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

被引:2
作者
Deguenon, Sena Donalde Dolores Marguerite [1 ,2 ]
Hounmenou, Castro Gbememali [3 ]
Adade, Richard [4 ]
Teka, Oscar [1 ]
Toko, Ismaila Imorou [5 ]
Aheto, Denis Worlanyo [4 ]
Sinsin, Brice [1 ]
机构
[1] Univ Abomey Calavi, Lab Appl Ecol, Fac Agron Sci, BP 526, Calavi, Benin
[2] Univ Cape Coast, Ctr Coastal Management, PMB TF0494, Cape Coast, Ghana
[3] Univ Abomey Calavi, Fac Agron Sci, Lab Biomath & Estimat Forestieres LABEF, BP 1525, Calavi, Benin
[4] Univ Cape Coast, Africa Ctr Excellence Coastal Resilience, Dept Fisheries & Aquat Sci, Sch Biol Sci,Ctr Coastal Management, PMB TF0494, Cape Coast, Ghana
[5] Univ Abomey Calavi, Lab Cartographie LaCarto, Inst Geog Amenagement Terr & Environm IGATE, BP 698, Calavi, Benin
关键词
sea-level rise; machine learning; SLAMM model; coastal ecosystems; Benin; CLIMATE-CHANGE;
D O I
10.3390/su152216001
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
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页数:17
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