Version 1
: Received: 22 April 2024 / Approved: 23 April 2024 / Online: 25 April 2024 (15:18:35 CEST)
How to cite:
SY, B.; BAH, F.B.; Dao, H. Flood Extent Delineation and Exposure Assessment in Senegal Using Google Earth Engine: The 2022 Event. Preprints2024, 2024041552. https://doi.org/10.20944/preprints202404.1552.v1
SY, B.; BAH, F.B.; Dao, H. Flood Extent Delineation and Exposure Assessment in Senegal Using Google Earth Engine: The 2022 Event. Preprints 2024, 2024041552. https://doi.org/10.20944/preprints202404.1552.v1
SY, B.; BAH, F.B.; Dao, H. Flood Extent Delineation and Exposure Assessment in Senegal Using Google Earth Engine: The 2022 Event. Preprints2024, 2024041552. https://doi.org/10.20944/preprints202404.1552.v1
APA Style
SY, B., BAH, F.B., & Dao, H. (2024). Flood Extent Delineation and Exposure Assessment in Senegal Using Google Earth Engine: The 2022 Event. Preprints. https://doi.org/10.20944/preprints202404.1552.v1
Chicago/Turabian Style
SY, B., Fatoumata Bintou BAH and Hy Dao. 2024 "Flood Extent Delineation and Exposure Assessment in Senegal Using Google Earth Engine: The 2022 Event" Preprints. https://doi.org/10.20944/preprints202404.1552.v1
Abstract
This study addresses the pressing need for flood extent and exposure information in data-scarce and vulnerable regions, with a specific focus on West Africa, particularly Senegal. Leveraging the Google Earth Engine (GEE) platform and integrating data from Sentinel-1 SAR, Global Surface Water, HydroSHEDS, Global Human Layer Settlement, and MODIS Land Cover, our primary ob-jective is to delineate flood extents and compare them with centennial flood-prone areas, offering a comprehensive assessment of exposure during the period from July to October 2022 across Sen-egal's 14 regions.
The findings underscore a total inundation area of 2 951 square kilometers, impacting 297 142 people, 175 square kilometers of urban and 16 square kilometers of crops. Notably, August wit-nessed the largest flooded areas, reaching 780 square kilometers, constituting 0.40% of the coun-try's surface. Subsequent regions, including Saint-Louis, Ziguinchor, Fatick, and Matam, experi-enced varying extents of flooding, with August data showing a 1.34% overall overlap compared to centennial flood-prone areas derived from hydrological and hydraulic modeling. This low per-centage reveals the distinct purposes and natures of the two approaches (remote sensing and modeling), as well as their complementarity.
Turning to flood exposure, August emerges as the most critical month, affecting 76 595 people (0.43% of the total population). Dakar, Diourbel, Thiès, and Saint-Louis regions bore substantial impacts, affecting 100 707, 57 648, 31 579, and 26 581 people, respectively. These findings emphasize the imperative for comprehensive disaster preparedness and mitigation efforts. The study provides a crucial national-scale perspective to guide Senegal's authorities in formulating effective flood management, intervention, and adaptation strategies.
Keywords
flood extent mapping; flood exposition assessment; remote sensing; Google Earth Engine; Sentinel-1; hydrological and hydraulic modelling
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
