Articles | Volume 24, issue 5
https://doi.org/10.5194/nhess-24-1579-2024
© 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-24-1579-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 May 2024
Research article |
| 06 May 2024
| 06 May 2024
Assessing locations susceptible to shallow landslide initiation during prolonged intense rainfall in the Lares, Utuado, and Naranjito municipalities of Puerto Rico
U.S. Geological Survey, Golden, Colorado 80401, USA
Dianne L. Brien
U.S. Geological Survey, Moffett Field, California 94035, USA
Mark E. Reid
U.S. Geological Survey, Moffett Field, California 94035, USA
William H. Schulz
U.S. Geological Survey, Golden, Colorado 80401, USA
Matthew J. Tello
U.S. Geological Survey, Golden, Colorado 80401, USA
currently at: Colorado Department of Transportation, Denver, Colorado 80204, USA
Related authors
S. Raia, M. Alvioli, M. Rossi, R. L. Baum, J. W. Godt, and F. Guzzetti
Geosci. Model Dev., 7, 495–514, https://doi.org/10.5194/gmd-7-495-2014, https://doi.org/10.5194/gmd-7-495-2014, 2014
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
Short summary
Short summary
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.
S. Raia, M. Alvioli, M. Rossi, R. L. Baum, J. W. Godt, and F. Guzzetti
Geosci. Model Dev., 7, 495–514, https://doi.org/10.5194/gmd-7-495-2014, https://doi.org/10.5194/gmd-7-495-2014, 2014
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
Invited perspectives: Integrating hydrologic information into the next generation of landslide early warning systems
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
The vulnerability of buildings to a large-scale debris flow and outburst flood hazard cascade that occurred on 30 August 2020 in Ganluo, southwest China
Optimizing rainfall-triggered landslide thresholds for daily landslide hazard warning in the Three Gorges Reservoir area
Is higher resolution always better? Open-access DEM comparison for Slope Units delineation and regional landslide prediction
Brief communication: Monitoring impending slope failure with very high-resolution spaceborne synthetic aperture radar
Size scaling of large landslides from incomplete inventories
InSAR-informed in situ monitoring for deep-seated landslides: insights from El Forn (Andorra)
A coupled hydrological and hydrodynamic modeling approach for estimating rainfall thresholds of debris-flow occurrence
More than one landslide per road kilometer – surveying and modeling mass movements along the Rishikesh–Joshimath (NH-7) highway, Uttarakhand, India
Temporal clustering of precipitation for detection of potential landslides
Shallow-landslide stability evaluation in loess areas according to the Revised Infinite Slope Model: a case study of the 7.25 Tianshui sliding-flow landslide events of 2013 in the southwest of the Loess Plateau, China
Comparative Analysis of μ (I) and Voellmy-Type Grain Flow Rheologies in Geophysical Mass Flows: Insights from Theoretical and Real Case Studies
From rockfall source areas identification to susceptibility zonation: a proposed workflow tested in El Hierro (Canary Islands, Spain)
Exploring implications of input parameter uncertainties on GLOF modelling results using the state-of-the-art modelling code, r.avaflow
Probabilistic assessment of postfire debris-flow inundation in response to forecast rainfall
Evaluating post-wildfire debris-flow rainfall thresholds and volume models at the 2020 Grizzly Creek Fire in Glenwood Canyon, Colorado, USA
Addressing class imbalance in soil movement predictions
Assessing the impact of climate change on landslides near Vejle, Denmark, using public data
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
Evaluation of debris-flow building damage forecasts
Characteristics of debris-flow-prone watersheds and debris-flow-triggering rainstorms following the Tadpole Fire, New Mexico, USA
Morphological characteristics and conditions of drainage basins contributing to the formation of debris flow fans: an examination of regions with different rock strength using decision tree analysis
Comparison of debris flow observations, including fine-sediment grain size and composition and runout model results, at Illgraben, Swiss Alps
Simulation analysis of 3D stability of a landslide with a locking segment: a case study of the Tizicao landslide in Maoxian County, southwest China
Brief Communication: Weak correlation between building damage and loss of life from landslides
Space–time landslide hazard modeling via Ensemble Neural Networks
Optimization strategy for flexible barrier structures: investigation and back analysis of a rockfall disaster case in southwestern China
Numerical-model-derived intensity–duration thresholds for early warning of rainfall-induced debris flows in a Himalayan catchment
Slope Unit Maker (SUMak): an efficient and parameter-free algorithm for delineating slope units to improve landslide modeling
Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling
A new analytical method for stability analysis of rock blocks with basal erosion in sub-horizontal strata by considering the eccentricity effect
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
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Benjamin B. Mirus, Thom Bogaard, Roberto Greco, and Manfred Stähli
Nat. Hazards Earth Syst. Sci., 25, 169–182, https://doi.org/10.5194/nhess-25-169-2025, https://doi.org/10.5194/nhess-25-169-2025, 2025
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Mahnoor Ahmed, Giacomo Titti, Sebastiano Trevisani, Lisa Borgatti, and Mirko Francioni
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-211, https://doi.org/10.5194/nhess-2024-211, 2024
Preprint under review for NHESS
Short summary
Short summary
Elevation models are compared with a true dataset for terrain characteristics which selects a better ranking model to test with different parameters for partitioning the terrain. The partitioning of the terrain is measured by how well a partitioned unit can support the mapped landslide area and number of landslides. The effect of this relationship is reflected with different metrics in the susceptibility maps.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
Aaron, J., McDougall, S., Moore, J. R., Coe, J. A., and Hungr, O.: The role of initial coherence and path materials in the dynamics of three rock avalanche case histories, Geoenvironmental Disasters, 4, 5, https://doi.org/10.1186/s40677-017-0070-4, 2017.
Alvioli, M. and Baum, R. L.: Parallelization of the TRIGRS model for rainfall-induced landslides using the message passing interface, Environ. Modell. Softw., 81, 122–135, https://doi.org/10.1016/j.envsoft.2016.04.002, 2016a.
Alvioli, M. and Baum, R. L.: Serial and parallel versions of the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model (TRIGRS, version 2.1), U.S. Geol. Surv. software release [code], https://doi.org/10.5066/F7M044QS, 2016b.
Alvioli, M., Marchesini, I., Reichenbach, P., Rossi, M., Ardizzone, F., Fiorucci, F., and Guzzetti, F.: Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modeling, Geosci. Model Dev., 9, 3975–3991, https://doi.org/10.5194/gmd-9-3975-2016, 2016.
Arnone, E., Noto, L., Lepore, C., and Bras, R.: Physically-based and distributed approach to analyze rainfall-triggered landslides at watershed scale, Geomorphology, 133, 121–131, https://doi.org/10.1016/j.geomorph.2011.03.019, 2011.
ASTM International: D2487-17e1, Standard Practice for Classification of Soils for Engineering Purposes (Unified Soil Classification System), https://doi.org/10.1520/D2487-17E01, 2020.
Baum, R. L.: Rapid sensitivity analysis for reducing uncertainty in landslide hazard assessments, in: Understanding and Reducing Landslide Disaster Risk, edited by: Guzzetti, F., Mihalić Arbanas, S., Reichenbach, P., Sassa, K., Bobrowsky, P. T., and Takara, K., Springer, Cham, Switzerland, 329–335, https://doi.org/10.1007/978-3-030-60227-7_37, 2021.
Baum, R. L.: Slabs3D – A Fortran 95 program for analyzing potential shallow landslides in a digital landscape, U.S. Geol. Surv. software release [code], https://doi.org/10.5066/P9G4I8IU, 2023.
Baum, R. L. and Lewis, A. C.: Engineering soil classification and geotechnical measurements in Lares, Naranjito, and Utuado, Puerto Rico: U.S. Geol. Surv. data release [data set], https://doi.org/10.5066/P9UXTQ4B, 2023.
Baum, R. L., Godt, J. W., and Savage, W. Z.: Estimating the timing and location of shallow rainfall-induced landslides using a model for transient, unsaturated infiltration, J. Geophys. Res.-Earth, 115, F03013, https://doi.org/10.1029/2009JF001321, 2010.
Baum, R. L., Godt, J. W., Coe, J. A., and Reid, M. E.: Assessment of shallow landslide potential using 1D and 3D slope stability analysis, in: Landslides and Engineered Slopes: Protecting Society through Improved Understanding, edited by: Eberhardt, E., Froese, C., Turner, A. K., and Leroueil, S., Taylor & Francis Group, London, 1667–1672, ISBN 978-0-415-62123-6, 2012.
Baum, R. L., Schulz, W. H., Brien, D. L., Burns, W. L., Reid, M. E., and Godt, J. W.: Progress in regional landslide hazard assessment – Examples from the USA, in: Landslide Science for a Safer Geoenvironment, edited by: Sassa, K., Canuti, P., and Yin, Y., Springer, Cham, Switzerland, 21–36, https://doi.org/10.1007/978-3-319-04999-1_2, 2014.
Baum, R. L., Cerovski-Darriau, C., Schulz, W. H., Bessette-Kirton, E., Coe, J. A., Smith, J. B., and Smoczyk, G. M.: Variability of hurricane María debris-flow source areas in Puerto Rico – Implications for hazard assessment, AGU Fall Meeting, Washington, DC 2018, NH14A-02, https://agu.confex.com/agu/fm18/meetingapp.cgi/Paper/412740 (last access: 10 August 2023), 2018.
Baum, R. L., Scheevel, C. R., and Jones, E. S.: Constraining parameter uncertainty in modeling debris-flow initiation during the September 2013 Colorado Front Range storm, in: Debris-flow Hazards Mitigation: Mechanics, Monitoring, Modeling, and Assessment, edited by: Kean, J. W., Coe, J. A., Santi, P. M., and Guillen, B. K., Association of Environmental and Engineering, Brunswick, Ohio, 249–256, https://doi.org/10.25676/11124/173212, 2019.
Baum, R. L., Bedinger, E. C., and Tello, M. J.: REGOLITH – A Fortran 95 program for estimating soil mantle thickness in a digital landscape for landslide and debris-flow hazard assessment, U.S. Geol. Surv. software release [code], https://doi.org/10.5066/P9U2RDWJ, 2021.
Baum, R. L., Brien, D. L., Reid, M. E., Schulz, W. H., Tello, M. J., and Bedinger, E. C.: Model input and output data covering Lares Municipio, Utuado Municipio, and Naranjito Municipio, Puerto Rico, for landslide initiation susceptibility assessment after Hurricane Maria, U.S. Geol. Surv. data release [data set], https://doi.org/10.5066/P9C1U0LP, 2023.
Bawiec, W. J.: Geologic terranes of Puerto Rico, in: Geology, geochemistry, geophysics, mineral occurrences, and mineral resource assessment for the commonwealth of Puerto Rico, edited by: Bawiec, W. J., U.S. Geol. Surv. Open-File Rep. 98–38, https://doi.org/10.3133/ofr9838, 1998.
Baxstrom, K. W., Einbund, M. M., and Schulz, W. H.: Map data from landslides triggered by Hurricane María in a section of Naranjito, Puerto Rico, U.S. Geol. Surv. data release [data set], https://doi.org/10.5066/P9GBGA4I, 2021a.
Baxstrom, K. W., Einbund, M. M., and Schulz, W. H.: Map data from landslides triggered by Hurricane María in the greater karst region of northwest Puerto Rico, U.S. Geol. Surv. data release [data set], https://doi.org/10.5066/P9YYU7W1, 2021b.
Begueria, S.: Validation and evaluation of predictive models in hazard assessment and risk management, Nat. Hazards, 37, 315–329, https://doi.org/10.1007/s11069-005-5182-6, 2006.
Benda, L., Miller, D., Andras, K., Bigelow, P., Reeves, G., and Michael, D.: NetMap: A new tool in support of watershed science and resource management, Forest Sci., 53, 206–219, https://doi.org/10.1093/forestscience/53.2.206, 2007.
Bessette-Kirton, E. K., Coe, J. A., Godt, J. W., Kean, J. W., Rengers, F. K., Schulz, W. H., Baum, R. L., Jones, E. S., and Staley, D. M.: Map data showing concentration of landslides caused by hurricane María in Puerto Rico, U.S. Geol. Surv. data release [data set], https://doi.org/10.5066/F7JD4VRF, 2017.
Bessette-Kirton, E. K., Cerovski-Darriau, C., Schulz, W. H., Coe, J. A., Kean, J. W., Godt, J. W., Thomas, M. A., and Hughes, K. S.: Landslides triggered by Hurricane María: Assessment of an extreme event in Puerto Rico, GSA Today, 29, 4–10, https://doi.org/10.1130/GSATG383A.1, 2019a.
Bessette-Kirton, E. K., Kean, J. W., Coe, J. A., Rengers, F. K., and Staley, D. M.: An evaluation of debris-flow runout model accuracy and complexity in Montecito, California: Towards a framework for regional inundation-hazard forecasting, in: Debris-flow Hazards Mitigation: Mechanics, Monitoring, Modeling, and Assessment, edited by: Kean, J. W., Coe, J. A., Santi, P. M., and Guillen, B. K., Association of Environmental and Engineering Geologists, Brunswick, Ohio, 257–264, https://doi.org/10.25676/11124/173211, 2019b.
Bessette-Kirton, E. K., Coe, J. A., Kelly, M. A., Cerovski-Darriau, C., and Schulz, W. H.: Map data from landslides triggered by Hurricane María in four study areas of Puerto Rico, U.S. Geol. Surv. data release [data set], https://doi.org/10.5066/P9OW4SLX, 2019c.
Bessette-Kirton, E. K., Coe, J. A., Schulz, W. H., Cerovski-Darriau, C., and Einbund, M. M.: Mobility characteristics of debris slides and flows triggered by Hurricane María in Puerto Rico, Landslides, 17, 2795–2809, https://doi.org/10.1007/s10346-020-01445-z, 2020.
Brien, D. L., Reid, M. E., Cronkite-Ratcliff, C., and Perkins, J. P.: Portraying runout and inundation from hurricane-induced landslides in Puerto Rico, Geological Society of America Abstracts with Programs, 53, 85-4, https://doi.org/10.1130/abs/2021AM-368632, 2021.
Catani, F., Segoni, S., and Falorni, G.: An empirical geomorphology-based approach to the spatial prediction of soil thickness at catchment scale, Water Resour. Res., 46, W05508, https://doi.org/10.1029/2008WR007450, 2010.
Canli, E., Mergili, M., Thiebes, B., and Glade, T.: Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology, Nat. Hazards Earth Syst. Sci., 18, 2183–2202, https://doi.org/10.5194/nhess-18-2183-2018, 2018.
Carrara, A., Guzzetti, F., Cardinali, M., and Reichenbach, P.: Use of GIS technology in the prediction and monitoring of landslide hazard, Nat. Hazards, 20, 117–135, https://doi.org/10.1023/A:1008097111310, 1999.
Chung, C. F. and Fabbri, A. G.: Validation of spatial prediction models for landslide hazard mapping, Nat. Hazards, 30, 451–472, https://doi.org/10.1023/B:NHAZ.0000007172.62651.2b, 2003.
DeRose, R. C., Trustrum, N. A., and Blaschke, P. M.: Geomorphic change implied by regolith-slope relationships on steepland hillslopes, Taranaki, New Zealand, Catena, 18, 489–514, https://doi.org/10.1016/0341-8162(91)90051-X, 1991.
Einbund, M. M., Baxstrom, K. S., and Schulz, W. H.: Map data from landslides triggered by Hurricane María in four study areas in the Utuado municipality, Puerto Rico, U.S. Geol. Surv. data release [data set], https://doi.org/10.5066/P9ZNUR1P, 2021a.
Einbund, M. M., Baxstrom, K. S., and Schulz, W. H.: Map data from landslides triggered by Hurricane María in four study areas in the Lares municipality, Puerto Rico, U.S. Geol. Surv. data release [data set], https://doi.org/10.5066/P9EASZZ7, 2021b.
Ellen, S. D., Mark, R. K., Cannon, S. H., and Knifong, D. L.: Map of debris-flow hazard in the Honolulu District of Oahu, Hawaii, U.S. Geol. Surv. Open-File Rep. 93-213, 28 pp., https://doi.org/10.3133/ofr93213, 1993.
Fan, L., Lehmann, P., McArdell, B., and Or, D.: Linking rainfall-induced landslides with debris flows runout patterns towards catchment scale hazard assessment, Geomorphology, 280, 1–15, https://doi.org/10.1016/j.geomorph.2016.10.007, 2017.
Fawcett, T.: An introduction to ROC analysis, Pattern Recogn. Lett., 27, 861–874, https://doi.org/10.1016/j.patrec.2005.10.010, 2006.
Formetta, G., Capparelli, G., and Versace, P.: Evaluating performance of simplified physically based models for shallow landslide susceptibility, Hydrol. Earth Syst. Sci., 20, 4585–4603, https://doi.org/10.5194/hess-20-4585-2016, 2016.
George, D. L. and Iverson, R. M.: A depth-averaged debris-flow model that includes the effects of evolving dilatancy: 2. Numerical predictions and experimental tests, P. Roy. Soc. A-Math. Phy., 470, 20130820, https://doi.org/10.1098/rspa.2013.0820, 2014.
Godt, J. W., Schulz, W. H., Baum, R. L., and Savage, W. Z.: Modeling rainfall conditions for shallow landsliding in Seattle, Washington, in: Landslides and Engineering Geology of the Seattle, Washington, Area, edited by: Baum, R. L., Godt, J. W., and Highland, L. M., Geological Society of America, Boulder, Colorado, 137–152, https://doi.org/10.1130/2008.4020(08), 2008a.
Godt, J. W., Baum, R. L., Savage, W. Z., Salciarini, D., Schulz, W. H., and Harp, E. L.: Transient deterministic shallow landslide modeling: Requirements for susceptibility and hazard assessments in a GIS framework, Eng. Geol., 102, 214–226, https://doi.org/10.1016/j.enggeo.2008.03.019, 2008b.
Gomes, G. J. C., Vrugt, J. A., and Vargas Jr., E. A.: Toward improved prediction of the bedrock depth underneath hillslopes: Bayesian inference of the bottom-up control hypothesis using high-resolution topographic data, Water Resour. Res., 52, 3085–3112, https://doi.org/10.1002/2015WR018147, 2016.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modeling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Heidemann, H. K.: Lidar base specification (ver. 1.3, February 2018), U.S. Geological Survey Techniques and Methods, 11, 102 pp., https://doi.org/10.3133/tm11b4, 2018.
Ho, J.-Y., Lee, K. T., Chang, T.-C., Wang, Z.-Y., and Liao, Y.-H.: Influences of spatial distribution of soil thickness on shallow landslide prediction, Eng. Geol., 124, 38–46, https://doi.org/10.1016/j.enggeo.2011.09.013, 2012.
Hovland, H. J.: Three-dimensional slope stability analysis method, J. Geotech. Eng.-ASCE, 103, 971–986, https://doi.org/10.1061/AJGEB6.0000493, 1977.
Hsu, Y. C. and Liu, K. F.: Combining TRIGRS and DEBRIS-2D models for the simulation of a rainfall infiltration induced shallow landslide and subsequent debris flow, Water, 11, 890, https://doi.org/10.3390/w11050890, 2019.
Hughes, K. S. and Schulz, W. H.: Map depicting susceptibility to landslides triggered by intense rainfall, Puerto Rico, U.S. Geol. Surv. Open-File Rep. 2020–1022, 91 pp., https://doi.org/10.3133/ofr20201022, 2020a.
Hughes, K. S. and Schulz, W. H.: Results from frequency-ratio analyses of soil classification and land use related to landslide locations in Puerto Rico following Hurricane María, U.S. Geol. Surv. data release [data set], https://doi.org/10.5066/P9VK2FAL, 2020b.
Hughes, K. S., Bayouth-García, D., Martínez-Milian, G. O., Schulz, W. H., and Baum, R. L.: Map of slope-failure locations in Puerto Rico after Hurricane María, U.S. Geol. Surv. data release [data set], https://doi.org/10.5066/P9BVMD74, 2019.
Iverson, R. M.: Landslide triggering by rain infiltration, Water Resour. Res., 36, 1897–1910, https://doi.org/10.1029/2000WR900090, 2000.
Jibson, R. W.: Debris flows in southern Puerto Rico, in: Landslide processes of the eastern United States and Puerto Rico, edited by: Schultz, A. P. and Jibson, R. W., Geol. Soc. Am. Spec. Pap., 236, 29–55, https://doi.org/10.1130/SPE236-p29, 1989.
Jolly, W. T., Lidiak, E. G., Dickin, A. P., and Wu, T.-W.: Geochemical diversity of Mesozoic island arc tectonic blocks in eastern Puerto Rico, in: Tectonics and Geochemistry of the Northeastern Caribbean, edited by: Likiak, E. G. and Larue, D. K., Geol. Soc. Am. Spec Pap., 322, 67–98, https://doi.org/10.1130/0-8137-2322-1.67, 1998.
Larsen, M. C.: Landslides and sediment budgets in four watersheds in eastern Puerto Rico, in: Water Quality and Landscape Processes of Four Watersheds in Eastern Puerto Rico, edited by: Murphy, S. F. and Stallard, R. F., U.S. Geol. Surv. Prof. Paper 1789, 153–178, https://doi.org/10.3133/pp1789, 2012.
Larsen, M. C. and Parks, J. E.: Map showing landslide susceptibility in the Comerio municipality, Puerto Rico, U.S. Geol. Surv. Open-File Rep. 98-566, https://doi.org/10.3133/ofr98566, 1998.
Larsen, M. C. and Simon, A.: A rainfall intensity-duration threshold for landslides in a humid-tropical environment, Puerto Rico, Geogr. Ann. A, 75, 13–23, https://doi.org/10.1080/04353676.1993.11880379, 1993.
Larsen, M. C. and Torres-Sanchez, A. J.: Landslides triggered by hurricane Hugo in eastern Puerto Rico, September 1989, Caribb. J. Sci., 28, 113–125, 1992.
Larsen, M. C. and Torres-Sanchez, A. J.: The frequency and distribution of recent landslides in three montane tropical regions of Puerto Rico, Geomorphology, 24, 309–331, https://doi.org/10.1016/S0169-555X(98)00023-3, 1998.
Larsen, M. C., Santiago, M., Jibson, R., and Questell, E.: Map showing susceptibility to rainfall-triggered landslides in the municipality of Ponce, Puerto Rico, U.S. Geol. Surv. Scientific Investigations Map 2818, https://doi.org/10.3133/sim2818, 2004.
Lee, S., Ryu, J.-H., Min, K., and Won, J.-S.: Landslide susceptibility analysis using GIS and artificial neural network, Earth Surf. Proc. Land., 28, 1361–1376, https://doi.org/10.1002/esp.593, 2003.
Lepore, C., Kamal, S. A., Shanahan, P., and Bras, R. L.: Rainfall-induced landslide susceptibility zonation of Puerto Rico, Environ. Earth Sci., 66, 1667–1681, https://doi.org/10.1007/s12665-011-0976-1, 2012.
Lepore, C., Arnone, E., Noto, L. V., Sivandran, G., and Bras, R. L.: Physically based modeling of rainfall-triggered landslides: a case study in the Luquillo forest, Puerto Rico, Hydrol. Earth Syst. Sci., 17, 3371–3387, https://doi.org/10.5194/hess-17-3371-2013, 2013.
Likos, W. J., Wayllace, A., Godt, J., and Lu, N.: Modified direct shear apparatus for unsaturated sands at low suction and stress, Geotech. Test. J., 33, 286–298, https://doi.org/10.1520/GTJ102927, 2010.
Medina, V., Hürlimann, M., Guo, Z., Lloret, A., and Vaunat, J.: Fast physically based model for rainfall-induced landslide susceptibility assessment at regional scale, Catena, 201, 105213, https://doi.org/10.1016/j.catena.2021.105213, 2021.
Mergili, M., Marchesini, I., Rossi, M., Guzzetti, F., and Fellin, W.: Spatially distributed three-dimensional slope stability modelling in a raster GIS, Geomorphology, 206, 178–195, https://doi.org/10.1016/j.geomorph.2013.10.008, 2014a.
Mergili, M., Marchesini, I., Alvioli, M., Metz, M., Schneider-Muntau, B., Rossi, M., and Guzzetti, F.: A strategy for GIS-based 3-D slope stability modelling over large areas, Geosci. Model Dev., 7, 2969–2982, https://doi.org/10.5194/gmd-7-2969-2014, 2014b.
Mergili, M., Schwarz, L., and Kociu, A.: Combining release and runout in statistical landslide susceptibility modeling, Landslides, 16, 2151–2165, https://doi.org/10.1007/s10346-019-01222-7, 2019.
Metz, C. E.: Basic principles of ROC analysis, Semin. Nucl. Med., 8, 283–298, https://doi.org/10.1016/S0001-2998(78)80014-2, 1978.
Milledge, D. G., Bellugi, D., McKean, J. A., Densmore, A. L., and Dietrich, W. E.: A multi-dimensional stability model for predicting shallow landslide size and shape across landscapes, J. Geophys. Res.-Earth, 119, 2481–2504, https://doi.org/10.1002/2014JF003135, 2015.
Montgomery, D. R. and Dietrich, W. E.: A physically-based model for the topographic control on shallow landsliding, Water Resour. Res., 30, 1153–1171, https://doi.org/10.1029/93WR02979, 1994.
Murphy, S. F., Stallard, R. F., Larsen, M. C., and Gould, W. A.: Physiography, geology, and land cover of four watersheds in eastern Puerto Rico, U.S. Geol. Surv. Prof. Paper 1789-A, 24 pp., https://doi.org/10.3133/pp1789A, 2012.
Monroe, W. H.: The karst landforms of Puerto Rico, U.S. Geol. Surv. Prof. Paper 899, 69 pp., https://doi.org/10.3133/pp899, 1976.
Nicótina, L., Tarboton, D. G., Tesfa, T. K., and Rinaldo, A.: Hydrologic controls on equilibrium soil depths, Water Resour. Res., 47, W04517, https://doi.org/10.1029/2010WR009538, 2011.
Pack, R. T., Tarboton, D. G., and Goodwin, C. N.: The SINMAP approach to terrain stability mapping, in: International Congress of the International Association of Engineering Geology and the Environment Proceedings, 8 September 21–25 1998, Vancouver, British Columbia, Canada, A. A. Balkema, Rotterdam, Netherlands, 2, 1157–1165, https://digitalcommons.usu.edu/cee_facpub/2583/ (last access: 13 March 2024), 1998.
Palacio Cordoba, J., Mergili, M., and Aristizábal, E.: Probabilistic landslide susceptibility analysis in tropical mountainous terrain using the physically based r.slope.stability model, Nat. Hazards Earth Syst. Sci., 20, 815–829, https://doi.org/10.5194/nhess-20-815-2020, 2020.
Pando, M. A., Ruiz, M. E., and Larsen, M. C.: Rainfall-induced landslides in Puerto Rico: An overview, in: Slopes and Retaining Structures Under Seismic and Static Conditions, edited by: Gabr, M. A., Bowders, J. J., Elton, D., and Zornberg, J. G., ASCE Geotech. SP., 140, 2911–2925, https://doi.org/10.1061/40787(166)25, 2005.
Patton, N. R., Lohse, K. A., Godsey, S. E., Crosby, B. T., and Seyfried, M. S.: Predicting soil thickness on soil mantled hillslopes, Nat. Commun., 9, 3329, https://doi.org/10.1038/s41467-018-05743-y, 2018.
Pelletier, J. D. and Rasmussen, C.: Geomorphically based predictive mapping of soil thickness in upland watersheds, Water Resour. Res., 45, W09417, https://doi.org/10.1029/2008WR007319, 2009.
Perkins, J. P., Baxstrom, K. W., Einbund, M. M., and Schulz, W. H.: Modified basal contact of the Tertiary Lares Limestone in the vicinity of Utuado, Puerto Rico, USA, derived from USGS Open-File Report 98-038, U.S. Geol. Surv. data release [data set], https://doi.org/10.5066/P9NL9EZG, 2022.
Raia, S., Alvioli, M., Rossi, M., Baum, R. L., Godt, J. W., and Guzzetti, F.: Improving predictive power of physically based rainfall-induced shallow landslide models: a probabilistic approach, Geosci. Model Dev., 7, 495–514, https://doi.org/10.5194/gmd-7-495-2014, 2014.
Ramos-Scharrón, C. E., Arima, E. Y., Guidry, A., Ruffe, D., and Vest, B.: Sediment mobilization by hurricane-driven shallow landsliding in a wet subtropical watershed, J. Geophys. Res.-Earth, 126, e2020JF006054, https://doi.org/10.1029/2020JF006054, 2021.
Reid, M. E., Christian, S. B., Brien, D. L., and Henderson, S. T.: Scoops3D – Software to analyze 3D slope stability throughout a digital landscape, U.S. Geol. Surv. Techniques and Methods 14-A1 [code], 218 pp. https://doi.org/10.3133/tm14A1, 2015.
Reid, M. E., Coe, J. A., and Brien, D. L.: Forecasting inundation from debris flows that grow volumetrically during travel, with application to the Oregon coast range, USA, Geomorphology, 273, 396–411, https://doi.org/10.1016/j.geomorph.2016.07.039, 2016.
Roering, J. J.: How well can hillslope evolution models “explain” topography?, Geol. Soc. Am. Bull., 120, 1248–1262, https://doi.org/10.1130/B26283.1, 2008.
Rossi, G., Catani, F., Leoni, L., Segoni, S., and Tofani, V.: HIRESSS: a physically based slope stability simulator for HPC applications, Nat. Hazards Earth Syst. Sci., 13, 151–166, https://doi.org/10.5194/nhess-13-151-2013, 2013.
Schulz, W. H., Jensen, E. K., Cerovski-Darriau, C. R., Baum, R. L., Thomas, M. A., and Coe, J. A.: Field observations of landslides and related materials following Hurricane Maria, Puerto Rico, U.S. Geol. Surv. data release [data set], https://doi.org/10.5066/P9T9KZ6T, 2023.
Segoni, S., Leoni, L., Benedetti, A. I., Catani, F., Righini, G., Falorni, G., Gabellani, S., Rudari, R., Silvestro, F., and Rebora, N.: Towards a definition of a real-time forecasting network for rainfall induced shallow landslides, Nat. Hazards Earth Syst. Sci., 9, 2119–2133, https://doi.org/10.5194/nhess-9-2119-2009, 2009.
Simon, A., Larsen, M. C., and Hupp, C. R.: The role of soil processes in determining mechanisms of slope failure and hillslope development in a humid-tropical forest eastern Puerto Rico, Geomorphology, 3, 263–286, https://doi.org/10.1016/0169-555X(90)90007-D, 1990.
Simoni, S., Zanotti, F., Bertoldi, G., and Rigon, R.: Modelling the probability of occurrence of shallow landslides and channelized debris flows using GEOtop-FS, Hydrol. Process., 22, 532–545, https://doi.org/10.1002/hyp.6886, 2008.
Smith, J. B., Thomas, M. A., Ashland, F., Michel, A. R., Wayllace, A., and Mirus, B. B.: Hillslope hydrologic monitoring data following Hurricane María in 2017, Puerto Rico, July 2018 to June 2020, U.S. Geol. Surv. data release [data set], https://doi.org/10.5066/P9548YK2, 2020.
Soil Survey Staff: Soil Survey Geographic (SSURGO) Database for Puerto Rico, all regions, U.S. Department of Agriculture Natural Resources Conservation Service, https://www.nrcs.usda.gov/resources/data-and-reports/web-soil-survey (last access: 10 August 2023), 2018.
Sowers, G. F.: Landslides in weathered volcanics in Puerto Rico, in: Proceedings of the Fourth Pan-American Conference on Soil Mechanics and Foundation Engineering, American Society of Civil Engineers, New York, 105–115, 1971.
Taggart, B. E. and Joyce, J.: Radiometrically dated marine terraces on northwestern Puerto Rico, in: Transactions of the 12th Caribbean Geological Conference, St. Croix, U.S. Virgin Islands, 7–11 August 1989, Miami Geological Society, South Miami, Florida, 248–258, 1991.
Taylor, D. W.: Fundamentals of Soil Mechanics, John Wiley & Sons, New York, 700 pp., 1948.
Thomas, M. A. and Cerovski-Darriau, C.: Infiltration data collected post-Hurricane María across landslide source area materials, Puerto Rico, USA, U.S. Geol. Surv. data release [data set], https://doi.org/10.5066/P9SCGVF7, 2019.
Tello, M.: Optimization of landslide susceptibility modeling: A Puerto Rico case study, Master of Science Thesis, Colorado School of Mines, Golden, Colorado, https://hdl.handle.net/11124/174137 (last access: 10 August 2023), 2020.
Terzaghi, K., Peck, R. B., and Mesri, G.: Soil Mechanics in Engineering Practice, 3rd edn., John Wiley & Sons: New York, 549 pp., ISBN 978-0-471-08658-1, 1996.
Tofani, V., Bicocchi, G., Rossi, G., Segoni, S., D'Ambrosio, M., Casagli, N., and Catani, F.: Soil characterization for shallow landslides modeling: a case study in the Northern Apennines (Central Italy), Landslides, 14, 755–770, https://doi.org/10.1007/s10346-017-0809-8, 2017.
U.S. Geological Survey: 2015–2016 USGS Puerto Rico LiDAR (project PR_PuertoRico_2015) [data set], https://apps.nationalmap.gov/lidar-explorer/#/ (last access: 10 August 2023), 2018.
U.S. Geological Survey: 2018 USGS Puerto Rico – Virgin Islands LiDAR (project PR_PRVI_A_2018), https://apps.nationalmap.gov/lidar-explorer/#/ (last access: 10 August 2023), 2020a.
U.S. Geological Survey: 2018 USGS Puerto Rico – Virgin Islands LiDAR (project PR_PRVI_D_2018), https://apps.nationalmap.gov/lidar-explorer/#/ (last access: 10 August 2023), 2020b.
U.S. Geological Survey: 2018 USGS Puerto Rico – Virgin Islands LiDAR (project PR_PRVI_H_2018), https://apps.nationalmap.gov/lidar-explorer/#/ (last access: 10 August 2023), 2020c.
von Ruette, J., Lehmann, P., and Or, D.: Rainfall-triggered shallow landslides at catchment scale: Threshold mechanics-based modeling for abruptness and localization, Water Resour. Res., 49, 6266–6285, https://doi.org/10.1002/wrcr.20418, 2013.
Wang, S., Zhang K., van Beek, L. P. H., Tian, X., and Bogaard, T. A.: Physically-based landslide prediction over a large region: Scaling low-resolution hydrological model results for high-resolution slope stability assessment, Environ. Modell. Softw., 124, 104607, https://doi.org/10.1016/j.envsoft.2019.104607, 2020.
Woodard, J. B., Mirus, B. B., Wood, N. J., Allstadt, K. E., Leshchinsky, B. A., and Crawford, M. M.: Slope Unit Maker (SUMak): an efficient and parameter-free algorithm for delineating slope units to improve landslide modeling, Nat. Hazards Earth Syst. Sci., 24, 1–12, https://doi.org/10.5194/nhess-24-1-2024, 2024.
Wu, W. and Sidle, R. C.: A distributed slope stability model for steep forested hillslopes, Water Resour. Res., 31, 2097–2110, https://doi.org/10.1029/95WR01136, 1995.
Xiao, T., Segoni, S., Liang, X., Yin, K., and Casagli, N.: Generating soil thickness maps by means of geomorphological-empirical approach and random forest algorithm in Wanzhou County, Three Gorges Reservoir, Geosci. Front., 14, 101514, https://doi.org/10.1016/j.gsf.2022.101514, 2023.
Yan, Q., Wainwright, H., Dafflon, B., Uhlemann, S., Steefel, C. I., Falco, N., Kwang, J., and Hubbard, S. S.: A hybrid data–model approach to map soil thickness in mountain hillslopes, Earth Surf. Dynam., 9, 1347–1361, https://doi.org/10.5194/esurf-9-1347-2021, 2021.
Zieher, T., Rutzinger, M., Schneider-Muntau, B., Perzl, F., Leidinger, D., Formayer, H., and Geitner, C.: Sensitivity analysis and calibration of a dynamic physically based slope stability model, Nat. Hazards Earth Syst. Sci., 17, 971–992, https://doi.org/10.5194/nhess-17-971-2017, 2017.
Short summary
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.
We mapped potential for heavy rainfall to cause landslides in part of the central mountains of...
Altmetrics
Final-revised paper
Preprint