Assessing Flood Adaptation Measures in Post-Cyclone Recovery and Reconstruction: The 2023 Cyclone Freddy Case in Kachulu, Malawi.
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| Title: | Assessing Flood Adaptation Measures in Post-Cyclone Recovery and Reconstruction: The 2023 Cyclone Freddy Case in Kachulu, Malawi. |
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| Authors: | Taghimolla, Ali1 (AUTHOR), Asgary, Ali1,2 (AUTHOR) asgary@yorku.ca, Aarabi, Mahbod1 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1593. 18p. |
| Subjects: | Flood damage prevention, Building foundations, Three-dimensional modeling, Cyclones, Disaster resilience, Floods, Flood control |
| Abstract: | Highlights: What are the main findings? Ground elevation of properties can mitigate the damage and inundation of properties in Kachulu settlement after Cyclone Freddy in future cyclones caused flooding. Scenario analysis suggests that building elevation up to 3 m could significantly reduce direct flood exposure under modeled conditions. What are the implications of the main findings? Property-level adaptation measures can reduce flooding risk significantly. The findings can inform discussions on targeted regional risk-mitigation strategies. Targeted regional risk-mitigation strategies, such as Property-Level Flood Risk Adaptation in high-risk areas, should be considered during post-disaster recovery and reconstruction. In 2023, Tropical Cyclone Freddy caused severe damage in southern Malawi, flooding much of the lowland area near Lake Chilwa and displacing many residents. This study evaluates long-term, region-specific mitigation strategies to lessen future risks, using a novel approach that combines drone and satellite data, building footprints, and 3D simulations to analyze how building elevation affects flood damage and assess Property-Level Flood Risk Adaptation measures. Results show a significant difference in ground elevation between affected and unaffected buildings, with damaged structures generally at lower levels. The 3D simulation confirmed a water-level rise of approximately 3.0 m caused by Freddy. Scenario analysis indicates that elevating buildings by 2.0, 2.5, and 3.0 m could reduce direct flood exposure and 64%, 76%, and 91% of damage, respectively. These insights can inform the development of targeted regional risk-mitigation strategies through Property-Level Flood Risk Adaptation in high-risk areas. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI 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: 194141118 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Assessing Flood Adaptation Measures in Post-Cyclone Recovery and Reconstruction: The 2023 Cyclone Freddy Case in Kachulu, Malawi. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Taghimolla%2C+Ali%22">Taghimolla, Ali</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Asgary%2C+Ali%22">Asgary, Ali</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> asgary@yorku.ca</i><br /><searchLink fieldCode="AR" term="%22Aarabi%2C+Mahbod%22">Aarabi, Mahbod</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 10, p1593. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Flood+damage+prevention%22">Flood damage prevention</searchLink><br /><searchLink fieldCode="DE" term="%22Building+foundations%22">Building foundations</searchLink><br /><searchLink fieldCode="DE" term="%22Three-dimensional+modeling%22">Three-dimensional modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Cyclones%22">Cyclones</searchLink><br /><searchLink fieldCode="DE" term="%22Disaster+resilience%22">Disaster resilience</searchLink><br /><searchLink fieldCode="DE" term="%22Floods%22">Floods</searchLink><br /><searchLink fieldCode="DE" term="%22Flood+control%22">Flood control</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? Ground elevation of properties can mitigate the damage and inundation of properties in Kachulu settlement after Cyclone Freddy in future cyclones caused flooding. Scenario analysis suggests that building elevation up to 3 m could significantly reduce direct flood exposure under modeled conditions. What are the implications of the main findings? Property-level adaptation measures can reduce flooding risk significantly. The findings can inform discussions on targeted regional risk-mitigation strategies. Targeted regional risk-mitigation strategies, such as Property-Level Flood Risk Adaptation in high-risk areas, should be considered during post-disaster recovery and reconstruction. In 2023, Tropical Cyclone Freddy caused severe damage in southern Malawi, flooding much of the lowland area near Lake Chilwa and displacing many residents. This study evaluates long-term, region-specific mitigation strategies to lessen future risks, using a novel approach that combines drone and satellite data, building footprints, and 3D simulations to analyze how building elevation affects flood damage and assess Property-Level Flood Risk Adaptation measures. Results show a significant difference in ground elevation between affected and unaffected buildings, with damaged structures generally at lower levels. The 3D simulation confirmed a water-level rise of approximately 3.0 m caused by Freddy. Scenario analysis indicates that elevating buildings by 2.0, 2.5, and 3.0 m could reduce direct flood exposure and 64%, 76%, and 91% of damage, respectively. These insights can inform the development of targeted regional risk-mitigation strategies through Property-Level Flood Risk Adaptation in high-risk areas. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18101593 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 1593 Subjects: – SubjectFull: Flood damage prevention Type: general – SubjectFull: Building foundations Type: general – SubjectFull: Three-dimensional modeling Type: general – SubjectFull: Cyclones Type: general – SubjectFull: Disaster resilience Type: general – SubjectFull: Floods Type: general – SubjectFull: Flood control Type: general Titles: – TitleFull: Assessing Flood Adaptation Measures in Post-Cyclone Recovery and Reconstruction: The 2023 Cyclone Freddy Case in Kachulu, Malawi. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Taghimolla, Ali – PersonEntity: Name: NameFull: Asgary, Ali – PersonEntity: Name: NameFull: Aarabi, Mahbod IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 10 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |