Articles | Volume 21, issue 9
https://doi.org/10.5194/hess-21-4573-2017
© Author(s) 2017. 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-21-4573-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
13 Sep 2017
Research article |
| 13 Sep 2017
Characterizing the spatial variations and correlations of large rainstorms for landslide study
Liang Gao
Department of Civil and Environmental Engineering, The
Hong Kong University of Science and Technology, Clear Water Bay, Hong
Kong
Department of Civil and Environmental Engineering, The
Hong Kong University of Science and Technology, Clear Water Bay, Hong
Kong
Mengqian Lu
Department of Civil and Environmental Engineering, The
Hong Kong University of Science and Technology, Clear Water Bay, Hong
Kong
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Cited
18 citations as recorded by crossref.
- Exploring the rainfall data from satellites to monitor rainfall induced landslides – A case study M. Kumar Thakur et al. 10.1016/j.asr.2020.05.015
- AI-powered landslide susceptibility assessment in Hong Kong H. Wang et al. 10.1016/j.enggeo.2021.106103
- Landslide identification using machine learning H. Wang et al. 10.1016/j.gsf.2020.02.012
- Hazard assessment of rainstorm-geohazard disaster chain based on multiple scenarios Q. Wang & J. Hou 10.1007/s11069-023-06020-y
- Spatiotemporal modelling of rainfall-induced landslides using machine learning C. Ng et al. 10.1007/s10346-021-01662-0
- Evaluating Metropolitan Flood Coping Capabilities under Heavy Storms L. Gao et al. 10.1061/(ASCE)HE.1943-5584.0001793
- Estimating the combined risks of sea level rise and storm surges using a numerical model: Application to Macao Z. Long & L. Gao 10.1016/j.jclepro.2023.137155
- Recent trends in the frequency and duration of global floods N. Najibi & N. Devineni 10.5194/esd-9-757-2018
- Landslide Trail Extraction Using Fire Extinguishing Model Z. Zhan et al. 10.3390/rs14020308
- Brief communication: SWM – stochastic weather model for precipitation-related hazard assessments using ERA5-Land data M. Whitehead & M. Bebbington 10.5194/nhess-24-1929-2024
- Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) W. Chen et al. 10.3390/app9183755
- Topography and geology effects on travel distances of natural terrain landslides: Evidence from a large multi-temporal landslide inventory in Hong Kong L. Gao et al. 10.1016/j.enggeo.2021.106266
- Relationships between natural terrain landslide magnitudes and triggering rainfall based on a large landslide inventory in Hong Kong L. Gao et al. 10.1007/s10346-017-0904-x
- On the importance of landslide management: Insights from a 32-year database of landslide consequences and rainfall in Hong Kong H. Yang et al. 10.1016/j.enggeo.2022.106578
- Assimilation of Multi-Source Precipitation Data over Southeast China Using a Nonparametric Framework Y. Zhou et al. 10.3390/rs13061057
- Flood hazards in urban environment L. Gao et al. 10.1080/17499518.2023.2201266
- EDDA 2.0: integrated simulation of debris flow initiation and dynamics considering two initiation mechanisms P. Shen et al. 10.5194/gmd-11-2841-2018
- Exploring and analyzing disaster coupling characteristics at the regional scale: evidence from Hubei Province Q. Wang et al. 10.1080/19475705.2024.2367639
18 citations as recorded by crossref.
- Exploring the rainfall data from satellites to monitor rainfall induced landslides – A case study M. Kumar Thakur et al. 10.1016/j.asr.2020.05.015
- AI-powered landslide susceptibility assessment in Hong Kong H. Wang et al. 10.1016/j.enggeo.2021.106103
- Landslide identification using machine learning H. Wang et al. 10.1016/j.gsf.2020.02.012
- Hazard assessment of rainstorm-geohazard disaster chain based on multiple scenarios Q. Wang & J. Hou 10.1007/s11069-023-06020-y
- Spatiotemporal modelling of rainfall-induced landslides using machine learning C. Ng et al. 10.1007/s10346-021-01662-0
- Evaluating Metropolitan Flood Coping Capabilities under Heavy Storms L. Gao et al. 10.1061/(ASCE)HE.1943-5584.0001793
- Estimating the combined risks of sea level rise and storm surges using a numerical model: Application to Macao Z. Long & L. Gao 10.1016/j.jclepro.2023.137155
- Recent trends in the frequency and duration of global floods N. Najibi & N. Devineni 10.5194/esd-9-757-2018
- Landslide Trail Extraction Using Fire Extinguishing Model Z. Zhan et al. 10.3390/rs14020308
- Brief communication: SWM – stochastic weather model for precipitation-related hazard assessments using ERA5-Land data M. Whitehead & M. Bebbington 10.5194/nhess-24-1929-2024
- Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) W. Chen et al. 10.3390/app9183755
- Topography and geology effects on travel distances of natural terrain landslides: Evidence from a large multi-temporal landslide inventory in Hong Kong L. Gao et al. 10.1016/j.enggeo.2021.106266
- Relationships between natural terrain landslide magnitudes and triggering rainfall based on a large landslide inventory in Hong Kong L. Gao et al. 10.1007/s10346-017-0904-x
- On the importance of landslide management: Insights from a 32-year database of landslide consequences and rainfall in Hong Kong H. Yang et al. 10.1016/j.enggeo.2022.106578
- Assimilation of Multi-Source Precipitation Data over Southeast China Using a Nonparametric Framework Y. Zhou et al. 10.3390/rs13061057
- Flood hazards in urban environment L. Gao et al. 10.1080/17499518.2023.2201266
- EDDA 2.0: integrated simulation of debris flow initiation and dynamics considering two initiation mechanisms P. Shen et al. 10.5194/gmd-11-2841-2018
- Exploring and analyzing disaster coupling characteristics at the regional scale: evidence from Hubei Province Q. Wang et al. 10.1080/19475705.2024.2367639
Latest update: 29 Jun 2024
Short summary
Rainfall is the primary trigger of landslides. However, the rainfall intensity is not uniform in space, which causes more landslides in the area of intense rainfall. The primary objective of this paper is to quantify spatial correlation characteristics of three landslide-triggering large storms in Hong Kong. The spatial maximum rolling rainfall is represented by a trend surface and a random field of residuals. The scales of fluctuation of the residuals are found between 5 km and 30 km.
Rainfall is the primary trigger of landslides. However, the rainfall intensity is not uniform in...
