Intensification of Global Hydrological Droughts Under Anthropogenic Climate Warming
Abstract Anthropogenic climate warming is expected to accelerate the hydrological cycle with significant consequences for hydrological droughts. However, a systematic understanding of climate warming impacts on the global hydrological droughts and their driving mechanisms is still lacking. Here, we...
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Wiley
2023-01-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2022WR032997 |
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| author | Lei Gu Jiabo Yin Louise J. Slater Jie Chen Hong Xuan Do Hui‐Min Wang Lu Chen Zhiqiang Jiang Tongtiegang Zhao |
| author_facet | Lei Gu Jiabo Yin Louise J. Slater Jie Chen Hong Xuan Do Hui‐Min Wang Lu Chen Zhiqiang Jiang Tongtiegang Zhao |
| author_sort | Lei Gu |
| collection | DOAJ |
| description | Abstract Anthropogenic climate warming is expected to accelerate the hydrological cycle with significant consequences for hydrological droughts. However, a systematic understanding of climate warming impacts on the global hydrological droughts and their driving mechanisms is still lacking. Here, we integrate bias‐corrected climate experiments, multiple hydrological models (HYs), and a multivariate analysis of variance (ANOVA) with a machine learning modeling framework, to examine the evolving frequency and multivariate characteristics of hydrological droughts and their mechanisms under climate warming for 6,688 catchments in the five principal Köppen‐Geiger climate zones. Results show that the total frequency of hydrological droughts is likely to stay unchanged while extreme hydrological droughts (e.g., events with a 30 yr joint return period, JRP) are projected to occur more frequently across the 21st century. The historical 30 yr JRP events assessed during the historical baseline period of 1985–2014 could become twice as frequent over ∼60% of global catchments by 2071–2100 under the middle and high emission scenarios (ESs). Climate uncertainty (i.e., from global climate models and ESs) is the major source of uncertainty over temperate and tropical catchments, versus HY uncertainty in arid catchments with locally complex runoff regimes. Our machine learning framework indicates that precipitation stress controls the development of historical droughts over ∼87% of global catchments. However, with climate warming, air temperature variations are expected to become the new primary driver of droughts in high‐latitude cold catchments. This study highlights an increasing risk of global extreme hydrological droughts with warming and suggests that rising temperatures in high latitudes may lead to more extreme hydrological droughts. |
| format | Article |
| id | doaj-art-ca048fa1558e4f63ba8fcbfde998fa22 |
| institution | Kabale University |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-ca048fa1558e4f63ba8fcbfde998fa222025-08-20T03:27:06ZengWileyWater Resources Research0043-13971944-79732023-01-01591n/an/a10.1029/2022WR032997Intensification of Global Hydrological Droughts Under Anthropogenic Climate WarmingLei Gu0Jiabo Yin1Louise J. Slater2Jie Chen3Hong Xuan Do4Hui‐Min Wang5Lu Chen6Zhiqiang Jiang7Tongtiegang Zhao8School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan ChinaState Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan University Wuhan ChinaSchool of Geography and the Environment University of Oxford Oxford UKState Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan University Wuhan ChinaFaculty of Environment and Natural Resources Nong Lam University Ho Chi Minh City VietnamDepartment of Civil and Environmental Engineering National University of Singapore Singapore SingaporeSchool of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan ChinaSchool of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan ChinaCenter of Water Resources and Environment Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) School of Civil Engineering Sun Yat‐Sen University Guangzhou ChinaAbstract Anthropogenic climate warming is expected to accelerate the hydrological cycle with significant consequences for hydrological droughts. However, a systematic understanding of climate warming impacts on the global hydrological droughts and their driving mechanisms is still lacking. Here, we integrate bias‐corrected climate experiments, multiple hydrological models (HYs), and a multivariate analysis of variance (ANOVA) with a machine learning modeling framework, to examine the evolving frequency and multivariate characteristics of hydrological droughts and their mechanisms under climate warming for 6,688 catchments in the five principal Köppen‐Geiger climate zones. Results show that the total frequency of hydrological droughts is likely to stay unchanged while extreme hydrological droughts (e.g., events with a 30 yr joint return period, JRP) are projected to occur more frequently across the 21st century. The historical 30 yr JRP events assessed during the historical baseline period of 1985–2014 could become twice as frequent over ∼60% of global catchments by 2071–2100 under the middle and high emission scenarios (ESs). Climate uncertainty (i.e., from global climate models and ESs) is the major source of uncertainty over temperate and tropical catchments, versus HY uncertainty in arid catchments with locally complex runoff regimes. Our machine learning framework indicates that precipitation stress controls the development of historical droughts over ∼87% of global catchments. However, with climate warming, air temperature variations are expected to become the new primary driver of droughts in high‐latitude cold catchments. This study highlights an increasing risk of global extreme hydrological droughts with warming and suggests that rising temperatures in high latitudes may lead to more extreme hydrological droughts.https://doi.org/10.1029/2022WR032997climate changehydrological droughtjoint return periodmachine learningglobal |
| spellingShingle | Lei Gu Jiabo Yin Louise J. Slater Jie Chen Hong Xuan Do Hui‐Min Wang Lu Chen Zhiqiang Jiang Tongtiegang Zhao Intensification of Global Hydrological Droughts Under Anthropogenic Climate Warming Water Resources Research climate change hydrological drought joint return period machine learning global |
| title | Intensification of Global Hydrological Droughts Under Anthropogenic Climate Warming |
| title_full | Intensification of Global Hydrological Droughts Under Anthropogenic Climate Warming |
| title_fullStr | Intensification of Global Hydrological Droughts Under Anthropogenic Climate Warming |
| title_full_unstemmed | Intensification of Global Hydrological Droughts Under Anthropogenic Climate Warming |
| title_short | Intensification of Global Hydrological Droughts Under Anthropogenic Climate Warming |
| title_sort | intensification of global hydrological droughts under anthropogenic climate warming |
| topic | climate change hydrological drought joint return period machine learning global |
| url | https://doi.org/10.1029/2022WR032997 |
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