Global mortality risk assessment from river flooding under climate change
Floods that cause yearly economic losses and casualties have increased in frequency with global warming. Assessing the mortality risks of populations due to flooding is important and necessary for risk management and disaster reduction. Thus, this paper develops a method for assessing global mortali...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2021-01-01
|
| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/abff87 |
| _version_ | 1797747694251278336 |
|---|---|
| author | Junlin Zhang Wei Xu Xinli Liao Shuo Zong Baoyin Liu |
| author_facet | Junlin Zhang Wei Xu Xinli Liao Shuo Zong Baoyin Liu |
| author_sort | Junlin Zhang |
| collection | DOAJ |
| description | Floods that cause yearly economic losses and casualties have increased in frequency with global warming. Assessing the mortality risks of populations due to flooding is important and necessary for risk management and disaster reduction. Thus, this paper develops a method for assessing global mortality risks due to river flooding. Global historical annual death tolls are first estimated during the historical period 1986–2005 (T _0 ) by using available mortality vulnerability functions of river flooding. Then, the best vulnerability function is selected according to lower root mean square errors (RMSE) and the differences in the multi-year mean (DMYM) values. Next, the adjustment coefficient K _c for each country (region) is calculated to use in the revision of the selected vulnerability function. Finally, the mortality risks are estimated based on an adjusted vulnerability function. As a case, the paper assessed and analysed the global mortality risks due to river flooding during 2016–2035 (2030s) and 2046–2065 (2050s) for the combined scenario of the Representative Concentration Pathway 4.5 (RCP4.5) and the Shared Socioeconomic Pathway 2 (SSP2), and the RCP8.5-SSP5 scenario. The results show that the estimation errors of the death tolls in most countries (regions) decrease after adjusting the vulnerability function. Under the current defense capacity and vulnerability level, the average annual death tolls of RCP4.5-SSP2 and RCP8.5-SSP5 in the 2030s will increase by 1.05 times and 0.93 times compared with the historical period. They will increase 1.89 and 2.20 times, respectively for the two scenarios during 2050s. High-risk areas are distributed in the south-eastern Eurasia. |
| first_indexed | 2024-03-12T15:54:11Z |
| format | Article |
| id | doaj.art-ed172cf268ba4b899bf7da1022ae4c93 |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T15:54:11Z |
| publishDate | 2021-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-ed172cf268ba4b899bf7da1022ae4c932023-08-09T15:00:57ZengIOP PublishingEnvironmental Research Letters1748-93262021-01-0116606403610.1088/1748-9326/abff87Global mortality risk assessment from river flooding under climate changeJunlin Zhang0https://orcid.org/0000-0003-1404-3549Wei Xu1Xinli Liao2Shuo Zong3Baoyin Liu4Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Faculty of Geographical Science, Beijing Normal University , Beijing 100875, People’s Republic of China; Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education, Beijing Normal University , Beijing 100875, People’s Republic of China; State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University , Beijing 100875, People’s Republic of ChinaKey Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Faculty of Geographical Science, Beijing Normal University , Beijing 100875, People’s Republic of China; Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education, Beijing Normal University , Beijing 100875, People’s Republic of China; State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University , Beijing 100875, People’s Republic of ChinaKey Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Faculty of Geographical Science, Beijing Normal University , Beijing 100875, People’s Republic of China; Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education, Beijing Normal University , Beijing 100875, People’s Republic of China; State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University , Beijing 100875, People’s Republic of ChinaKey Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Faculty of Geographical Science, Beijing Normal University , Beijing 100875, People’s Republic of China; Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education, Beijing Normal University , Beijing 100875, People’s Republic of China; State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University , Beijing 100875, People’s Republic of ChinaInstitutes of Science and Development, Chinese Academy of Sciences , Beijing 100190, People’s Republic of ChinaFloods that cause yearly economic losses and casualties have increased in frequency with global warming. Assessing the mortality risks of populations due to flooding is important and necessary for risk management and disaster reduction. Thus, this paper develops a method for assessing global mortality risks due to river flooding. Global historical annual death tolls are first estimated during the historical period 1986–2005 (T _0 ) by using available mortality vulnerability functions of river flooding. Then, the best vulnerability function is selected according to lower root mean square errors (RMSE) and the differences in the multi-year mean (DMYM) values. Next, the adjustment coefficient K _c for each country (region) is calculated to use in the revision of the selected vulnerability function. Finally, the mortality risks are estimated based on an adjusted vulnerability function. As a case, the paper assessed and analysed the global mortality risks due to river flooding during 2016–2035 (2030s) and 2046–2065 (2050s) for the combined scenario of the Representative Concentration Pathway 4.5 (RCP4.5) and the Shared Socioeconomic Pathway 2 (SSP2), and the RCP8.5-SSP5 scenario. The results show that the estimation errors of the death tolls in most countries (regions) decrease after adjusting the vulnerability function. Under the current defense capacity and vulnerability level, the average annual death tolls of RCP4.5-SSP2 and RCP8.5-SSP5 in the 2030s will increase by 1.05 times and 0.93 times compared with the historical period. They will increase 1.89 and 2.20 times, respectively for the two scenarios during 2050s. High-risk areas are distributed in the south-eastern Eurasia.https://doi.org/10.1088/1748-9326/abff87globallyriver floodingmortality riskvulnerability |
| spellingShingle | Junlin Zhang Wei Xu Xinli Liao Shuo Zong Baoyin Liu Global mortality risk assessment from river flooding under climate change Environmental Research Letters globally river flooding mortality risk vulnerability |
| title | Global mortality risk assessment from river flooding under climate change |
| title_full | Global mortality risk assessment from river flooding under climate change |
| title_fullStr | Global mortality risk assessment from river flooding under climate change |
| title_full_unstemmed | Global mortality risk assessment from river flooding under climate change |
| title_short | Global mortality risk assessment from river flooding under climate change |
| title_sort | global mortality risk assessment from river flooding under climate change |
| topic | globally river flooding mortality risk vulnerability |
| url | https://doi.org/10.1088/1748-9326/abff87 |
| work_keys_str_mv | AT junlinzhang globalmortalityriskassessmentfromriverfloodingunderclimatechange AT weixu globalmortalityriskassessmentfromriverfloodingunderclimatechange AT xinliliao globalmortalityriskassessmentfromriverfloodingunderclimatechange AT shuozong globalmortalityriskassessmentfromriverfloodingunderclimatechange AT baoyinliu globalmortalityriskassessmentfromriverfloodingunderclimatechange |