Assessing the Risk of Natural and Socioeconomic Hazards Caused by Rainfall in the Middle Yellow River Basin
Extreme rainfall events directly increase flood risks and further trigger environmental geological hazards (i.e., landslides and debris flows). Meanwhile, rainfall-induced risks are determined by climate and geographical factors and spatial socioeconomic factors (e.g., population density and gross d...
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MDPI AG
2025-05-01
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| Series: | Hydrology |
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| author | Yufeng Zhao Shun Xiao Xinshuang Wu Shuitao Guo Yingying Yao |
| author_facet | Yufeng Zhao Shun Xiao Xinshuang Wu Shuitao Guo Yingying Yao |
| author_sort | Yufeng Zhao |
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| description | Extreme rainfall events directly increase flood risks and further trigger environmental geological hazards (i.e., landslides and debris flows). Meanwhile, rainfall-induced risks are determined by climate and geographical factors and spatial socioeconomic factors (e.g., population density and gross domestic product). However, the middle stream of Yellow River Basin, where geological hazards frequently occur, lacks systematic analyses of rainfall-induced risks. In this study, we propose a comprehensive quantification framework and apply it to the Loess Plateau of northern China based on 40 years of climate data, streamflow measurements, and multiple spatial and geographical attribute datasets. A deep learning algorithm of long short-term memory (LSTM) was used to predict runoff, and the analytic hierarchy index was utilized to evaluate the comprehensive spatial risk considering natural and socioeconomic factors. Despite a decrease in annual precipitation in our study area of 1.46 mm per year, the intensity of heavy rainfall has increased since the 1980s, characterized by increases in rainstorm intensity (+4.68%), rainfall intensity (+7.07%), and rainfall amount (+5.34%). A comprehensive risk assessment indicated that high-risk areas accounted for 20.30% of the total area, with rainfall, geographical factors, and socioeconomic variables accounting for 53.90%, 29.72%, and 16.38% of risk areas, respectively. Rainfall was the dominant factor that determined the risk, and geographical and socioeconomic properties characterized the vulnerability and resilience of disasters. Our study provided an evaluation framework for multi-hazard risk assessment and insights for the development of disaster prevention and reduction policies. |
| format | Article |
| id | doaj-art-14ea5c8b0d304ecfb0dae73d25b2bb3a |
| institution | Kabale University |
| issn | 2306-5338 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Hydrology |
| spelling | doaj-art-14ea5c8b0d304ecfb0dae73d25b2bb3a2025-08-20T03:24:33ZengMDPI AGHydrology2306-53382025-05-0112613410.3390/hydrology12060134Assessing the Risk of Natural and Socioeconomic Hazards Caused by Rainfall in the Middle Yellow River BasinYufeng Zhao0Shun Xiao1Xinshuang Wu2Shuitao Guo3Yingying Yao4Institute of Global Environmental Change, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, ChinaPower China Northwest Engineering Corporation Limited, Xi’an 710065, ChinaInstitute of Global Environmental Change, Xi’an Jiaotong University, Xi’an 710049, ChinaInstitute of Global Environmental Change, Xi’an Jiaotong University, Xi’an 710049, ChinaExtreme rainfall events directly increase flood risks and further trigger environmental geological hazards (i.e., landslides and debris flows). Meanwhile, rainfall-induced risks are determined by climate and geographical factors and spatial socioeconomic factors (e.g., population density and gross domestic product). However, the middle stream of Yellow River Basin, where geological hazards frequently occur, lacks systematic analyses of rainfall-induced risks. In this study, we propose a comprehensive quantification framework and apply it to the Loess Plateau of northern China based on 40 years of climate data, streamflow measurements, and multiple spatial and geographical attribute datasets. A deep learning algorithm of long short-term memory (LSTM) was used to predict runoff, and the analytic hierarchy index was utilized to evaluate the comprehensive spatial risk considering natural and socioeconomic factors. Despite a decrease in annual precipitation in our study area of 1.46 mm per year, the intensity of heavy rainfall has increased since the 1980s, characterized by increases in rainstorm intensity (+4.68%), rainfall intensity (+7.07%), and rainfall amount (+5.34%). A comprehensive risk assessment indicated that high-risk areas accounted for 20.30% of the total area, with rainfall, geographical factors, and socioeconomic variables accounting for 53.90%, 29.72%, and 16.38% of risk areas, respectively. Rainfall was the dominant factor that determined the risk, and geographical and socioeconomic properties characterized the vulnerability and resilience of disasters. Our study provided an evaluation framework for multi-hazard risk assessment and insights for the development of disaster prevention and reduction policies.https://www.mdpi.com/2306-5338/12/6/134rainfallYellow River Basindeep learningspatiotemporalrisk of geo-hazards |
| spellingShingle | Yufeng Zhao Shun Xiao Xinshuang Wu Shuitao Guo Yingying Yao Assessing the Risk of Natural and Socioeconomic Hazards Caused by Rainfall in the Middle Yellow River Basin Hydrology rainfall Yellow River Basin deep learning spatiotemporal risk of geo-hazards |
| title | Assessing the Risk of Natural and Socioeconomic Hazards Caused by Rainfall in the Middle Yellow River Basin |
| title_full | Assessing the Risk of Natural and Socioeconomic Hazards Caused by Rainfall in the Middle Yellow River Basin |
| title_fullStr | Assessing the Risk of Natural and Socioeconomic Hazards Caused by Rainfall in the Middle Yellow River Basin |
| title_full_unstemmed | Assessing the Risk of Natural and Socioeconomic Hazards Caused by Rainfall in the Middle Yellow River Basin |
| title_short | Assessing the Risk of Natural and Socioeconomic Hazards Caused by Rainfall in the Middle Yellow River Basin |
| title_sort | assessing the risk of natural and socioeconomic hazards caused by rainfall in the middle yellow river basin |
| topic | rainfall Yellow River Basin deep learning spatiotemporal risk of geo-hazards |
| url | https://www.mdpi.com/2306-5338/12/6/134 |
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