A copula-based multivariate flood frequency analysis under climate change effects
Abstract Floods are among the most severe natural hazards, causing substantial damage and affecting millions of lives. These events are inherently multi-dimensional, requiring analysis across multiple factors. Traditional research often uses a bivariate framework relying on historical data, but clim...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-024-84543-5 |
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| author | Marzieh Khajehali Hamid R. Safavi Mohammad Reza Nikoo Mohammad Reza Najafi Reza Alizadeh-Sh |
| author_facet | Marzieh Khajehali Hamid R. Safavi Mohammad Reza Nikoo Mohammad Reza Najafi Reza Alizadeh-Sh |
| author_sort | Marzieh Khajehali |
| collection | DOAJ |
| description | Abstract Floods are among the most severe natural hazards, causing substantial damage and affecting millions of lives. These events are inherently multi-dimensional, requiring analysis across multiple factors. Traditional research often uses a bivariate framework relying on historical data, but climate change is expected to influence flood frequency analysis and flood system design in the future. This study assesses the projected changes in flood characteristics based on eight downscaled and bias-corrected General Circulation Models (GCMs) that participated in the Coupled Model Intercomparison Project Phase 6. The analysis considers two emission scenarios, including SSP2-4.5 and SSP5-8.5 for far-future (2070–2100), mid-term future (2040–2070), and historical (1982–2012) periods. Downscaled GCM outputs are utilized as predictors of the machine learning model to simulate daily streamflow. Then, a trivariate copula-based framework assesses flood events in terms of duration, volume, and flood peak in the Kan River basin, Iran. These analyses are carried out using the hierarchical Archimedean copula in three structures, and their accuracy in estimating the flood frequencies is ultimately compared. The results show that a heterogeneous asymmetric copula offers more flexibility to capture varying degrees of asymmetry across different parts of the distribution, leading to more accurate modeling results compared to homogeneous asymmetric and symmetric copulas. Also it has been found that climate change can influence the trivariate joint return periods, particularly in the far future. In other words, flood frequency may increase by approximately 50% in some cases in the far future compared to the mid-term future and historical period. This demonstrates that flood characteristics are expected to show nonstationary behavior in the future as a result of climate change. The results provide insightful information for managing and accessing flood risk in a dynamic environment. |
| format | Article |
| id | doaj-art-d040408c80754660969d3208de2d7b10 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-d040408c80754660969d3208de2d7b102025-01-05T12:19:41ZengNature PortfolioScientific Reports2045-23222025-01-0115111610.1038/s41598-024-84543-5A copula-based multivariate flood frequency analysis under climate change effectsMarzieh Khajehali0Hamid R. Safavi1Mohammad Reza Nikoo2Mohammad Reza Najafi3Reza Alizadeh-Sh4Department of Civil Engineering, Isfahan University of TechnologyDepartment of Civil Engineering, Isfahan University of TechnologyDepartment of Civil and Architectural Engineering, Sultan Qaboos UniversityDepartment of Civil and Environmental Engineering, Western UniversityDepartment of Civil Engineering, Isfahan University of TechnologyAbstract Floods are among the most severe natural hazards, causing substantial damage and affecting millions of lives. These events are inherently multi-dimensional, requiring analysis across multiple factors. Traditional research often uses a bivariate framework relying on historical data, but climate change is expected to influence flood frequency analysis and flood system design in the future. This study assesses the projected changes in flood characteristics based on eight downscaled and bias-corrected General Circulation Models (GCMs) that participated in the Coupled Model Intercomparison Project Phase 6. The analysis considers two emission scenarios, including SSP2-4.5 and SSP5-8.5 for far-future (2070–2100), mid-term future (2040–2070), and historical (1982–2012) periods. Downscaled GCM outputs are utilized as predictors of the machine learning model to simulate daily streamflow. Then, a trivariate copula-based framework assesses flood events in terms of duration, volume, and flood peak in the Kan River basin, Iran. These analyses are carried out using the hierarchical Archimedean copula in three structures, and their accuracy in estimating the flood frequencies is ultimately compared. The results show that a heterogeneous asymmetric copula offers more flexibility to capture varying degrees of asymmetry across different parts of the distribution, leading to more accurate modeling results compared to homogeneous asymmetric and symmetric copulas. Also it has been found that climate change can influence the trivariate joint return periods, particularly in the far future. In other words, flood frequency may increase by approximately 50% in some cases in the far future compared to the mid-term future and historical period. This demonstrates that flood characteristics are expected to show nonstationary behavior in the future as a result of climate change. The results provide insightful information for managing and accessing flood risk in a dynamic environment.https://doi.org/10.1038/s41598-024-84543-5Flood frequency analysisHierarchical archimedean copulaFlood forecastingClimate change scenariosJoint return period |
| spellingShingle | Marzieh Khajehali Hamid R. Safavi Mohammad Reza Nikoo Mohammad Reza Najafi Reza Alizadeh-Sh A copula-based multivariate flood frequency analysis under climate change effects Scientific Reports Flood frequency analysis Hierarchical archimedean copula Flood forecasting Climate change scenarios Joint return period |
| title | A copula-based multivariate flood frequency analysis under climate change effects |
| title_full | A copula-based multivariate flood frequency analysis under climate change effects |
| title_fullStr | A copula-based multivariate flood frequency analysis under climate change effects |
| title_full_unstemmed | A copula-based multivariate flood frequency analysis under climate change effects |
| title_short | A copula-based multivariate flood frequency analysis under climate change effects |
| title_sort | copula based multivariate flood frequency analysis under climate change effects |
| topic | Flood frequency analysis Hierarchical archimedean copula Flood forecasting Climate change scenarios Joint return period |
| url | https://doi.org/10.1038/s41598-024-84543-5 |
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