Articles | Volume 20, issue 12
https://doi.org/10.5194/hess-20-5035-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-20-5035-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
iCRESTRIGRS: a coupled modeling system for cascading flood–landslide disaster forecasting
Cooperative Institute for Mescoscale Meteorological Studies,
University of Oklahoma, Norman, OK 73072, USA
Hydrometeorology and Remote Sensing (HyDROS) Laboratory, School of
Civil Engineering and Environmental Science, and Advanced Radar Research
Center, University of Oklahoma, Norman, OK 73072, USA
State Key Laboratory of Hydrology-Water Resources and Hydraulic
Engineering, Hohai University, Nanjiang, Jiangsu, 210098, China
Xianwu Xue
Hydrometeorology and Remote Sensing (HyDROS) Laboratory, School of
Civil Engineering and Environmental Science, and Advanced Radar Research
Center, University of Oklahoma, Norman, OK 73072, USA
Yang Hong
CORRESPONDING AUTHOR
Hydrometeorology and Remote Sensing (HyDROS) Laboratory, School of
Civil Engineering and Environmental Science, and Advanced Radar Research
Center, University of Oklahoma, Norman, OK 73072, USA
Department of Hydraulic Engineering, Tsinghua University, Beijing,
China
Jonathan J. Gourley
NOAA/National Severe Storms Laboratory, Norman, OK 73072, USA
Ning Lu
Department of Civil & Environmental Engineering, Colorado School of
Mines, Golden, CO 80401, USA
Zhanming Wan
Hydrometeorology and Remote Sensing (HyDROS) Laboratory, School of
Civil Engineering and Environmental Science, and Advanced Radar Research
Center, University of Oklahoma, Norman, OK 73072, USA
Zhen Hong
Hydrometeorology and Remote Sensing (HyDROS) Laboratory, School of
Civil Engineering and Environmental Science, and Advanced Radar Research
Center, University of Oklahoma, Norman, OK 73072, USA
Rick Wooten
North Carolina Geological Survey, North Carolina Department of
Environmental Quality, Swannanoa, NC 28778, USA
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- Rainfall-Induced Landslide Prediction Using Machine Learning Models: The Case of Ngororero District, Rwanda M. Kuradusenge et al. 10.3390/ijerph17114147
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44 citations as recorded by crossref.
- Adequacy of Near Real-Time Satellite Precipitation Products in Driving Flood Discharge Simulation in the Fuji River Basin, Japan L. Zhou et al. 10.3390/app11031087
- Characteristics and influencing factors of rainfall-induced landslide and debris flow hazards in Shaanxi Province, China K. Zhang et al. 10.5194/nhess-19-93-2019
- Landslides Triggered by the 2016 Heavy Rainfall Event in Sanming, Fujian Province: Distribution Pattern Analysis and Spatio-Temporal Susceptibility Assessment S. Ma et al. 10.3390/rs15112738
- The application of cascading consequences for emergency management operations J. Cuartas et al. 10.1007/s11069-021-04807-5
- Numerical Simulation of Na-Tech Cascading Disasters in a Large Oil Depot S. Zhang et al. 10.3390/ijerph17228620
- An integrated flood risk assessment approach based on coupled hydrological-hydraulic modeling and bottom-up hazard vulnerability analysis K. Zhang et al. 10.1016/j.envsoft.2021.105279
- Assessing global parameters of slope stability model using Earth data observations for forecasting rainfall – induced shallow landslides J. Thomas et al. 10.1016/j.jappgeo.2023.104994
- Flood inundation mapping in data‐scarce areas: A case of Mbire District, Zimbabwe T. Manyangadze et al. 10.1002/geo2.105
- Insight from a Physical-Based Model for the Triggering Mechanism of Loess Landslides Induced by the 2013 Tianshui Heavy Rainfall Event S. Ma et al. 10.3390/w15030443
- Geographically weighted regression based methods for merging satellite and gauge precipitation L. Chao et al. 10.1016/j.jhydrol.2018.01.042
- A new approach for gridded risk assessment of rainfall-triggered flood and landslide hazards over a large region based on coupled flood-landslide modelling and ensemble simulation S. Wang et al. 10.1016/j.envsoft.2023.105917
- Investigation of inducements and defenses of flash floods and urban waterlogging in Fuzhou, China, from 1950 to 2010 M. Ma et al. 10.1007/s11069-017-3156-0
- PHyL v1.0: A parallel, flexible, and advanced software for hydrological and slope stability modeling at a regional scale G. Chen et al. 10.1016/j.envsoft.2023.105882
- Rainfall-Induced Landslide Prediction Using Machine Learning Models: The Case of Ngororero District, Rwanda M. Kuradusenge et al. 10.3390/ijerph17114147
- Historical Data for Natural Hazard Risk Mitigation and Land Use Planning F. Luino et al. 10.3390/land12091777
- Application of the GPM-IMERG Products in Flash Flood Warning: A Case Study in Yunnan, China M. Ma et al. 10.3390/rs12121954
- Evaluating performance dependency of a geomorphologic instantaneous unit hydrograph-based hydrological model on DEM resolution C. Yao et al. 10.1016/j.wse.2022.04.002
- Landslide susceptibility mapping of mountain roads based on machine learning combined model H. Dou et al. 10.1007/s11629-022-7657-2
- A comprehensive flash flood defense system in China: overview, achievements, and outlook L. Guo et al. 10.1007/s11069-018-3221-3
- Flood hazard mapping and assessment in data-scarce Nyaungdon area, Myanmar Z. Khaing et al. 10.1371/journal.pone.0224558
- Sensitivity analysis of regional rainfall-induced landslide based on UAV photogrammetry and LSTM neural network L. Zhao et al. 10.1007/s11629-023-7991-z
- A decadal review of the CREST model family: Developments, applications, and outlook Z. Li et al. 10.1016/j.hydroa.2023.100159
- Limit equilibrium method-based 3D slope stability analysis for wide area considering influence of rainfall K. Tozato et al. 10.1016/j.enggeo.2022.106808
- Investigating the Feasibility of Using Satellite Rainfall for the Integrated Prediction of Flood and Landslide Hazards over Shaanxi Province in Northwest China S. Wang et al. 10.3390/rs15092457
- Characterizing the Distribution Pattern and a Physically Based Susceptibility Assessment of Shallow Landslides Triggered by the 2019 Heavy Rainfall Event in Longchuan County, Guangdong Province, China S. Ma et al. 10.3390/rs14174257
- Spatiotemporal Characteristics of US Floods: Current Status and Forecast Under a Future Warmer Climate Z. Li et al. 10.1029/2022EF002700
- Triggering of Rain-Induced Landslides, with Applications in Southern Italy A. D’Ippolito et al. 10.3390/w15020277
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- Exploring the utility of radar and satellite-sensed precipitation and their dynamic bias correction for integrated prediction of flood and landslide hazards S. Wang et al. 10.1016/j.jhydrol.2021.126964
- GA-PIC: An improved Green-Ampt rainfall-runoff model with a physically based infiltration distribution curve for semi-arid basins W. Huo et al. 10.1016/j.jhydrol.2020.124900
- iHydroSlide3D v1.0: an advanced hydrological–geotechnical model for hydrological simulation and three-dimensional landslide prediction G. Chen et al. 10.5194/gmd-16-2915-2023
- Dynamic Risk Assessment of Landslide Hazard for Large-Scale Photovoltaic Power Plants under Extreme Rainfall Conditions R. Li et al. 10.3390/w15152832
- Comparison of hybrid data-driven and physical models for landslide susceptibility mapping at regional scales X. Wei et al. 10.1007/s11440-023-01841-4
- Development of Topography-Based River Width Estimation Model for Medium-Sized Mountainous Watersheds B. Tong et al. 10.1061/(ASCE)HE.1943-5584.0001888
- Predictive performance of ensemble hydroclimatic forecasts: Verification metrics, diagnostic plots and forecast attributes Z. Huang & T. Zhao 10.1002/wat2.1580
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- Integration of two-phase solid fluid equations in a catchment model for flashfloods, debris flows and shallow slope failures B. Bout et al. 10.1016/j.envsoft.2018.03.017
- A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping X. Wei et al. 10.1007/s11069-021-04844-0
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- Evaluation of Flood Prediction Capability of the WRF-Hydro Model Based on Multiple Forcing Scenarios M. Sun et al. 10.3390/w12030874
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- A D-vine copula-based quantile regression towards merging satellite precipitation products over rugged topography: a case study in the upper Tekeze–Atbara Basin M. Abdallah et al. 10.5194/hess-28-1147-2024
Discussed (final revised paper)
Latest update: 04 Nov 2024
Short summary
We developed a new approach to couple a distributed hydrological model, CREST, to a geotechnical landslide model, TRIGRS, to simulate both flood- and rainfall-triggered landslide hazards. By implementing more sophisticated and realistic representations of hydrological processes in the coupled model system, it shows better performance than the standalone landslide model in the case study. It highlights the important physical connection between rainfall, hydrological processes and slope stability.
We developed a new approach to couple a distributed hydrological model, CREST, to a geotechnical...