Simulation of the Impacts of Sea-Level Rise on Coastal Ecosystems in Benin Using a Combined Approach of Machine Learning and the Sea Level Affecting Marshes Model
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Published:2023-11-16
Issue:22
Volume:15
Page:16001
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ISSN:2071-1050
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Container-title:Sustainability
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language:en
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Short-container-title:Sustainability
Author:
Deguenon Sèna Donalde Dolorès Marguerite12ORCID, Hounmenou Castro Gbêmêmali3ORCID, Adade Richard4ORCID, Teka Oscar1, Toko Ismaila Imorou5, Aheto Denis Worlanyo4, Sinsin Brice1
Affiliation:
1. Laboratory of Applied Ecology, Faculty of Agronomic Sciences, University of Abomey-Calavi, Calavi BP 526, Benin 2. Centre for Coastal Management, University of Cape Coast, Cape Coast PMB TF0494, Ghana 3. Laboratoire de Biomatématiques et d’Estimations Forestières (LABEF), Faculty of Agronomic Sciences, University of Abomey-Calavi, Calavi BP 1525, Benin 4. Africa Centre of Excellence in Coastal Resilience—Centre for Coastal Management, Department of Fisheries and Aquatic Sciences, School of Biological Sciences, University of Cape Coast, Cape Coast PMB TF0494, Ghana 5. Laboratoire de Cartographie (LaCarto), Institut de Géographie, de l’Aménagement du Territoire et de l’Environnement (IGATE), University of Abomey-Calavi, Calavi BP 698, Benin
Abstract
Sea-level rise in Benin coastal zones leads to risks of erosion and flooding, which have significant consequences on the socio-economic life of the local population. In this paper, erosion, flood risk, and greenhouse gas sequestration resulting from sea-level rise in the coastal zone of the Benin coast were assessed with the Sea Level Affecting Marshes Model (SLAMM) using ArcGIS Pro 3.1 tools. The input features used were the Digital Elevation Map (DEM), the National Wetland Inventory (NWI) categories, and the slope of each cell. National Wetland Inventory (NWI) categories were then created using Support Vector Machines (SVMs), a supervised machine learning technique. The research simulated the effects of a 1.468 m sea-level rise in the study area from 2021 to 2090, considering wetland types, marsh accretion, wave erosion, and surface elevation changes. The largest land cover increases were observed in Estuarine Open Water and Open Ocean, expanding by approximately 106.2 hectares across different sea-level rise scenarios (RCP 8.5_Upper Limit). These gains were counterbalanced by losses of approximately 106.2 hectares in Inland Open Water, Ocean Beaches, Mangroves, Regularly Flooded Marsh, Swamp, Undeveloped, and Developed Dryland. Notably, Estuarine Open Water (97.7 hectares) and Open Ocean (8.5 hectares) experienced the most significant expansion, indicating submergence and saltwater intrusion by 2090 due to sea-level rise. The largest reductions occurred in less tidally influenced categories like Inland Open Water (−81.4 hectares), Ocean Beach (−7.9 hectares), Swamp (−5.1 hectares), Regularly Flooded Marsh (−4.6 hectares), and Undeveloped Dryland (−2.9 hectares). As the sea-level rises by 1.468 m, these categories are expected to be notably diminished, with Estuarine Open Water and Open Ocean becoming dominant. Erosion and flooding in the coastal zone are projected to have severe adverse impacts, including a gradual decline in greenhouse gas sequestration capacity. The outputs of this research will aid coastal management organizations in evaluating the consequences of sea-level rise and identifying areas with high mitigation requirements.
Funder
Organization for Women in Science for the Developing World (OWSD) and the Swedish International Development Cooperation Agency
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
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