76 citations as recorded by crossref.

1. Integrated debris flow hazard assessment in critical infrastructure zones: a case study of the 2022 Cubatão event, Serra do Mar, Brazil V. Veloso et al. https://doi.org/10.1007/s11069-026-07969-2
2. Initiation and Flow Conditions of Contemporary Flows in Martian Gullies T. de Haas et al. https://doi.org/10.1029/2018JE005899
3. Multi‐Model Comparison of Computed Debris Flow Runout for the 9 January 2018 Montecito, California Post‐Wildfire Event K. Barnhart et al. https://doi.org/10.1029/2021JF006245
4. 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
5. From snowmelt to disaster: modeling debris flow dynamics in the Shatoobar Nala watershed, Hunza, Pakistan H. Khan et al. https://doi.org/10.1007/s11069-026-08068-y
6. Predicting storm-triggered debris flow events: application to the 2009 Ionian Peloritan disaster (Sicily, Italy) M. Cama et al. https://doi.org/10.5194/nhess-15-1785-2015
7. Forecasting the Frequency and Magnitude of Postfire Debris Flows Across Southern California J. Kean & D. Staley https://doi.org/10.1029/2020EF001735
8. Learning Debris Flow Dynamics with a Deep Learning Fourier Neural Operator: Application to the Rendinara–Morino Area M. Secchi et al. https://doi.org/10.3390/geosciences16020055
9. Comparing single‐phase, non‐Newtonian approaches with experimental results: Validating flume‐scale mud and debris flow in HEC‐RAS S. Gibson et al. https://doi.org/10.1002/esp.5044
10. Torrential Hazard Prevention in Alpine Small Basin through Historical, Empirical and Geomorphological Cross Analysis in NW Italy L. Turconi et al. https://doi.org/10.3390/land11050699
11. El rol de los términos de fricción y cohesión en la modelización bidimensional de fluidos no Newtonianos: avalanchas de nieve densa M. Sanz-Ramos et al. https://doi.org/10.4995/ia.2023.20080
12. Numerical Modelling and Sensitivity Analysis of the Pitztal Valley Debris Flow Event M. Naqvi et al. https://doi.org/10.3390/geosciences13120378
13. A Study of Landslide Susceptibility Assessment and Trend Prediction Using a Rule-Based Discrete Grid Model Y. Duan et al. https://doi.org/10.3390/rs16244740
14. Numerical Simulation of Tailings Flow from Dam Failure over Complex Terrain Y. Yang et al. https://doi.org/10.3390/ma15062288
15. Comparative analysis of μ(I) and Voellmy-type grain flow rheologies in geophysical mass flows: insights from theoretical and real case studies Y. Zhuang et al. https://doi.org/10.5194/nhess-25-1901-2025
16. Channel position-dependent erosivity of steep debris flows combining gravitational and erosive processes: Towards predictive modeling V. Stammberger et al. https://doi.org/10.1016/j.enggeo.2026.108919
17. 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
18. Influence of flow resistance stresses on debris flow runout S. Lee & C. Song https://doi.org/10.1007/s12665-018-7604-2
19. Effects of defense-structure system for bridge piers on two-phase debris flow wakes Z. Chen et al. https://doi.org/10.1007/s11440-021-01296-5
20. Safeguarding Cultural Heritage: Integrating laser scanning, InSAR, vibration monitoring and rockfall/granular flow runout modelling at the Temple of Hatshepsut, Egypt B. Jacobs et al. https://doi.org/10.5194/esurf-14-55-2026
21. 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
22. Numerical simulation of debris-flow impact on multiple check dams using the material point method Y. Yang et al. https://doi.org/10.1063/5.0299689
23. Modelling future lahars controlled by different volcanic eruption scenarios at Cotopaxi (Ecuador) calibrated with the massively destructive 1877 lahar T. Frimberger et al. https://doi.org/10.1002/esp.5056
24. Modelling of debris-flow deposition: Terrain slope, mobility coefficient, and back-calculated basal friction coefficient D. Rickenmann et al. https://doi.org/10.1051/e3sconf/202341507013
25. Artificial Intelligence for Learning 2D Debris-Flow Dynamics: Application of Fourier Neural Operators and Synthetic Data to a Case Study in Central Italy M. Secchi et al. https://doi.org/10.3390/land15050759
26. Application of Sensitivity Analysis for Process Model Calibration of Natural Hazards C. Chow et al. https://doi.org/10.3390/geosciences8060218
27. Multi-Source Glacial Lake Outburst Flood Hazard Assessment and Mapping for Huaraz, Cordillera Blanca, Peru H. Frey et al. https://doi.org/10.3389/feart.2018.00210
28. Debris Flow Modelling Using RAMMS Model in the Alpine Environment With Focus on the Model Parameters and Main Characteristics M. Mikoš & N. Bezak https://doi.org/10.3389/feart.2020.605061
29. Computationally-feasible uncertainty quantification in model-based landslide risk assessment A. Yildiz et al. https://doi.org/10.3389/feart.2022.1032438
30. Pore pressure evolution and entrainment mechanism of convex unsaturated colluvial beds with divergent compositions under debris flow overriding P. Li et al. https://doi.org/10.1016/j.jrmge.2025.03.040
31. 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
32. Cluster landslides and associated damage in the Dima Hasao district of Assam, India due to heavy rainfall in May 2022 P. Roy et al. https://doi.org/10.1007/s10346-022-01977-6
33. Effects of interactions between transient granular flows and macroscopically rough beds and their implications for bulk flow dynamics C. Choi & S. Goodwin https://doi.org/10.1139/cgj-2020-0160
34. Three-dimensional numerical simulation of mud flow from a tailing dam failure across complex terrain D. Yu et al. https://doi.org/10.5194/nhess-20-727-2020
35. Implications of landslide runout modeling for vulnerability assessment: Benchmarking from a case study in the andean region M. Alvarez Jaimes et al. https://doi.org/10.1016/j.ijdrr.2025.105920
36. An analysis of rheological models for lahar modelling with Iber M. Sanz-Ramos et al. https://doi.org/10.22201/igc.20072902e.2025.3.1882
37. Stress anisotropy in natural debris flows during impacting a monitoring structure G. Nagl et al. https://doi.org/10.1007/s10346-021-01779-2
38. UAV-based topography and RAMMS-DF simulation for debris flow impact assessment: Insights from the typhoon Molave in Phuoc Thanh commune of Vietnam Q. Bui et al. https://doi.org/10.1016/j.geomat.2026.100091
39. Data Mining and Statistical Approaches in Debris-Flow Susceptibility Modelling Using Airborne LiDAR Data U. Lay et al. https://doi.org/10.3390/s19163451
40. Application of the debris-flow hazard index for pipelines in the context of the hydrogeological disaster of February 2023 in São Sebastião, Serra do Mar, Brazil V. Veloso et al. https://doi.org/10.1007/s10346-024-02319-4
41. Predictive simulation of concurrent debris flows: How slope failure locations affect predicted damage K. Yamanoi et al. https://doi.org/10.1111/jfr3.12776
42. Debris flows in Lebai gully along the Yarlung Tsangpo River in Tibet: characterization, causes, and dynamic prediction of potential debris flows M. Zhang et al. https://doi.org/10.1007/s12665-022-10694-1
43. Measurements of debris flow entrainment and dynamics H. Vicari et al. https://doi.org/10.1051/e3sconf/202341501028
44. Numerical modelling of dense snow avalanches with a well-balanced scheme based on the 2D shallow water equations M. Sanz-Ramos et al. https://doi.org/10.1017/jog.2023.48
45. Bayesian active learning for parameter calibration of landslide run-out models H. Zhao & J. Kowalski https://doi.org/10.1007/s10346-022-01857-z
46. Rockfall and Debris Flow Hazard Assessment in the SW Escarpment of Montagna del Morrone Ridge (Abruzzo, Central Italy) M. Calista et al. https://doi.org/10.3390/w12041206
47. Study of the 2012 lahar on Iztaccíhuatl Volcano (Central Mexico) based on geomorphology, botanical evidence and 2D modeling J. Figueroa-García et al. https://doi.org/10.18268/BSGM2025v77n3a300625
48. A dilatant, two-layer debris flow model validated by flow density measurements at the Swiss illgraben test site G. Meyrat et al. https://doi.org/10.1007/s10346-021-01733-2
49. Analyzing landslide-induced debris flow and flow-bridge interaction by using a hybrid model of depth-averaged model and discrete element method W. Shiu et al. https://doi.org/10.1007/s10346-022-01963-y
50. A multi-step hazard assessment for debris-flow prone areas influenced by hydroclimatic events V. Cabral et al. https://doi.org/10.1016/j.enggeo.2022.106961
51. Time and path prediction of landslides using InSAR and flow model P. Roy et al. https://doi.org/10.1016/j.rse.2022.112899
52. Evaluating Catchment-Scale Physically Based Modeling of Sediment Deposition During an Extreme Rainfall Event S. Emtehani et al. https://doi.org/10.3390/geosciences16020088
53. Numerical Modeling and a Parametric Study of Various Mass Flows Based on a Multi-Phase Computational Framework M. Naqvi et al. https://doi.org/10.3390/geotechnics2030025
54. Runout modelling and hazard assessment of Tangni debris flow in Garhwal Himalayas, India R. Dash et al. https://doi.org/10.1007/s12665-021-09637-z
55. Influence of rheology on landslide-dammed lake impoundment and sediment trapping: Back-analysis of the Hintersee landslide dam A. Argentin et al. https://doi.org/10.1016/j.geomorph.2022.108363
56. Assessment of barrier location effect on debris flow based on smoothed particle hydrodynamics (SPH) simulation on 3D terrains S. Choi et al. https://doi.org/10.1007/s10346-020-01477-5
57. Enhancing debris flow run-out hazard assessment through probabilistic post-event back analysis P. Zeng et al. https://doi.org/10.1007/s10346-026-02746-5
58. Deciphering controls for debris‐flow erosion derived from a LiDAR‐recorded extreme event and a calibrated numerical model (Roßbichelbach, Germany) A. Dietrich & M. Krautblatter https://doi.org/10.1002/esp.4578
59. A robust debris-flow and GLOF risk management strategy for a data-scarce catchment in Santa Teresa, Peru H. Frey et al. https://doi.org/10.1007/s10346-015-0669-z
60. Breaking Limits of Remote Sensing by Deep Learning From Simulated Data for Flood and Debris-Flow Mapping N. Yokoya et al. https://doi.org/10.1109/TGRS.2020.3035469
61. Integrated debris flow hazard and risk assessment using UAV data and RAMMS, a case study in northern Pakistan N. Ahmad et al. https://doi.org/10.1007/s11069-024-06862-0
62. How memory effects, check dams, and channel geometry control erosion and deposition by debris flows T. de Haas et al. https://doi.org/10.1038/s41598-020-71016-8
63. The Impact of DSM Resolutİon and Block Geometry on 3D Rockfall Modeling: The Case of Başköy Heritage Site M. Utlu et al. https://doi.org/10.46453/jader.1955333
64. Ecosystem-based disaster risk reduction in mountains C. Moos et al. https://doi.org/10.1016/j.earscirev.2017.12.011
65. Preliminary results of numerical modelling of debris flow ‐ case study Leva reka, Serbia J. KRUŠIĆ et al. https://doi.org/10.1002/cepa.753
66. Variable hydrograph inputs for a numerical debris-flow runout model A. Mitchell et al. https://doi.org/10.5194/nhess-22-1627-2022
67. Impact of a Random Sequence of Debris Flows on Torrential Fan Formation N. Bezak et al. https://doi.org/10.3390/geosciences9020064
68. Derivation of voellmy model parameters for landslide runout based on co-seismic rock avalanche inventory J. Huang & D. Zekkos https://doi.org/10.1007/s10346-026-02736-7
69. The Value of Using Multiple Hydrometeorological Variables to Predict Temporal Debris Flow Susceptibility in an Alpine Environment D. Prenner et al. https://doi.org/10.1029/2018WR022985
70. A Comprehensive Review on Debris Flow Landslide Assessment Using Rapid Mass Movement Simulation (RAMMS) S. Kumar et al. https://doi.org/10.1007/s10706-024-02887-1
71. 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
72. Emulator-based global sensitivity analysis for flow-like landslide run-out models H. Zhao et al. https://doi.org/10.1007/s10346-021-01690-w
73. Exploring new methods to analyse spatial impact distributions on debris‐flow fans using data from south‐western British Columbia S. Zubrycky et al. https://doi.org/10.1002/esp.5184
74. Controls on the formation and size of potential landslide dams and dammed lakes in the Austrian Alps A. Argentin et al. https://doi.org/10.5194/nhess-21-1615-2021
75. Reconstructing the Snow Avalanche of Coll de Pal 2018 (SE Pyrenees) M. Sanz-Ramos et al. https://doi.org/10.3390/geohazards2030011
76. Analysing debris flow impact in Upper Svaneti, Georgia: investigating triggering factors and runout dynamics A. Mehrpajouh & J. Engel https://doi.org/10.1080/19386362.2025.2574594