199 citations as recorded by crossref.

1. Development of an initiation criterion for debris flows based on local topographic properties and applicability assessment at a regional scale S. Kang et al. https://doi.org/10.1016/j.enggeo.2017.09.017
2. Characterisation and multiphase flow modelling of a rock avalanche turned into a supraglacial debris flow: Insights from Mt Job, British Columbia, Canada J. Singh et al. https://doi.org/10.1002/esp.70308
3. Modelling of debris-flow susceptibility and propagation: a case study from Northwest Himalaya H. Daud et al. https://doi.org/10.1007/s11629-023-7966-0
4. Development of structural type-based physical vulnerability curves to debris flow using numerical analysis and regression model J. Lee et al. https://doi.org/10.1016/j.ijdrr.2024.104431
5. Mapping release and propagation areas of permafrost-related rock slope failures in the French Alps: A new methodological approach at regional scale M. Cathala et al. https://doi.org/10.1016/j.geomorph.2023.109032
6. Probabilistic evaluation of landslide influence zones considering stratigraphic dips and nonstationarity of soil properties J. Li et al. https://doi.org/10.1016/j.compgeo.2024.106815
7. Analysis of Differences in Geomorphological Characteristics on Initiation of Landslides and Debris Flows S. Kang et al. https://doi.org/10.9798/KOSHAM.2015.15.2.249
8. Modeling the Propagation of Landslide-Derived Flow in Tropical Mountain Basins: La Liboriana and La Argelia Basins (Colombia) M. Hurtado et al. https://doi.org/10.1177/11786221261416964
9. An overview of debris-flow mathematical modelling M. Trujillo-Vela et al. https://doi.org/10.1016/j.earscirev.2022.104135
10. Debris flow susceptibility assessment based on an empirical approach in the central region of South Korea S. Kang & S. Lee https://doi.org/10.1016/j.geomorph.2018.01.025
11. Simulating Debris Flow and Levee Formation in the 2D Shallow Flow Model D‐Claw: Channelized and Unconfined Flow R. Jones et al. https://doi.org/10.1029/2022EA002590
12. Empirical quantification of rockfall propagation probability: Robust determination of an appropriate topographic descriptor M. Peruzzetto et al. https://doi.org/10.1016/j.enggeo.2025.108370
13. Debris flow triggering characterization through a comparative analysis among different mountain catchments R. Pastorello et al. https://doi.org/10.1016/j.catena.2019.104348
14. Conventional data-driven landslide susceptibility models may only tell us half of the story: Potential underestimation of landslide impact areas depending on the modeling design P. Lima et al. https://doi.org/10.1016/j.geomorph.2023.108638
15. Spatial differentiation and risk zonation of debris flow hazards in Tajikistan W. JIA et al. https://doi.org/10.1016/j.regsus.2026.100299
16. An energy-based approach to predict debris flow mobility and analyze empirical relationships F. Federico & C. Cesali https://doi.org/10.1139/cgj-2015-0107
17. Integrating machine learning and numerical methods for enhanced landslide susceptibility and hazard mapping in the Bhotekoshi watershed, central Nepal B. Gurung et al. https://doi.org/10.1007/s11629-025-9951-2
18. Debris-Flow Hazard Assessments: A Practitioner's View M. Jakob https://doi.org/10.2113/EEG-D-20-00110
19. Governance strategies and tools towards the improvement of emergency management of natural disasters in transboundary areas D. Bignami et al. https://doi.org/10.1016/j.ijdrr.2024.104704
20. Combined hillslope diffusion and sediment transport simulation applied to landscape dynamics modelling T. Salles & G. Duclaux https://doi.org/10.1002/esp.3674
21. A progressive flow-routing model for rapid assessment of debris-flow inundation A. Gorr et al. https://doi.org/10.1007/s10346-022-01890-y
22. Modelling debris flood propagation in scarce data terrain, case study in the Colombian Andes F. Cardona et al. https://doi.org/10.1016/j.jsames.2025.105660
23. The roles and seismic expressions of turbidites and mass transport deposits using stratigraphic forward modeling and seismic forward modeling L. Wan et al. https://doi.org/10.1016/j.jseaes.2022.105110
24. Assessment of Debris-Flow Erosion and Deposit Areas by Morphometric Analysis and a GIS-Based Simplified Procedure: A Case Study of Paupisi in the Southern Apennines G. Grelle et al. https://doi.org/10.3390/su11082382
25. A methodological framework for flood hazard assessment for land transport infrastructures C. Samela et al. https://doi.org/10.1016/j.ijdrr.2022.103491
26. A Simple Method of Mapping Landslides Runout Zones Considering Kinematic Uncertainties J. Liu et al. https://doi.org/10.3390/rs14030668
27. Predicting debris flow pathways using volume-based thresholds for effective risk assessment A. Rajaneesh et al. https://doi.org/10.1038/s44304-024-00055-2
28. Changing significance of landslide Hazard and risk after the 2015 Mw 7.8 Gorkha, Nepal Earthquake N. Rosser et al. https://doi.org/10.1016/j.pdisas.2021.100159
29. Risk Assessment of Debris Flow at Regional Scale considering Catchment Area in Chuncheon, Korea G. Choi et al. https://doi.org/10.1007/s12205-021-0136-4
30. Performance and sensitivity analysis of the Morpho2DH model in extreme events in southern Brazil A. Franck & M. Kobiyama https://doi.org/10.1016/j.jsames.2024.105275
31. Modelling Spatiotemporal Dynamics of Large Wood Recruitment, Transport, and Deposition at the River Reach Scale during Extreme Floods A. Zischg et al. https://doi.org/10.3390/w10091134
32. Topographic controls on landslide mobility: modeling hurricane-induced landslide runout and debris-flow inundation in Puerto Rico D. Brien et al. https://doi.org/10.5194/nhess-25-1229-2025
33. Classic, modern, and physics-based rheological laws for geophysical granular flows in a landslide hazard chain M. Kamali Zarch et al. https://doi.org/10.1016/j.earscirev.2025.105204
34. Numerical simulation for run-out extent of debris flows using an improved cellular automaton model Z. Han et al. https://doi.org/10.1007/s10064-016-0902-6
35. 2014 Canadian Geotechnical Colloquium: Landslide runout analysis — current practice and challenges S. McDougall https://doi.org/10.1139/cgj-2016-0104
36. Process-driven susceptibility assessment of glacial lake outburst debris flow in the Himalayas under climate change B. Zhou et al. https://doi.org/10.1016/j.accre.2023.11.002
37. Modelling rapid mass movements using the shallow water equations in Cartesian coordinates S. Hergarten & J. Robl https://doi.org/10.5194/nhess-15-671-2015
38. Lahars and debris flows: Characteristics and impacts J. Thouret et al. https://doi.org/10.1016/j.earscirev.2019.103003
39. An interpretable framework for the hazard assessment of debris flow based on an improved AHP-EWM method and the SHAP model: a case study of Heishuiwan gully Y. Ji et al. https://doi.org/10.1007/s10064-023-03462-3
40. Lahar flow characteristic of Semeru Volcano based on Flow-R Model and its countermeasure M. Munir et al. https://doi.org/10.1088/1755-1315/1343/1/012032
41. Post-wildfire debris-flow runout mapping using geospatial analysis S. Praskievicz & R. Sigdel https://doi.org/10.1007/s11069-024-06684-0
42. A hybrid approach to earthquake-preconditioned debris flow: regional susceptibility and event-based hazard assessments Q. Mehmood et al. https://doi.org/10.1007/s11069-026-08181-y
43. Probabilistic Analysis of the Obstruction of Water Sources Due to the Occurrence of Rainfall-Triggered Mass Movements L. Giraldo et al. https://doi.org/10.1088/1755-1315/906/1/012040
44. Evaluation of spatiotemporal effects of soil depth on shallow landslides and debris flows via coupled numerical analysis S. Lee et al. https://doi.org/10.1016/j.pdisas.2025.100470
45. Debris Flow Susceptibility Assessment in the Wudongde Dam Area, China Based on Rock Engineering System and Fuzzy C-Means Algorithm Y. Li et al. https://doi.org/10.3390/w9090669
46. A landscape scale model to predict post-fire debris flow impact zones T. Keeble et al. https://doi.org/10.1016/j.geomorph.2024.109175
47. Robustness evaluation of the probability-based HTCA model for simulating debris-flow run-out extent: Case study of the 2010 Hongchun event, China Y. Ma et al. https://doi.org/10.1016/j.enggeo.2022.106918
48. runoutSIM v1.0: an R package for regionally simulating landslide runout and connectivity using random walks J. Goetz https://doi.org/10.5194/gmd-19-5709-2026
49. Fusion Analysis of Optical Satellite Images and Digital Elevation Model for Quantifying Volume in Debris Flow Disaster H. Miura https://doi.org/10.3390/rs11091096
50. Robustness Evaluation of the Probability-Based Htca Model for Simulating Debris Flow Run-Out Extent: A Case Study of the 2010 Hongchun Event in China Y. Ma et al. https://doi.org/10.2139/ssrn.4005765
51. Effect of Boulder Size on Debris Flow Impact Pressure Using a CFD-DEM Numerical Model R. Chehade et al. https://doi.org/10.3390/geosciences12050188
52. Comprehensive risk evaluation in Rapti Valley, Nepal: A multi-hazard approach S. Bhatta & B. Adhikari https://doi.org/10.1016/j.pdisas.2024.100346
53. Identifying human activities and rainfall impacts in landslide: a case study from southwestern China X. Wang et al. https://doi.org/10.1007/s11069-025-07728-9
54. Dating of avalanches in Pirin mountains in Bulgaria by tree-ring analysis of Pinus peuce and Pinus heldriechii trees M. Panayotov & N. Tsvetanov https://doi.org/10.1016/j.dendro.2024.126206
55. r.randomwalk v1, a multi-functional conceptual tool for mass movement routing M. Mergili et al. https://doi.org/10.5194/gmd-8-4027-2015
56. Multi-scale debris flow risk assessment using morphometrics in high-altitude catchments of Northern Pakistan Y. Ullah et al. https://doi.org/10.1007/s11069-025-07833-9
57. Hydrodynamic and topography based cellular automaton model for simulating debris flow run-out extent and entrainment behavior Z. Han et al. https://doi.org/10.1016/j.watres.2021.116872
58. Optimisation and application of a high-resolution shallow landslide model at regional scale C. Schaller et al. https://doi.org/10.1007/s11069-025-07701-6
59. International Frameworks for Disaster Risk Reduction: Useful Guidance for Sustainable Mountain Development? M. Zimmermann & M. Keiler https://doi.org/10.1659/MRD-JOURNAL-D-15-00006.1
60. Debris Flow Susceptibility Evaluation in Meizoseismal Region: A Case Study in Jiuzhaigou, China Y. Li et al. https://doi.org/10.1007/s12583-022-1803-1
61. Curvature derived from LiDAR digital elevation models as simple indicators of debris-flow susceptibility A. Nonomura et al. https://doi.org/10.1007/s11629-018-5098-8
62. Predicting the thickness of shallow landslides in Switzerland using machine learning C. Schaller et al. https://doi.org/10.5194/nhess-25-467-2025
63. Modeling debris flow initiation and run-out in recently burned areas using data-driven methods R. Melo & J. Zêzere https://doi.org/10.1007/s11069-017-2921-4
64. Numerical simulation of rainfall-induced debris flow in the Hongchun gully based on the coupling of the LHT model and the Pudasaini model H. Yin et al. https://doi.org/10.1007/s11069-023-05956-5
65. Évaluation de l’aléa glissements de terrain : état de l’art et perspectives pour la cartographie réglementaire en France Y. Thiery & M. Terrier https://doi.org/10.1051/geotech/2019003
66. Building risk amplification effect under loess landslides-hydraulic erosion-debris flow cascade in China Y. Chen et al. https://doi.org/10.1016/j.ijdrr.2024.105061
67. Elementary analysis on the bed-sediment entrainment by debris flow and its application using the TopFlowDF model Z. Han et al. https://doi.org/10.1080/19475705.2014.966868
68. Improvement of landslide hazard assessments for regulatory zoning in France: STATE–OF–THE-ART perspectives and considerations Y. Thiery et al. https://doi.org/10.1016/j.ijdrr.2020.101562
69. Physically based deterministic rockfall hazard assessment integrating multi-failure modes at large scale: A case study of Tiefeng Township, Chongqing, China J. Du et al. https://doi.org/10.1016/j.jrmge.2024.12.023
70. Risk Assessment and Control of Geological Hazards in Towns of Complex Mountainous Areas Based on Remote Sensing and Geological Survey W. Ding et al. https://doi.org/10.3390/w15183170
71. Simulating the Extent of Landslide Damage using LAHARZ: A Case Study for Yecheon-gun, Gyeongsangbuk-do J. Kim & H. Kim https://doi.org/10.11628/ksppe.2025.28.1.35
72. Combination of statistical and conceptual approaches for debris-flow susceptibility modelling at a regional scale, British Columbia, Canada T. Bornaetxea et al. https://doi.org/10.1016/j.catena.2025.109044
73. Potential rock fall source areas identification and rock fall propagation in the province of Potenza territory using an empirically distributed approach L. Losasso et al. https://doi.org/10.1007/s10346-017-0807-x
74. A multivariate statistical method for susceptibility analysis of debris flow in southwestern China F. Ji et al. https://doi.org/10.5194/nhess-20-1321-2020
75. Modeling Shallow Landslide Runout Distance in Eocene Flysch Facies Using Empirical–Statistical Models (Western Black Sea Region of Türkiye) M. Komu et al. https://doi.org/10.3390/ijgi13030084
76. Some Considerations for Using Numerical Methods to Simulate Possible Debris Flows: The Case of the 2013 and 2020 Wayao Debris Flows (Sichuan, China) X. Zhang et al. https://doi.org/10.3390/w14071050
77. DFLOWZ: A free program to evaluate the area potentially inundated by a debris flow M. Berti & A. Simoni https://doi.org/10.1016/j.cageo.2014.02.002
78. Universal Snow Avalanche Modeling Index Based on SAFI–Flow-R Approach in Poorly-Gauged Regions U. Durlević et al. https://doi.org/10.3390/ijgi13090315
79. An Integrated Approach for Simulating Debris-Flow Dynamic Process Embedded with Physically Based Initiation and Entrainment Models Z. Han et al. https://doi.org/10.3390/w15081592
80. The dynamic threat from landslides following large continental earthquakes K. Arrell et al. https://doi.org/10.1371/journal.pone.0308444
81. Integrating dendrochronology and geomatics to monitor natural hazards and landscape changes M. Ciolli et al. https://doi.org/10.1007/s12518-018-0236-0
82. Tree-Based Machine Learning and Flow Simulation for Debris Flow Susceptibility, Runout Propagation, and Dynamics in the Higher Himalayas H. Daud et al. https://doi.org/10.1007/s11004-025-10196-3
83. Study on the Prediction of Debris Flow Risk Using LS-RAPID N. Kim et al. https://doi.org/10.9798/KOSHAM.2023.23.1.73
84. A novel approach of multi-hazard integrated zonation on the ancient Silk Road Q. Zou et al. https://doi.org/10.1016/j.ijdrr.2022.103325
85. Integrating expert opinion with modelling for quantitative multi-hazard risk assessment in the Eastern Italian Alps L. Chen et al. https://doi.org/10.1016/j.geomorph.2016.07.041
86. Geospatial modeling of snow avalanches on the Sar Mountains, Balkan Peninsula U. Durlevic https://doi.org/10.2298/GSGD2501163D
87. Debris Flow Assessment in the Gaizi-Bulunkou Section of Karakoram Highway N. Jiang et al. https://doi.org/10.3389/feart.2021.660579
88. The catastrophic debris flow in the Qinling Mountains of Xi’an, Shaanxi province, China, from 11 August 2023 D. Yang et al. https://doi.org/10.1080/19475705.2025.2584696
89. Semi-quantitative risk assessment: From rainfall-induced landslides to the risk of persons in buildings H. Nguyen et al. https://doi.org/10.1007/s10064-025-04420-x
90. Multi-hazard risk assessment of Waling Municipality, Syangja, Nepal P. Rai et al. https://doi.org/10.1080/19475705.2025.2485327
91. The impact of terrain model source and resolution on snow avalanche modeling A. Miller et al. https://doi.org/10.5194/nhess-22-2673-2022
92. Application of different watershed units to debris flow susceptibility mapping: A case study of Northeast China J. Lv et al. https://doi.org/10.3389/feart.2023.1118160
93. Flow-Py v1.0: a customizable, open-source simulation tool to estimate runout and intensity of gravitational mass flows C. D'Amboise et al. https://doi.org/10.5194/gmd-15-2423-2022
94. Automated avalanche hazard indication mapping on a statewide scale Y. Bühler et al. https://doi.org/10.5194/nhess-22-1825-2022
95. Optimizing the Numerical Simulation of Debris Flows: A New Exploration of the Hexagonal Cellular Automaton Method Z. Han et al. https://doi.org/10.3390/w16111536
96. Assessment of Potential Landslide Scenarios Using Morphometry, Geomorphological Constraints, and Run-Out Analysis: A Case Study from Central Apennines (Italy) G. Paglia et al. https://doi.org/10.3390/land14112109
97. A nation-wide classification of the Italian Railway Network susceptibility to flood hazard G. Lelli et al. https://doi.org/10.1016/j.ijdrr.2025.105946
98. Morphometry and Debris-Flow Susceptibility Map in Mountain Drainage Basins of the Vallo di Diano, Southern Italy S. Giano et al. https://doi.org/10.3390/rs13163254
99. Exploring best-fit parameters for propagation modeling of shallow landslides, using Flow-R J. Palacio et al. https://doi.org/10.1016/j.jsames.2025.105739
100. Combining release and runout in statistical landslide susceptibility modeling M. Mergili et al. https://doi.org/10.1007/s10346-019-01222-7
101. Detection of Flash Flood Inundated Areas Using Relative Difference in NDVI from Sentinel-2 Images: A Case Study of the August 2020 Event in Charikar, Afghanistan M. Atefi & H. Miura https://doi.org/10.3390/rs14153647
102. Semi-empirical prediction of dam height and stability of dams formed by rock slope failures in Norway T. Oppikofer et al. https://doi.org/10.5194/nhess-20-3179-2020
103. LARMSU: A semi-empirical landslide runout model based on slope units J. Liu et al. https://doi.org/10.1016/j.enggeo.2026.108964
104. A landslide runout model for sediment transport, landscape evolution, and hazard assessment applications J. Keck et al. https://doi.org/10.5194/esurf-12-1165-2024
105. The seismic microzonation study of Pescara del Tronto (Central Italy) during and after the Central Italy earthquake sequence. M. Amanti et al. https://doi.org/10.1007/s10518-020-00927-8
106. Development and Application of Urban Landslide Vulnerability Assessment Methodology Reflecting Social and Economic Variables Y. Park et al. https://doi.org/10.1155/2016/4572498
107. DebrisFlow Predictor: an agent-based runout program for shallow landslides R. Guthrie & A. Befus https://doi.org/10.5194/nhess-21-1029-2021
108. Rockfall Modelling in Forested Areas: The Role of Digital Terrain Model Grid Cell Size B. Žabota et al. https://doi.org/10.3390/app11041461
109. Landslide initiation and runout susceptibility modeling in the context of hill cutting and rapid urbanization: a combined approach of weights of evidence and spatial multi-criteria M. Rahman et al. https://doi.org/10.1007/s11629-016-4220-z
110. An integrated assessment of landslide: Type, causes, pre-post scenario and risk impact of the Pettimudi incident in Idukki District, India A. S. et al. https://doi.org/10.1016/j.rines.2024.100050
111. Rock avalanche runout prediction using stochastic analysis of a regional dataset A. Mitchell et al. https://doi.org/10.1007/s10346-019-01331-3
112. Modelling of mobility of Rissa landslide and following tsunami Z. Liu et al. https://doi.org/10.1016/j.compgeo.2021.104388
113. Physically based probabilistic prediction of regional rainfall-induced shallow landslides: from initiation to runout analysis C. Wu et al. https://doi.org/10.1080/17499518.2024.2422481
114. Practices in disaster mitigation in the case of the 2015 Typhoon Koppu debris flows in Nueva Ecija, Philippines F. Llanes et al. https://doi.org/10.1007/s11069-022-05407-7
115. Highly energetic rockfalls: back analysis of the 2015 event from the Mel de la Niva, Switzerland F. Noël et al. https://doi.org/10.1007/s10346-023-02054-2
116. Identifikasi Longsor dan Pemodelan Probabilitas Jarak Luncur Longsor di Lereng Waduk Bener, Purworejo, Jawa Tengah E. Ningrum & G. Samodra https://doi.org/10.14710/jil.22.5.1242-1254
117. What drives landslide risk? Disaggregating risk analyses, an example from the Franz Josef Glacier and Fox Glacier valleys, New Zealand S. de Vilder et al. https://doi.org/10.5194/nhess-22-2289-2022
118. Dependence of debris flow susceptibility maps on sampling strategy with data-driven grid-based model N. Jiang et al. https://doi.org/10.1016/j.ecolind.2024.112534
119. Debris flow susceptibility and propagation modeling: a deep learning and flow-R framework M. Khalid et al. https://doi.org/10.1007/s10064-025-04496-5
120. Modelling post‐earthquake cascading hazards: Changing patterns of landslide runout following the 2015 Gorkha earthquake, Nepal M. Kincey et al. https://doi.org/10.1002/esp.5501
121. ASCHFLOW - A dynamic landslide run-out model for medium scale hazard analysis B. Quan Luna et al. https://doi.org/10.1186/s40677-016-0064-7
122. Rockfall impacts on the road network of the Metropolitan Area of Rome: a risk prioritisation framework for decision-makers G. Marmoni et al. https://doi.org/10.1016/j.ijdrr.2026.106182
123. A comparative study on the integrative ability of the analytical hierarchy process, weights of evidence and logistic regression methods with the Flow-R model for landslide susceptibility assessment H. Do et al. https://doi.org/10.1080/19475705.2020.1846086
124. Mud and debris flow modeling as hazard assessment tool J. Páez Pedraza https://doi.org/10.11144/Javeriana.iued29.mdfm
125. Regional risk assessment of debris flows in China—An HRU-based approach Q. Zou et al. https://doi.org/10.1016/j.geomorph.2019.04.027
126. Parameter Analysis of Flow-R Model for Physical Vulnerability Assessment of Debris Flow Disaster in Regional Scale H. Kang & Y. Kim https://doi.org/10.9798/KOSHAM.2017.17.4.233
127. Data Mining and Statistical Approaches in Debris-Flow Susceptibility Modelling Using Airborne LiDAR Data U. Lay et al. https://doi.org/10.3390/s19163451
128. Runout Number: A New Metric for Landslide Runout Characterization C. Wallace & P. Santi https://doi.org/10.2113/EEG-D-20-00144
129. Environmental controls on the generation of submarine landslides in Arctic fjords: Insight from Pangnirtung Fjord, Baffin Island, Nunavut P. Sedore et al. https://doi.org/10.1016/j.margeo.2024.107290
130. Coupled limit equilibrium/depth-averaged approach for debris flow hazard assessment: the 2012 Te Maari avalanche J. Vicente & S. Mead https://doi.org/10.1680/jgeot.25.00702
131. A numerical model for cascading glacier mass flow analysis (GMFA): Erosion-deposition dynamics, phase changes, and multi-hazard chain transformations R. Jiang et al. https://doi.org/10.1016/j.jrmge.2025.02.010
132. Evaluations and proposals for the debris flow hazard mapping method of the GIDES Project G. Facuri & J. de Lima Picanço https://doi.org/10.1007/s10346-020-01480-w
133. Prompt Quantitative Risk Assessment for Rain-Induced Landslides J. He et al. https://doi.org/10.1061/JGGEFK.GTENG-10980
134. Temporal evolution of flow-like landslide hazard for a road infrastructure in the municipality of Nocera Inferiore (southern Italy) under the effect of climate change M. Uzielli et al. https://doi.org/10.5194/nhess-18-3019-2018
135. Natural Landing Pads on the Moon and Mars J. Menges & K. Cannon https://doi.org/10.1016/j.actaastro.2026.01.045
136. Susceptibility Assessment of Debris Flows Coupled with Ecohydrological Activation in the Eastern Qinghai-Tibet Plateau H. Jiang et al. https://doi.org/10.3390/rs14061444
137. Susceptibility areas identification and risk assessment of debris flow using the Flow-R model: a case study of Basu County of Tibet H. Xu et al. https://doi.org/10.1186/s40677-022-00216-3
138. Semi-quantitative method to identify the vulnerable areas in terms of building aggregation for probable landslide runout at the regional scale: a case study from Soacha Province, Colombia A. Pradhan et al. https://doi.org/10.1007/s10064-019-01533-y
139. Application of Machine Learning to Debris Flow Susceptibility Mapping along the China–Pakistan Karakoram Highway F. Qing et al. https://doi.org/10.3390/rs12182933
140. Analogy between grid-based modeling of landslide and avalanche using GIS with surface flow analysis S. Kim & H. Park https://doi.org/10.1007/s10064-017-1144-y
141. Morphometric analysis of debris flow hazard and risk assessment in the mountain terrains of northern Pakistan using remote sensing and field data N. Shah et al. https://doi.org/10.1007/s12145-025-01807-y
142. Coupled model for simulation of landslides and debris flows at local scale D. Park et al. https://doi.org/10.1007/s11069-016-2150-2
143. Glacial debris flow susceptibility mapping based on combined models in the Parlung Tsangpo Basin, China Y. Zhou et al. https://doi.org/10.1007/s11629-023-8500-0
144. Urban debris flow vulnerability map: an application to the metropolitan cities in Korea Y. Park et al. https://doi.org/10.5004/dwt.2018.21822
145. Multi-hazard probability assessment in Quinali A Watershed, Albay, Philippines J. Mendoza et al. https://doi.org/10.1016/j.pdisas.2025.100408
146. Scoring system to predict landslide runout in the Pacific Northwest, USA C. Wallace et al. https://doi.org/10.1007/s10346-021-01839-7
147. GIS-based assessment of debris flow hazards in Kulekhani Watershed, Nepal B. Paudel et al. https://doi.org/10.1007/s11069-020-03867-3
148. Spatial model integration for shallow landslide susceptibility and its runout using a GIS-based approach in Yongin, Korea A. Pradhan et al. https://doi.org/10.1080/10106049.2016.1155658
149. Establishing a GIS-based evaluation method considering spatial heterogeneity for debris flow susceptibility mapping at the regional scale S. Qin et al. https://doi.org/10.1007/s11069-022-05487-5
150. Debris flows analysis through quantitative evaluation of soil depth distribution under limited data S. Lee et al. https://doi.org/10.1016/j.catena.2024.108379
151. GIS tools for preliminary debris-flow assessment at regional scale M. Cavalli et al. https://doi.org/10.1007/s11629-017-4573-y
152. Integrating experimental and numerical approaches to simulate viscous debris flows using an HBP-SPH framework N. Pandey et al. https://doi.org/10.1038/s41598-025-01603-0
153. Debris-flow risk-to-life: Preliminary screening T. Davies et al. https://doi.org/10.1016/j.ijdrr.2023.104158
154. Multihazards Scenario Generator: A Network‐Based Simulation of Natural Disasters A. Dunant et al. https://doi.org/10.1111/risa.13723
155. Regional-scale landslide risk assessment on Mt. Umyeon using risk index estimation B. Nguyen & Y. Kim https://doi.org/10.1007/s10346-021-01622-8
156. Hazard zonation for rock avalanche and subsequent debris flow propagation in a frontally confined context based on the analysis of numerical simulations and empirical models C. Wolff et al. https://doi.org/10.1007/s11069-025-07342-9
157. The Gravitational Process Path (GPP) model (v1.0) – a GIS-based simulation framework for gravitational processes V. Wichmann https://doi.org/10.5194/gmd-10-3309-2017
158. Exploring the impact of introducing a physical model into statistical methods on the evaluation of regional scale debris flow susceptibility J. Sun et al. https://doi.org/10.1007/s11069-020-04498-4
159. Assessment of exposed elements in a changing built environment by using an integrated model of debris flow initiation and runout (Kalimpong region, Himalaya) S. Bera et al. https://doi.org/10.1007/s10064-021-02352-w
160. Dynamic hazard assessment of debris flow based on TRIGRS and flow-R coupled models Y. Nie et al. https://doi.org/10.1007/s00477-021-02093-y
161. Debris flow susceptibility mapping using a qualitative heuristic method and Flow-R along the Yukon Alaska Highway Corridor, Canada A. Blais-Stevens & P. Behnia https://doi.org/10.5194/nhess-16-449-2016
162. Numerical modeling using an elastoplastic-adhesive discrete element code for simulating hillslope debris flows and calibration against field experiments A. Albaba et al. https://doi.org/10.5194/nhess-19-2339-2019
163. Susceptibility assessment of soil-water hazard chain on a small catchment in gully region of Loess Plateau: Implications for artificially-induced mountaintop removal filling valley and geoheritage H. Shu & F. Zhang https://doi.org/10.1016/j.geomorph.2023.108949
164. Dynamic hazard assessment of group-occurring debris flows based on a coupled model Y. Nie et al. https://doi.org/10.1007/s11069-021-04558-3
165. Research on Uncertainty of Landslide Susceptibility Prediction—Bibliometrics and Knowledge Graph Analysis Z. Yang et al. https://doi.org/10.3390/rs14163879
166. The SWADE model for landslide dating in time series of optical satellite imagery S. Fu et al. https://doi.org/10.1007/s10346-022-02012-4
167. Modeling and mapping the environmental impact of debris flow hazard on alluvial fans for sustainable development in Bangga and Poi Villages, Sigi, Central Sulawesi M. Putra et al. https://doi.org/10.1088/1755-1315/1201/1/012028
168. Operational Estimation of Landslide Runout: Comparison of Empirical and Numerical Methods M. Peruzzetto et al. https://doi.org/10.3390/geosciences10110424
169. Mud and debris flow modeling as hazard assessment tool J. Páez Pedraza https://doi.org/10.11144/Javeriana.iued28.mdfm
170. A new approach to assess landslide susceptibility based on slope failure mechanisms Q. Zou et al. https://doi.org/10.1016/j.catena.2021.105388
171. National-scale assessment of railways exposure to rapid flow-like landslides I. Marchesini et al. https://doi.org/10.1016/j.enggeo.2024.107474
172. Mapping debris flow susceptibility based on watershed unit and grid cell unit: a comparison study S. Qin et al. https://doi.org/10.1080/19475705.2019.1604572
173. A combined triggering-propagation modeling approach for the assessment of rainfall induced debris flow susceptibility L. Stancanelli et al. https://doi.org/10.1016/j.jhydrol.2017.04.038
174. Debris flow susceptibility assessment based on topo-hydrological factors at different unit scales: a case study of Mentougou district, Beijing Z. Li et al. https://doi.org/10.1007/s12665-021-09665-9
175. Geometric approach based tool for shallow landslides propagation assessment (ShaLPA) at basin scale L. Falconi et al. https://doi.org/10.1016/j.envsoft.2025.106512
176. Integration of two-phase solid fluid equations in a catchment model for flashfloods, debris flows and shallow slope failures B. Bout et al. https://doi.org/10.1016/j.envsoft.2018.03.017
177. Landslide and debris flow susceptibility zonation using TRIGRS for the 2011 Seoul landslide event D. Park et al. https://doi.org/10.5194/nhess-13-2833-2013
178. A progressive entrainment runout model for debris flow analysis and its application C. Kang & D. Chan https://doi.org/10.1016/j.geomorph.2018.09.003
179. Quantifying human risk from rainfall‐induced landslides across different return periods: A case study of Ba To, Quang Ngai, Vietnam B. Nguyen & T. Nguyen https://doi.org/10.1002/esp.70210
180. Optimizing and validating the Gravitational Process Path model for regional debris-flow runout modelling J. Goetz et al. https://doi.org/10.5194/nhess-21-2543-2021
181. Comparing Flow-R, Rockyfor3D and RAMMS to Rockfalls from the Mel de la Niva Mountain: A Benchmarking Exercise F. Noël et al. https://doi.org/10.3390/geosciences13070200
182. Hybrid method for rainfall-induced regional landslide susceptibility mapping S. Wu et al. https://doi.org/10.1007/s00477-024-02753-9
183. Analysis of lateral sediment connectivity and its connection to debris flow intensity patterns at different return periods in the Fella River system in northeastern Italy N. Schopper et al. https://doi.org/10.1016/j.scitotenv.2018.12.288
184. Gis-Based Assessment of Debris Flow Runout in Kulekhani Watershed, Nepal B. Paudel et al. https://doi.org/10.1007/s10706-020-01655-1
185. Combining stratigraphic forward modeling and susceptibility mapping to investigate the origin and evolution of submarine canyons L. Wan et al. https://doi.org/10.1016/j.geomorph.2021.108047
186. Probabilistic cascading multi-hazard risk assessment methodology using graph theory, a New Zealand trial A. Dunant et al. https://doi.org/10.1016/j.ijdrr.2020.102018
187. Investigation and mapping of natural hazards areas related to mass movements in a geopark, in Southern Brazil A. Franck & M. Kobiyama https://doi.org/10.1016/j.jsames.2024.104926
188. Debris-Flow and Debris-Flood Susceptibility Mapping for Geohazard Risk Prioritization M. Sturzenegger et al. https://doi.org/10.2113/EEG-D-20-00006
189. Evaluating Catchment-Scale Physically Based Modeling of Sediment Deposition During an Extreme Rainfall Event S. Emtehani et al. https://doi.org/10.3390/geosciences16020088
190. HyLands 1.0: a hybrid landscape evolution model to simulate the impact of landslides and landslide-derived sediment on landscape evolution B. Campforts et al. https://doi.org/10.5194/gmd-13-3863-2020
191. Modeling the geomorphologic susceptibility to debris flow initiation with geomorphometric parameters and fuzzy logic in the mountainous region of Serra do Mar (state of Paraná, Brazil) R. Silveira & C. Silveira https://doi.org/10.1007/s11069-025-07264-6
192. r.avaflow v1, an advanced open-source computational framework for the propagation and interaction of two-phase mass flows M. Mergili et al. https://doi.org/10.5194/gmd-10-553-2017
193. Risk assessment of glacial debris flow on highway under warming climate: A case study of Tianmo Gully in the southeastern Tibetan Plateau Y. Li et al. https://doi.org/10.1016/j.ecolind.2024.112606
194. Integrating machine learning and deep learning for enhanced landslide susceptibility and propagation dynamics in Northwestern Pakistan H. Ullah et al. https://doi.org/10.1007/s10064-026-04940-0
195. Snow avalanche hazard of the Krkonoše National Park, Czech Republic J. Blahut et al. https://doi.org/10.1080/17445647.2016.1262794
196. Heavy Rainfall Events and Mass Movements in the Funchal Area (Madeira, Portugal): Spatial Analysis and Susceptibility Assessment S. Lopes et al. https://doi.org/10.3390/atmos11010104
197. From start to stop: Simple methods for mapping susceptibility to landslide runout and debris-flow inundation M. Reid et al. https://doi.org/10.1007/s10346-026-02751-8
198. On the criteria to create a susceptibility map to debris flow at a regional scale using Flow-R R. Pastorello et al. https://doi.org/10.1007/s11629-016-4077-1
199. Understanding the Future Dynamics of Kashang Glacial Lake in Indian Himalaya: Remote Sensing and Modeling-Based Hazard Assessment R. Kumar et al. https://doi.org/10.1007/s12524-025-02250-8