Potential effects of projected global sea level rise on Sundarbans mangrove wetland ecosystem: insights from SLAMM and hybrid machine learning models.
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| Title: | Potential effects of projected global sea level rise on Sundarbans mangrove wetland ecosystem: insights from SLAMM and hybrid machine learning models. |
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| Authors: | Mondal, Ismail1 (AUTHOR) ismailmondal58@gmail.com, Ariful Hossain, SK2 (AUTHOR) hossainskariful@gmail.com, Bandyopadhyay, Jatisankar3 (AUTHOR) jatib@mail.vidyasagar.ac.in, Altuwaijri, Hamad Ahmed4 (AUTHOR) haaltuwaijri@ksu.edu.sa, Das, Anirjita1 (AUTHOR) anirjitadas@gmail.com, Jose, Felix5 (AUTHOR) fjose@fgcu.edu, Saha, Nilanjan6 (AUTHOR) nilanjan@smail.iitm.ac.in, Juliev, Mukhiddin7,8,9 (AUTHOR) mukhiddinjuliev@gmail.com |
| Source: | Environment, Development & Sustainability. Jun2025, Vol. 27 Issue 6, p14879-14912. 34p. |
| Subject Terms: | *Machine learning, *Geographic information systems, *Environmental sciences, *Coastal zone management, *Absolute sea level change |
| Geographic Terms: | Sundarbans (Bangladesh & India) |
| Abstract: | The Sundarbans coastal delta, spread across the international boundary between Bangladesh and India, is a globally recognized priority for conserving biodiversity. This region is particularly vulnerable to frequent flooding and the degradation of its fragile wetland environment, with an average elevation of just 2 m above sea level. The extent of this potential loss can be predicted with acceptable confidence since the average sea level is expected to increase 2 m globally by 2100, compared to the baseline period (2022). We investigated the possible impacts of three sea-level rise (SLR) scenarios on the Sundarbans using field and remote measurements, simulation modelling, and geographic information systems. Hindcast's modelling efforts using the Sea Level Affecting Marshes Model (SLAMM) and machine learning (ML) algorithms accurate predictions of reported area declines during the 1990–2022 period. The input characteristics applied were the National Wetland Inventory (NWI) classifications, the slope of each cell, and the Digital Elevation Map (DEM). Next, using ML approaches, NWI categories were developed. We examined the effects of varying sea levels at 0.49 m in 2022, 0.79 m in 2050, 1.52 m in 2075, and 2 m in 2100, considering different wetland types, marsh accretion, wave erosion, and changes in surface elevation. According to estimates, the mangrove wetland area will decrease by ~ 46 km2 between 2022 and 2050 under the 1.5-m and 1-m SLR scenarios. The decline in mangrove area by 2100 is estimated to be 81 km2, 111 km2, and 583 km2 under the 1-m, 1.5-m, and 2-m SLR scenarios, respectively. Our results suggest that in a 1-m inundation scenario, approximately 325.36 km2 of land may be submerged, whereas, for a 2-m inundation, this area increases substantially to 874.49 km2, more than 2.5 times the area impacted by the 1-m scenario. Both scenarios resulted in significant land loss in the Sundarbans. Severe adverse effects from erosion and floods are expected in the coastal zone, including decreased capacity to sequester carbon gases. This study will help coastal management organizations estimate the impacts of SLR and pinpoint places that need significant mitigating efforts. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 185942435 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Potential effects of projected global sea level rise on Sundarbans mangrove wetland ecosystem: insights from SLAMM and hybrid machine learning models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mondal%2C+Ismail%22">Mondal, Ismail</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ismailmondal58@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Ariful+Hossain%2C+SK%22">Ariful Hossain, SK</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> hossainskariful@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Bandyopadhyay%2C+Jatisankar%22">Bandyopadhyay, Jatisankar</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> jatib@mail.vidyasagar.ac.in</i><br /><searchLink fieldCode="AR" term="%22Altuwaijri%2C+Hamad+Ahmed%22">Altuwaijri, Hamad Ahmed</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> haaltuwaijri@ksu.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Das%2C+Anirjita%22">Das, Anirjita</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> anirjitadas@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Jose%2C+Felix%22">Jose, Felix</searchLink><relatesTo>5</relatesTo> (AUTHOR)<i> fjose@fgcu.edu</i><br /><searchLink fieldCode="AR" term="%22Saha%2C+Nilanjan%22">Saha, Nilanjan</searchLink><relatesTo>6</relatesTo> (AUTHOR)<i> nilanjan@smail.iitm.ac.in</i><br /><searchLink fieldCode="AR" term="%22Juliev%2C+Mukhiddin%22">Juliev, Mukhiddin</searchLink><relatesTo>7,8,9</relatesTo> (AUTHOR)<i> mukhiddinjuliev@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Environment%2C+Development+%26+Sustainability%22">Environment, Development & Sustainability</searchLink>. Jun2025, Vol. 27 Issue 6, p14879-14912. 34p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Geographic+information+systems%22">Geographic information systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Environmental+sciences%22">Environmental sciences</searchLink><br />*<searchLink fieldCode="DE" term="%22Coastal+zone+management%22">Coastal zone management</searchLink><br />*<searchLink fieldCode="DE" term="%22Absolute+sea+level+change%22">Absolute sea level change</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Sundarbans+%28Bangladesh+%26+India%29%22">Sundarbans (Bangladesh & India)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The Sundarbans coastal delta, spread across the international boundary between Bangladesh and India, is a globally recognized priority for conserving biodiversity. This region is particularly vulnerable to frequent flooding and the degradation of its fragile wetland environment, with an average elevation of just 2 m above sea level. The extent of this potential loss can be predicted with acceptable confidence since the average sea level is expected to increase 2 m globally by 2100, compared to the baseline period (2022). We investigated the possible impacts of three sea-level rise (SLR) scenarios on the Sundarbans using field and remote measurements, simulation modelling, and geographic information systems. Hindcast's modelling efforts using the Sea Level Affecting Marshes Model (SLAMM) and machine learning (ML) algorithms accurate predictions of reported area declines during the 1990–2022 period. The input characteristics applied were the National Wetland Inventory (NWI) classifications, the slope of each cell, and the Digital Elevation Map (DEM). Next, using ML approaches, NWI categories were developed. We examined the effects of varying sea levels at 0.49 m in 2022, 0.79 m in 2050, 1.52 m in 2075, and 2 m in 2100, considering different wetland types, marsh accretion, wave erosion, and changes in surface elevation. According to estimates, the mangrove wetland area will decrease by ~ 46 km2 between 2022 and 2050 under the 1.5-m and 1-m SLR scenarios. The decline in mangrove area by 2100 is estimated to be 81 km2, 111 km2, and 583 km2 under the 1-m, 1.5-m, and 2-m SLR scenarios, respectively. Our results suggest that in a 1-m inundation scenario, approximately 325.36 km2 of land may be submerged, whereas, for a 2-m inundation, this area increases substantially to 874.49 km2, more than 2.5 times the area impacted by the 1-m scenario. Both scenarios resulted in significant land loss in the Sundarbans. Severe adverse effects from erosion and floods are expected in the coastal zone, including decreased capacity to sequester carbon gases. This study will help coastal management organizations estimate the impacts of SLR and pinpoint places that need significant mitigating efforts. [ABSTRACT FROM AUTHOR] |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10668-025-06193-5 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 34 StartPage: 14879 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Geographic information systems Type: general – SubjectFull: Environmental sciences Type: general – SubjectFull: Coastal zone management Type: general – SubjectFull: Absolute sea level change Type: general – SubjectFull: Sundarbans (Bangladesh & India) Type: general Titles: – TitleFull: Potential effects of projected global sea level rise on Sundarbans mangrove wetland ecosystem: insights from SLAMM and hybrid machine learning models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mondal, Ismail – PersonEntity: Name: NameFull: Ariful Hossain, SK – PersonEntity: Name: NameFull: Bandyopadhyay, Jatisankar – PersonEntity: Name: NameFull: Altuwaijri, Hamad Ahmed – PersonEntity: Name: NameFull: Das, Anirjita – PersonEntity: Name: NameFull: Jose, Felix – PersonEntity: Name: NameFull: Saha, Nilanjan – PersonEntity: Name: NameFull: Juliev, Mukhiddin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 1387585X Numbering: – Type: volume Value: 27 – Type: issue Value: 6 Titles: – TitleFull: Environment, Development & Sustainability Type: main |
| ResultId | 1 |
