Utilizing non-stationary extreme value model to quantify extreme rainfall in two major cities in Bangladesh.
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| Title: | Utilizing non-stationary extreme value model to quantify extreme rainfall in two major cities in Bangladesh. |
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| Authors: | Dey, Asim K.1 (AUTHOR) a.dey@ttu.edu, Patwary, Mohammad Shaha A.2 (AUTHOR) mpatwary@butler.edu |
| Source: | Stochastic Environmental Research & Risk Assessment. Jun2025, Vol. 39 Issue 6, p2281-2296. 16p. |
| Subjects: | Atmospheric sciences, Rainfall probabilities, Earth sciences, Emergency management, Extreme value theory, Flood warning systems |
| Abstract: | Bangladesh is highly susceptible to the impacts of climate change, particularly extreme rainfall during the monsoon season, leading to severe floods and landslides. This study introduces a nonstationary generalized extreme value (GEV) modeling framework, which integrates atmospheric dry bulb temperatures as a covariate to capture the seasonal and dynamic characteristics of extreme rainfall events. Using daily rainfall and temperature data from Dhaka (1990–2015) and Chattogram (1999–2015), the study identifies optimal models based on AIC, BIC, and goodness-of-fit criteria. Uncertainties in the predictions are quantified using the delta method and parametric bootstrap approaches. The results indicate a higher likelihood of extreme rainfall events in Chattogram compared to Dhaka, as reflected in the predictions and probabilities in return levels. Diagnostic evaluations confirm that the models effectively capture the variability in monthly maximum rainfall during the monsoon. These findings offer valuable information for flood risk management, urban planning, and disaster preparedness. By incorporating temperature effects and quantifying prediction uncertainties, the study addresses key limitations in existing methodologies. Future work will expand this framework to assess spatiotemporal rainfall variability in Bangladesh and explore advanced machine learning approaches to simultaneously model the bulk and tail of rainfall distributions. [ABSTRACT FROM AUTHOR] |
| Copyright of Stochastic Environmental Research & Risk Assessment is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 186136343 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Utilizing non-stationary extreme value model to quantify extreme rainfall in two major cities in Bangladesh. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Dey%2C+Asim+K%2E%22">Dey, Asim K.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> a.dey@ttu.edu</i><br /><searchLink fieldCode="AR" term="%22Patwary%2C+Mohammad+Shaha+A%2E%22">Patwary, Mohammad Shaha A.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> mpatwary@butler.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Stochastic+Environmental+Research+%26+Risk+Assessment%22">Stochastic Environmental Research & Risk Assessment</searchLink>. Jun2025, Vol. 39 Issue 6, p2281-2296. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Atmospheric+sciences%22">Atmospheric sciences</searchLink><br /><searchLink fieldCode="DE" term="%22Rainfall+probabilities%22">Rainfall probabilities</searchLink><br /><searchLink fieldCode="DE" term="%22Earth+sciences%22">Earth sciences</searchLink><br /><searchLink fieldCode="DE" term="%22Emergency+management%22">Emergency management</searchLink><br /><searchLink fieldCode="DE" term="%22Extreme+value+theory%22">Extreme value theory</searchLink><br /><searchLink fieldCode="DE" term="%22Flood+warning+systems%22">Flood warning systems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Bangladesh is highly susceptible to the impacts of climate change, particularly extreme rainfall during the monsoon season, leading to severe floods and landslides. This study introduces a nonstationary generalized extreme value (GEV) modeling framework, which integrates atmospheric dry bulb temperatures as a covariate to capture the seasonal and dynamic characteristics of extreme rainfall events. Using daily rainfall and temperature data from Dhaka (1990–2015) and Chattogram (1999–2015), the study identifies optimal models based on AIC, BIC, and goodness-of-fit criteria. Uncertainties in the predictions are quantified using the delta method and parametric bootstrap approaches. The results indicate a higher likelihood of extreme rainfall events in Chattogram compared to Dhaka, as reflected in the predictions and probabilities in return levels. Diagnostic evaluations confirm that the models effectively capture the variability in monthly maximum rainfall during the monsoon. These findings offer valuable information for flood risk management, urban planning, and disaster preparedness. By incorporating temperature effects and quantifying prediction uncertainties, the study addresses key limitations in existing methodologies. Future work will expand this framework to assess spatiotemporal rainfall variability in Bangladesh and explore advanced machine learning approaches to simultaneously model the bulk and tail of rainfall distributions. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Stochastic Environmental Research & Risk Assessment is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=186136343 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00477-025-02969-3 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 2281 Subjects: – SubjectFull: Atmospheric sciences Type: general – SubjectFull: Rainfall probabilities Type: general – SubjectFull: Earth sciences Type: general – SubjectFull: Emergency management Type: general – SubjectFull: Extreme value theory Type: general – SubjectFull: Flood warning systems Type: general Titles: – TitleFull: Utilizing non-stationary extreme value model to quantify extreme rainfall in two major cities in Bangladesh. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Dey, Asim K. – PersonEntity: Name: NameFull: Patwary, Mohammad Shaha A. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 14363240 Numbering: – Type: volume Value: 39 – Type: issue Value: 6 Titles: – TitleFull: Stochastic Environmental Research & Risk Assessment Type: main |
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