Articles | Volume 24, issue 6
https://doi.org/10.5194/nhess-24-1897-2024
© Author(s) 2024. 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-24-1897-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Jun 2024
Research article |
| 03 Jun 2024
| 03 Jun 2024
Assessing the impact of climate change on landslides near Vejle, Denmark, using public data
Geological Survey of Denmark and Greenland (GEUS), Copenhagen, Denmark
Julian Koch
Geological Survey of Denmark and Greenland (GEUS), Copenhagen, Denmark
Marie Keiding
Geological Survey of Denmark and Greenland (GEUS), Copenhagen, Denmark
Gregor Luetzenburg
Geological Survey of Denmark and Greenland (GEUS), Copenhagen, Denmark
Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
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A tsunami wave will leave evidence of erosion and deposition in coastal lakes, making it possible to determine the runup height and when it occurred. Here, we use four lakes now located at elevations of 19–91 m a.s.l. close to the settlement of Saqqaq, West Greenland, to show that at least two giant tsunamis occurred 7300–7600 years ago with runup heights larger than 40 m. We infer that any tsunamis from at least nine giga-scale landslides must have happened 8500–10 000 years ago.
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Nat. Hazards Earth Syst. Sci., 24, 4617–4630, https://doi.org/10.5194/nhess-24-4617-2024, https://doi.org/10.5194/nhess-24-4617-2024, 2024
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This paper proposes a 3D limit analysis for seismic stability of soil slopes to address the influence of earthquakes on slope stabilities with nonlinear and linear criteria. Comparison results illustrate that the use of a linear envelope leads to the non-negligible overestimation of steep-slope stability, and this overestimation will be significant with increasing earthquakes. Earthquakes have a smaller influence on slope slip surfaces with a nonlinear envelope than those with a linear envelope.
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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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Communicating uncertainties is a crucial yet challenging aspect of spatial modelling – especially in applied research, where results inform decisions. In disaster risk reduction, susceptibility maps for natural hazards guide planning and risk assessment, yet their uncertainties are often overlooked. We present a new type of landslide susceptibility map that visualizes both susceptibility and associated uncertainty, alongside guidelines for creating such maps using free and open-source software.
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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.
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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.
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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.
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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.
Chenchen Qiu and Xueyu Geng
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-156, https://doi.org/10.5194/nhess-2024-156, 2024
Revised manuscript accepted for NHESS
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We proposed an interated method with the combination of a physical vulnerability matric and a machine learning model to estimate the potential physical damage and associated economic loss caused by future debris flows based on the collected historical data on the Qinghai-Tibet Plateau regions.
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.
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.
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.
Cited articles
Abbott, M. B., Bathurst, J. C., Cunge, J. A., O'Connell, P. E., and Rasmussen, J.: An introduction to the European Hydrological System – Systeme Hydrologique Europeen, “SHE”, 1: History and philosophy of a physically-based, distributed modelling system, J. Hydrol., 87, 45–59, 1986.
Alberti, S., Olsen, M. J., Allan, J., and Leshchinsky, B.: Feedback thresholds between coastal retreat and landslide activity, Eng. Geol., 301, 106620, https://doi.org/10.1016/j.enggeo.2022.106620, 2022.
Bennett, G. L., Roering, J. J., Mackey, B. H., Handwerger, A. L., Schmidt, D. A., and Guillod, B. P.: Historic drought puts the brakes on earthflows in Northern California, Geophys. Res. Lett., 43, 5725–5731, https://doi.org/10.1002/2016GL068378, 2016.
Cappelen, J.: Ekstrem nedbør i Danmark – opgørelser og analyser til og med 2018, Danmarks Meteorologiske Institut, Copenhagen, https://www.dmi.dk/fileadmin/Rapporter/2021/DMIRap21-06.pdf (last access: 30 May 2024), 2019.
Coe, J. A.: Regional moisture balance control of landslide motion: Implications for landslide forecasting in a changing climate, Geology, 40, 323–326, https://doi.org/10.1130/G32897.1, 2012.
Coe, J. A., Michael, J. A., Crovelli, R. A., Savage, W. Z., Laprade, W. T., and Nashem, W. D.: Probabilistic assessment of precipitation-triggered landslides using historical records of landslide occurence, Seattle, Washington, Environ. Eng. Geosci., 10, 103–122, https://doi.org/10.2113/10.2.103, 2004.
Cohen-Waeber, J., Bürgmann, R., Chaussard, E., Giannico, C., and Ferretti, A.: Spatiotemporal Patterns of Precipitation-Modulated Landslide Deformation From Independent Component Analysis of InSAR Time Series, Geophys. Res. Lett., 45, 1878–1887, https://doi.org/10.1002/2017GL075950, 2018.
Collison, A., Wade, S., Gri, J., and Dehn, M.: Modelling the impact of predicted climate change on landslide frequency and magnitude in SE England, Eng. Geol., 55, 205–218, 2000.
Copernicus Land Monitoring Service: European Ground Motion Service, https://land.copernicus.eu/en/products/european-ground-motion-service (last access: 29 January 2024), 2024.
Corominas, J., Moya, J., Ledesma, A., Lloret, A., and Gili, J. A.: Prediction of ground displacements and velocities from groundwater level changes at the Vallcebre landslide (Eastern Pyrenees, Spain), Landslides, 2, 83–96, https://doi.org/10.1007/s10346-005-0049-1, 2005.
Costantini, M., Minati, F., Trillo, F., Ferretti, A., Passera, E., Rucci, A., Dehls, J., Larsen, Y., Marinkovic, P., Eineder, M., Brcic, R., Siegmund, R., Kotzerke, P., Kenyeres, A., Costantini, V., Proietti, S., Solari, L., and Andersen, H. S.: EGMS: Europe-Wide Ground Motion Monitoring based on Full Resolution Insar Processing of All Sentinel-1 Acquisitions, in: IGARSS 2022 – 2022 IEEE International Geoscience and Remote Sensing Symposium, 17–22 July 2022, Kuala Lumpur, Malaysia, 5093–5096, https://doi.org/10.1109/IGARSS46834.2022.9884966, 2022.
Crosetto, M., Monserrat, O., Cuevas-González, M., Devanthéry, N., and Crippa, B.: Persistent Scatterer Interferometry: A review, ISPRS J. Photogram. Remote Sens., 115, 78–89, https://doi.org/10.1016/j.isprsjprs.2015.10.011, 2016.
Crosetto, M., Solari, L., Mróz, M., Balasis-Levinsen, J., Casagli, N., Frei, M., Oyen, A., Moldestad, D. A., Bateson, L., Guerrieri, L., Comerci, V., and Andersen, H. S.: The evolution of wide-area DInSAR: From regional and national services to the European ground motion service, Remote Sens., 12, 1–20, https://doi.org/10.3390/RS12122043, 2020.
Crozier, M. J.: Deciphering the effect of climate change on landslide activity: A review, Geomorphology, 124, 260–267, https://doi.org/10.1016/j.geomorph.2010.04.009, 2010.
Dixon, N. and Brook, E.: Impact of predicted climate change on landslide reactivation: Case study of Mam Tor, UK, Landslides, 4, 137–147, https://doi.org/10.1007/s10346-006-0071-y, 2007.
DMI: Klimaatlas, https://www.dmi.dk/klima-atlas/data-i-klimaatlas/ (last access: 30 January 2023), 2023.
DMI: Weather archive Vejle, https://www.dmi.dk/lokationarkiv/show/DK/2621215/Greve?cHash=cd7318c15ef5cd901a6038c352a7d4ed (last access: 25 January 2024), 2024.
Ferretti, A., Prati, C., and Rocca, F.: Permanent Scatterers in SAR Interferometry, IEEE T. Geosc. Remote, 39, 8–20, 2001.
Froude, M. J. and Petley, D. N.: Global fatal landslide occurrence from 2004 to 2016, Nat. Hazards Earth Syst. Sci., 18, 2161–2181, https://doi.org/10.5194/nhess-18-2161-2018, 2018.
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.
Håkansson, E. and Pedersen, S. A. S.: Geologisk Kort over den Danske Undergrund, Varv, https://tidsskrift.dk/varv/issue/archive (last access: 30 May 2024), 1992.
Handwerger, A. L., Fielding, E. J., Huang, M. H., Bennett, G. L., Liang, C., and Schulz, W. H.: Widespread Initiation, Reactivation, and Acceleration of Landslides in the Northern California Coast Ranges due to Extreme Rainfall, J. Geophys. Res.-Earth, 124, 1782–1797, https://doi.org/10.1029/2019JF005035, 2019.
Handwerger, A. L., Fielding, E. J., Sangha, S. S., and Bekaert, D. P. S.: Landslide Sensitivity and Response to Precipitation Changes in Wet and Dry Climates, Geophys. Res. Lett., 49, 1–12, https://doi.org/10.1029/2022GL099499, 2022.
Heilmann-Clausen, C., Nielsen, O. B., and Gersner, F.: Lithostratigraphy and depositional environments in the Upper Paleocene and Eocene of Denmark, Bull. Geol. Soc. Denmark, 33, 287–323, 1985.
Henriksen, H. J., Troldborg, L., Nyegaard, P., Sonnenborg, T. O., Refsgaard, J. C., and Madsen, B.: Methodology for construction, calibration and validation of a national hydrological model for Denmark, J. Hydrol., 280, 52–71, 2003.
Henriksen, H. J., Kragh, S. J., Gotfredsen, J., Ondracek, M., van Til, M., Jakobsen, A., Schneider, R. J. M., Koch, J., Troldborg, L., Rasmussen, P., Pasten-Zapata, E., and Stisen, S.: Dokumentationsrapport vedr. modelleverancer til Hydrologisk Informations- og Prognosesystem, https://sdfe.dk/media/2920242/hip4plus_dokumentationsrapport31jan2021.pdf (last access: 30 May 2024), 2020.
Hermanns, R. L., Niedermann, S., Villanueva Garcia, A., and Schellenberger, A.: Rock avalanching in the NW argentine andes as a result of complex interactions of lithologic, structural and topographic boundary conditions, climate change and active tectonics, in: Landslides from Massive Rock Slope Failure, edited by: Evans, S. G., Scarawcia Mugnozza, G., Strom, A. L., and Hermanns, R. L., Springer Netherlands, Celano, 497–520, 2006.
Herrera, G., Mateos, R. M., García-Davalillo, J. C., Grandjean, G., Poyiadji, E., Maftei, R., Filipciuc, T. C., Jemec Auflič, M., Jež, J., Podolszki, L., Trigila, A., Iadanza, C., Raetzo, H., Kociu, A., Przyłucka, M., Kułak, M., Sheehy, M., Pellicer, X. M., McKeown, C., Ryan, G., Kopačková, V., Frei, M., Kuhn, D., Hermanns, R. L., Koulermou, N., Smith, C. A., Engdahl, M., Buxó, P., Gonzalez, M., Dashwood, C., Reeves, H., Cigna, F., Lik, P., Pauditš, P., Mikulėnas, V., Demir, V., Raha, M., Quental, L., Sandić, C., Fusi, B., and Jensen, O. A.: Landslide databases in the Geological Surveys of Europe, Landslides, 15, 359–379, https://doi.org/10.1007/s10346-017-0902-z, 2018.
Højberg, A. L., Troldborg, L., Stisen, S., Christensen, B. B., and Henriksen, H. J.: Stakeholder driven update and improvement of a national water resources model, Environ. Model. Softw., 40, 202–213, 2013.
Hungr, O., Leroueil, S., and Picarelli, L.: The Varnes classification of landslide types, an update, Landslides, 11, 167–194, https://doi.org/10.1007/s10346-013-0436-y, 2014.
Hydrologisk Informations- og Prognosesystem: https://hip.dataforsyningen.dk/ (last access: 29 January 2024), 2024.
Iverson, R. M.: Landslide triggering by rain infiltration, Water Resour. Res., 36, 1897–1910, https://doi.org/10.1029/2000WR900090, 2000.
Jupiter database drill log: https://data.geus.dk/JupiterWWW/borerapport.jsp?borid=458263 (last access: 13 December 2023), 2023.
Kashyap, R., Pandey, A. C., and Parida, B. R.: Spatio-temporal variability of monsoon precipitation and their effect on precipitation triggered landslides in relation to relief in Himalayas, Spat. Inform. Res., 29, 857–869, https://doi.org/10.1007/s41324-021-00392-8, 2021.
Koch, J., Gotfredsen, J., Schneider, R., Troldborg, L., Stisen, S., and Henriksen, H. J.: High resolution water table modelling of the shallow groundwater using a knowledge-guided gradient boosting decision tree model, Front. Water, 3, 701726, https://doi.org/10.3389/frwa.2021.701726, 2021.
Lin, Q., Steger, S., Pittore, M., Zhang, J., Wang, L., Jiang, T., and Wang, Y.: Evaluation of potential changes in landslide susceptibility and landslide occurrence frequency in China under climate change, Sci. Total Environ., 850, 158049, https://doi.org/10.1016/j.scitotenv.2022.158049, 2022.
Luetzenburg, G., Svennevig, K., Bjørk, A. A., Keiding, M., and Kroon, A.: A national landslide inventory for Denmark, Earth Syst. Sci. Data, 14, 3157–3165, https://doi.org/10.5194/essd-14-3157-2022, 2022.
Luna, L. V. and Korup, O.: Seasonal Landslide Activity Lags Annual Precipitation Pattern in the Pacific Northwest, Geophys. Res. Lett., 49, 1–11, https://doi.org/10.1029/2022gl098506, 2022.
Magnin, F., Josnin, J. Y., Ravanel, L., Pergaud, J., Pohl, B., and Deline, P.: Modelling rock wall permafrost degradation in the Mont Blanc massif from the LIA to the end of the 21st century, The Cryosphere, 11, 1813–1834, https://doi.org/10.5194/tc-11-1813-2017, 2017.
Magnin, F., Etzelmüller, B., Westermann, S., Isaksen, K., Hilger, P., and Hermanns, R. L.: Permafrost distribution in steep rock slopes in Norway: measurements, statistical modelling and implications for geomorphological processes, Earth Surf. Dynam., 7, 1019–1040, https://doi.org/10.5194/esurf-7-1019-2019, 2019.
Malet, J.-P., van Asch, T. W. J., van Beek, R., and Maquaire, O.: Forecasting the behaviour of complex landslides with a spatially distributed hydrological model, Nat. Hazards Earth Syst. Sci., 5, 71–85, https://doi.org/10.5194/nhess-5-71-2005, 2005.
Mateos, R. M., López-Vinielles, J., Poyiadji, E., Tsagkas, D., Sheehy, M., Hadjicharalambous, K., Liscák, P., Podolski, L., Laskowicz, I., Iadanza, C., Gauert, C., Todorović, S., Auflič, M. J., Maftei, R., Hermanns, R. L., Kociu, A., Sandić, C., Mauter, R., Sarro, R., Béjar, M., and Herrera, G.: Integration of landslide hazard into urban planning across Europe, Landsc. Urban Plan., 196, 103740, https://doi.org/10.1016/j.landurbplan.2019.103740, 2020.
Moreiras, S., Lisboa, M. S., and Mastrantonio, L.: The role of snow melting upon landslides in the central Argentinean Andes, Earth Surf. Proc. Land., 37, 1106–1119, https://doi.org/10.1002/esp.3239, 2012.
Nuth, C. and Kääb, A.: Co-registration and bias corrections of satellite elevation data sets for quantifying glacier thickness change, The Cryosphere, 5, 271–290, https://doi.org/10.5194/tc-5-271-2011, 2011.
Pasten-Zapata, E., Sonnenborg, T. O., and Refsgaard, J. C.: Climate change: Sources of uncertainty in precipitation and temperature projections for Denmark, GEUS Bull., 43, e2019430102, https://doi.org/10.34194/GEUSB-201943-01-02, 2019.
Penna, I. M., Magnin, F., Nicolet, P., Etzelmüller, B., Hermanns, R. L., Böhme, M., Kristensen, L., Nöel, F., Bredal, M., and Dehls, J. F.: Permafrost controls the displacement rates of large unstable rock-slopes in subarctic environments, Global Planet. Change, 220, 104017, https://doi.org/10.1016/j.gloplacha.2022.104017, 2023.
Peres, D. J. and Cancelliere, A.: Modeling impacts of climate change on return period of landslide triggering, J. Hydrol., 567, 420–434, https://doi.org/10.1016/j.jhydrol.2018.10.036, 2018.
Pfeiffer, J., Zieher, T., Bremer, M., Wichmann, V., and Rutzinger, M.: Derivation of Three-Dimensional Displacement Vectors from Multi-Temporal Long-Range Terrestrial Laser Scanning at the Reissenschuh Landslide (Tyrol, Austria), Remote Sens., 10, 1688, https://doi.org/10.3390/rs10111688, 2018.
Pollock, W. and Wartman, J.: Human Vulnerability to Landslides, GeoHealth, 4, 1–17, https://doi.org/10.1029/2020GH000287, 2020.
Rasmussen, E. S., Dybkjær, K., and Piasecki, S.: Lithostratigraphy of the Upper Oligocene – Miocene succession of Denmark, Geological Survey of Denmark and Greenland Bulletin, 92 pp., https://doi.org/10.34194/geusb.v22.4733, 2010.
Rosen, P. A., Hensley, S., Joughin, I. R., Li, F. K., Madsen, S. N., Rodriguez, E., and Goldstein, R.: Synthetic aperture radar interferometry, Proc. IEEE, 88, 333–382, https://doi.org/10.1109/5.838084, 2000.
R Project: A language and environment for statistical computing: https://www.r-project.org/ (last access: 30 January 2023), 2023.
Saba, S. B., van der Meijde, M., and van der Werff, H.: Spatiotemporal landslide detection for the 2005 Kashmir earthquake region, Geomorphology, 124, 17–25, https://doi.org/10.1016/j.geomorph.2010.07.026, 2010.
Scaioni, M., Longoni, L., Melillo, V., and Papini, M.: Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives, Remote Sens., 6, 9600–9652, https://doi.org/10.3390/rs6109600, 2014.
Scharling, M.: Klimagrid Danmark Nedbør 10×10 km (ver. 2) – Metodebeskrivelse, Danish Meteorological Institute Technical Report, Danish Meteorological Institute, Copenhagen, https://www.dmi.dk/fileadmin/user_upload/Rapporter/TR/1999/tr99-15.pdf (last access: 30 May 2024), 1999.
SDFI: Denmark's Elevation Model, https://eng.sdfi.dk/data/the-danish-elevation-model-dk-dem (last access: 30 May 2024), 2020.
Svennevig, K. and Keiding, M.: En dansk nomenklatur for landskred, Geologisk Tidsskrift, 2020, 19–30, 2020.
Svennevig, K., Luetzenburg, G., Keiding, M. K., Pedersen, S. A. S., Asbjørn, S., and Pedersen, S. A. S.: Preliminary landslide mapping in Denmark indicates an underestimated geohazard, GEUS Bull., 44, 1–6, https://doi.org/10.34194/geusb.v44.5302, 2020.
Svennevig, K., Hermanns, R. L., Keiding, M., Binder, D., Citterio, M., Dahl-Jensen, T., Mertl, S., Sørensen, E. V., and Voss, P. H.: A large frozen debris avalanche entraining warming permafrost ground – the June 2021 Assapaat landslide, West Greenland, Landslides, 19, 2549–2567, https://doi.org/10.1007/s10346-022-01922-7, 2022.
Svennevig, K., Keiding, M., Korsgaard, N. J., Lucas, A., Owen, M., Poulsen, M. D., Priebe, J., Sørensen, E. V., and Morino, C.: Uncovering a 70-year-old permafrost degradation induced disaster in the Arctic, the 1952 Niiortuut landslide-tsunami in central West Greenland, Sci. Total Environ., 859, 160110, https://doi.org/10.1016/j.scitotenv.2022.160110, 2023.
Terzaghi, K.: Mechanism of Landslides, edited by: Paige, S., Geological Society of America, https://doi.org/10.1130/Berkey.1950.83, 1950.
Uhlemann, S., Smith, A., Chambers, J., Dixon, N., Dijkstra, T., Haslam, E., Meldrum, P., Merritt, A., Gunn, D., and Mackay, J.: Assessment of ground-based monitoring techniques applied to landslide investigations, Geomorphology, 253, 438–451, https://doi.org/10.1016/j.geomorph.2015.10.027, 2016.
van Asch, T. W. J. and Buma, J. T.: Modelling groundwater fluctuations and the frequency of movement of a landslide in the Terres Noires region of Barcelonnette (France), Earth Surf. Proc. Land., 22, 131–141, https://doi.org/10.1002/(SICI)1096-9837(199702)22:2<131::AID-ESP679>3.0.CO;2-J, 1997.
van Asch, T. W. J., Buma, J., and Van Beek, L. P. H.: A view on some hydrological triggering systems in landslides, Geomorphology, 30, 25–32, https://doi.org/10.1016/S0169-555X(99)00042-2, 1999.
van Asch, T. W. J., Malet, J. P., and Bogaard, T. A.: The effect of groundwater fluctuations on the velocity pattern of slow-moving landslides, Nat. Hazards Earth Syst. Sci., 9, 739–749, https://doi.org/10.5194/nhess-9-739-2009, 2009.
Van Beek, L. P. H. and Van Asch, T. W. J.: Regional assessment of the effects of land-use change on landslide hazard by means of physically based modelling, Nat. Hazards, 31, 289–304, https://doi.org/10.1023/B:NHAZ.0000020267.39691.39, 2004.
Vitousek, S., Buscombe, D., Vos, K., Barnard, P. L., Ritchie, A. C., and Warrick, J. A.: The future of coastal monitoring through satellite remote sensing, Cambridge Prisms: Coastal Futures, 1, e10, https://doi.org/10.1017/cft.2022.4, 2023.
Wistuba, M., Gorczyca, E., and Malik, I.: Inferring precipitation thresholds of landslide activity from long-term dendrochronological and precipitation data: Case study on the unstable slope at Karpenciny, Poland, Eng. Geol., 294, 106398, https://doi.org/10.1016/j.enggeo.2021.106398, 2021.
Zieher, T., Gallotti, G., Rianna, G., Reder, A., and Pfeiffer, J.: Exploring the effects of climate change on the water balance of a continuously moving deep-seated landslide, Nat. Hazards, 115, 357–387, https://doi.org/10.1007/s11069-022-05558-7, 2023.
Short summary
In our study, we analysed publicly available data in order to investigate the impact of climate change on landslides in Denmark. Our research indicates that the rising groundwater table due to climate change will result in an increase in landslide activity. Previous incidents of extremely wet winters have caused damage to infrastructure and buildings due to landslides. This study is the first of its kind to exclusively rely on public data and examine landslides in Denmark.
In our study, we analysed publicly available data in order to investigate the impact of climate...
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