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She Z, Liu Z, Cai H, Liu H, Song Y, Li B, Lan X, Jiang T. A framework to evaluate the impact of a hazard chain and geographical covariates on spatial extreme water levels: A case study in the Pearl River Delta. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 926:172066. [PMID: 38556022 DOI: 10.1016/j.scitotenv.2024.172066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 02/06/2024] [Accepted: 03/26/2024] [Indexed: 04/02/2024]
Abstract
The interactions and collective impacts of different types of hazards within a compound hazard system, along with the influence of geographical covariates on flooding are presently unclear. Understanding these relationships is crucial for comprehending the formation and dynamic processes of the hazard chain and improving the ability to identify flood warning signals in complex hazard scenarios. In this study, we presented a multivariate spatial extreme value hierarchical (MSEVH) framework to assess the spatial extreme water levels (EWL) at different return levels under the influence of a hazard chain and geographical covariates. The Pearl River Delta (PRD) was selected as a research example to assess the effectiveness of the MSEVH framework. Firstly, we identified a hazard chain (extreme streamflow from the Xijiang River (XR) - extreme streamflow from the Beijiang River (BR) - extreme sea level) and three geographical covariates influencing EWL in the PRD. Then, we compared four hazard scenarios in the MSEVH framework to evaluate the spatial EWL at different return levels under the influence of the hazard chain in the PRD. The final step involves assessing spatial EWL with the effect of the hazard chain and geographical covariates. The results indicate that when extreme streamflow from XR and BR occurs concurrently, the extreme streamflow from BR weakens the influence of extreme streamflow from XR on EWL in the PRD. However, it cannot fully offset the overall impact of extreme streamflow from XR on EWL. In addition, when extreme streamflow from XR, extreme streamflow from BR, and extreme sea level occur simultaneously, the extreme sea level enhances the influence of concurrent extreme streamflow from XR and BR on EWL in the PRD. The proposed MSEVH is not only applicable to the PRD but also shows promising potential for evaluating extreme hydrometeorological variables under the influence of other hazard chains.
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Affiliation(s)
- Zhenyan She
- Institute of Estuarine and Coastal Research, School of Ocean Engineering and Technology, Sun Yat-sen University, 519082 Zhuhai, China
| | - Zhiyong Liu
- Center for Water Resources and Environment, School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), 519082 Zhuhai, China.
| | - Huayang Cai
- Institute of Estuarine and Coastal Research, School of Ocean Engineering and Technology, Sun Yat-sen University, 519082 Zhuhai, China.
| | - Haibo Liu
- Powerchina Eco-environmental Group Co., Ltd., Shenzhen 518101, China
| | - Yunlong Song
- VAST Institute of Water Ecology and Environment, Shenzhen 518101, China
| | - Bo Li
- Institute of Estuarine and Coastal Research, School of Ocean Engineering and Technology, Sun Yat-sen University, 519082 Zhuhai, China
| | - Xin Lan
- Center for Water Resources and Environment, School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), 519082 Zhuhai, China
| | - Tao Jiang
- School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510006, China
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Jiang N, Zhu C, Hu ZZ, McPhaden MJ, Chen D, Liu B, Ma S, Yan Y, Zhou T, Qian W, Luo J, Yang X, Liu F, Zhu Y. Enhanced risk of record-breaking regional temperatures during the 2023-24 El Niño. Sci Rep 2024; 14:2521. [PMID: 38424053 PMCID: PMC10904789 DOI: 10.1038/s41598-024-52846-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 01/24/2024] [Indexed: 03/02/2024] Open
Abstract
In 2023, the development of El Niño is poised to drive a global upsurge in surface air temperatures (SAT), potentially resulting in unprecedented warming worldwide. Nevertheless, the regional patterns of SAT anomalies remain diverse, obscuring where historical warming records may be surpassed in the forthcoming year. Our study underscores the significant influence of El Niño and the persistence of climate signals on the inter-annual variability of regional SAT, both in amplitude and spatial distribution. The likelihood of global mean SAT exceeding historical records, calculated from July 2023 to June 2024, is estimated at 90%, contingent upon annual-mean sea surface temperature anomalies in the eastern equatorial Pacific exceeding 0.6 °C. Regions particularly susceptible to recording record-high SAT include coastal and adjacent areas in Asia such as the Bay of Bengal and the South China Sea, as well as Alaska, the Caribbean Sea, and the Amazon. This impending warmth heightens the risk of year-round marine heatwaves and escalates the threat of wildfires and other negative consequences in Alaska and the Amazon basin, necessitating strategic mitigation measures to minimize potential worst-case impacts.
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Affiliation(s)
- Ning Jiang
- State Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Sciences, Beijing, 100081, China
| | - Congwen Zhu
- State Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Sciences, Beijing, 100081, China.
| | - Zeng-Zhen Hu
- Climate Prediction Center, NCEP/NWS/NOAA, College Park, MD, USA
| | | | - Deliang Chen
- Department of Earth Sciences, University of Gothenburg, 40530, Gothenburg, Sweden
| | - Boqi Liu
- State Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Sciences, Beijing, 100081, China
| | - Shuangmei Ma
- State Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Sciences, Beijing, 100081, China
| | - Yuhan Yan
- State Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Sciences, Beijing, 100081, China
| | - Tianjun Zhou
- State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Weihong Qian
- Department of Atmospheric and Oceanic Sciences, Peking University, Beijing, 100871, China
- Guangzhou Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction, China Meteorological Administration, Guangzhou, 510641, China
| | - Jingjia Luo
- Institute for Climate and Application Research (ICAR), Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Xiuqun Yang
- School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
| | - Fei Liu
- Key Laboratory of Tropical Atmosphere-Ocean System Ministry of Education, and Southern Marine Science and Engineering Guangdong Laboratory, School of Atmospheric Sciences Sun Yat-Sen University, Zhuhai, 519082, China
| | - Yuejian Zhu
- Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing, 100081, China
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