Quantifying the relative contributions of climate change and ENSO to flood occurrence in Bangladesh
Bangladesh is highly vulnerable to flood hazards, and its flood risk is projected to increase with global warming. In addition to climate change, internal climate variation, such as the El Niño–Southern Oscillation (ENSO), influences flooding in many rivers worldwide. However, the impact of internal...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2023-01-01
|
| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/acfa11 |
| _version_ | 1797672963023044608 |
|---|---|
| author | Shahab Uddin Menaka Revel Prakat Modi Dai Yamazaki |
| author_facet | Shahab Uddin Menaka Revel Prakat Modi Dai Yamazaki |
| author_sort | Shahab Uddin |
| collection | DOAJ |
| description | Bangladesh is highly vulnerable to flood hazards, and its flood risk is projected to increase with global warming. In addition to climate change, internal climate variation, such as the El Niño–Southern Oscillation (ENSO), influences flooding in many rivers worldwide. However, the impact of internal climate variability on flooding in Bangladesh remains unclear due to the limited observations. Here, we assess the impacts of ENSO and climate change on flood occurrence in Bangladesh using a large-ensemble climate simulation dataset and a global river model (CaMa-Flood). After separating 6000 years of simulation (100-member ensemble river simulations for 1950–2010) into El Niño, La Niña, and neutral years, we calculated the extent to which each ENSO stage increased flood occurrence probability relative to the neutral state using the fraction of attributable risk method. In addition, we estimated the impact of historical climate change on past flood occurrence through a comparison of simulations with and without historical global warming. Under the no-global-warming climate, La Niña increased the occurrence probability of a 10 year return period flood at Hardinge Bridge on the Ganges River by 38% compared to neutral years. The influence of La Niña or El Niño state on flood occurrence probability in the Brahmaputra River at Bahadurabad station is negligible. Historical global warming increased the occurrence of flooding in the Ganges River, the Brahmaputra River, and their confluence by 59%, 44%, and 55%, respectively. The impact of ENSO on flood occurrence probability decreased in the historical simulation, likely due to the conflation of ENSO and climate change signals, and no significant correlation between ENSO and flood occurrence was detected when only small-ensemble simulations were used. These findings suggest that the use of large-ensemble climate simulation datasets is essential for precise attribution of the impacts of internal climate variability on flooding in Bangladesh. |
| first_indexed | 2024-03-11T21:38:41Z |
| format | Article |
| id | doaj.art-636c277f7d994fb69d7819e9d66c3a76 |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-11T21:38:41Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-636c277f7d994fb69d7819e9d66c3a762023-09-26T14:56:30ZengIOP PublishingEnvironmental Research Letters1748-93262023-01-01181010402710.1088/1748-9326/acfa11Quantifying the relative contributions of climate change and ENSO to flood occurrence in BangladeshShahab Uddin0https://orcid.org/0000-0002-1753-2279Menaka Revel1https://orcid.org/0000-0003-0390-8279Prakat Modi2https://orcid.org/0000-0002-2751-1594Dai Yamazaki3https://orcid.org/0000-0002-6478-1841Global Hydrological Prediction Center, Institute of Industrial Science, The University of Tokyo , 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan; Department of Civil Engineering, Dhaka University of Engineering & Technology , Gazipur, BangladeshGlobal Hydrological Prediction Center, Institute of Industrial Science, The University of Tokyo , 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, JapanGlobal Hydrological Prediction Center, Institute of Industrial Science, The University of Tokyo , 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan; Department of Civil Engineering, Shibaura Institute of Technology , Tokyo, JapanGlobal Hydrological Prediction Center, Institute of Industrial Science, The University of Tokyo , 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, JapanBangladesh is highly vulnerable to flood hazards, and its flood risk is projected to increase with global warming. In addition to climate change, internal climate variation, such as the El Niño–Southern Oscillation (ENSO), influences flooding in many rivers worldwide. However, the impact of internal climate variability on flooding in Bangladesh remains unclear due to the limited observations. Here, we assess the impacts of ENSO and climate change on flood occurrence in Bangladesh using a large-ensemble climate simulation dataset and a global river model (CaMa-Flood). After separating 6000 years of simulation (100-member ensemble river simulations for 1950–2010) into El Niño, La Niña, and neutral years, we calculated the extent to which each ENSO stage increased flood occurrence probability relative to the neutral state using the fraction of attributable risk method. In addition, we estimated the impact of historical climate change on past flood occurrence through a comparison of simulations with and without historical global warming. Under the no-global-warming climate, La Niña increased the occurrence probability of a 10 year return period flood at Hardinge Bridge on the Ganges River by 38% compared to neutral years. The influence of La Niña or El Niño state on flood occurrence probability in the Brahmaputra River at Bahadurabad station is negligible. Historical global warming increased the occurrence of flooding in the Ganges River, the Brahmaputra River, and their confluence by 59%, 44%, and 55%, respectively. The impact of ENSO on flood occurrence probability decreased in the historical simulation, likely due to the conflation of ENSO and climate change signals, and no significant correlation between ENSO and flood occurrence was detected when only small-ensemble simulations were used. These findings suggest that the use of large-ensemble climate simulation datasets is essential for precise attribution of the impacts of internal climate variability on flooding in Bangladesh.https://doi.org/10.1088/1748-9326/acfa11flood riskattributionclimate changeENSO |
| spellingShingle | Shahab Uddin Menaka Revel Prakat Modi Dai Yamazaki Quantifying the relative contributions of climate change and ENSO to flood occurrence in Bangladesh Environmental Research Letters flood risk attribution climate change ENSO |
| title | Quantifying the relative contributions of climate change and ENSO to flood occurrence in Bangladesh |
| title_full | Quantifying the relative contributions of climate change and ENSO to flood occurrence in Bangladesh |
| title_fullStr | Quantifying the relative contributions of climate change and ENSO to flood occurrence in Bangladesh |
| title_full_unstemmed | Quantifying the relative contributions of climate change and ENSO to flood occurrence in Bangladesh |
| title_short | Quantifying the relative contributions of climate change and ENSO to flood occurrence in Bangladesh |
| title_sort | quantifying the relative contributions of climate change and enso to flood occurrence in bangladesh |
| topic | flood risk attribution climate change ENSO |
| url | https://doi.org/10.1088/1748-9326/acfa11 |
| work_keys_str_mv | AT shahabuddin quantifyingtherelativecontributionsofclimatechangeandensotofloodoccurrenceinbangladesh AT menakarevel quantifyingtherelativecontributionsofclimatechangeandensotofloodoccurrenceinbangladesh AT prakatmodi quantifyingtherelativecontributionsofclimatechangeandensotofloodoccurrenceinbangladesh AT daiyamazaki quantifyingtherelativecontributionsofclimatechangeandensotofloodoccurrenceinbangladesh |