfall alarm system, which has operated since 2018 at a high frequency under all illumination and weather conditions at a site where frequent rockfall events threaten a village and road. The new data set is used to investigate rockfall triggers in an active rockslide complex.
Annette I. Patton, Lisa V. Luna, Joshua J. Roering, Aaron Jacobs, Oliver Korup, and Benjamin B. Mirus
Nat. Hazards Earth Syst. Sci., 23, 3261–3284, https://doi.org/10.5194/nhess-23-3261-2023, https://doi.org/10.5194/nhess-23-3261-2023, 2023
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Landslide warning systems often use statistical models to predict landslides based on rainfall. They are typically trained on large datasets with many landslide occurrences, but in rural areas large datasets may not exist. In this study, we evaluate which statistical model types are best suited to predicting landslides and demonstrate that even a small landslide inventory (five storms) can be used to train useful models for landslide early warning when non-landslide events are also included.
Sandra Melzner, Marco Conedera, Johannes Hübl, and Mauro Rossi
Nat. Hazards Earth Syst. Sci., 23, 3079–3093, https://doi.org/10.5194/nhess-23-3079-2023, https://doi.org/10.5194/nhess-23-3079-2023, 2023
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The estimation of the temporal frequency of the involved rockfall processes is an important part in hazard and risk assessments. Different methods can be used to collect and analyse rockfall data. From a statistical point of view, rockfall datasets are nearly always incomplete. Accurate data collection approaches and the application of statistical methods on existing rockfall data series as reported in this study should be better considered in rockfall hazard and risk assessments in the future.
Stefan Hergarten
Nat. Hazards Earth Syst. Sci., 23, 3051–3063, https://doi.org/10.5194/nhess-23-3051-2023, https://doi.org/10.5194/nhess-23-3051-2023, 2023
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Rockslides are a major hazard in mountainous regions. In formerly glaciated regions, the disposition mainly arises from oversteepened topography and decreases through time. However, little is known about this decrease and thus about the present-day hazard of huge, potentially catastrophic rockslides. This paper presents a new theoretical framework that explains the decrease in maximum rockslide size through time and predicts the present-day frequency of large rockslides for the European Alps.
Colin K. Bloom, Corinne Singeisen, Timothy Stahl, Andrew Howell, Chris Massey, and Dougal Mason
Nat. Hazards Earth Syst. Sci., 23, 2987–3013, https://doi.org/10.5194/nhess-23-2987-2023, https://doi.org/10.5194/nhess-23-2987-2023, 2023
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Landslides are often observed on coastlines following large earthquakes, but few studies have explored this occurrence. Here, statistical modelling of landslides triggered by the 2016 Kaikōura earthquake in New Zealand is used to investigate factors driving coastal earthquake-induced landslides. Geology, steep slopes, and shaking intensity are good predictors of landslides from the Kaikōura event. Steeper slopes close to the coast provide the best explanation for a high landslide density.
Yi-Min Huang
Nat. Hazards Earth Syst. Sci., 23, 2649–2662, https://doi.org/10.5194/nhess-23-2649-2023, https://doi.org/10.5194/nhess-23-2649-2023, 2023
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Debris flows are common hazards in Taiwan, and debris-flow early warning is important for disaster responses. The rainfall thresholds of debris flows are analyzed and determined in terms of rainfall intensity, accumulated rainfall, and rainfall duration, based on case histories in Taiwan. These thresholds are useful for disaster management, and the cases in Taiwan are useful for global debris-flow databases.
Davide Notti, Martina Cignetti, Danilo Godone, and Daniele Giordan
Nat. Hazards Earth Syst. Sci., 23, 2625–2648, https://doi.org/10.5194/nhess-23-2625-2023, https://doi.org/10.5194/nhess-23-2625-2023, 2023
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We developed a cost-effective and user-friendly approach to map shallow landslides using free satellite data. Our methodology involves analysing the pre- and post-event NDVI variation to semi-automatically detect areas potentially affected by shallow landslides (PLs). Additionally, we have created Google Earth Engine scripts to rapidly compute NDVI differences and time series of affected areas. Datasets and codes are stored in an open data repository for improvement by the scientific community.
Simon Seelig, Thomas Wagner, Karl Krainer, Michael Avian, Marc Olefs, Klaus Haslinger, and Gerfried Winkler
Nat. Hazards Earth Syst. Sci., 23, 2547–2568, https://doi.org/10.5194/nhess-23-2547-2023, https://doi.org/10.5194/nhess-23-2547-2023, 2023
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A rapid sequence of cascading events involving thermokarst lake outburst, rock glacier front failure, debris flow development, and river blockage hit an alpine valley in Austria during summer 2019. We analyze the environmental conditions initiating the process chain and identify the rapid evolution of a thermokarst channel network as the main driver. Our results highlight the need to account for permafrost degradation in debris flow hazard assessment studies.
Camilla Lanfranconi, Paolo Frattini, Gianluca Sala, Giuseppe Dattola, Davide Bertolo, Juanjuan Sun, and Giovanni Battista Crosta
Nat. Hazards Earth Syst. Sci., 23, 2349–2363, https://doi.org/10.5194/nhess-23-2349-2023, https://doi.org/10.5194/nhess-23-2349-2023, 2023
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This paper presents a study on rockfall dynamics and hazard, examining the impact of the presence of trees along slope and block fragmentation. We compared rockfall simulations that explicitly model the presence of trees and fragmentation with a classical approach that accounts for these phenomena in model parameters (both the hazard and the kinetic energy change). We also used a non-parametric probabilistic rockfall hazard analysis method for hazard mapping.
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.
Cited articles
Ayalew, L. and Yamagishi, H.: The application of GIS-based logistic
regression for landslide susceptibility mapping in the Kakuda-Yahiko
Mountains, Central Japan, Geomorphology, 65, 12–31,
https://doi.org/10.1016/j.geomorph.2004.06.010, 2005.
Bai, S. B., Wang, J., Lu, G. N., Zhou, P. G., Hou, S. S., and Xu, S. N.:
Gis-based logistic regression fro landslide susceptibility mapping of the
Zhongxian segment in the Three Gorges area, China, Geomorphology, 115,
23–31, https://doi.org/10.1016/j.geomorph.2009.09.025, 2010.
Bathurst, J. C., Burton, A., and Ward, T. J.: Debris flow run-out and
landslide sediment delivery model test, J. Hydraul. Eng., 123, 410–419,
https://doi.org/10.1061/(ASCE)0733-9429(1997)123:5(410), 1997.
Begueria, S.: Changes in land cover and shallow landslide activity: a case
study in the Spanish Pyrenees, Geomorphology, 74, 196–206,
https://doi.org/10.1016/j.geomorph.2005.07.018, 2006.
Belsley, D. A., Kuh, E., and Welsch, R. E. (Eds.): Regression diagnostics:
identifying influential data and sources of collinearity, John Wiley and
Sons, New York, USA, 1980.
Bil, M., Kubecek, J., and Andrasik, R.: An epidemiological approach to
determining the risk of road damage due to landslides, Nat. Hazards, 73,
1323–1335, https://doi.org/10.1007/s11069-014-1141-4, 2014.
Bil, M., Andrasik, R., Kubecek, J., Krivankova, Z., and Vodak, R.: RUPOK: An
online landslide risk tool for road networks, in: Advancing culture of living
with landslides, edited by: Mikos, M., Vilimek, V., Yin, Y., and Sassa, K.,
Springer, Cham, 19–26, 2017.
Bordoni, M., Meisina, C., Valentino, R., Bittelli, M., and Chersich, S.:
Site-specific to local-scale shallow landslides triggering zones assessment
using TRIGRS, Nat. Hazards Earth Syst. Sci., 15, 1025–1050,
https://doi.org/10.5194/nhess-15-1025-2015, 2015.
Borselli, L., Cassi, P., and Torri, D.: Prolegomena to sediment and flow
connectivity in the landscape: a GIS and field numerical assessment, Catena,
75, 268–277, https://doi.org/10.1016/j.catena.2008.07.006, 2008.
Brenning, A., Schwinn, M., Ruiz-Páez, A. P., and Muenchow, J.: Landslide
susceptibility near highways is increased by 1 order of magnitude in the
Andes of southern Ecuador, Loja province, Nat. Hazards Earth Syst. Sci., 15,
45–57, https://doi.org/10.5194/nhess-15-45-2015, 2015.
Budetta, P.: Assessment of rockfall risk along roads, Nat. Hazards Earth
Syst. Sci., 4, 71–81, https://doi.org/10.5194/nhess-4-71-2004, 2004.
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.
Cavalli, M. and Marchi, L.: Characterisation of the surface morphology of an
alpine alluvial fan using airborne LiDAR, Nat. Hazards Earth Syst. Sci., 8,
323–333, https://doi.org/10.5194/nhess-8-323-2008, 2008.
Cavalli, M., Tarolli, P., Marchi, L., and Dalla Fontana, G.: The
effectiveness of airborne LiDAR data in the recognition of channel-bed
morphology, Catena, 73, 249–260, https://doi.org/10.1016/j.catena.2007.11.001, 2008.
Cavalli, M., Trevisani, S., Comiti, F., and Marchi, L.: Geomorphometric
assessment of spatial sediment connectivity in small alpine catchments,
Geomorphology, 188, 31–41, https://doi.org/10.1016/j.geomorph.2012.05.007, 2013.
Cevasco, A., Pepe, G., and Brandolini, P.: The influences of geological and
land-use settings on shallow landslides triggered by an intense rainfall
event in a coastal terraced environment, B. Eng. Geol. Environ., 73,
859–875, https://doi.org/10.1007/s10064-013-0544-x, 2014.
Chau, K. T., Sze, Y. L., Fung, M. K., Wong, W. Y., Fong, E. L., and Chan, L.
C. P.: Landslide hazard analysis for Hong Kong using landslide inventory and
GIS, Comp. Geosci., 30, 429–443, https://doi.org/10.1016/j.cageo.2003.08.013, 2004.
Chen, Z. and Wang, J.: Landslide hazard mapping using logistic regression
model in Mackenzie Valley, Canada, Nat. Hazards, 42, 75–89,
https://doi.org/10.1007/s11069-006-9061-6, 2007.
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L.,
Wehberg, J., Wichmann, V., and Böhner, J.: System for Automated
Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991–2007,
https://doi.org/10.5194/gmd-8-1991-2015, 2015.
Corominas, J., Van Westen, C., Frattini, P., Cascini, L., Malet, J. P.,
Fotopoulou, S., Catani, F., Van Den Eeckhaut, M., Mavrouli, O., Agliardi, F.,
Pitilakis, K., Winter, M. G., Pastor, M., Ferlisi, S., Tofani, V., Hervas,
J., and Smith, J. T.: Recommendations for the quantitative analysis of
landslide risk, B. Eng. Geol. Environ., 73, 209–263,
https://doi.org/10.1007/s10064-013-0538-8 2014.
Crema, S. and Cavalli, M.: SedInConnect: A stand-alone, free and open source
tool for the assessment of sediment connectivity, Comp. Geosci., 111, 39–45,
https://doi.org/10.1016/j.cageo.2017.10.009, 2018.
Cruden, D. M. and Varnes, D. J.: Landslide types and processes, in:
Landslides: investigation and mitigation, edited by: Turner, A. K. and
Schuster, R. L., National Academy Press, Washington, D.C., 36–75, 1996.
Dai, F. C. and Lee, C. F.: Landslide characteristics and slope instability
modeling using GIS, Lantau Island, Hong Kong, Geomorphology, 42, 213–228,
https://doi.org/10.1016/S0169-555X(01)00087-3, 2002.
Dai, F. C., Lee, C. F., and Ngai, Y. Y.: Landslide risk assessment and
management: an overview, Eng. Geol., 64, 65–87,
https://doi.org/10.1016/S0013-7952(01)00093-X, 2002.
D'Amato Avanzi, G., Galanti, Y., Giannecchini, R., and Puccinelli, A.:
Fragility of territory and infrastructures resulting from rainstorms in
Northern Tuscany (Italy), in: Landslide science and practice, Vol. 6, edited
by: Margottini, C., Canuti, P., and Sassa, K., Springer-Verlag Berlin
Heidelberg, 239–246, 2013.
Donnini, M., Napolitano, E., Salvati, P., Ardizzone, F., Bucci, F., Fiorucci,
F., Santangelo, M., Cardinali, M., and Guzzetti, F.: Impact of event
landslides on road networks: a statistical analysis of two Italian case
studies, Landslides, 14, 1521–1535, https://doi.org/10.1007/s10346-017-0829-4, 2017.
Fan, L., Lehmann, P., McArdell, B., and Or, D.: Linking rainfall-induced
landslides with debris flows runout patterns towards catchment scale hazard
assessment, Geomorphology, 280, 1–15, https://doi.org/10.1016/j.geomorph.2016.10.007,
2017.
Fannin, R. and Wise, M.: An empirical-statistical model for debris flow
travel distance, Can. Geotech. J., 38, 982–994, https://doi.org/10.1139/t01-030, 2001.
Farrar, D. E. and Glauber, R. R.: Multicollinearity in regression analysis:
the problem revisited, Rev. Econ. Stat., 49, 92–107, https://doi.org/10.2307/1937887,
1967.
Fasolini, D: La cartografia dell'uso e copertura del suolo: uno strumento per rilevare il cambiamento del territorio lombardo, Ri-Vista, 1–2, 76–87, 2014.
Fathani, T. F., Legono, D., and Karnawati, D.: A numerical model for the
analysis of rapid landslide motion, Geotech. Geol. Eng., 35, 2253–2268,
https://doi.org/10.1007/s10706-017-0241-9, 2017.
Foerster, S., Wilczok, C., Brosinsky, A., and Segl, K.: Assessment of
sediment connectivity from vegetation cover and topography using remotely
sensed data in a dryland catchment in the Spanish Pyrenees, J. Soils
Sediments, 14, 1982–2000, https://doi.org/10.1007/s11368-014-0992-3, 2014.
Fryirs, K. A., Brierley, G. J., Preston, N. J., and Kasai, M.: Buffers,
barriers and blankets: the (dis)connectivity of catchment-scale sediment
cascades, Catena, 70, 49–67, https://doi.org/10.1016/j.catena.2006.07.007, 2007.
Fu, B. J., Zhang, Q. J., Chen, L. D., Zhao, W. W., Gulinck, H., Liu, G. B.,
Yang, Q. K., and Zhu, Y. G.: Temporal change in land use and its relationship
to slope degree and soil type in a small catchment on the Loess Plateau of
China, Catena, 65, 41–48, https://doi.org/10.1016/j.catena.2005.07.005, 2006.
Galve, J. P., Cevasco, A., Brandolini, P., and Soldati, M.: Assessment of
shallow landslide risk mitigation measures based on land use planning through
probabilistic modelling, Landslides, 12, 101–114,
https://doi.org/10.1007/s10346-014-0478-9, 2015.
Gariano, S. L. and Guzzetti, F.: Landslides in a changing climate, Earth Sci.
Rev., 162, 227–252, https://doi.org/10.1016/j.earscirev.2016.08.011, 2016.
Gay, A., Cerdan, O., Mardhel, V., and Desmet, M.: Application of an index of
sediment connectivity in a lowland area, J. Soils Sediments, 16, 280–293,
https://doi.org/10.1007/s11368-015-1235-y, 2015.
Glade, T.: Landslide occurrence as a response to land use change: a review of
evidence from New Zealand, Catena, 51, 297–314,
https://doi.org/10.1016/S0341-8162(02)00170-4, 2003.
Goetz, J. N., Guthrie, R. H., and Brenning, A.: Integrating physical and
empirical landslide susceptibility models using generalized additive models,
Geomorphology, 129, 376–386, https://doi.org/10.1016/j.geomorph.2011.03.001, 2011.
Guzzetti, F., Galli, M., Reichenbach, P., Ardizzone, F., and Cardinali, M.:
Landslide hazard assessment in the Collazzone area, Umbria, Central Italy,
Nat. Hazards Earth Syst. Sci., 6, 115–131,
https://doi.org/10.5194/nhess-6-115-2006, 2006.
Hastie, T.: gam: Generalized Additive Models, R package version 1.08,
available at: http://CRAN.R-project.org/package=gam, last access: 29
August 2013.
Hastie, T. J. and Tibshirani, R. (Eds.): Generalized additive models, Chapman
& Hall, London, UK, 1990.
Hearn, G., Hunt, T., Aubert, J., and Howell, J.: Landslide impacts on the
road network of Lao PDR and the feasibility of implementing a slope
management programme, South East Asia Community Access Programme (SEACAP),
Department for International Development, UK, 2008.
Hosmer, D. W. and Lemeshow, S.: Applied logistic regression, Wiley, New York,
USA, 2000.
Hungr, O.: A model for the runout analysis of rapid flow slides, debris
flows, and avalanches, Can. Geotech. J., 32, 610–623, https://doi.org/10.1139/t95-063,
1995.
Jaiswal, P., Van Westen, C. J., and Jetten, V.: Quantitative landslide hazard
assessment along a transportation corridor in southern India, Eng. Geol.,
116, 236–250, https://doi.org/10.1016/j.enggeo.2010.09.005, 2010a.
Jaiswal, P., van Westen, C. J., and Jetten, V.: Quantitative assessment of
direct and indirect landslide risk along transportation lines in southern
India, Nat. Hazards Earth Syst. Sci., 10, 1253–1267,
https://doi.org/10.5194/nhess-10-1253-2010, 2010b.
Jaiswal, P., Van Westen, C. J., and Jetten, V.: Quantitative assessment of
landslide hazard along transportation lines using historical records,
Landslides, 8, 279–291, https://doi.org/10.1007/s10346-011-0252-1, 2011.
Jenks, G. F.: The data model concept in statistical mapping, Int. Year.
Cart., 7, 186–190, 1967.
Jia, G., Yuan, T., Liu, Y., and Zhang, Y.: A static and dynamic
factors-coupled forecasting model or regional rainfall induced landslides: a
case study of Shenzhen, Sci. China Ser. E., 51, 164–175, 2008.
Jollifee, I. T. and Stephenson, D. B. (Eds.): Forecast verification: A
practitioner's guide in atmospheric science, John Wiley and Sons, New York,
USA, 2003.
Kalantari, Z., Cavalli, M., Cantone, C., Crema, S., and Destouni, G.: Flood
probability quantification for road infrastructure: Data-driven
spatial-statistical approach and case study applications, Sci. Total
Environ., 581–582, 386–398, https://doi.org/10.1016/j.scitotenv.2016.12.147, 2017.
Klose, M., Damm, B., and Terhorstet, B.: Landslide cost modeling for
transportation infrastructures: a methodological approach, Landslides, 12,
321–334, https://doi.org/10.1007/s10346-014-0481-1, 2015.
Klose, M., Auerbach, M., Herrmann, C., Kumerics, C., and Gratzki, A.:
Landslide hazards and climate change adaptation of transport infrastructures
in Germany, in: Advancing culture of living with landslides, edited by:
Mikos, M., Vilimek, V., Yin, Y., and Sassa, K., Springer, Cham, 535–541,
2017.
Kritikos, T. and Davies, T.: Assessment of rainfall-generated shallow
landslide/debris-flow susceptibility and runout using a GIS-based approach:
application to western Southern Alps of New Zealand, Landslides, 12,
1051–1075, https://doi.org/10.1007/s10346-014-0533-6, 2015.
Lieskovsky, L. and Kenderessy, P.: Modelling the effect of vegetation cover
and different tillage practices on soil erosion in vineyards: a case study in
Vrable (Slovakia) using WATEM/SEDEM, Land Degrad. Dev., 25, 188–196,
https://doi.org/10.1002/ldr.2162, 2014.
Lopez-Vicente, M., Poesen, J., Navas, A., and Gaspar, L.: Predicting runoff
and sediment connectivity and soil erosion by water for different land use
scenarios in the Spanish Pre-Pyrenees, Catena, 102, 62–73,
https://doi.org/10.1016/j.catena.2011.01.001, 2013.
Lopez-Vicente, M., Nadal-Romero, E., and Cammeraat, E. L. H.: Hydrological
connectivity does change over 70 years of abandonment and afforestation in
the Spanish Pyrenees, Land Degrad. Dev., 28, 1298–1310,
https://doi.org/10.1002/ldr.2531, 2016.
Maindonald, J. and Braun, W. J. (Eds.): Data analysis and graphics using R:
an example based approach, Cambridge Series in Statistical and Probabilistic
Mathematics, Cambridge, UK, 2010.
Martinovic, K., Gavin, K., and Reale, C.: Development of a landslide
susceptibility assessment for a rail network, Eng. Geol., 215, 1–9,
https://doi.org/10.1016/j.enggeo.2016.10.011, 2016.
Martinovic, K., Gavin, K., Reale, C., and Mangan, C.: Rainfall thresholds as
a landslide indicator for engineered slopes on the Irish Rail network,
Geomorphology, 306, 40–50, https://doi.org/10.1016/j.geomorph.2018.01.006, 2018.
Matulla, C., Hollsi, B., Andre, K., Gringinger, J., Chimani, B., Namyslo, J.,
Fuchs, T., Auerbach, M., Herrmann, C., Sladek, B., Berghold, H., Gschier, R.,
and Eichinger-Vill, E.: Climate Change driven evolution of hazards to
Europe's transport infrastructure throughout the twenty-first century, Theor.
Appl. Climatol., https://doi.org/10.1007/s00704-017-2127-4, 2017.
McLachlan, G. J. (Ed.): Discriminant analysis and statistical pattern
recognition, John Wiley & Sons, New York, USA, 1992.
Michaelides, S.: Vulnerability of transportation to extreme weather and
climate change, Nat. Hazards, 72, 1–4, https://doi.org/10.1007/s11069-013-0975-5, 2014.
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.
Molinaro, A. M., Simon, R., and Pfeiffer, R. M.: Prediction error estimation:
a comparison of resampling methods, Bioinformatics, 21, 3301–3307, https://doi.org/10.1093/bioinformatics/bti499, 2005.
Muenchow, J., Brenning, A., and Richter, M.: Geomorphic process rates of
landslides along a humidity gradient in the tropical Andes, Geomorphology,
139, 271–284, https://doi.org/10.1016/j.geomorph.2011.10.029, 2012.
Nemry, F. and Demirel, H.: Impacts of Climate Change on transport: a focus on
road and rail transport infrastructures. Publications Office of the European
Union, Luxembourg, Luxembourg, 93 pp., 2012.
Olaya, V.: A gentle introduction to SAGA GIS, Edition 1.1 – Rev. 9 December,
216 pp., 2004.
Pastor, M., Blanc, T., Haddad, B., Petrone, S., Sanchez Morles, M.,
Drempetic, V., Issler, D., Crosta, G. B., Cascini, L., Sorbino, G., and
Cuomo, S.: Application of a SPH depth-integrated model to landslide run-out
analysis, Landslides, 11, 793–812, https://doi.org/10.1007/s10346-014-0484-y, 2014.
Pellicani, R., Argentiero, I., and Spilotro, G.: GIS-based predictive models
for regional-scale landslide susceptibility assessment and risk mapping along
road corridors, Geomat. Nat. Haz. Risk, 8, 1012–1033,
https://doi.org/10.1080/19475705.2017.1292411, 2017.
Penna, D., Borga, M., Aronica, G. T., Brigandì, G., and Tarolli, P.: The
influence of grid resolution on the prediction of natural and road-related
shallow landslides, Hydrol. Earth Syst. Sci., 18, 2127–2139,
https://doi.org/10.5194/hess-18-2127-2014, 2014.
Persichillo, M. G., Bordoni, M., and Meisina, C.: The role of land use
changes in the distribution of shallow landslides, Sci. Total Environ., 574,
924–937, https://doi.org/10.1016/j.scitotenv.2016.09.125, 2017a.
Persichillo, M. G., Bordoni, M., Meisina, C., Bartelletti, C., Barsanti, M.,
Giannecchini, R., D'Amato Avanzi, G., Galanti, Y., Cevasco, A., Brandolini,
P., and Galve, J. P.: Shallow landslides susceptibility assessment in
different environments, Geomat. Nat. Haz. Risk, 8, 748–771,
https://doi.org/10.1080/19475705.2016.1265011, 2017b.
Persichillo, M. G., Bordoni, M., Cavalli, M., Crema, S., and Meisina, C.: The
role of human activities on sediment connectivity of shallow landslides,
Catena, 160, 261–274, https://doi.org/10.1016/j.catena.2017.09.025, 2018.
Petschko, H., Brenning, A., Bell, R., Goetz, J., and Glade, T.: Assessing the
quality of landslide susceptibility maps – case study Lower Austria, Nat.
Hazards Earth Syst. Sci., 14, 95–118,
https://doi.org/10.5194/nhess-14-95-2014, 2014.
Phillips, J. D.: Sources of nonlinearity and complexity in geomorphic
systems, Prog. Phys. Geogr., 27, 1–23, https://doi.org/10.1191/0309133303pp340ra, 2003.
Phillips, J. D.: Evolutionary geomorphology: thresholds and nonlinearity in
landform response to environmental change, Hydrol. Earth Syst. Sci., 10,
731–742, https://doi.org/10.5194/hess-10-731-2006, 2006.
Postance, B., Hillier, J., Dijkstra, T., and Dixon, N.: Extending natural
hazard impacts: an assessment of landslide disruptions on a national road
transportation network, Environ. Res. Lett., 12, 14010,
https://doi.org/10.1088/1748-9326/aa5555, 2017.
Prosdocimi, M., Cerdà, A., and Tarolli, P.: Soil water erosion on
Mediterranean vineyards: A review, Catena, 141, 1–21,
https://doi.org/10.1016/j.catena.2016.02.010, 2016.
Quinn, P., Beven, K., Chevallier, P., and Planchon, O.: The prediction of
hillslope flow paths for distributed hydrological modelling using digital
terrain models, Hydrol. Process., 5, 59–79, https://doi.org/10.1002/hyp.3360050106,
1991.
Quinn, P. E., Hutchinson, D. J., Diederichs, M. S., and Rowe, R. K.:
Regional-scale landslide susceptibility mapping using the weights of evidence
method: an example applied to linear infrastructure, Can. Geotech. J., 47,
905–927, https://doi.org/10.1139/T09-144, 2010.
Ramesh, V. and Anbazhagan, S.: Landslide susceptibility mapping along Kolli
hills Ghat road section (India) using frequency ratio, relative effect and
fuzzy logic models, Environ. Earth Sci., 73, 8009–8021,
https://doi.org/10.1007/s12665-014-3954-6, 2015.
Reichenbach, P., Busca, C., Mondini, A. C., and Rossi, M.: The influence of
land use change on landslide susceptibility zonation: the Briga catchment
test site (Messina, Italy), Environ. Manage., 54, 1372–1384,
https://doi.org/10.1007/s00267-014-0357-0, 2014.
Rural Police Regulation: Regolamento di Polizia Rurale, Comune di Canneto
Pavese, Italy, 17 pp., 2008.
Salvati, P., Bianchi, C., Fiorucci, F., Giostrella, P., Marchesini, I., and
Guzzetti, F.: Perception of flood and landslide risk in Italy: a preliminary
analysis, Nat. Hazards Earth Syst. Sci., 14, 2589–2603,
https://doi.org/10.5194/nhess-14-2589-2014, 2014.
Seibert, J. and McGlynn, B. L.: A new triangular multiple flow direction
algorithm for computing upslope areas from gridded digital elevation models,
Water Resour. Res., 43, W04501, https://doi.org/10.1029/2006WR005128, 2007.
Seibert, J., Stendahl, J., and Sorensen, R.: Topographical influences on soil
properties in boreal forests, Geoderma, 141, 139–148,
https://doi.org/10.1016/j.geoderma.2007.05.013, 2007.
Sidle, R. C. and Ochiai, H.: Landslides: Processes, prediction, and land use,
Water Resources Monograph, AGU, Washington D.C., 2006.
Sidle, R. C., Ghestem, M., and Stokes, A.: Epic landslide erosion from
mountain roads in Yunnan, China – challenges for sustainable development,
Nat. Hazards Earth Syst. Sci., 14, 3093–3104,
https://doi.org/10.5194/nhess-14-3093-2014, 2014.
Spitalnic, S.: Test properties 2: likelihood ratios, Bayes' formula, and
receiver operating characteristic curves, Hosp. Physician, 40, 53–58, 2004.
Strauch, R. L., Raymond, C. L., Rochefort, R. M., Hamlet, A. F., and Lauver,
C.: Adapting transportation to climate change on federal lands in Washington
State, U.S.A, Climatic Change, 130, 185–199, https://doi.org/10.1007/s10584-015-1357-7,
2015.
Surian, N., Righini, M., Lucia, A., Nardi, L., Amponsah, W., Benvenuti, M.,
Borga, M., Cavalli, M., Comiti, F., Marchi, L., Rinaldi, M., and Viero, A.:
Channel response to extreme floods: Insights on controlling factors from six
mountain rivers in northern Apennines, Italy, Geomorphology, 272, 78–91,
https://doi.org/10.1016/j.geomorph.2016.02.002, 2016.
Tarolli, P. and Sofia, G.: Human topographic signatures and derived
geomorphic processes across landscapes, Geomorphology, 255, 140–161,
https://doi.org/10.1016/j.geomorph.2015.12.007, 2016.
Tarolli, P., Calligaro, S., Cazorzi, F., and Dalla Fontana, G.: Recognition
of surface flow processes influenced by roads and trails in mountain areas
using high-resolution topography, Eur. J. Remote Sens., 46, 176–197,
https://doi.org/10.5721/EuJRS20134610, 2013.
Tarolli, P., Preti, F., and Romano, N.: Terraced landscapes: from an old best
practice to a potential hazard for soil degradation due to land abandonment,
Anthropocene, 6, 10–25, https://doi.org/10.1016/j.ancene.2014.03.002, 2014.
Tarolli, P., Sofia, G., Calligaro, S., Prosdocimi, M., Preti, F., and Dalla
Fontana, G.: Vineyards in terraced landscapes: New opportunities from LIDAR
data, Land Degrad. Dev., 26, 92–102, https://doi.org/10.1002/ldr.2311, 2015.
Tiranti, D., Cavalli, M., Crema, S., Zerbato, M., Graziadei, M., Barbero, S.,
Cremonini, R., Silvestro, C., Bodrato, G., and Tresso, F.: Semi-quantitative
method for the assessment of debris supply from slopes to river in ungauged
catchments, Sci. Total Environ., 554–555, 337–348,
https://doi.org/10.1016/j.scitotenv.2016.02.150, 2016.
Van Westen, C. J., Asch T. W. J., and Soeters, R.: Landslide hazard and risk
zonation – why is it still so difficult?, B. Eng. Geol. Environ., 65,
67–184, https://doi.org/10.1007/s10064-005-0023-0, 2006.
Van Westen, C. J., Castellanos, E., and Kuriakose, S. L.: Spatial data for
landslide susceptibility, hazard, and vulnerability assessment: an overview,
Eng. Geol., 102, 112–131, https://doi.org/10.1016/j.enggeo.2008.03.010, 2008.
Varnes, D. J. (Ed.): Landslide hazard zonation – a review of principles and
practice, UNESCO, Paris, France, 1984.
Vercesi, P. and Scagni, G.: Osservazioni sui depositi conglomeratici dello
sperone collinare di Stradella, Rendiconti della Società Geologica
Italiana, 7, 23–26, 1984.
Winchell, M. F., Jackson, S. H., Wadley, A. M., and Srinivasan, R.: Extension
and validation of a geographic information system-based method for
calculating the revised universal soil loss equation length-slop factor for
erosion risk assessments in large watersheds, J. Soil Water Conserv., 63,
105–111, 2008.
Winter, M. G., Shearer, B., Palmer, D., Peeling, D., Harmer, C., and Sharpe
J.: The economic impact of landslides and floods on the road network,
Procedia Eng., 143, 1425–1434, https://doi.org/10.1016/j.proeng.2016.06.168, 2016.
Zaffaroni, P.: Confronto fra CLC 2006 e DUSAF 2.1 della Regione Lombardia,
9–12 November 2010, ASITA, Brescia, 2010.
Wischmeier, W. H. and Smith, D. D.: Predicting rainfall erosion losses: a
guide to conservation planning with Universal Soil Loss Equation (USLE), in:
Agriculture Handbook, No. 703, Department of Agriculture, Washington, D. C.,
1978.
Zezere, J. L., Oliveira, S. C., Garcia, R. A. C., and Reis, E.: Landslide
risk analysis in the area North of Lisbon (Portugal): evaluation of direct
and indirect costs resulting from a motorway disruption by slope movements,
Landslides, 4, 123–136, https://doi.org/10.1007/s10346-006-0070-z, 2007.
Zevenbergen, L. W. and Thorne, C. R.: Quantitative analysis of land surface
topography, Earth Surf. Proc. Land., 12, 47–56,
https://doi.org/10.1002/esp.3290120107, 1987.
Zizioli, D., Meisina, C., Valentino, R., and Montrasio, L.: Comparison
between different approaches to modeling shallow landslide susceptibility: a
case history in Oltrepo Pavese, Northern Italy, Nat. Hazards Earth Syst.
Sci., 13, 559–573, https://doi.org/10.5194/nhess-13-559-2013, 2013.
Short summary
This paper aimed to develop and test a data-driven model for the identification of road sectors that are susceptible to be hit by shallow landslides triggered in slopes upstream of infrastructure. Most susceptible road traits were those located below steep slopes with a limited height (lower than 50 m), where sediment connectivity is high. The results of the susceptibility analysis can give asset managers indispensable information on the relative criticality of the different roads.
This paper aimed to develop and test a data-driven model for the identification of road sectors...
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