Articles | Volume 24, issue 10
https://doi.org/10.5194/nhess-24-3357-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-24-3357-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Oct 2024
Research article |
| 01 Oct 2024
| 01 Oct 2024
A coupled hydrological and hydrodynamic modeling approach for estimating rainfall thresholds of debris-flow occurrence
Zhen Lei Wei
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, China
School of Architecture, Building and Civil Engineering, Loughborough University, Loughborough, LE11 3TU, UK
Yue Quan Shang
College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, 310058, China
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, China
School of Architecture, Building and Civil Engineering, Loughborough University, Loughborough, LE11 3TU, UK
Xi Lin Xia
School of Engineering, University of Birmingham, Birmingham, B15 2TT, UK
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Cited
17 citations as recorded by crossref.
- Accelerating high-fidelity debris-flow modeling with a super-resolution deep-learning framework Y. Lv et al. https://doi.org/10.1016/j.enggeo.2026.108851
- Identification of rainfall thresholds for debris-flow occurrence through field monitoring data E. Ioriatti et al. https://doi.org/10.5194/nhess-25-4941-2025
- Discrete Boltzmann modeling of overland flow X. Gao et al. https://doi.org/10.1016/j.jhydrol.2025.134456
- Probabilistic rainfall thresholds of post-earthquake debris flows by integrating observed soil moisture content and calculated water runoff Z. Wei et al. https://doi.org/10.1007/s10346-025-02623-7
- Performance Evaluation of Urban Drainage Systems Under Sedimentation and Solid Waste Influence I. Angin et al. https://doi.org/10.70211/ijesi.v3i1.406
- Constructing regional catchment-specific triggering threshold for runoff-generated debris flows: a case study in the Bailong River Y. Huang et al. https://doi.org/10.1080/19475705.2025.2574301
- Evaluating the thresholds for predicting post-earthquake debris flows: Comparison of meteorological, hydro-meteorological and critical discharge approaches Z. Wei et al. https://doi.org/10.1016/j.enggeo.2024.107773
- Hydrologically guided transfer learning for ungauged basin prediction: similarity-based adaptation and interpretation of runoff generation mechanisms J. Li et al. https://doi.org/10.1016/j.jhydrol.2026.135872
- Machine learning prediction of post-earthquake debris flows incorporating hydrological variables X. Wang et al. https://doi.org/10.1007/s11069-026-07970-9
- Mudflow hazard mapping and nonlinear hydrological response in arid basins of southern Peru using coupled hydrological–hydraulic modeling J. Qquenta et al. https://doi.org/10.3389/frwa.2026.1822397
- Statistical analysis of the relationship between catchment morphometric parameters and debris-flow-triggering rainfall intensity J. Huo et al. https://doi.org/10.1007/s11069-025-07434-6
- Development of a Precipitation-Induced Moisture-Driven Landslide Threshold Atlas for the Northwestern and Northeastern Himalayas D. Monga & P. Ganguli https://doi.org/10.1061/JHYEFF.HEENG-6745
- A quantitative framework to assess the debris flow hazard at a catchment scale under climate change Z. Guo et al. https://doi.org/10.1002/esp.70278
- Spatial differentiation and risk zonation of debris flow hazards in Tajikistan W. JIA et al. https://doi.org/10.1016/j.regsus.2026.100299
- Machine learning enhanced framework for rainfall-induced debris flows spatio-temporal prediction: Integrating mobilized materials and hydrological processes W. Zhou et al. https://doi.org/10.1016/j.ejrh.2025.103046
- Evaluating the applicability of simulated soil moisture index in forecasting post-earthquake debris flows Z. Wei et al. https://doi.org/10.1016/j.enggeo.2026.108557
- Integrated hydrological model based on undirected MDPM: A case study of rainfall process simulation for mountain highways W. Zheng et al. https://doi.org/10.1016/j.jhydrol.2025.133130
17 citations as recorded by crossref.
- Accelerating high-fidelity debris-flow modeling with a super-resolution deep-learning framework Y. Lv et al. https://doi.org/10.1016/j.enggeo.2026.108851
- Identification of rainfall thresholds for debris-flow occurrence through field monitoring data E. Ioriatti et al. https://doi.org/10.5194/nhess-25-4941-2025
- Discrete Boltzmann modeling of overland flow X. Gao et al. https://doi.org/10.1016/j.jhydrol.2025.134456
- Probabilistic rainfall thresholds of post-earthquake debris flows by integrating observed soil moisture content and calculated water runoff Z. Wei et al. https://doi.org/10.1007/s10346-025-02623-7
- Performance Evaluation of Urban Drainage Systems Under Sedimentation and Solid Waste Influence I. Angin et al. https://doi.org/10.70211/ijesi.v3i1.406
- Constructing regional catchment-specific triggering threshold for runoff-generated debris flows: a case study in the Bailong River Y. Huang et al. https://doi.org/10.1080/19475705.2025.2574301
- Evaluating the thresholds for predicting post-earthquake debris flows: Comparison of meteorological, hydro-meteorological and critical discharge approaches Z. Wei et al. https://doi.org/10.1016/j.enggeo.2024.107773
- Hydrologically guided transfer learning for ungauged basin prediction: similarity-based adaptation and interpretation of runoff generation mechanisms J. Li et al. https://doi.org/10.1016/j.jhydrol.2026.135872
- Machine learning prediction of post-earthquake debris flows incorporating hydrological variables X. Wang et al. https://doi.org/10.1007/s11069-026-07970-9
- Mudflow hazard mapping and nonlinear hydrological response in arid basins of southern Peru using coupled hydrological–hydraulic modeling J. Qquenta et al. https://doi.org/10.3389/frwa.2026.1822397
- Statistical analysis of the relationship between catchment morphometric parameters and debris-flow-triggering rainfall intensity J. Huo et al. https://doi.org/10.1007/s11069-025-07434-6
- Development of a Precipitation-Induced Moisture-Driven Landslide Threshold Atlas for the Northwestern and Northeastern Himalayas D. Monga & P. Ganguli https://doi.org/10.1061/JHYEFF.HEENG-6745
- A quantitative framework to assess the debris flow hazard at a catchment scale under climate change Z. Guo et al. https://doi.org/10.1002/esp.70278
- Spatial differentiation and risk zonation of debris flow hazards in Tajikistan W. JIA et al. https://doi.org/10.1016/j.regsus.2026.100299
- Machine learning enhanced framework for rainfall-induced debris flows spatio-temporal prediction: Integrating mobilized materials and hydrological processes W. Zhou et al. https://doi.org/10.1016/j.ejrh.2025.103046
- Evaluating the applicability of simulated soil moisture index in forecasting post-earthquake debris flows Z. Wei et al. https://doi.org/10.1016/j.enggeo.2026.108557
- Integrated hydrological model based on undirected MDPM: A case study of rainfall process simulation for mountain highways W. Zheng et al. https://doi.org/10.1016/j.jhydrol.2025.133130
Saved (final revised paper)
Latest update: 21 Jul 2026
Short summary
The initiation of debris flows is significantly influenced by rainfall-induced hydrological processes. We propose a novel framework based on an integrated hydrological and hydrodynamic model and aimed at estimating intensity–duration (ID) rainfall thresholds responsible for triggering debris flows. In comparison to traditional statistical approaches, this physically based framework is particularly suitable for application in ungauged catchments where historical debris flow data are scarce.
The initiation of debris flows is significantly influenced by rainfall-induced hydrological...
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