Articles | Volume 25, issue 1
https://doi.org/10.5194/nhess-25-169-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-169-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Invited perspectives
| 
07 Jan 2025
Invited perspectives |
| 07 Jan 2025
| 07 Jan 2025
Invited perspectives: Integrating hydrologic information into the next generation of landslide early warning systems
United States Geological Survey (USGS), Geologic Hazards Science Center, Golden, Colorado, USA
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Mountain Hydrology and Mass Movements, Birmensdorf, Switzerland
Thom Bogaard
Department of Water Management, Delft University of Technology, Delft, the Netherlands
Roberto Greco
Department of Engineering, University of Campania “Luigi Vanvitelli”, Aversa, Italy
Manfred Stähli
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Mountain Hydrology and Mass Movements, Birmensdorf, Switzerland
Related authors
Lisa V. Luna, Jacob B. Woodard, Janice L. Bytheway, Gina M. Belair, and Benjamin B. Mirus
EGUsphere, https://doi.org/10.5194/egusphere-2025-947, https://doi.org/10.5194/egusphere-2025-947, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
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Landslide frequency (how often landslides occur) is needed to assess landslide hazard and risk but has rarely been quantified at near continental scales. Here, we used statistical models to estimate landslide frequency across the United States while addressing gaps in landslide reporting. Our results showed strong variations in landslide frequency that followed topography, earthquake probability, and ecological region and highlighted areas with potential for widespread landsliding.
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.
Annette I. Patton, Lisa V. Luna, Joshua J. Roering, Aaron Jacobs, Oliver Korup, and Benjamin B. Mirus
Nat. Hazards Earth Syst. Sci., 23, 3261–3284, https://doi.org/10.5194/nhess-23-3261-2023, https://doi.org/10.5194/nhess-23-3261-2023, 2023
Short summary
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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.
Lisa V. Luna, Jacob B. Woodard, Janice L. Bytheway, Gina M. Belair, and Benjamin B. Mirus
EGUsphere, https://doi.org/10.5194/egusphere-2025-947, https://doi.org/10.5194/egusphere-2025-947, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
Landslide frequency (how often landslides occur) is needed to assess landslide hazard and risk but has rarely been quantified at near continental scales. Here, we used statistical models to estimate landslide frequency across the United States while addressing gaps in landslide reporting. Our results showed strong variations in landslide frequency that followed topography, earthquake probability, and ecological region and highlighted areas with potential for widespread landsliding.
Daniel Camilo Roman Quintero, Pasquale Marino, Abdullah Abdullah, Giovanni Francesco Santonastaso, and Roberto Greco
EGUsphere, https://doi.org/10.5194/egusphere-2024-2329, https://doi.org/10.5194/egusphere-2024-2329, 2024
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Local thresholds for landslide forecasting, combining hydrologic predisposing factors and rainfall features, are developed from a physically based model of a slope. To extend their application to a wide area, uncertainty due to spatial variability of geomorphological and hydrologic variables is introduced. The obtained hydrometeorological thresholds, integrating root zone soil moisture and aquifer water level with rainfall depth, outperform thresholds based on rain intensity and duration.
Jacob B. Woodard, Benjamin B. Mirus, Nathan J. Wood, Kate E. Allstadt, Benjamin A. Leshchinsky, and Matthew M. Crawford
Nat. Hazards Earth Syst. Sci., 24, 1–12, https://doi.org/10.5194/nhess-24-1-2024, https://doi.org/10.5194/nhess-24-1-2024, 2024
Short summary
Short summary
Dividing landscapes into hillslopes greatly improves predictions of landslide potential across landscapes, but their scaling is often arbitrarily set and can require significant computing power to delineate. Here, we present a new computer program that can efficiently divide landscapes into meaningful slope units scaled to best capture landslide processes. The results of this work will allow an improved understanding of landslide potential and can help reduce the impacts of landslides worldwide.
Adriaan L. van Natijne, Thom A. Bogaard, Thomas Zieher, Jan Pfeiffer, and Roderik C. Lindenbergh
Nat. Hazards Earth Syst. Sci., 23, 3723–3745, https://doi.org/10.5194/nhess-23-3723-2023, https://doi.org/10.5194/nhess-23-3723-2023, 2023
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Landslides are one of the major weather-related geohazards. To assess their potential impact and design mitigation solutions, a detailed understanding of the slope is required. We tested if the use of machine learning, combined with satellite remote sensing data, would allow us to forecast deformation. Our results on the Vögelsberg landslide, a deep-seated landslide near Innsbruck, Austria, show that the formulation of such a machine learning system is not as straightforward as often hoped for.
Daniel Camilo Roman Quintero, Pasquale Marino, Giovanni Francesco Santonastaso, and Roberto Greco
Hydrol. Earth Syst. Sci., 27, 4151–4172, https://doi.org/10.5194/hess-27-4151-2023, https://doi.org/10.5194/hess-27-4151-2023, 2023
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This study shows a methodological approach using machine learning techniques to disentangle the relationships among the variables in a synthetic dataset to identify suitable variables that control the hydrologic response of the slopes. It has been found that not only is the rainfall responsible for the water accumulation in the slope; the ground conditions (soil water content and aquifer water level) also indicate the activation of natural slope drainage mechanisms.
Annette I. Patton, Lisa V. Luna, Joshua J. Roering, Aaron Jacobs, Oliver Korup, and Benjamin B. Mirus
Nat. Hazards Earth Syst. Sci., 23, 3261–3284, https://doi.org/10.5194/nhess-23-3261-2023, https://doi.org/10.5194/nhess-23-3261-2023, 2023
Short summary
Short summary
Landslide warning systems often use statistical models to predict landslides based on rainfall. They are typically trained on large datasets with many landslide occurrences, but in rural areas large datasets may not exist. In this study, we evaluate which statistical model types are best suited to predicting landslides and demonstrate that even a small landslide inventory (five storms) can be used to train useful models for landslide early warning when non-landslide events are also included.
Adrian Wicki, Peter Lehmann, Christian Hauck, and Manfred Stähli
Nat. Hazards Earth Syst. Sci., 23, 1059–1077, https://doi.org/10.5194/nhess-23-1059-2023, https://doi.org/10.5194/nhess-23-1059-2023, 2023
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Soil wetness measurements are used for shallow landslide prediction; however, existing sites are often located in flat terrain. Here, we assessed the ability of monitoring sites at flat locations to detect critically saturated conditions compared to if they were situated at a landslide-prone location. We found that differences exist but that both sites could equally well distinguish critical from non-critical conditions for shallow landslide triggering if relative changes are considered.
Yi Luo, Jiaming Zhang, Zhi Zhou, Juan P. Aguilar-Lopez, Roberto Greco, and Thom Bogaard
Hydrol. Earth Syst. Sci., 27, 783–808, https://doi.org/10.5194/hess-27-783-2023, https://doi.org/10.5194/hess-27-783-2023, 2023
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This paper describes an experiment and modeling of the hydrological response of desiccation cracks under long-term wetting–drying cycles. We developed a new dynamic dual-permeability model to quantify the dynamic evolution of desiccation cracks and associated preferential flow and moisture distribution. Compared to other models, the dynamic dual-permeability model could describe the experimental data much better, but it also provided an improved description of the underlying physics.
Judith Uwihirwe, Alessia Riveros, Hellen Wanjala, Jaap Schellekens, Frederiek Sperna Weiland, Markus Hrachowitz, and Thom A. Bogaard
Nat. Hazards Earth Syst. Sci., 22, 3641–3661, https://doi.org/10.5194/nhess-22-3641-2022, https://doi.org/10.5194/nhess-22-3641-2022, 2022
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This study compared gauge-based and satellite-based precipitation products. Similarly, satellite- and hydrological model-derived soil moisture was compared to in situ soil moisture and used in landslide hazard assessment and warning. The results reveal the cumulative 3 d rainfall from the NASA-GPM to be the most effective landslide trigger. The modelled antecedent soil moisture in the root zone was the most informative hydrological variable for landslide hazard assessment and warning in Rwanda.
Jan Pfeiffer, Thomas Zieher, Jan Schmieder, Thom Bogaard, Martin Rutzinger, and Christoph Spötl
Nat. Hazards Earth Syst. Sci., 22, 2219–2237, https://doi.org/10.5194/nhess-22-2219-2022, https://doi.org/10.5194/nhess-22-2219-2022, 2022
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The activity of slow-moving deep-seated landslides is commonly governed by pore pressure variations within the shear zone. Groundwater recharge as a consequence of precipitation therefore is a process regulating the activity of landslides. In this context, we present a highly automated geo-statistical approach to spatially assess groundwater recharge controlling the velocity of a deep-seated landslide in Tyrol, Austria.
Judith Uwihirwe, Markus Hrachowitz, and Thom Bogaard
Nat. Hazards Earth Syst. Sci., 22, 1723–1742, https://doi.org/10.5194/nhess-22-1723-2022, https://doi.org/10.5194/nhess-22-1723-2022, 2022
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This research tested the value of regional groundwater level information to improve landslide predictions with empirical models based on the concept of threshold levels. In contrast to precipitation-based thresholds, the results indicated that relying on threshold models exclusively defined using hydrological variables such as groundwater levels can lead to improved landslide predictions due to their implicit consideration of long-term antecedent conditions until the day of landslide occurrence.
Punpim Puttaraksa Mapiam, Monton Methaprayun, Thom Bogaard, Gerrit Schoups, and Marie-Claire Ten Veldhuis
Hydrol. Earth Syst. Sci., 26, 775–794, https://doi.org/10.5194/hess-26-775-2022, https://doi.org/10.5194/hess-26-775-2022, 2022
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The density of rain gauge networks plays an important role in radar rainfall bias correction. In this work, we aimed to assess the extent to which daily rainfall observations from a dense network of citizen scientists improve the accuracy of hourly radar rainfall estimates in the Tubma Basin, Thailand. Results show that citizen rain gauges significantly enhance the performance of radar rainfall bias adjustment up to a range of about 40 km from the center of the citizen rain gauge network.
Adrian Wicki, Per-Erik Jansson, Peter Lehmann, Christian Hauck, and Manfred Stähli
Hydrol. Earth Syst. Sci., 25, 4585–4610, https://doi.org/10.5194/hess-25-4585-2021, https://doi.org/10.5194/hess-25-4585-2021, 2021
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Soil moisture information was shown to be valuable for landslide prediction. Soil moisture was simulated at 133 sites in Switzerland, and the temporal variability was compared to the regional occurrence of landslides. We found that simulated soil moisture is a good predictor for landslides, and that the forecast goodness is similar to using in situ measurements. This encourages the use of models for complementing existing soil moisture monitoring networks for regional landslide early warning.
Luca Comegna, Emilia Damiano, Roberto Greco, Lucio Olivares, and Luciano Picarelli
Earth Syst. Sci. Data, 13, 2541–2553, https://doi.org/10.5194/essd-13-2541-2021, https://doi.org/10.5194/essd-13-2541-2021, 2021
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The set-up of an automatic field station allowed for the monitoring of the annual cyclic hydrological response of a deposit in pyroclastic air-fall soils covering a steep mountainous area in Campania region (Italy), which in 1999 was involved in a rainfall-induced flowslide. Data highlight the influence of the initial conditions, governed by the antecedent wetting/drying history, on the weather-induced hydraulic paths, allowing us to estimate their influence on the local stability conditions.
Rolf Hut, Thanda Thatoe Nwe Win, and Thom Bogaard
Geosci. Instrum. Method. Data Syst., 9, 435–442, https://doi.org/10.5194/gi-9-435-2020, https://doi.org/10.5194/gi-9-435-2020, 2020
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GPS drifters that float down rivers are important tools in studying rivers, but they can be expensive. Recently, both GPS receivers and cellular modems have become available at lower prices to tinkering scientists due to the rise of open hardware and the Arduino. We provide detailed instructions on how to build a low-power GPS drifter with local storage and a cellular model that we tested in a fieldwork in Myanmar. These instructions allow fellow geoscientists to recreate the device.
Leonie Kiewiet, Ilja van Meerveld, Manfred Stähli, and Jan Seibert
Hydrol. Earth Syst. Sci., 24, 3381–3398, https://doi.org/10.5194/hess-24-3381-2020, https://doi.org/10.5194/hess-24-3381-2020, 2020
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The sources of stream water are important, for instance, for predicting floods. The connectivity between streams and different (ground-)water sources can change during rain events, which affects the stream water composition. We investigated this for stream water sampled during four events and found that stream water came from different sources. The stream water composition changed gradually, and we showed that changes in solute concentrations could be partly linked to changes in connectivity.
César Dionisio Jiménez-Rodríguez, Miriam Coenders-Gerrits, Thom Bogaard, Erika Vatiero, and Hubert Savenije
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-344, https://doi.org/10.5194/hess-2019-344, 2019
Revised manuscript not accepted
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Knowing the isotopic composition of water vapor in the air is a difficult task. The estimation of δ18O and δ2H has to be done carefully, because it is accompanied by a high risk of methodological errors (if it is sampled) or wrong assumptions that can lead to incorrect values (if it is modeled). The aim of this work was to compare available sampling methods for water vapor in the air and estimate their isotopic composition, comparing the results against direct measurements of the sampled air.
César~Dionisio Jiménez-Rodríguez, Miriam Coenders-Gerrits, Thom Bogaard, Erika Vatiero, and Hubert Savenije
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-538, https://doi.org/10.5194/hess-2018-538, 2018
Manuscript not accepted for further review
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The measurement of stable isotopes in water vapor has been improved with the use of laser technologies. Its direct application in the field depends on the availability of infrastructure or the budget of the project. For those cases when it is not possible, we provide an alternative method to sample the air for its later measurement. This method is based on the use of a low-cost polyethylene bag, getting stable measurements with a volume of 450 mL of air reducing the risk of sample deterioration.
Petra Hulsman, Thom A. Bogaard, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 22, 5081–5095, https://doi.org/10.5194/hess-22-5081-2018, https://doi.org/10.5194/hess-22-5081-2018, 2018
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In many river basins, the development of hydrological models is challenged by poor discharge data availability and quality. In contrast, water level data are more reliable, as these are direct measurements and are unprocessed. In this study, an alternative calibration method is presented using water-level time series and the Strickler–Manning formula instead of discharge. This is applied to a semi-distributed rainfall-runoff model for the semi-arid, poorly gauged Mara River basin in Kenya.
David J. Peres, Antonino Cancelliere, Roberto Greco, and Thom A. Bogaard
Nat. Hazards Earth Syst. Sci., 18, 633–646, https://doi.org/10.5194/nhess-18-633-2018, https://doi.org/10.5194/nhess-18-633-2018, 2018
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We investigate the influence of imprecise identification of triggering instants on landslide early warning thresholds by perturbing an error-free synthetic dataset. Combined impacts of uncertainty with respect to temporal discretization of data and criteria for singling out rainfall events are assessed as well. Results show that thresholds can be significantly affected by these uncertainty sources.
Thom Bogaard and Roberto Greco
Nat. Hazards Earth Syst. Sci., 18, 31–39, https://doi.org/10.5194/nhess-18-31-2018, https://doi.org/10.5194/nhess-18-31-2018, 2018
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The vast majority of shallow landslides and debris flows are precipitation initiated and predicted using historical landslides plotted versus observed precipitation information. However, this approach has severe limitations. This is partly due to the fact that it is not precipitation that initiates a landslide or debris flow but rather the hydrological dynamics in the soil and slope. We propose to include hydrological information in the regional hydro-meteorological hazard assessment.
Roberto Greco and Luca Pagano
Nat. Hazards Earth Syst. Sci., 17, 2213–2227, https://doi.org/10.5194/nhess-17-2213-2017, https://doi.org/10.5194/nhess-17-2213-2017, 2017
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The paper focuses on the main features characterizing predictive models working in early warning systems (EWS), by discussing their aims, the evolution stage of the phenomenon where they should be incardinated, and their architecture, regardless of the specific application field. With reference to flow-like landslide and earth flows, some alternative approaches to the development of the predictive tool and to its implementation in an EWS are described.
Marie K. M. Charrière and Thom A. Bogaard
Nat. Hazards Earth Syst. Sci., 16, 1175–1188, https://doi.org/10.5194/nhess-16-1175-2016, https://doi.org/10.5194/nhess-16-1175-2016, 2016
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This paper present the results of interviews that were conducted with the developers of apps dedicated to avalanche risk communication. The study investigates the context of their development to determine how choices of content and visualization were made as well as how their effectiveness is evaluated. Results show that consensus is achieved in terms of message but not in terms of visualization. However, progress remains in terms of effectiveness evaluation.
W. Shao, T. A. Bogaard, M. Bakker, and R. Greco
Hydrol. Earth Syst. Sci., 19, 2197–2212, https://doi.org/10.5194/hess-19-2197-2015, https://doi.org/10.5194/hess-19-2197-2015, 2015
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The effect of preferential flow on the stability of landslides is studied through numerical simulation of two types of rainfall events on a hypothetical hillslope. A model is developed that consists of two parts. The first part is a model for combined saturated/unsaturated subsurface flow and is used to compute the spatial and temporal water pressure response to rainfall. Preferential flow is simulated with a dual-permeability continuum model consisting of a matrix/preferential flow domain.
M. Stähli, M. Sättele, C. Huggel, B. W. McArdell, P. Lehmann, A. Van Herwijnen, A. Berne, M. Schleiss, A. Ferrari, A. Kos, D. Or, and S. M. Springman
Nat. Hazards Earth Syst. Sci., 15, 905–917, https://doi.org/10.5194/nhess-15-905-2015, https://doi.org/10.5194/nhess-15-905-2015, 2015
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This review paper describes the state of the art in monitoring and predicting rapid mass movements for early warning. It further presents recent innovations in observation technologies and modelling to be used in future early warning systems (EWS). Finally, the paper proposes avenues towards successful implementation of next-generation EWS.
M. Stähli, C. Graf, C. Scheidl, C. R. Wyss, and A. Volkwein
Geogr. Helv., 70, 1–9, https://doi.org/10.5194/gh-70-1-2015, https://doi.org/10.5194/gh-70-1-2015, 2015
V. J. Cortes Arevalo, M. Charrière, G. Bossi, S. Frigerio, L. Schenato, T. Bogaard, C. Bianchizza, A. Pasuto, and S. Sterlacchini
Nat. Hazards Earth Syst. Sci., 14, 2681–2698, https://doi.org/10.5194/nhess-14-2681-2014, https://doi.org/10.5194/nhess-14-2681-2014, 2014
D. M. Krzeminska, T. A. Bogaard, T.-H. Debieche, F. Cervi, V. Marc, and J.-P. Malet
Earth Surf. Dynam., 2, 181–195, https://doi.org/10.5194/esurf-2-181-2014, https://doi.org/10.5194/esurf-2-181-2014, 2014
U. Ehret, H. V. Gupta, M. Sivapalan, S. V. Weijs, S. J. Schymanski, G. Blöschl, A. N. Gelfan, C. Harman, A. Kleidon, T. A. Bogaard, D. Wang, T. Wagener, U. Scherer, E. Zehe, M. F. P. Bierkens, G. Di Baldassarre, J. Parajka, L. P. H. van Beek, A. van Griensven, M. C. Westhoff, and H. C. Winsemius
Hydrol. Earth Syst. Sci., 18, 649–671, https://doi.org/10.5194/hess-18-649-2014, https://doi.org/10.5194/hess-18-649-2014, 2014
R. Greco, L. Comegna, E. Damiano, A. Guida, L. Olivares, and L. Picarelli
Hydrol. Earth Syst. Sci., 17, 4001–4013, https://doi.org/10.5194/hess-17-4001-2013, https://doi.org/10.5194/hess-17-4001-2013, 2013
J. E. van der Spek, T. A. Bogaard, and M. Bakker
Hydrol. Earth Syst. Sci., 17, 2171–2183, https://doi.org/10.5194/hess-17-2171-2013, https://doi.org/10.5194/hess-17-2171-2013, 2013
D. M. Krzeminska, T. A. Bogaard, J.-P. Malet, and L. P. H. van Beek
Hydrol. Earth Syst. Sci., 17, 947–959, https://doi.org/10.5194/hess-17-947-2013, https://doi.org/10.5194/hess-17-947-2013, 2013
M. Hrachowitz, H. Savenije, T. A. Bogaard, D. Tetzlaff, and C. Soulsby
Hydrol. Earth Syst. Sci., 17, 533–564, https://doi.org/10.5194/hess-17-533-2013, https://doi.org/10.5194/hess-17-533-2013, 2013
Related subject area
Landslides and Debris Flows Hazards
Topographic controls on landslide mobility: modeling hurricane-induced landslide runout and debris-flow inundation in Puerto Rico
Characterizing the scale of regional landslide triggering from storm hydrometeorology
A participatory approach to determine the use of road cut slope design guidelines in Nepal to lessen landslides
An integrated method for assessing vulnerability of buildings caused by debris flows in mountainous areas
Identifying unrecognised risks to life from debris flows
Predicting the thickness of shallow landslides in Switzerland using machine learning
Unraveling landslide failure mechanisms with seismic signal analysis for enhanced pre-survey understanding
Comparison of conditioning factor classification criteria in large-scale statistically based landslide susceptibility models
Predicting deep-seated landslide displacement on Taiwan's Lushan through the integration of convolutional neural networks and the Age of Exploration-Inspired Optimizer
Limit analysis of earthquake-induced landslides considering two strength envelopes
Brief communication: Visualizing uncertainties in landslide susceptibility modeling using bivariate mapping
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Optimizing rainfall-triggered landslide thresholds for daily landslide hazard warning in the Three Gorges Reservoir area
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Addressing class imbalance in soil movement predictions
Assessing the impact of climate change on landslides near Vejle, Denmark, using public data
A regional analysis of paraglacial landslide activation in southern coastal Alaska
Analysis of three-dimensional slope stability combined with rainfall and earthquake
Assessing landslide damming susceptibility in Central Asia
Assessing locations susceptible to shallow landslide initiation during prolonged intense rainfall in the Lares, Utuado, and Naranjito municipalities of Puerto Rico
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Comparison of debris flow observations, including fine-sediment grain size and composition and runout model results, at Illgraben, Swiss Alps
Simulation analysis of 3D stability of a landslide with a locking segment: a case study of the Tizicao landslide in Maoxian County, southwest China
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Rockfall monitoring with a Doppler radar on an active rockslide complex in Brienz/Brinzauls (Switzerland)
Landslide initiation thresholds in data-sparse regions: application to landslide early warning criteria in Sitka, Alaska, USA
Lessons learnt from a rockfall time series analysis: data collection, statistical analysis, and applications
The concept of event-size-dependent exhaustion and its application to paraglacial rockslides
Coastal earthquake-induced landslide susceptibility during the 2016 Mw 7.8 Kaikōura earthquake, New Zealand
Characteristics of debris flows recorded in the Shenmu area of central Taiwan between 2004 and 2021
Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine
Dianne L. Brien, Mark E. Reid, Collin Cronkite-Ratcliff, and Jonathan P. Perkins
Nat. Hazards Earth Syst. Sci., 25, 1229–1253, https://doi.org/10.5194/nhess-25-1229-2025, https://doi.org/10.5194/nhess-25-1229-2025, 2025
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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. Landslide runout may occur on hillslopes or in channels, requiring different modeling approaches. We develop methods to identify potential runout zones and apply these methods to identify susceptible areas for three municipalities in Puerto Rico.
Jonathan Perkins, Nina S. Oakley, Brian D. Collins, Skye C. Corbett, and W. Paul Burgess
Nat. Hazards Earth Syst. Sci., 25, 1037–1056, https://doi.org/10.5194/nhess-25-1037-2025, https://doi.org/10.5194/nhess-25-1037-2025, 2025
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Rainfall-induced landslides result in deaths and economic losses annually across the globe. However, it is unclear how storm severity relates to landslide severity across large regions. Here we develop a method to dynamically map landslide-affected areas, and we compare this to meteorological estimates of storm severity. We find that preconditioning by earlier storms and the location of rainfall bursts, rather than atmospheric storm strength, dictate landslide magnitude and pattern.
Ellen B. Robson, Bhim Kumar Dahal, and David G. Toll
Nat. Hazards Earth Syst. Sci., 25, 949–973, https://doi.org/10.5194/nhess-25-949-2025, https://doi.org/10.5194/nhess-25-949-2025, 2025
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Slopes excavated alongside roads in Nepal frequently fail (a landslide), resulting in substantial losses. Our participatory approach study with road engineers aimed to assess how road slope design guidelines in Nepal can be improved. Our study revealed inconsistent guideline adherence due to a lack of user-friendliness and inadequate training. We present general recommendations to enhance road slope management, as well as technical recommendations to improve the guidelines.
Chenchen Qiu and Xueyu Geng
Nat. Hazards Earth Syst. Sci., 25, 709–726, https://doi.org/10.5194/nhess-25-709-2025, https://doi.org/10.5194/nhess-25-709-2025, 2025
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We propose an integrated method using a combination of a physical vulnerability matrix and a machine learning model to estimate the potential physical damage and associated economic loss caused by future debris flows based on collected historical data on the Qinghai–Tibet Plateau region.
Mark Bloomberg, Tim Davies, Elena Moltchanova, Tom Robinson, and David Palmer
Nat. Hazards Earth Syst. Sci., 25, 647–656, https://doi.org/10.5194/nhess-25-647-2025, https://doi.org/10.5194/nhess-25-647-2025, 2025
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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 that pose an unacceptable risk to life, and identify the “window of non-recognition” where debris flows are infrequent enough that their hazard is unrecognised yet frequent enough to pose a risk to life.
Christoph Schaller, Luuk Dorren, Massimiliano Schwarz, Christine Moos, Arie C. Seijmonsbergen, and E. Emiel van Loon
Nat. Hazards Earth Syst. Sci., 25, 467–491, https://doi.org/10.5194/nhess-25-467-2025, https://doi.org/10.5194/nhess-25-467-2025, 2025
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We developed a machine-learning-based approach to predict the potential thickness of shallow landslides to generate improved inputs for slope stability models. We selected 21 explanatory variables, including metrics on terrain, geomorphology, vegetation height, and lithology, and used data from two Swiss field inventories to calibrate and test the models. The best-performing machine learning model consistently reduced the mean average error by at least 20 % compared to previous models.
Jui-Ming Chang, Che-Ming Yang, Wei-An Chao, Chin-Shang Ku, Ming-Wan Huang, Tung-Chou Hsieh, and Chi-Yao Hung
Nat. Hazards Earth Syst. Sci., 25, 451–466, https://doi.org/10.5194/nhess-25-451-2025, https://doi.org/10.5194/nhess-25-451-2025, 2025
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The study on the Cilan landslide (CL) demonstrates the utilization of seismic analysis results as preliminary data for geologists during field surveys. Spectrograms revealed that the first event of CL consisted of four sliding failures accompanied by a gradual reduction in landslide volume. The second and third events were minor toppling and rockfalls. Then combining the seismological-based knowledge and field survey results, the spatiotemporal variation in landslide evolution is proposed.
Marko Sinčić, Sanja Bernat Gazibara, Mauro Rossi, and Snježana Mihalić Arbanas
Nat. Hazards Earth Syst. Sci., 25, 183–206, https://doi.org/10.5194/nhess-25-183-2025, https://doi.org/10.5194/nhess-25-183-2025, 2025
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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 5 statistical methods. The novelty of the research is that using stretched landslide conditioning factor values results in models with higher accuracy and that certain statistical methods are more sensitive to the landslide conditioning factor classification criteria than others.
Jui-Sheng Chou, Hoang-Minh Nguyen, Huy-Phuong Phan, and Kuo-Lung Wang
Nat. Hazards Earth Syst. Sci., 25, 119–146, https://doi.org/10.5194/nhess-25-119-2025, https://doi.org/10.5194/nhess-25-119-2025, 2025
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This study enhances landslide prediction using advanced machine learning, including new algorithms inspired by historical explorations. The research accurately forecasts landslide movements by analyzing 8 years of data from Taiwan's Lushan, improving early warning and potentially saving lives and infrastructure. This integration marks a significant advancement in environmental risk management.
Di Wu, Yuke Wang, and Xin Chen
Nat. Hazards Earth Syst. Sci., 24, 4617–4630, https://doi.org/10.5194/nhess-24-4617-2024, https://doi.org/10.5194/nhess-24-4617-2024, 2024
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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.
Matthias Schlögl, Anita Graser, Raphael Spiekermann, Jasmin Lampert, and Stefan Steger
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.
Li Wei, Kaiheng Hu, Shuang Liu, Lan Ning, Xiaopeng Zhang, Qiyuan Zhang, and Md. Abdur Rahim
Nat. Hazards Earth Syst. Sci., 24, 4179–4197, https://doi.org/10.5194/nhess-24-4179-2024, https://doi.org/10.5194/nhess-24-4179-2024, 2024
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The damage patterns of the buildings were classified into three types: (I) buried by primary debris flow, (II) inundated by secondary dam-burst flood, and (III) sequentially buried by debris flow and inundated by dam-burst flood. The threshold of the impact pressures in Zones (II) and (III) where vulnerability is equal to 1 is 84 kPa and 116 kPa, respectively. Heavy damage occurs at an impact pressure greater than 50 kPa, while slight damage occurs below 30 kPa.
Bo Peng and Xueling Wu
Nat. Hazards Earth Syst. Sci., 24, 3991–4013, https://doi.org/10.5194/nhess-24-3991-2024, https://doi.org/10.5194/nhess-24-3991-2024, 2024
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Our research enhances landslide prevention using advanced machine learning to forecast heavy-rainfall-triggered landslides. By analyzing regions and employing various models, we identified optimal ways to predict high-risk rainfall events. Integrating multiple factors and models, including a neural network, significantly improves landslide predictions. Real data validation confirms our approach's reliability, aiding communities in mitigating landslide impacts and safeguarding lives and property.
Andrea Manconi, Yves Bühler, Andreas Stoffel, Johan Gaume, Qiaoping Zhang, and Valentyn Tolpekin
Nat. Hazards Earth Syst. Sci., 24, 3833–3839, https://doi.org/10.5194/nhess-24-3833-2024, https://doi.org/10.5194/nhess-24-3833-2024, 2024
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Our research reveals the power of high-resolution satellite synthetic-aperture radar (SAR) imagery for slope deformation monitoring. Using ICEYE data over the Brienz/Brinzauls instability, we measured surface velocity and mapped the landslide event with unprecedented precision. This underscores the potential of satellite SAR for timely hazard assessment in remote regions and aiding disaster mitigation efforts effectively.
Oliver Korup, Lisa V. Luna, and Joaquin V. Ferrer
Nat. Hazards Earth Syst. Sci., 24, 3815–3832, https://doi.org/10.5194/nhess-24-3815-2024, https://doi.org/10.5194/nhess-24-3815-2024, 2024
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Catalogues of mapped landslides are useful for learning and forecasting how frequently they occur in relation to their size. Yet, rare and large landslides remain mostly uncertain in statistical summaries of these catalogues. We propose a single, consistent method of comparing across different data sources and find that landslide statistics disclose more about subjective mapping choices than trigger types or environmental settings.
Rachael Lau, Carolina Seguí, Tyler Waterman, Nathaniel Chaney, and Manolis Veveakis
Nat. Hazards Earth Syst. Sci., 24, 3651–3661, https://doi.org/10.5194/nhess-24-3651-2024, https://doi.org/10.5194/nhess-24-3651-2024, 2024
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This work examines the use of interferometric synthetic-aperture radar (InSAR) alongside in situ borehole measurements to assess the stability of deep-seated landslides for the case study of El Forn (Andorra). Comparing InSAR with borehole data suggests a key trade-off between accuracy and precision for various InSAR resolutions. Spatial interpolation with InSAR informed how many remote observations are necessary to lower error in a remote sensing re-creation of ground motion over the landslide.
Zhen Lei Wei, Yue Quan Shang, Qiu Hua Liang, and Xi Lin Xia
Nat. Hazards Earth Syst. Sci., 24, 3357–3379, https://doi.org/10.5194/nhess-24-3357-2024, https://doi.org/10.5194/nhess-24-3357-2024, 2024
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The initiation of debris flows is significantly influenced by rainfall-induced hydrological processes. We propose a novel framework based on an integrated hydrological and hydrodynamic model and aimed at estimating intensity–duration (ID) rainfall thresholds responsible for triggering debris flows. In comparison to traditional statistical approaches, this physically based framework is particularly suitable for application in ungauged catchments where historical debris flow data are scarce.
Jürgen Mey, Ravi Kumar Guntu, Alexander Plakias, Igo Silva de Almeida, and Wolfgang Schwanghart
Nat. Hazards Earth Syst. Sci., 24, 3207–3223, https://doi.org/10.5194/nhess-24-3207-2024, https://doi.org/10.5194/nhess-24-3207-2024, 2024
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The Himalayan road network links remote areas, but fragile terrain and poor construction lead to frequent landslides. This study on the NH-7 in India's Uttarakhand region analyzed 300 landslides after heavy rainfall in 2022 . Factors like slope, rainfall, rock type and road work influence landslides. The study's model predicts landslide locations for better road maintenance planning, highlighting the risk from climate change and increased road use.
Fabiola Banfi, Emanuele Bevacqua, Pauline Rivoire, Sérgio C. Oliveira, Joaquim G. Pinto, Alexandre M. Ramos, and Carlo De Michele
Nat. Hazards Earth Syst. Sci., 24, 2689–2704, https://doi.org/10.5194/nhess-24-2689-2024, https://doi.org/10.5194/nhess-24-2689-2024, 2024
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Landslides are complex phenomena causing important impacts in vulnerable areas, and they are often triggered by rainfall. Here, we develop a new approach that uses information on the temporal clustering of rainfall, i.e. multiple events close in time, to detect landslide events and compare it with the use of classical empirical rainfall thresholds, considering as a case study the region of Lisbon, Portugal. The results could help to improve the prediction of rainfall-triggered landslides.
Jianqi Zhuang, Jianbing Peng, Chenhui Du, Yi Zhu, and Jiaxu Kong
Nat. Hazards Earth Syst. Sci., 24, 2615–2631, https://doi.org/10.5194/nhess-24-2615-2024, https://doi.org/10.5194/nhess-24-2615-2024, 2024
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The Revised Infinite Slope Model (RISM) is proposed using the equal differential unit method and correcting the deficiency of the safety factor increasing with the slope increasing when the slope is larger than 40°, as calculated using the Taylor slope infinite model. The intensity–duration (I–D) prediction curve of the rainfall-induced shallow loess landslides with different slopes was constructed and can be used in forecasting regional shallow loess landslides.
Yu Zhuang, Brian W. McArdell, and Perry Bartelt
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-87, https://doi.org/10.5194/nhess-2024-87, 2024
Revised manuscript accepted for NHESS
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This study reformulates the μ(I) rheology into a Voellmy-type relationship to elucidate its physical implications. The μ(I) rheology, incorporating a dimensionless inertial number, mimics granular temperature effects, reflecting shear thinning behavior of mass flows. However, its constant Coulomb friction coefficient limits accuracy in modeling deposition. Comparing μ(I) with Voellmy-type rheologies reveals strengths and limitations, enhancing mass flow modeling and engineering applications.
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.
Sonam Rinzin, Stuart Dunning, Rachel Carr, Ashim Sattar, and Martin Mergili
EGUsphere, https://doi.org/10.5194/egusphere-2024-1819, https://doi.org/10.5194/egusphere-2024-1819, 2024
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We evaluated the sensitivity of model outputs to input parameter uncertainties by performing multiple GLOF simulations using the r.avaflow model. We found out that GLOF modelling outputs are highly sensitive to six parameters: volume of mass movements entering lakes, DEM datasets, origin of mass movements, mesh size, basal frictional angle, and entrainment coefficient. Future modelling should carefully consider the output uncertainty from these sensitive parameters.
Alexander B. Prescott, Luke A. McGuire, Kwang-Sung Jun, Katherine R. Barnhart, and Nina S. Oakley
Nat. Hazards Earth Syst. Sci., 24, 2359–2374, https://doi.org/10.5194/nhess-24-2359-2024, https://doi.org/10.5194/nhess-24-2359-2024, 2024
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Fire can dramatically increase the risk of debris flows to downstream communities with little warning, but hazard assessments have not traditionally included estimates of inundation. We unify models developed by the scientific community to create probabilistic estimates of inundation area in response to rainfall at forecast lead times (≥ 24 h) needed for decision-making. This work takes an initial step toward a near-real-time postfire debris-flow inundation hazard assessment product.
Francis K. Rengers, Samuel Bower, Andrew Knapp, Jason W. Kean, Danielle W. vonLembke, Matthew A. Thomas, Jaime Kostelnik, Katherine R. Barnhart, Matthew Bethel, Joseph E. Gartner, Madeline Hille, Dennis M. Staley, Justin K. Anderson, Elizabeth K. Roberts, Stephen B. DeLong, Belize Lane, Paxton Ridgway, and Brendan P. Murphy
Nat. Hazards Earth Syst. Sci., 24, 2093–2114, https://doi.org/10.5194/nhess-24-2093-2024, https://doi.org/10.5194/nhess-24-2093-2024, 2024
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Every year the U.S. Geological Survey produces 50–100 postfire debris-flow hazard assessments using models for debris-flow likelihood and volume. To refine these models they must be tested with datasets that clearly document rainfall, debris-flow response, and debris-flow volume. These datasets are difficult to obtain, but this study developed and analyzed a postfire dataset with more than 100 postfire storm responses over a 2-year period. We also proposed ways to improve these models.
Praveen Kumar, Priyanka Priyanka, Kala Venkata Uday, and Varun Dutt
Nat. Hazards Earth Syst. Sci., 24, 1913–1928, https://doi.org/10.5194/nhess-24-1913-2024, https://doi.org/10.5194/nhess-24-1913-2024, 2024
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Our study focuses on predicting soil movement to mitigate landslide risks. We develop machine learning models with oversampling techniques to address the class imbalance in monitoring data. The dynamic ensemble model with K-means SMOTE (synthetic minority oversampling technique) achieves high precision, high recall, and a high F1 score. Our findings highlight the potential of these models with oversampling techniques to improve soil movement predictions in landslide-prone areas.
Kristian Svennevig, Julian Koch, Marie Keiding, and Gregor Luetzenburg
Nat. Hazards Earth Syst. Sci., 24, 1897–1911, https://doi.org/10.5194/nhess-24-1897-2024, https://doi.org/10.5194/nhess-24-1897-2024, 2024
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In our study, we analysed publicly available data in order to investigate the impact of climate change on landslides in Denmark. Our research indicates that the rising groundwater table due to climate change will result in an increase in landslide activity. Previous incidents of extremely wet winters have caused damage to infrastructure and buildings due to landslides. This study is the first of its kind to exclusively rely on public data and examine landslides in Denmark.
Jane Walden, Mylène Jacquemart, Bretwood Higman, Romain Hugonnet, Andrea Manconi, and Daniel Farinotti
EGUsphere, https://doi.org/10.5194/egusphere-2024-1086, https://doi.org/10.5194/egusphere-2024-1086, 2024
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In a study of eight landslides adjacent to glaciers in Alaska, we found that landslide movement increased as the glacier retreated past the landslide at four sites. Movement at other sites coincided with heavy precipitation or increased glacier thinning, and two sites showed little-to-no motion. We suggest that landslides next to water-terminating glaciers may be especially vulnerable to acceleration, which we guess is due to faster retreat rates and water replacing ice at the landslide edge.
Jiao Wang, Zhangxing Wang, Guanhua Sun, and Hongming Luo
Nat. Hazards Earth Syst. Sci., 24, 1741–1756, https://doi.org/10.5194/nhess-24-1741-2024, https://doi.org/10.5194/nhess-24-1741-2024, 2024
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With a simplified formula linking rainfall and groundwater level, the rise of the phreatic surface within the slope can be obtained. Then, a global analysis method that considers both seepage and seismic forces is proposed to determine the safety factor of slopes subjected to the combined effect of rainfall and earthquakes. By taking a slope in the Three Gorges Reservoir area as an example, the safety evolution of the slope combined with both rainfall and earthquake is also examined.
Carlo Tacconi Stefanelli, William Frodella, Francesco Caleca, Zhanar Raimbekova, Ruslan Umaraliev, and Veronica Tofani
Nat. Hazards Earth Syst. Sci., 24, 1697–1720, https://doi.org/10.5194/nhess-24-1697-2024, https://doi.org/10.5194/nhess-24-1697-2024, 2024
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Central Asia regions are marked by active tectonics, high mountains with glaciers, and strong rainfall. These predisposing factors make large landslides a serious threat in the area and a source of possible damming scenarios, which endanger the population. To prevent this, a semi-automated geographic information system (GIS-)based mapping method, centered on a bivariate correlation of morphometric parameters, was applied to give preliminary information on damming susceptibility in Central Asia.
Rex L. Baum, Dianne L. Brien, Mark E. Reid, William H. Schulz, and Matthew J. Tello
Nat. Hazards Earth Syst. Sci., 24, 1579–1605, https://doi.org/10.5194/nhess-24-1579-2024, https://doi.org/10.5194/nhess-24-1579-2024, 2024
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We mapped potential for heavy rainfall to cause landslides in part of the central mountains of Puerto Rico using new tools for estimating soil depth and quasi-3D slope stability. Potential ground-failure locations correlate well with the spatial density of landslides from Hurricane Maria. The smooth boundaries of the very high and high ground-failure susceptibility zones enclose 75 % and 90 %, respectively, of observed landslides. The maps can help mitigate ground-failure hazards.
Katherine R. Barnhart, Christopher R. Miller, Francis K. Rengers, and Jason W. Kean
Nat. Hazards Earth Syst. Sci., 24, 1459–1483, https://doi.org/10.5194/nhess-24-1459-2024, https://doi.org/10.5194/nhess-24-1459-2024, 2024
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Debris flows are a type of fast-moving landslide that start from shallow landslides or during intense rain. Infrastructure located downstream of watersheds susceptible to debris flows may be damaged should a debris flow reach them. We present and evaluate an approach to forecast building damage caused by debris flows. We test three alternative models for simulating the motion of debris flows and find that only one can forecast the correct number and spatial pattern of damaged buildings.
Luke A. McGuire, Francis K. Rengers, Ann M. Youberg, Alexander N. Gorr, Olivia J. Hoch, Rebecca Beers, and Ryan Porter
Nat. Hazards Earth Syst. Sci., 24, 1357–1379, https://doi.org/10.5194/nhess-24-1357-2024, https://doi.org/10.5194/nhess-24-1357-2024, 2024
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Runoff and erosion increase after fire, leading to a greater likelihood of floods and debris flows. We monitored debris flow activity following a fire in western New Mexico, USA, and observed 16 debris flows over a <2-year monitoring period. Rainstorms with recurrence intervals of approximately 1 year were sufficient to initiate debris flows. All debris flows initiated during the first several months following the fire, indicating a rapid decrease in debris flow susceptibility over time.
Ken'ichi Koshimizu, Satoshi Ishimaru, Fumitoshi Imaizumi, and Gentaro Kawakami
Nat. Hazards Earth Syst. Sci., 24, 1287–1301, https://doi.org/10.5194/nhess-24-1287-2024, https://doi.org/10.5194/nhess-24-1287-2024, 2024
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Morphological conditions of drainage basins that classify the presence or absence of debris flow fans were analyzed in areas with different rock strength using decision tree analysis. The relief ratio is the most important morphological factor regardless of the geology. However, the thresholds of morphological parameters needed for forming debris flow fans differ depending on the geology. Decision tree analysis is an effective tool for evaluating the debris flow risk for each geology.
Daniel Bolliger, Fritz Schlunegger, and Brian W. McArdell
Nat. Hazards Earth Syst. Sci., 24, 1035–1049, https://doi.org/10.5194/nhess-24-1035-2024, https://doi.org/10.5194/nhess-24-1035-2024, 2024
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We analysed data from the Illgraben debris flow monitoring station, Switzerland, and we modelled these flows with a debris flow runout model. We found that no correlation exists between the grain size distribution, the mineralogical composition of the matrix, and the debris flow properties. The flow properties rather appear to be determined by the flow volume, from which most other parameters can be derived.
Yuntao Zhou, Xiaoyan Zhao, Guangze Zhang, Bernd Wünnemann, Jiajia Zhang, and Minghui Meng
Nat. Hazards Earth Syst. Sci., 24, 891–906, https://doi.org/10.5194/nhess-24-891-2024, https://doi.org/10.5194/nhess-24-891-2024, 2024
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We developed three rock bridge models to analyze 3D stability and deformation behaviors of the Tizicao landslide and found that the contact surface model with high strength parameters combines advantages of the intact rock mass model in simulating the deformation of slopes with rock bridges and the modeling advantage of the Jennings model. The results help in choosing a rock bridge model to simulate landslide stability and reveal the influence laws of rock bridges on the stability of landslides.
Maximillian Van Wyk de Vries, Alexandre Dunant, Amy L. Johnson, Erin L. Harvey, Sihan Li, Katherine Arrell, Jeevan Baniya, Dipak Basnet, Gopi K. Basyal, Nyima Dorjee Bhotia, Simon J. Dadson, Alexander L. Densmore, Tek Bahadur Dong, Mark E. Kincey, Katie Oven, Anuradha Puri, and Nick J. Rosser
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-40, https://doi.org/10.5194/nhess-2024-40, 2024
Revised manuscript accepted for NHESS
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Mapping exposure to landslides is necessary to mitigate risk and reduce vulnerability. In this study, we show that there is a poor correlation between building damage and deaths from landslides- such that the deadliest landslides do not always destroy the most buildings and vice versa. This has important implications for our management on landslide risk.
Ashok Dahal, Hakan Tanyas, Cees van Westen, Mark van der Meijde, Paul Martin Mai, Raphaël Huser, and Luigi Lombardo
Nat. Hazards Earth Syst. Sci., 24, 823–845, https://doi.org/10.5194/nhess-24-823-2024, https://doi.org/10.5194/nhess-24-823-2024, 2024
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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.
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Short summary
Early warning of increased landslide potential provides situational awareness to reduce landslide-related losses from major storm events. For decades, landslide forecasts relied on rainfall data alone, but recent research points to the value of hydrologic information for improving predictions. In this paper, we provide our perspectives on the value and limitations of integrating subsurface hillslope hydrologic monitoring data and mathematical modeling for more accurate landslide forecasts.
Early warning of increased landslide potential provides situational awareness to reduce...
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