Global Flood Projection and Socioeconomic Implications Under a Deep Learning Framework
Abstract As the planet warms, the frequency and severity of weather‐related hazards such as floods are intensifying, posing substantial threats to communities around the globe. Rising flood peaks and volumes claim lives, damage infrastructure, and compromise access to essential services. However, th...
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| Format: | Article |
| Language: | English |
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Wiley
2025-05-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2024WR037139 |
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| author | Shengyu Kang Jiabo Yin Louise Slater Pan Liu Fubao Sun Dedi Liu Jun Xia |
| author_facet | Shengyu Kang Jiabo Yin Louise Slater Pan Liu Fubao Sun Dedi Liu Jun Xia |
| author_sort | Shengyu Kang |
| collection | DOAJ |
| description | Abstract As the planet warms, the frequency and severity of weather‐related hazards such as floods are intensifying, posing substantial threats to communities around the globe. Rising flood peaks and volumes claim lives, damage infrastructure, and compromise access to essential services. However, the physical mechanisms behind global flood evolution are still uncertain, and their implications for socioeconomic systems remain unclear. In this study, we leverage a supervised machine learning technique to identify the dominant factors influencing daily streamflow. We then develop a physics‐constrained cascade model chain which assimilates water and heat transport processes to project the bivariate risk of flood peak and volume, along with its socioeconomic consequences. To achieve this, we develop a hybrid deep‐learning‐hydrological model with bias‐corrected outputs from 20 global climate models from CMIP6 under four shared socioeconomic pathways. Our results project considerable increases in flood risk under the medium to high‐end emission scenario (SSP3‐7.0) over most catchments of the globe. The median future joint return period decreases from 50 years to around 27.6 years, with 186 trillion USD and 4 billion people exposed. Downwelling shortwave radiation is identified as the dominant factor driving changes in daily streamflow, accelerating both terrestrial evapotranspiration and snowmelt. As future scenarios project enhanced global warming along with an increase in precipitation extremes, a heightened risk of widespread flooding is foreseen. This study aims to provide valuable insights for policymakers developing proactive strategies to mitigate the risks associated with river flooding under climate change. |
| format | Article |
| id | doaj-art-d804c78b9fbe4ba7a5a7aba7fcc08666 |
| institution | OA Journals |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-d804c78b9fbe4ba7a5a7aba7fcc086662025-08-20T02:36:28ZengWileyWater Resources Research0043-13971944-79732025-05-01615n/an/a10.1029/2024WR037139Global Flood Projection and Socioeconomic Implications Under a Deep Learning FrameworkShengyu Kang0Jiabo Yin1Louise Slater2Pan Liu3Fubao Sun4Dedi Liu5Jun Xia6State Key Laboratory of Water Resources Engineering and Management Wuhan University Wuhan PR. ChinaState Key Laboratory of Water Resources Engineering and Management Wuhan University Wuhan PR. ChinaSchool of Geography and the Environment University of Oxford Oxford UKState Key Laboratory of Water Resources Engineering and Management Wuhan University Wuhan PR. ChinaXinjiang Key Laboratory of Water Cycle and Utilization in Arid Zone Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences Urumqi ChinaState Key Laboratory of Water Resources Engineering and Management Wuhan University Wuhan PR. ChinaState Key Laboratory of Water Resources Engineering and Management Wuhan University Wuhan PR. ChinaAbstract As the planet warms, the frequency and severity of weather‐related hazards such as floods are intensifying, posing substantial threats to communities around the globe. Rising flood peaks and volumes claim lives, damage infrastructure, and compromise access to essential services. However, the physical mechanisms behind global flood evolution are still uncertain, and their implications for socioeconomic systems remain unclear. In this study, we leverage a supervised machine learning technique to identify the dominant factors influencing daily streamflow. We then develop a physics‐constrained cascade model chain which assimilates water and heat transport processes to project the bivariate risk of flood peak and volume, along with its socioeconomic consequences. To achieve this, we develop a hybrid deep‐learning‐hydrological model with bias‐corrected outputs from 20 global climate models from CMIP6 under four shared socioeconomic pathways. Our results project considerable increases in flood risk under the medium to high‐end emission scenario (SSP3‐7.0) over most catchments of the globe. The median future joint return period decreases from 50 years to around 27.6 years, with 186 trillion USD and 4 billion people exposed. Downwelling shortwave radiation is identified as the dominant factor driving changes in daily streamflow, accelerating both terrestrial evapotranspiration and snowmelt. As future scenarios project enhanced global warming along with an increase in precipitation extremes, a heightened risk of widespread flooding is foreseen. This study aims to provide valuable insights for policymakers developing proactive strategies to mitigate the risks associated with river flooding under climate change.https://doi.org/10.1029/2024WR037139flooddeep learninghydrological modelingsocioeconomic implicationclimate change |
| spellingShingle | Shengyu Kang Jiabo Yin Louise Slater Pan Liu Fubao Sun Dedi Liu Jun Xia Global Flood Projection and Socioeconomic Implications Under a Deep Learning Framework Water Resources Research flood deep learning hydrological modeling socioeconomic implication climate change |
| title | Global Flood Projection and Socioeconomic Implications Under a Deep Learning Framework |
| title_full | Global Flood Projection and Socioeconomic Implications Under a Deep Learning Framework |
| title_fullStr | Global Flood Projection and Socioeconomic Implications Under a Deep Learning Framework |
| title_full_unstemmed | Global Flood Projection and Socioeconomic Implications Under a Deep Learning Framework |
| title_short | Global Flood Projection and Socioeconomic Implications Under a Deep Learning Framework |
| title_sort | global flood projection and socioeconomic implications under a deep learning framework |
| topic | flood deep learning hydrological modeling socioeconomic implication climate change |
| url | https://doi.org/10.1029/2024WR037139 |
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