Articles | Volume 19, issue 11
https://doi.org/10.5194/nhess-19-2477-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-19-2477-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Nov 2019
Research article |
| 12 Nov 2019
| 12 Nov 2019
A new approach to mapping landslide hazards: a probabilistic integration of empirical and physically based models in the North Cascades of Washington, USA
Ronda Strauch
CORRESPONDING AUTHOR
Seattle City Light, Seattle, WA, USA
Civil & Environmental Engineering, College of Engineering, University of Washington, Seattle, WA, USA
Erkan Istanbulluoglu
Civil & Environmental Engineering, College of Engineering, University of Washington, Seattle, WA, USA
Jon Riedel
National Park Service, US Department of the Interior, Sedro-Woolley, WA, USA
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Ronda Strauch, Erkan Istanbulluoglu, Sai Siddhartha Nudurupati, Christina Bandaragoda, Nicole M. Gasparini, and Gregory E. Tucker
Earth Surf. Dynam., 6, 49–75, https://doi.org/10.5194/esurf-6-49-2018, https://doi.org/10.5194/esurf-6-49-2018, 2018
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We develop a model of annual probability of shallow landslide initiation triggered by soil water from a hydrologic model. Our physically based model accommodates data uncertainty using a Monte Carlo approach. We found elevation-dependent patterns in probability related to the stabilizing effect of forests and soil and slope limitation at high elevations. We demonstrate our model in Washington, USA, but it is designed to run elsewhere with available data for risk planning using the Landlab.
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MassWastingRunout (MWR) is a new landslide runout model designed for sediment transport, landscape evolution, and hazard assessment applications. MWR is written in Python and includes a calibration utility that automatically determines best-fit parameters for a site and empirical probability density functions of each parameter for probabilistic model implementation. MWR and Jupyter Notebook tutorials are available as part of the Landlab package at https://github.com/landlab/landlab.
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Landlab is a Python package to support the creation of numerical models in Earth surface dynamics. Since the release of the 1.0 version in 2017, Landlab has grown and evolved: it contains 31 new process components, a refactored model grid, and additional utilities. This contribution describes the new elements of Landlab, discusses why certain backward-compatiblity-breaking changes were made, and reflects on the process of community open-source software development.
Ronda Strauch, Erkan Istanbulluoglu, Sai Siddhartha Nudurupati, Christina Bandaragoda, Nicole M. Gasparini, and Gregory E. Tucker
Earth Surf. Dynam., 6, 49–75, https://doi.org/10.5194/esurf-6-49-2018, https://doi.org/10.5194/esurf-6-49-2018, 2018
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We develop a model of annual probability of shallow landslide initiation triggered by soil water from a hydrologic model. Our physically based model accommodates data uncertainty using a Monte Carlo approach. We found elevation-dependent patterns in probability related to the stabilizing effect of forests and soil and slope limitation at high elevations. We demonstrate our model in Washington, USA, but it is designed to run elsewhere with available data for risk planning using the Landlab.
Jordan M. Adams, Nicole M. Gasparini, Daniel E. J. Hobley, Gregory E. Tucker, Eric W. H. Hutton, Sai S. Nudurupati, and Erkan Istanbulluoglu
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Daniel E. J. Hobley, Jordan M. Adams, Sai Siddhartha Nudurupati, Eric W. H. Hutton, Nicole M. Gasparini, Erkan Istanbulluoglu, and Gregory E. Tucker
Earth Surf. Dynam., 5, 21–46, https://doi.org/10.5194/esurf-5-21-2017, https://doi.org/10.5194/esurf-5-21-2017, 2017
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Many geoscientists use computer models to understand changes in the Earth's system. However, typically each scientist will build their own model from scratch. This paper describes Landlab, a new piece of open-source software designed to simplify creation and use of models of the Earth's surface. It provides off-the-shelf tools to work with models more efficiently, with less duplication of effort. The paper explains and justifies how Landlab works, and describes some models built with it.
Gregory E. Tucker, Daniel E. J. Hobley, Eric Hutton, Nicole M. Gasparini, Erkan Istanbulluoglu, Jordan M. Adams, and Sai Siddartha Nudurupati
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This paper presents a new Python-language software library, called CellLab-CTS, that enables rapid creation of continuous-time stochastic (CTS) cellular automata models. These models are quite useful for simulating the behavior of natural systems, but can be time-consuming to program. CellLab-CTS allows users to set up models with a minimum of effort, and thereby focus on the science rather than the software.
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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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Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-86, https://doi.org/10.5194/nhess-2024-86, 2024
Revised manuscript accepted for NHESS
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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.
Benjamin B. Mirus, Thom A. Bogaard, Roberto Greco, and Manfred Stähli
EGUsphere, https://doi.org/10.5194/egusphere-2024-1219, https://doi.org/10.5194/egusphere-2024-1219, 2024
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Early warning of increased landslide potential provides situational awareness to reduce landslide-related losses from major storm events. For decades, landslide forecasts relied on rainfall data alone, but recent research points to the value of hydrologic information for improving predictions. In this article, we provide our perspectives on the value and limitations of integrating subsurface hillslope hydrologic monitoring data and mathematical modeling for more accurate landslide forecasts.
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.
Marko Sinčić, Sanja Bernat Gazibara, Mauro Rossi, and Snježana Mihalić Arbanas
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-29, https://doi.org/10.5194/nhess-2024-29, 2024
Revised manuscript accepted for NHESS
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The paper focuses on classifying continuous landslide conditioning factors for susceptibility modelling, which resulted in 54 landslide susceptibility models that tested 11 classification criteria in combination with five statistical methods. The novelty of the research is that using stretched landslide conditioning factor values results in models with higher accuracy and that certain statistical methods are more sensitive to the landslide conditioning factor classification criteria than others.
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.
Di Wu, Yuke Wang, and Xin Chen
EGUsphere, https://doi.org/10.5194/egusphere-2023-2318, https://doi.org/10.5194/egusphere-2023-2318, 2023
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This paper proposed 3D limit analysis for seismic stability of soil slopes to address the influence of earthquake on slope stabilities with nonlinear and linear criteria. Comparison results illustrated that the use of linear envelope leads to the non-negligible overestimation of steep slope stability and this overestimation will be significant with the increasing earthquake. Earthquake has a smaller influence on slope slip surface with nonlinear envelope than that with linear envelope.
Mark Bloomberg, Tim Davies, Elena Moltchanova, Tom Robinson, and David Palmer
EGUsphere, https://doi.org/10.5194/egusphere-2023-2695, https://doi.org/10.5194/egusphere-2023-2695, 2023
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Debris flows occur infrequently, with average recurrence intervals (ARIs) ranging from decades to millennia. Consequently, they pose an underappreciated hazard. We describe how to make a preliminary identification of debris flow-susceptible catchments, estimate threshold ARIs for debris flows which pose an unacceptable risk to life, and identify the "window of non-recognition" where debris flows are infrequent enough that their hazard is unrecognised, yet frequent enough to pose a risk to life.
Xushan Shi, Bo Chai, Juan Du, Wei Wang, and Bo Liu
Nat. Hazards Earth Syst. Sci., 23, 3425–3443, https://doi.org/10.5194/nhess-23-3425-2023, https://doi.org/10.5194/nhess-23-3425-2023, 2023
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A 3D stability analysis method is proposed for biased rockfall with external erosion. Four failure modes are considered according to rockfall evolution processes, including partial damage of underlying soft rock and overall failure of hard rock blocks. This method is validated with the biased rockfalls in the Sichuan Basin, China. The critical retreat ratio from low to moderate rockfall susceptibility is 0.33. This method could facilitate rockfall early identification and risk mitigation.
Marius Schneider, Nicolas Oestreicher, Thomas Ehrat, and Simon Loew
Nat. Hazards Earth Syst. Sci., 23, 3337–3354, https://doi.org/10.5194/nhess-23-3337-2023, https://doi.org/10.5194/nhess-23-3337-2023, 2023
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Rockfalls and their hazards are typically treated as statistical events based on rockfall catalogs, but only a few complete rockfall inventories are available today. Here, we present new results from a Doppler radar rockfall alarm system, which has operated since 2018 at a high frequency under all illumination and weather conditions at a site where frequent rockfall events threaten a village and road. The new data set is used to investigate rockfall triggers in an active rockslide complex.
Annette I. Patton, Lisa V. Luna, Joshua J. Roering, Aaron Jacobs, Oliver Korup, and Benjamin B. Mirus
Nat. Hazards Earth Syst. Sci., 23, 3261–3284, https://doi.org/10.5194/nhess-23-3261-2023, https://doi.org/10.5194/nhess-23-3261-2023, 2023
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Landslide warning systems often use statistical models to predict landslides based on rainfall. They are typically trained on large datasets with many landslide occurrences, but in rural areas large datasets may not exist. In this study, we evaluate which statistical model types are best suited to predicting landslides and demonstrate that even a small landslide inventory (five storms) can be used to train useful models for landslide early warning when non-landslide events are also included.
Sandra Melzner, Marco Conedera, Johannes Hübl, and Mauro Rossi
Nat. Hazards Earth Syst. Sci., 23, 3079–3093, https://doi.org/10.5194/nhess-23-3079-2023, https://doi.org/10.5194/nhess-23-3079-2023, 2023
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The estimation of the temporal frequency of the involved rockfall processes is an important part in hazard and risk assessments. Different methods can be used to collect and analyse rockfall data. From a statistical point of view, rockfall datasets are nearly always incomplete. Accurate data collection approaches and the application of statistical methods on existing rockfall data series as reported in this study should be better considered in rockfall hazard and risk assessments in the future.
Stefan Hergarten
Nat. Hazards Earth Syst. Sci., 23, 3051–3063, https://doi.org/10.5194/nhess-23-3051-2023, https://doi.org/10.5194/nhess-23-3051-2023, 2023
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Rockslides are a major hazard in mountainous regions. In formerly glaciated regions, the disposition mainly arises from oversteepened topography and decreases through time. However, little is known about this decrease and thus about the present-day hazard of huge, potentially catastrophic rockslides. This paper presents a new theoretical framework that explains the decrease in maximum rockslide size through time and predicts the present-day frequency of large rockslides for the European Alps.
Colin K. Bloom, Corinne Singeisen, Timothy Stahl, Andrew Howell, Chris Massey, and Dougal Mason
Nat. Hazards Earth Syst. Sci., 23, 2987–3013, https://doi.org/10.5194/nhess-23-2987-2023, https://doi.org/10.5194/nhess-23-2987-2023, 2023
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Landslides are often observed on coastlines following large earthquakes, but few studies have explored this occurrence. Here, statistical modelling of landslides triggered by the 2016 Kaikōura earthquake in New Zealand is used to investigate factors driving coastal earthquake-induced landslides. Geology, steep slopes, and shaking intensity are good predictors of landslides from the Kaikōura event. Steeper slopes close to the coast provide the best explanation for a high landslide density.
Yi-Min Huang
Nat. Hazards Earth Syst. Sci., 23, 2649–2662, https://doi.org/10.5194/nhess-23-2649-2023, https://doi.org/10.5194/nhess-23-2649-2023, 2023
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Debris flows are common hazards in Taiwan, and debris-flow early warning is important for disaster responses. The rainfall thresholds of debris flows are analyzed and determined in terms of rainfall intensity, accumulated rainfall, and rainfall duration, based on case histories in Taiwan. These thresholds are useful for disaster management, and the cases in Taiwan are useful for global debris-flow databases.
Davide Notti, Martina Cignetti, Danilo Godone, and Daniele Giordan
Nat. Hazards Earth Syst. Sci., 23, 2625–2648, https://doi.org/10.5194/nhess-23-2625-2023, https://doi.org/10.5194/nhess-23-2625-2023, 2023
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We developed a cost-effective and user-friendly approach to map shallow landslides using free satellite data. Our methodology involves analysing the pre- and post-event NDVI variation to semi-automatically detect areas potentially affected by shallow landslides (PLs). Additionally, we have created Google Earth Engine scripts to rapidly compute NDVI differences and time series of affected areas. Datasets and codes are stored in an open data repository for improvement by the scientific community.
Li Wei, Kaiheng Hu, Shuang Liu, Nan Ning, Xiaopeng Zhang, Qiyuan Zhang, and Md Abdur Rahim
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-75, https://doi.org/10.5194/nhess-2023-75, 2023
Revised manuscript accepted for NHESS
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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) buried by debris flow and inundated by dam-burst flood sequentially. The threshold of the impact pressures in Zones II and III where vulnerability is equal to 1 are 88 kPa and 106 kPa, respectively. Heavy damage occurs at an impact pressure greater than 40 kPa, while slight damage occurs below 20 kPa.
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.
Cited articles
Agee, J. K. and Kertis, J.: Forest types of the north Cascades National Park Service complex, Can. J. Bot., 65, 1520–1530, 1987.
Aleotti, P. and Chowdhury, R.: Landslide hazard assessment: summary review
and new perspectives, B. Eng. Geol. Environ., 58, 21–44, 1999.
Anagnostopoulos, G. G., Fatichi, S., and Burlando, P.: An advanced
process-based distributed model for the investigation of rainfall-induced
landslides: The effect of process representation and boundary conditions,
Water Resour. Res., 51, 7501–7523, 2015.
Ayalew, L., Yamagishi, H., and Ugawa, N.: Landslide susceptibility mapping
using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan, Landslides, 1, 73–81, 2004.
Baum, R. L., Galloway, D. L., and Harp, E. L.: Landslide and land subsidence
hazards to pipelines, US Geological Survey Open-File Report 2008-1164, US Geological Survey, Reston, Virginia, 192 pp., 2008.
Beaty, C. B.: Landslides and Slope Exposure, J. Geol., 64, 70–74, 1956.
Bellugi, D., Milledge, D. G., Dietrich, W. E., Perron, J. T., and McKean, J.:
Predicting shallow landslide size and location across a natural landscape:
Application of a spectral clustering search algorithm, J. Geophys.
Res.-Earth, 120, 2552–2585, 2015.
Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing area model of basin hydrology, Hydrolog. Sci. J., 24, 43–69, 1979.
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.
Borga, M., Fontana, G. D., and Cazorzi, F.: Analysis of topographic and climatic control on rainfall-triggered shallow landsliding using a quasi-dynamic wetness index, J. Hydrol., 268, 56–71, 2002.
Carrara, A., Cardinali, M., Guzzetti, F., and Reichenback, P.: GIS technology in mapping landslide hazards, in: Geographical Information System in Assessing Natural Hazard, edited by: Carrara, A. and Guzzetti, F., Springer, Dordrecht, 135–175, 1995.
Carson, M. A. and Kirkby, M. J.: Hillslope Form and Process, Cambridge University Press, Cambridge, UK, 475 pp., 1972.
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, 2014.
Chalkias, C., Ferentinou, M., and Polykretis, C.: GIS-based landslide
susceptibility mapping on the Peloponnese Peninsula, Greece, Geosciences, 4, 176–190, 2014.
Chung, C. J. and Fabbri, A. G.: Modeling the conditional probability of the occurrence of future landslides in a study area characterized by spatial data, Int. Arch. Photogram. Remote Sens. Spat. Inform. Sci., 34, 124–131, 2002.
Coe, J. A.: Landslide hazards and climate change: A perspective from the United States, in: Slope safety preparedness for impact of climate change,
Chapter: 14, edited by: Ho, K., Lacasse, S., and Picarelli, L., CRC Press,
Boca Raton, FL, 479–523, 2016.
Collins, B. D. and Montgomery, D. R.: The legacy of Pleistocene glaciation and the organization of lowland alluvial process domains in the Puget Sound
region, Geomorphology, 126, 174–185, 2011.
Corominas, J., Van Westen, C., Frattini, P., Cascini, L., Malet, J. P., Fotopoulou, S., Catani, F., Van Den Eeckhaut, M., Mavrouli, O., Agliardi, F., and Pitilakis, K.: Recommendations for the quantitative analysis of landslide risk, B. Eng. Geol. Environ., 73, 209–263, 2014.
Croke, J. C. and Hairsine, P. B.: Sediment delivery in managed forests: a
review, Environ. Rev., 14, 59–87, 2006.
Crozier, M. J.: Deciphering the effect of climate change on landslide activity: A review, Geomorphology, 124, 260–267, 2010.
Dai, F. C. and Lee, C. F.: Landslide characteristics and slope instability
modeling using GIS, Lantau Island, Hong Kong, Geomorphology, 42, 213–228, 2002.
Densmore, A. L., Anderson, R. S., McAdoo, B. G., and Ellis, M. A.: Hillslope
evolution by bedrock landslides, Science, 275, 369–372, 1997.
DOI-NPS – United States Department of the Interior, National Park Service:
Foundation Document, North Cascades National Park Complex, Washington,
available at:
https://www.nps.gov/noca/learn/management/upload/North-Cascades-NP-Complex-Foundation-Document_small.pdf, (last access: 23 January 2017), 2012.
El-Ramly, H., Morgenstern, N. R., and Cruden, D. M.: Probabilistic slope stability analysis for practice, Can. Geotech. J., 39, 665–683, 2002.
Ercanoglu, M. and Sonmez, H.: General Trends and New Perspectives on Landslide Mapping and Assessment Methods. In Environmental Information
Systems: Concepts, Methodologies, Tools, and Applications, IGI Global, Hershey, Pennsylvania, 64–93, https://doi.org/10.4018/978-1-5225-7033-2.ch004, 2019.
Evans, R. D. and Fonda, R. W.: The influence of snow on subalpine meadow
community pattern, North Cascades, Washington, Can. J. Bot., 68, 212–220, 1990.
Fawcett, T.: An introduction to ROC analysis, Pattern Recogn. Lett., 27, 861–874, 2006.
Fischer, L., Kääb, A., Huggel, C., and Noetzli, J.: Geology, glacier retreat and permafrost degradation as controlling factors of slope instabilities in a high-mountain rock wall: the Monte Rosa east face, Nat. Hazards Earth Syst. Sci., 6, 761–772, https://doi.org/10.5194/nhess-6-761-2006, 2006.
Gabet, E. J.: Sediment transport by dry ravel, J. Geophys. Res.-Solid,
108, 2049, https://doi.org/10.1029/2001JB001686, 2003.
Geroy, I. J., Gribb, M. M., Marshall, H. P., Chandler, D. G., Benner, S. G., and McNamara, J. P.: Aspect influences on soil water retention and storage,
Hydrol. Process., 25, 3836–3842, https://doi.org/10.1002/hyp.8281, 2011.
Ghirotti, M.: The 1963 Vaiont landslide, Italy, in: Landslides: Types,
mechanisms and modeling, edited by: Claque, J. J. and Stead, D., Cambridge
University Press, New York, NY, 359 pp., 2012.
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.
Gokceoglu, C., Sonmez, H., Nefeslioglu, H. A., Duman, T. Y., and Can, T.:
The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity, Eng. Geol., 81, 65–83,
2005.
Gupta, R. P. and Joshi, B. C.: Landslide hazard zoning using the GIS approach – a case study from the Ramganga catchment, Himalayas, Eng. Geol., 28, 119–131, 1990.
Haeberli, W., Schaub, Y., and Huggel, C.: Increasing risks related to landslides from degrading permafrost into new lakes in de-glaciating mountain ranges, Geomorphology, 293, 405–417, https://doi.org/10.1016/j.geomorph.2016.02.009, 2017.
Hales, T. C., Ford, C. R., Hwang, T., Vose, J. M., and Band, L. E.: Topographic and ecologic controls on root reinforcement, J. Geophys. Res.,
114, F03013, https://doi.org/10.1029/2008JF001168, 2009.
Hamlet, A. F., Elsner, M. M., Mauger, G., Lee, S., and Tohver, I.: An Overview of the Columbia Basin Climate Change Scenarios Project: Approach,
Methods, and Summary of Key Results, Atmos. Ocean., 51, 392–415, 2013.
Hanley, J. A. and McNeil, B. J.: The meaning and use of the area under a
receiver operating characteristic (ROC) curve, Radiology, 143, 29–36, 1982.
Haugerud, R. A. and Tabor, R. W.: Geologic map of the North Cascade Range,
Washington, US Department of the Interior, US Geological Survey, Denver, Colorado, 29 pp., 2009.
Hobley, D. E. J., Adams, J. M., Nudurupati, S. S., Hutton, E. W. H., Gasparini, N. M., Istanbulluoglu, E., and Tucker, G. E.: Creative computing
with Landlab: an open-source toolkit for building, coupling, and exploring
two-dimensional numerical models of Earth-surface dynamics, Earth Surf. Dynam., 5, 21–46, https://doi.org/10.5194/esurf-5-21-2017, 2017.
Hong, H., Chen, W., Xu, C., Youssef, A. M., Pradhan, B., and Tien Bui, D.:
Rainfall-induced landslide susceptibility assessment at the Chongren area
(China) using frequency ratio, certainty factor, and index of entropy,
Geocarto Int., 32, 139–154, 2017.
Hungr, O.: A review of landslide hazard and risk assessment methodology,
in: Landslides and engineered slopes. Experience, theory and practice, edited
by: Aversa, S., Cascini, L., Picarelli, L., and Scavia, C., CRC Press, Boca
Raton, FL, 3–27, 2018.
Hungr, O., Leroueil, S., and Picarelli, L.: The Varnes classification of
landslide types, an update. Landslides, 11, 167–194, 2014.
Jin, S., Yang, L., Danielson, P., Homer, C., Fry, J., and Xian, G.: A comprehensive change detection method for updating the National Land Cover Database to circa 2011, Remote Sens. Environ., 132, 159–175, 2013.
Kelsey, H. M.: Formation of inner gorges, Catena, 15, 433–458, 1988.
Kirschbaum, D. B., Adler, R., Hong, Y., Kumar, S., Peters-Lidard, C., and
Lerner-Lam, A.: Advances in landslide nowcasting: evaluation of global and
regional modeling approach, Environ. Earth. Sci., 66, 1683–1696, 2012.
LaHusen, S. R., Duvall, A. R., Booth, A. M., and Montgomery, D. R.: Surface
roughness dating of long-runout landslides near Oso, Washington (USA),
reveals persistent postglacial hillslope instability, Geology, 44, 111–114, 2016.
Lee, S. and Pradhan, B.: Landslide hazard mapping at Selangor, Malaysia using
frequency ratio and logistic regression models, Landslides, 4, 33–41, 2007.
Lee, S., Ryu, J. H., and Kim, I. S.: Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial
neural network models: Case study of Youngin, Korea, Landslides, 4, 327–338,
2007.
Lepore, C., Kamal, S. A., Shanahan, P., and Bras, R. L.: Rainfall-induced
landslide susceptibility zonation of Puerto Rico, Environ. Earth Sci., 66, 1667–1681, 2012.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple
hydrologically based model of land surface water and energy fluxes for GSMs,
J. Geophys. Res., 99, 14415–14428, 1994.
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.
May, C. L., Pryor, B., Lisle, T. E., and Lang, M.: Coupling hydrodynamic modeling and empirical measures of bed mobility to predict the risk of scour and fill of salmon redds in a large regulated river, Water Resour. Res., 45, W05402, https://doi.org/10.1029/2007WR006498, 2009.
Megahan, W. F., Day, N. F., and Bliss, T. M.: Landslide occurrence in the
western and central Northern Rocky Mountain physiographic province in Idaho,
in: Forest Soils and Land Use: Proceedings of the Fifth North American Forest
Soils Conference, edited by: Youngberg, C. T., CSU, Ft. Collins, CO, 116–139, 1978.
Miller, D. J. and Burnett, K. M.: Effects of forest cover, topography, and
sampling extent on the measured density of shallow, translational landslides, Water Resour. Res., 43, W03433, https://doi.org/10.1029/2005WR004807, 2007.
Montgomery, D. R.: Road surface drainage, channel initiation, and slope
instability, Water Resour. Res., 30, 1925–1932, 1994.
Montgomery, D. R.: Slope distributions, threshold hillslopes, and steady-state topography, Am. J. Sci., 301, 432–454, 2001.
Mustoe, G. E. and Leopold, E. B.: Paleobotanical evidence for the post-Miocene uplift of the Cascade Range, Can. J. Earth Sci., 51, 809–824, 2014.
O'Loughlin, E. M.: Prediction of surface saturation zones in natural catchments by topographic analysis, Water Resour. Res., 22, 794–804, 1986.
Pachauri, A. K. and Pant, M.: Landslide hazard mapping based on geological
attributes, Eng. Geol., 32, 81–100, 1992.
Pack, R. T., Tarboton, D. G., and Goodwin, C. N.: The SINMAP approach to terrain stability mapping, in: Proceedings of the 8th international congress of the international association of engineering geology and the environment, vol. 2, 21–25 September 1998, Vancouver, British Columbia, Canada, AA Balkema, Rotterdam, 1157–1165, 1998.
Pelto, M. S. and Riedel, J.: Spatial and temporal variations in annual balance of North Cascade glaciers, Washington 1984–2000, Hydrol. Process.,
15, 3461–3472, 2001.
Pollock, M. M.: Biodiversity, in: River Ecology and Management: Lessons From
the Pacific Coastal Ecoregion, edited by: Naiman, R. J. and Bilby, R. E.,
Springer-Verlag, New York, 430–452, 1998.
Poulos, M. J., Pierce, J. L., Flores, A. N., and Benner, S. G.: Hillslope
asymmetry maps reveal widespread, multi-scale organization, Geophys. Res. Lett., 39, L06406, https://doi.org/10.1029/2012GL051283, 2012.
Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., and Guzzetti, F.: A
review of statistically-based landslide susceptibility models, Earth-Sci. Rev., 180, 60–91, 2018.
Riedel, J. L. and Probala, J.: Mapping ecosystems at the landform scale in Washington state, Park Science, 23, 37–42, 2005.
Riedel, J. L., Haugerud, R. A., and Clague, J. J.: Geomorphology of a Cordilleran Ice Sheet drainage network through breached divides in the North
Cascades Mountains of Washington and British Columbia, Geomorphology, 91, 1–18, 2007.
Riedel, J. L., Dorsch, S., and Wenger, J.: Geomorphology of the Stehekin River watershed: Landform mapping at North Cascades National Park Service Complex, Washington, Natural Resource Technical Report NPS/NCCN/NRTR-2012/566, National Park Service, Fort Collins, Colorado, 90 pp., 2012.
Roe, G. H.: Orographic Precipitation, Annu. Rev. Earth Planet. Sci., 33, 645–671, 2005.
Roering, J. J., Schmidt, K. M., Stock, J. D., Dietrich, W. E., and Montgomery, D. R.: Shallow landsliding, root reinforcement, and the spatial
distribution of trees in the Oregon Coast Range, Can. Geotech. J., 40,
237–253, 2003.
Sidle, R. C. and Ochiai, H.: Landslides: processes, prediction, and land use, Water Resources Monogram 18, American Geophysical Union, Washington, D.C., 2006.
Strauch, R., Istanbulluoglu, E., Nudurupati, S. S., Bandaragoda, C., Gasparini, N. M., and Tucker, G. E.: A hydro-climatological approach to
predicting regional landslide probability using Landlab, Earth Surf. Dynam.,
6, 49–75, https://doi.org/10.5194/esurf-6-49-2018, 2018.
Strauch, R., Istanbulluoglu, E., Riedel, J.: A New Approach to Mapping Landslide hazards: a probabilistic integration of empirical and physically based models in the North Cascades of Washington, USA – Research Data, HydroShare, https://doi.org/10.4211/hs.6d8c3c46f4c8422796f28584eb9bdfaa, 2019.
Swanson, F. J. and Dyrness, C . T .: Impact of clear-cutting and road construction on soil erosion by landslides in the western Cascade Range,
Oregon, Geology, 3, 393–396, 1975.
Tabor, R. W. and Haugerud, R. A.: Geology of the North Cascades: a mountain
mosaic, The Mountaineers Books, Seattle, WA, 1999.
Taylor, F. A. and Brabb, E. E.: Map showing landslides in California that have caused fatalities or at least $1,000,000 in damages from 1906 to 1984, US Geological Survey Miscellaneous Field Studies Map, MF-1867,
scale: 1:1 000 000, US Geological Survey, Reston, Virginia, https://doi.org/10.3133/ofr86100, 1986.
USGS – United States Geological Survey: National Elevation Data last modified March 6, 2014, National Map Viewer, available at: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/national/?&cid=nrcs143_021626 (last access: 24 November 2014), 2014a.
USGS: National Land Cover Data (NLCD) version Marched 31, 2014, National Map
Viewer, available at: https://www.usgs.gov/core-science-systems/national-geospatial-program/land-cover (last access: 25 November 2014), 2014b.
Van Westen, C. J., Van Asch, T. W., and Soeters, R.: Landslide hazard and risk zonation – why is it still so difficult?, B. Eng. Geol. Environ., 65,
167–184, 2006.
WADNR – Washington State Department of Natural Resources:
Geologic_unit_poly_100k, Vector digital data, published June 2010, Division of Geology and Earth Resources, Olympia, WA, available at: https://www.dnr.wa.gov/programs-and-services/geology/publications-and-data/gis-data-and-databases, last access: 27 March 2014.
Wartman, J., Montgomery, D. R., Anderson, S. A., Keaton, J. R., Benoît,
J., dela Chapelle, J., and Gilbert, R.: The 22 March 2014 Oso landslide,
Washington, USA, Geomorphology, 253, 275–288, 2016.
Wooten, R. M., Witt, A. C., Miniat, C. F., Hales, T. C., and Aldred, J. L.:
Frequency and magnitude of selected historical landslide events in the
southern Appalachian Highlands of North Carolina and Virginia: relationships
to rainfall, geological and ecohydrological controls, and effects, in: Natural Disturbances and Historic Range of Variation, edited by: Greenberg,
C. H. and Collins, B. S., Springer International Publishing, Switzerland,
203–262, https://doi.org/10.1007/978-3-319-21527-3, 2016.
Wu, Z., Wu, Y., Yang, Y., Chen, F., Zhang, N., Ke, Y., and Li, W.: A comparative study on the landslide susceptibility mapping using logistic
regression and statistical index models, Arab. J. Geosci., 10, 187, 2017.
Short summary
Identifying landslide hazards is challenging but important for understanding risks to people and both built and natural resources. We use models to identify landslide hazards based on observed landslides and local site traits such as slope and on physical mechanisms such as soil moisture. Integrating both approaches improves hazard detection by accounting for processes not captured in the physically based model. Hazard maps are made for the North Cascades National Park Complex (Washington, USA).
Identifying landslide hazards is challenging but important for understanding risks to people and...
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