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  A satellite imagery-driven framework for rapid resource allocation in flood scenarios to enhance loss and damage fund effectiveness

Eudaric, J., Kreibich, H., Camero, A., Rafiezadeh Shahi, K., Martinis, S., Zhu, X. X. (2024): A satellite imagery-driven framework for rapid resource allocation in flood scenarios to enhance loss and damage fund effectiveness. - Scientific Reports, 14, 19290.
https://doi.org/10.1038/s41598-024-69977-1

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This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Eudaric, Jeremy1, Autor
Kreibich, H.2, Autor              
Camero, Andrés1, Autor
Rafiezadeh Shahi, Kasra2, Autor              
Martinis, Sandro1, Autor
Zhu, Xiao Xiang1, Autor
Affiliations:
1External Organizations, ou_persistent22              
24.4 Hydrology, 4.0 Geosystems, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, ou_146048              

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 Zusammenfassung: The impact of climate change and urbanization has increased the risk of flooding. During the UN Climate Change Conference 28 (COP 28), an agreement was reached to establish “The Loss and Damage Fund” to assist low-income countries impacted by climate change. However, allocating the resources required for post-flood reconstruction and reimbursement is challenging due to the limited availability of data and the absence of a comprehensive tool. Here, we propose a novel resource allocation framework based on remote sensing and geospatial data near the flood peak, such as buildings and population. The quantification of resource distribution utilizes an exposure index for each municipality, which interacts with various drivers, including flood hazard drivers, buildings exposure, and population exposure. The proposed framework asses the flood extension using pre- and post-flood Sentinel-1 Synthetic Aperture Radar (SAR) data. To demonstrate the effectiveness of this framework, an analysis was conducted on the flood that occurred in the Thessaly region of Greece in September 2023. The study revealed that the municipality of Palamas has the highest need for resource allocation, with an exposure index rating of 5/8. Any government can use this framework for rapid decision-making and to expedite post-flood recovery.

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Sprache(n): eng - Englisch
 Datum: 2024-08-202024
 Publikationsstatus: Final veröffentlicht
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 Identifikatoren: DOI: 10.1038/s41598-024-69977-1
GFZPOF: p4 T5 Future Landscapes
OATYPE: Gold Open Access
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Titel: Scientific Reports
Genre der Quelle: Zeitschrift, SCI, Scopus, OA
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Seiten: - Band / Heft: 14 Artikelnummer: 19290 Start- / Endseite: - Identifikator: CoNE: https://gfzpublic.gfz-potsdam.de/cone/journals/resource/journals2_395
Publisher: Springer Nature