Analysis and Evaluation of Extreme Rainfall Trends and Geological Hazards Risk in the Lower Jinshajiang River
Source abstract
Abstract
This research studied the risk assessment of geological hazards, such as landslides and debris flow, under the time series and trend characteristics of extreme precipitation events in the last 60 years in nine typical regions of the lower Jinshajiang River Basin. Nine indicators, including slope, engineering geological rock group, slope structure type, distance to road, topographic relief, distance to fault, distance to the water system, vegetation cover and profile curvature, were selected as the index factors for landslide susceptibility evaluation, and the information quantity method was used to obtain the landslide susceptibility evaluation of the study area. Based on the susceptibility evaluation, the spatial analysis function of GIS was used to derive the geological hazard zoning under the extreme rainfall trend. The results showed that the areas with high extreme rainfall trends have higher densities of geological hazard development and they are concentrated, while areas with low extreme rainfall trends have relatively less geological hazard development, and what development exists is scattered.
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Research context
OpenAlex topics: Landslides and related hazards · Cryospheric studies and observations · Climate change and permafrost
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China
Sichuan Province Engineering Technology Research Center of Support Software of Informatization Application, Chengdu 610225, China
School of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, China
Operational System Development and Maintenance Division, National Climate Center, Beijing 100081, China
Funding
Key Projects of Global Change and Response of Ministry of Science and Technology of China · Research and Demonstration of Key Technologies for Cultivated Land Remote Sensing Intelligent Monitoring · China Three Gorges · Scientific Research Talent Fund of the Chengdu University of Information
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