Articles | Volume 23, issue 4
https://doi.org/10.5194/nhess-23-1631-2023
© Author(s) 2023. 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-23-1631-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 May 2023
Research article |
| 02 May 2023
| 02 May 2023
A data-driven evaluation of post-fire landslide susceptibility
Department of Civil, Architectural and Environmental Engineering, University of Colorado Boulder, Boulder, Colorado, USA
Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder, Boulder, Colorado, USA
Ben Livneh
Department of Civil, Architectural and Environmental Engineering, University of Colorado Boulder, Boulder, Colorado, USA
Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder, Boulder, Colorado, USA
Balaji Rajagopalan
Department of Civil, Architectural and Environmental Engineering, University of Colorado Boulder, Boulder, Colorado, USA
Kristy F. Tiampo
Department of Geological Sciences, University of Colorado Boulder, Boulder, Colorado, USA
Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder, Boulder, Colorado, USA
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Parthkumar A. Modi, Jared C. Carbone, Keith S. Jennings, Hannah Kamen, Joseph R. Kasprzyk, Bill Szafranski, Cameron W. Wobus, and Ben Livneh
EGUsphere, https://doi.org/10.5194/egusphere-2024-4046, https://doi.org/10.5194/egusphere-2024-4046, 2025
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This study shows that in unmanaged snow-dominated basins, high forecast accuracy doesn’t always lead to high economic value, especially during extreme conditions like droughts. It highlights how irregular errors in modern forecasting systems weaken the connection between accuracy and value. These findings call for forecast evaluations to focus not only on accuracy but also on economic impacts, providing valuable guidance for better water resource management under uncertainty.
Sebastian I. Cantarero, Edgart Flores, Harry Allbrook, Paulina Aguayo, Cristian A. Vargas, John E. Tamanaha, J. Bentley C. Scholz, Lennart T. Bach, Carolin R. Löscher, Ulf Riebesell, Balaji Rajagopalan, Nadia Dildar, and Julio Sepúlveda
Biogeosciences, 21, 3927–3958, https://doi.org/10.5194/bg-21-3927-2024, https://doi.org/10.5194/bg-21-3927-2024, 2024
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Our study explores lipid remodeling in response to environmental stress, specifically how cell membrane chemistry changes. We focus on intact polar lipids in a phytoplankton community exposed to diverse stressors in a mesocosm experiment. The observed remodeling indicates acyl chain recycling for energy storage in intact polar lipids during stress, reallocating resources based on varying growth conditions. This understanding is essential to grasp the system's impact on cellular pools.
Sandeep Kumar Mondal, Rishikesh Bharti, and Kristy F. Tiampo
EGUsphere, https://doi.org/10.5194/egusphere-2023-2253, https://doi.org/10.5194/egusphere-2023-2253, 2023
Preprint archived
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This study explains the capabilities of C-band Synthetic Aperture Radar (SAR) system in detection of glacial deformation due to earthquakes. Due to the remote location of the Himalayan glaciers associated with harsh weather conditions and rugged topography, the method of differential interferometric SAR (DInSAR) can be very useful in studying the impact of earthquakes in the glaciated regions where field-based seismological study is extremely tough to execute.
Justin M. Pflug, Yiwen Fang, Steven A. Margulis, and Ben Livneh
Hydrol. Earth Syst. Sci., 27, 2747–2762, https://doi.org/10.5194/hess-27-2747-2023, https://doi.org/10.5194/hess-27-2747-2023, 2023
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Wolverine denning habitat inferred using a snow threshold differed for three different spatial representations of snow. These differences were based on the annual volume of snow and the elevation of the snow line. While denning habitat was most influenced by winter meteorological conditions, our results show that studies applying thresholds to environmental datasets should report uncertainties stemming from different spatial resolutions and uncertainties introduced by the thresholds themselves.
Aaron Heldmyer, Ben Livneh, James McCreight, Laura Read, Joseph Kasprzyk, and Toby Minear
Hydrol. Earth Syst. Sci., 26, 6121–6136, https://doi.org/10.5194/hess-26-6121-2022, https://doi.org/10.5194/hess-26-6121-2022, 2022
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Measurements of channel characteristics are important for accurate forecasting in the NOAA National Water Model (NWM) but are scarcely available. We seek to improve channel representativeness in the NWM by updating channel geometry and roughness parameters using a large, previously unpublished, dataset of approximately 48 000 gauges. We find that the updated channel parameterization from this new dataset leads to improvements in simulated streamflow performance and channel representation.
Álvaro Ossandón, Manuela I. Brunner, Balaji Rajagopalan, and William Kleiber
Hydrol. Earth Syst. Sci., 26, 149–166, https://doi.org/10.5194/hess-26-149-2022, https://doi.org/10.5194/hess-26-149-2022, 2022
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Timely projections of seasonal streamflow extremes on a river network can be useful for flood risk mitigation, but this is challenging, particularly under space–time nonstationarity. We develop a space–time Bayesian hierarchical model (BHM) using temporal climate covariates and copulas to project seasonal streamflow extremes and the attendant uncertainties. We demonstrate this on the Upper Colorado River basin to project spring flow extremes using the preceding winter’s climate teleconnections.
Sergey Samsonov, Kristy Tiampo, and Ryan Cassotto
The Cryosphere, 15, 4221–4239, https://doi.org/10.5194/tc-15-4221-2021, https://doi.org/10.5194/tc-15-4221-2021, 2021
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The direction and intensity of glacier surface flow adjust in response to a warming climate, causing sea level rise, seasonal flooding and droughts, and changing landscapes and habitats. We developed a technique that measures the evolution of surface flow for a glaciated region in three dimensions with high temporal and spatial resolution and used it to map the temporal evolution of glaciers in southeastern Alaska (Agassiz, Seward, Malaspina, Klutlan, Walsh, and Kluane) during 2016–2021.
Mylène Jacquemart and Kristy Tiampo
Nat. Hazards Earth Syst. Sci., 21, 629–642, https://doi.org/10.5194/nhess-21-629-2021, https://doi.org/10.5194/nhess-21-629-2021, 2021
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We used interferometric radar coherence – a data quality indicator typically used to assess the reliability of radar interferometry data – to document the destabilization of the Mud Creek landslide in California, 5 months prior to its catastrophic failure. We calculated a time series of coherence on the slide relative to the surrounding hillslope and suggest that this easy-to-compute metric might be useful for assessing the stability of a hillslope.
P. Sharma, J. Wang, M. Zhang, C. Woods, B. Kar, D. Bausch, Z. Chen, K. Tiampo, M. Glasscoe, G. Schumann, M. Pierce, and R. Eguchi
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VI-3-W1-2020, 107–113, https://doi.org/10.5194/isprs-annals-VI-3-W1-2020-107-2020, https://doi.org/10.5194/isprs-annals-VI-3-W1-2020-107-2020, 2020
Qinghuan Zhang, Qiuhong Tang, John F. Knowles, and Ben Livneh
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-52, https://doi.org/10.5194/hess-2019-52, 2019
Manuscript not accepted for further review
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The uncertainty from model parameters is not well understood compared to that from climate data in hydrologic modeling. This study quantifies the projection uncertainty in three hydrologic variables using a group of best performing parameter sets. It shows that model parameter uncertainty takes an important role in hydrologic modeling, especially for seasonal projections. Thus it is necessary to consider multiple optimal parameter sets in hydrologic projection and water resources management.
Peter J. Shellito, Eric E. Small, and Ben Livneh
Hydrol. Earth Syst. Sci., 22, 1649–1663, https://doi.org/10.5194/hess-22-1649-2018, https://doi.org/10.5194/hess-22-1649-2018, 2018
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After soil gets wet, much of the surface moisture evaporates directly back into the air. Recent satellite data show that this process is enhanced when there is more water in the soil, less humidity in the air, and less vegetation covering the ground. A widely used model shows similar effects of soil water and humidity, but it largely misses the role of vegetation and assigns outsized importance to soil type. These results are encouraging evidence that the satellite can be used to improve models.
S. V. Samsonov, P. J. González, K. F. Tiampo, and N. d'Oreye
Nat. Hazards Earth Syst. Sci., 14, 247–257, https://doi.org/10.5194/nhess-14-247-2014, https://doi.org/10.5194/nhess-14-247-2014, 2014
S. J. Nanda, K. F. Tiampo, G. Panda, L. Mansinha, N. Cho, and A. Mignan
Nonlin. Processes Geophys., 20, 143–162, https://doi.org/10.5194/npg-20-143-2013, https://doi.org/10.5194/npg-20-143-2013, 2013
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Nat. Hazards Earth Syst. Sci., 24, 4617–4630, https://doi.org/10.5194/nhess-24-4617-2024, https://doi.org/10.5194/nhess-24-4617-2024, 2024
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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.
Dianne L. Brien, Mark E. Reid, Collin Cronkite-Ratcliff, and Jonathan P. Perkins
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-141, https://doi.org/10.5194/nhess-2024-141, 2024
Revised manuscript accepted for NHESS
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Landslide runout zones are the areas downslope or downstream of landslide initiation. People often live and work in these areas, leading to property damage and deaths. We develop methods to identify potential runout zones from landslides. We apply our methods to create susceptibility maps for three study areas in Puerto Rico and assess the success of our methods based on mapped landslides from Hurricane Maria.
Jürgen Mey, Ravi Kumar Guntu, Alexander Plakias, Igo Silva de Almeida, and Wolfgang Schwanghart
Nat. Hazards Earth Syst. Sci., 24, 3207–3223, https://doi.org/10.5194/nhess-24-3207-2024, https://doi.org/10.5194/nhess-24-3207-2024, 2024
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The Himalayan road network links remote areas, but fragile terrain and poor construction lead to frequent landslides. This study on the NH-7 in India's Uttarakhand region analyzed 300 landslides after heavy rainfall in 2022 . Factors like slope, rainfall, rock type and road work influence landslides. The study's model predicts landslide locations for better road maintenance planning, highlighting the risk from climate change and increased road use.
Fabiola Banfi, Emanuele Bevacqua, Pauline Rivoire, Sérgio C. Oliveira, Joaquim G. Pinto, Alexandre M. Ramos, and Carlo De Michele
Nat. Hazards Earth Syst. Sci., 24, 2689–2704, https://doi.org/10.5194/nhess-24-2689-2024, https://doi.org/10.5194/nhess-24-2689-2024, 2024
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Landslides are complex phenomena causing important impacts in vulnerable areas, and they are often triggered by rainfall. Here, we develop a new approach that uses information on the temporal clustering of rainfall, i.e. multiple events close in time, to detect landslide events and compare it with the use of classical empirical rainfall thresholds, considering as a case study the region of Lisbon, Portugal. The results could help to improve the prediction of rainfall-triggered landslides.
Jianqi Zhuang, Jianbing Peng, Chenhui Du, Yi Zhu, and Jiaxu Kong
Nat. Hazards Earth Syst. Sci., 24, 2615–2631, https://doi.org/10.5194/nhess-24-2615-2024, https://doi.org/10.5194/nhess-24-2615-2024, 2024
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The Revised Infinite Slope Model (RISM) is proposed using the equal differential unit method and correcting the deficiency of the safety factor increasing with the slope increasing when the slope is larger than 40°, as calculated using the Taylor slope infinite model. The intensity–duration (I–D) prediction curve of the rainfall-induced shallow loess landslides with different slopes was constructed and can be used in forecasting regional shallow loess landslides.
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.
Cited articles
AghaKouchak, A., Chiang, F., Huning, L. S., Love, C. A., Mallakpour, I., Mazdiyasni, O., Moftakhari, H., Papalexiou, S. M., Ragno, E., and Sadegh, M.: Climate extremes and compound hazards in a warming world, Annu. Rev. Earth Pl. Sc., 48, 519–548, 2020. a
Araújo, J. R., Ramos, A. M., Soares, P. M., Melo, R., Oliveira, S. C., and Trigo, R. M.: Impact of extreme rainfall events on landslide activity in Portugal under climate change scenarios, Landslides, 19, 2279–2293, https://doi.org/10.1007/s10346-022-01895-7, 2022. a
Balling, R. C., Meyer, G. A., and Wells, S. G.: Climate change in Yellowstone National Park: is the drought-related risk of wildfires increasing?, Clim. Change, 22, 35–45, 1992. a
Becker, R. A. and Wilks, A. R.: maps: Draw Geographical Maps, R package version 3.4.0, https://CRAN.R-project.org/package=maps (last access: 2 March 2023), 2021. a
Benda, L. and Dunne, T.: Stochastic forcing of sediment supply to channel networks from landsliding and debris flow, Water Resour. Res., 33, 2849–2863, 1997. a
Budimir, M. E. A., Atkinson, P. M., and Lewis, H. G.: A Systematic Review of Landslide Probability Mapping Using Logistic Regression, Landslides, 12, 419–436, https://doi.org/10.1007/s10346-014-0550-5, 2015. a
Burrows, K., Walters, R. J., Milledge, D., Spaans, K., and Densmore, A. L.: A New Method for Large-Scale Landslide Classification from Satellite Radar, Remote Sensing, 11, 237, https://doi.org/10.3390/rs11030237, 2019. a
Cannon, S. H. and DeGraff, J.: The Increasing Wildfire and Post-Fire Debris-Flow Threat in Western USA, and Implications for Consequences of Climate Change, in: Landslides – Disaster Risk Reduction, Springer, Berlin, Heidelberg, 177–190, https://doi.org/10.1007/978-3-540-69970-5_9, 2009. a
Cannon, S. H. and Gartner, J. E.: Wildfire-Related Debris Flow from a Hazards Perspective, in: Debris-flow Hazards and Related Phenomena, edited by: Jakob, M. and Hungr, O., Springer Praxis Books, Springer, Berlin, Heidelberg, 363–385, https://doi.org/10.1007/3-540-27129-5_15, 2005. a, b, c, d, e, f
Cannon, S. H., Gartner, J. E., Wilson, R. C., Bowers, J. C., and Laber, J. L.: Storm Rainfall Conditions for Floods and Debris Flows from Recently Burned Areas in Southwestern Colorado and Southern California, Geomorphology, 96, 250–269, https://doi.org/10.1016/j.geomorph.2007.03.019, 2008. a, b
Culler, E., Livneh, B., Rajagopalan, B., and Tiampo, K.: Analysis code: A data-driven evaluation of post-fire landslide susceptibility (v1.0.1), Zenodo [code], https://doi.org/10.5281/zenodo.7653683, 2023a. a
Culler, E., Livneh, B., Rajagopalan, B., and Tiampo, K.: Precipitation and fire history at landslide sites (1.0.0), Zenodo [data set], https://doi.org/10.5281/zenodo.7653639, 2023b. a
Deckmyn, A., Becker, R. A., Wilks, A. R., Brownrigg, R., and Minka, T. P.: Maps: Draw Geographical Maps, https://cran.r-project.org/web/packages/maps/ (last access: 8 December 2022), 2018. a
DeGraff, J. V., Cannon, S. H., and Gartner, J. E.: The Timing of Susceptibility to Post-Fire Debris Flows in the Western United States, Environmental and Engineering Geoscience, 21, 277–292, https://doi.org/10.2113/gseegeosci.21.4.277, 2015. a, b
Donnellan, A., Parker, J., Milliner, C., Farr, T. G., Glasscoe, M., Lou, Y., Zheng, Y., and Hawkins, B.: UAVSAR and Optical Analysis of the Thomas Fire Scar and Montecito Debris Flows: Case Study of Methods for Disaster Response Using Remote Sensing Products, Earth Space Sci., 5, 339–347, https://doi.org/10.1029/2018EA000398, 2018. a
Ebel, B. A., Moody, J. A., and Martin, D. A.: Hydrologic Conditions Controlling Runoff Generation Immediately after Wildfire, Water Resour. Res., 48, W03529, https://doi.org/10.1029/2011WR011470, 2012. a
Felicísimo, Á. M., Cuartero, A., Remondo, J., and Quirós, E.: Mapping Landslide Susceptibility with Logistic Regression, Multiple Adaptive Regression Splines, Classification and Regression Trees, and Maximum Entropy Methods: A Comparative Study, Landslides, 10, 175–189, https://doi.org/10.1007/s10346-012-0320-1, 2013. a
Froude, M. J. and Petley, D. N.: Global fatal landslide occurrence from 2004 to 2016, Nat. Hazards Earth Syst. Sci., 18, 2161–2181, https://doi.org/10.5194/nhess-18-2161-2018, 2018. a
Funk, C. C., Peterson, P. J., Landsfeld, M. F., Pedreros, D. H., Verdin, J. P., Rowland, J. D., Romero, B. E., Husak, G. J., Michaelsen, J. C., and Verdin, A. P.: A quasi-global precipitation time series for drought monitoring: U.S. Geological Survey Data Series 832, 4 p., Reston, VA, U.S. Geological Survey [code], https://doi.org/10.3133/ds832, 2014. a
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The Climate Hazards Infrared Precipitation with Stations – a New Environmental Record for Monitoring Extremes, Scientific Data, 2, 1–21, https://doi.org/10.1038/sdata.2015.66, 2015. a, b, c
Garcia-Urquia, E. and Axelsson, K.: Rainfall thresholds for the occurrence of urban landslides in Tegucigalpa, Honduras: an application of the critical rainfall intensity, Geogr. Ann. A, 97, 61–83, 2015. a
Gartner, J. E.: Relations between Wildfire Related Debris-Flow Volumes and Basin Morphology, Burn Severity, Material Properties and Triggering Storm Rainfall, M. A., University of Colorado at Boulder, United States –
Colorado, https://www.proquest.com/docview/305008518 (last access: 2 March 2023), 2005. a
Gartner, J. E., Cannon, S. H., Helsel, D. R., and Bandurraga, M.: Mutivariate Statistical Models for Predicting Sediment Yields from Southern California Watersheds, Open-File Report, U.S. Geological Survey, Reston, VA, 2009–1200, https://doi.org/10.3133/ofr20091200, 2009. a, b, c, d
Giglio, L., Justice, C., Boschetti, L., and Roy, D.: MCD64A1 MODIS/Terra+Aqua Burned Area Monthly L3 Global 500 m SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD64A1.006, 2015. a
Giglio, L., Boschetti, L., Roy, D. P., Humber, M. L., and Justice, C. O.: The Collection 6 MODIS burned area mapping algorithm and product, Remote Sens. Environ., 217, 72–85, https://doi.org/10.1016/j.rse.2018.08.005, 2018. a, b, c
Glade, T. and Crozier, M. J.: The nature of landslide hazard impact, Landslide hazard and risk, 43–74, https://doi.org/10.1002/9780470012659.ch2, 2005. a
Grolemund, G. and Wickham, H.: Dates and Times Made Easy with lubridate, J. Stat. Softw., 40, 1–25, 2011. a
Gudmundsson, L., Rego, F. C., Rocha, M., and Seneviratne, S. I.: Predicting above normal wildfire activity in southern Europe as a function of meteorological drought, Environ. Res. Lett., 9, 084008, https://doi.org/10.1088/1748-9326/9/8/084008, 2014. a
Gupta, V., Jain, M. K., Singh, P. K., and Singh, V.: An assessment of global satellite-based precipitation datasets in capturing precipitation extremes: A comparison with observed precipitation dataset in India, Int. J. Climatol., 40, 3667–3688, 2020. a
Handwerger, A. L., Huang, M.-H., Fielding, E. J., Booth, A. M., and Bürgmann, R.: A shift from drought to extreme rainfall drives a stable landslide to catastrophic failure, Scientific Reports, 9, 1–12, 2019. a
Harp, E. L., Keefer, D. K., Sato, H. P., and Yagi, H.: Landslide inventories: The essential part of seismic landslide hazard analyses, Eng. Geol., 122, 9–21, https://doi.org/10.1016/j.enggeo.2010.06.013, 2011. a
Helsel, D., Hirsch, R., Ryberg, K., Archfield, S., and Gilroy, E.: Statistical methods in water resources: Techniques and Methods 4-A3, U.S. Geological Survey, https://doi.org/10.3133/tm4A3, 2020. a
Henry, L. and Wickham, H.: purrr: Functional Programming Tools, R package version 0.3.4, https://CRAN.R-project.org/package=purrr (last access: 2 March 2023), 2020. a
Hong, Y., Adler, R., and Huffman, G.: Use of Satellite Remote Sensing Data in the Mapping of Global Landslide Susceptibility, Nat. Hazards, 43, 245–256, https://doi.org/10.1007/s11069-006-9104-z, 2007. a
Hoyer, S. and Hamman, J.: xarray: N-D labeled Arrays and Datasets in Python, J. Open Res. Softw. [code], 5, p. 10, https://doi.org/10.5334/jors.148, 2017. a
James, A. L. and Roulet, N. T.: Antecedent Moisture Conditions and Catchment Morphology as Controls on Spatial Patterns of Runoff Generation in Small Forest Catchments, J. Hydrol., 377, 351–366, https://doi.org/10.1016/j.jhydrol.2009.08.039, 2009. a
Jordan, P.: Post-wildfire debris flows in southern British Columbia, Canada, Int. J. Wildland Fire, 25, 322–336, 2015. a
Kaufman, L. and Rousseeuw, P. J.: Finding Groups in Data: An Introduction to Cluster Analysis, vol. 344, John Wiley & Sons, https://doi.org/10.1002/9780470316801.ch5, 2009. a
Kirschbaum, D. and Stanley, T.: Satellite-Based Assessment of Rainfall-Triggered Landslide Hazard for Situational Awareness, Earth's Future, 6, 505–523, https://doi.org/10.1002/2017EF000715, 2018. a, b, c
Kirschbaum, D., Kapnick, S. B., Stanley, T., and Pascale, S.: Changes in extreme precipitation and landslides over High Mountain Asia, Geophys. Res. Lett., 47, e2019GL085347, https://doi.org/10.1029/2019GL085347, 2020. a, b
Kirschbaum, D. B., Adler, R., Hong, Y., Kumar, S., Peters-Lidard, C., and Lerner-Lam, A.: Advances in Landslide Nowcasting: Evaluation of a Global and Regional Modeling Approach, Environ. Earth Sci., 66, 1683–1696, https://doi.org/10.1007/s12665-011-0990-3, 2012. a
Klose, M. (Ed.): Introduction, in: Landslide Databases as Tools for Integrated Assessment of Landslide Risk, Springer Theses, Springer International Publishing, Cham, 1–24, https://doi.org/10.1007/978-3-319-20403-1_1, 2015a. a
Klose, M. (Ed.): Methodology, in: Landslide Databases as Tools for Integrated Assessment of Landslide Risk, Springer Theses, Springer International Publishing, Cham, 59–105, https://doi.org/10.1007/978-3-319-20403-1_4, 2015b. a
Krkač, M., Špoljarić, D., Bernat, S., and Arbanas, S. M.: Method for prediction of landslide movements based on random forests, Landslides, 14, 947–960, 2017. a
Lee, J.-O., Lee, D.-K., and Song, Y.-I.: Analysis of the potential landslide hazard after wildfire considering compound disaster effect, Journal of the Korean Society of Environmental Restoration Technology, 22, 33–45, 2019. a
Lee, S.: Application and Verification of Fuzzy Algebraic Operators to Landslide Susceptibility Mapping, Environ. Geol., 52, 615–623, https://doi.org/10.1007/s00254-006-0491-y, 2007. a
Lee, S. and Pradhan, B.: Landslide Hazard Mapping at Selangor, Malaysia Using Frequency Ratio and Logistic Regression Models, Landslides, 4, 33–41, https://doi.org/10.1007/s10346-006-0047-y, 2007. a
Liu, Y., Goodrick, S., and Heilman, W.: Wildland Fire Emissions, Carbon, and Climate: Wildfire–Climate Interactions, Forest Ecol. Manag., 317, 80–96, https://doi.org/10.1016/j.foreco.2013.02.020, 2014. a
Lu, P., Casagli, N., Catani, F., and Tofani, V.: Persistent Scatterers Interferometry Hotspot and Cluster Analysis (PSI-HCA) for Detection of Extremely Slow-Moving Landslides, Int. J. Remote Sens., 33, 466–489, https://doi.org/10.1080/01431161.2010.536185, 2012. a
Martha, T. R., van Westen, C. J., Kerle, N., Jetten, V., and Vinod Kumar, K.: Landslide Hazard and Risk Assessment Using Semi-Automatically Created Landslide Inventories, Geomorphology, 184, 139–150, https://doi.org/10.1016/j.geomorph.2012.12.001, 2013. a
Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., and Hornik, K.: cluster: Cluster Analysis Basics and Extensions, R package version 2.1.3, For new features, see the “Changelog” file (in the package source) [code], https://CRAN.R-project.org/package=cluster (last access: 2 March 2023), 2022. a
McGuire, L. A., Rengers, F. K., Oakley, N., Kean, J. W., Staley, D. M., Tang, H., de Orla-Barile, M., and Youberg, A. M.: Time Since Burning and Rainfall Characteristics Impact Post-Fire Debris-Flow Initiation and Magnitude, Environ. Eng. Geosci., 27, 43–56, 2021. a
McKinney, W.: Data structures for statistical computing in python, in: Proceedings of the 9th Python in Science Conference, edited by: van der Walt, S. and Millman, J., Vol. 445, 51–56 pp., AQR Capital Management, LLC [code], https://doi.org/10.25080/Majora-92bf1922-00a, 2010. a
Meena, S. R. and Tavakkoli Piralilou, S.: Comparison of Earthquake-Triggered Landslide Inventories: A Case Study of the 2015 Gorkha Earthquake, Nepal, Geosciences, 9, 437, https://doi.org/10.3390/geosciences9100437, 2019. a
Mezaal, M. R., Pradhan, B., Sameen, M. I., Mohd Shafri, H. Z., and Yusoff, Z. M.: Optimized Neural Architecture for Automatic Landslide Detection from High-Resolution Airborne Laser Scanning Data, Applied Sciences, 7, 730, https://doi.org/10.3390/app7070730, 2017. a
Monsieurs, E., Dewitte, O., and Demoulin, A.: A susceptibility-based rainfall threshold approach for landslide occurrence, Nat. Hazards Earth Syst. Sci., 19, 775–789, https://doi.org/10.5194/nhess-19-775-2019, 2019. a
Neary, D. G., Ryan, K. C., and DeBano, L. F.: Wildland Fire in Ecosystems: Effects of Fire on Soils and Water, U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Ft. Collins, CO, Tech. Rep. RMRS-GTR-42-V4, https://doi.org/10.2737/RMRS-GTR-42-V4, 2005. a
Nefeslioglu, H. A., Sezer, E., Gokceoglu, C., Bozkir, A. S., and Duman, T. Y.: Assessment of Landslide Susceptibility by Decision Trees in the Metropolitan Area of Istanbul, Turkey, Math. Probl. Eng., 2010, 901095, https://doi.org/10.1155/2010/901095, 2010. a
Neuwirth, E.: RColorBrewer: ColorBrewer Palettes, R package version 1.1-2 [code], https://CRAN.R-project.org/package=RColorBrewer (last access: 2 March 2023), 2014. a
Nyman, P., Sheridan, G. J., Smith, H. G., and Lane, P. N. J.: Evidence of Debris Flow Occurrence after Wildfire in Upland Catchments of South-East Australia, Geomorphology, 125, 383–401, https://doi.org/10.1016/j.geomorph.2010.10.016, 2011. a, b
Ohlmacher, G. C. and Davis, J. C.: Using Multiple Logistic Regression and GIS Technology to Predict Landslide Hazard in Northeast Kansas, USA, Eng. Geol., 69, 331–343, https://doi.org/10.1016/S0013-7952(03)00069-3, 2003. a
Parise, M. and Cannon, S. H.: Wildfire impacts on the processes that generate debris flows in burned watersheds, Nat. Hazards, 61, 217–227, 2012. a
Pelletier, J. D. and Orem, C. A.: How do sediment yields from post-wildfire debris-laden flows depend on terrain slope, soil burn severity class, and drainage basin area? Insights from airborne-LiDAR change detection, Earth
Surf. Proc. Land., 39, 1822–1832, 2014. a
R Core Team: _R: A Language and Environment for Statistical Computing_.R Foundation for Statistical Computing, Vienna, Austria https://www.R-project.org/ (last access: 2 March 2023), 2021. a
Reneau, S. L., Katzman, D., Kuyumjian, G. A., Lavine, A., and Malmon, D. V.: Sediment Delivery after a Wildfire, Geology, 35, 151–154, https://doi.org/10.1130/G23288A.1, 2007. a
Rengers, F. K., McGuire, L. A., Oakley, N. S., Kean, J. W., Staley, D. M., and Tang, H.: Landslides after Wildfire: Initiation, Magnitude, and Mobility, Landslides, 17, 2631–2641, https://doi.org/10.1007/s10346-020-01506-3, 2020. a, b
Santi, P. M. and Rengers, F. K.: Wildfire and Landscape Change, in: Reference Module in Earth Systems and Environmental Sciences, Elsevier, https://doi.org/10.1016/B978-0-12-818234-5.00017-1, 2020. a
Schuster, R. and Highland, L.: Socioeconomic and Environmental Impacts of Landslides in the Western Hemisphere, Open-File Report, U.S. Geological Survey, 2001–276, https://doi.org/10.3133/ofr01276, 2001. a
Shakesby, R. A. and Doerr, S. H.: Wildfire as a Hydrological and Geomorphological Agent, Earth-Sci. Rev., 74, 269–307, https://doi.org/10.1016/j.earscirev.2005.10.006, 2006. a, b, c, d
Spiker, E. C. and Gori, P. L.: National Landslide Hazards Mitigation Strategy – A Framework for Loss Reduction, U.S. Geological Survey, Circular 1244, https://pubs.usgs.gov/circ/c1244/ (last access: 2 March 2023), 2002. a
Spittler, T.: Fire and Debris Flow Potential of Winter Storms, in: Brushfires in California Wildlands: Ecology and Resource Management, International Association of Wildland Fire, Fairfield, Washington, 113–120, 1995. a
Staley, D., Negri, J., Kean, J., Tillery, A., and Youberg, A.: Updated Logistic Regression Equations for the Calculation of Post-Fire Debris-Flow Likelihood in the Western United States, Open-File Report, U. S. Geological Survey, 2016–1106, https://doi.org/10.3133/ofr20161106, 2016. a, b, c
Staley, D. M., Tillery, A. C., Kean, J. W., McGuire, L. A., Pauling, H. E., Rengers, F. K., and Smith, J. B.: Estimating post-fire debris-flow hazards prior to wildfire using a statistical analysis of historical distributions of fire severity from remote sensing data, Int. J. Wildland Fire, 27, 595–608, 2018. a, b
Stanley, T. A., Kirschbaum, D. B., Sobieszczyk, S., Jasinski, M. F., Borak, J. S., and Slaughter, S. L.: Building a landslide hazard indicator with machine learning and land surface models, Environ. Modell. Softw., 129, 104692, https://doi.org/10.1016/j.envsoft.2020.104692, 2020. a
Struyf, A., Hubert, M., and Rousseeuw, P.: Clustering in an Object-Oriented Environment, J. Stat. Softw., 1, 1–30, https://doi.org/10.18637/jss.v001.i04, 1997. a
Thornton, M. M., Shrestha, R., Wei, Y., Thornton, P. E., Kao, S.-C., and Wilson, B. E.: Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4 R1, ORNL DAAC, Oak Ridge, Tennessee, USA [data set], https://doi.org/10.3334/ORNLDAAC/2129, 2022. a
Van Den Eeckhaut, M. and Hervás, J.: State of the Art of National Landslide Databases in Europe and Their Potential for Assessing Landslide Susceptibility, Hazard and Risk, Geomorphology, 139–140, 545–558, https://doi.org/10.1016/j.geomorph.2011.12.006, 2012. a
Van Rossum, G. and Drake, F. L.: Python 3 Reference Manual, Scotts Valley, CA: CreateSpace, ISBN 978-1-4414-1269-0, 242 pp., 2009. a
van Westen, C. J., van Asch, T. W. J., and Soeters, R.: Landslide Hazard and Risk Zonation – Why Is It Still so Difficult?, B. Eng. Geol. Environ., 65, 167–184, https://doi.org/10.1007/s10064-005-0023-0, 2006. a
Westerling, A. L. and Swetnam, T. W.: Interannual to decadal drought and wildfire in the western United States, EOS T. Am. Geophys. Un., 84, 545–555, 2003. a
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., Takahashi, K., Vaughan, D., Wilke, C., Woo, K., and Yutani, H.: Welcome to the tidyverse, J. Open Source Softw., 4, 1686, https://doi.org/10.21105/joss.01686, 2019. a
Wilke, C., Fox, S. J., Bates, T., Manalo, K., Lang, B., Barrett, M., Stoiber, M., Philipp, A., Denney, B., Hesselberth, J., wsteenhu, van der Bijl, W., Grenié, M., Selker, R., Uhlitz, F., and zaczap: wilkelab/cowplot, 1.1.1 (1.1.1), Zenodo [data set], https://doi.org/10.5281/zenodo.4411966, 2021. a
Zeileis, A. and Grothendieck, G.: zoo: S3 Infrastructure for Regular and Irregular Time Series, J. Stat. Softw., 14, 1–27, https://doi.org/10.18637/jss.v014.i06, 2005. a
Short summary
Landslides have often been observed in the aftermath of wildfires. This study explores regional patterns in the rainfall that caused landslides both after fires and in unburned locations. In general, landslides that occur after fires are triggered by less rainfall, confirming that fire helps to set the stage for landslides. However, there are regional differences in the ways in which fire impacts landslides, such as the size and direction of shifts in the seasonality of landslides after fires.
Landslides have often been observed in the aftermath of wildfires. This study explores regional...
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