Alex Smith , Jacob Lehner , Charlotte Wills , Grace Johnson , Chris Houser
{"title":"沙丘脚趾提取方法的比较分析及其对前丘动力学评价的意义","authors":"Alex Smith , Jacob Lehner , Charlotte Wills , Grace Johnson , Chris Houser","doi":"10.1016/j.scitotenv.2025.179277","DOIUrl":null,"url":null,"abstract":"<div><div>An acceleration in Sea Level Rise (SLR) and change in storm activity is likely to increase erosion and flood hazards over the coming decades, representing a significant economic burden and threat to coastal environments globally. Foredune systems may mitigate hazards by providing a natural flood protection to back barrier resources, but only if they can maintain their volume and elevation in pace with SLR and changing storm climates. The beach–dune interface, or dune toe (dt), is an important process boundary that has been used to classify and assess the resiliency of coastal landscapes, however, concerns with inconsistent methodologies have been raised in several recent works. This study provides the first comparative analysis of dt extraction methodologies, discusses the potential impacts on coastal research, and presents the Minimum Averaged Relative Relief (MARR) machine learning model aimed at improving the consistency of dt classifications. Results indicate that interannual horizontal (±29 m) and vertical positions (±1.5 m) of dt predictions are significantly different and the consistency in classifications range widely between approaches and study sites. Overall, MARR was the most consistent displaying its potential to provide an improved methodology for comparisons between sites. Over a period of decades, a disparity in the rates of change in the horizontal (±1 m/y) and vertical (±14 mm/y) dt position were also significantly different among methodologies, which can lead to a divergence in vulnerability of predicted dt elevations (−1 to +1.5 m) relative to a 1–100-year extreme sea level event by 2100. These findings suggest that studies focused on the dt may not be directly comparable, and there is a further need to improve the repeatability and transferability of all coastal landscape classifications and metrics to better inform coastal management and address uncertainties of foredune response to changing sea levels and storm climates.</div></div>","PeriodicalId":422,"journal":{"name":"Science of the Total Environment","volume":"979 ","pages":"Article 179277"},"PeriodicalIF":8.2000,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A comparative analysis of dune toe extraction methodologies and implications for assessment of foredune dynamics\",\"authors\":\"Alex Smith , Jacob Lehner , Charlotte Wills , Grace Johnson , Chris Houser\",\"doi\":\"10.1016/j.scitotenv.2025.179277\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>An acceleration in Sea Level Rise (SLR) and change in storm activity is likely to increase erosion and flood hazards over the coming decades, representing a significant economic burden and threat to coastal environments globally. Foredune systems may mitigate hazards by providing a natural flood protection to back barrier resources, but only if they can maintain their volume and elevation in pace with SLR and changing storm climates. The beach–dune interface, or dune toe (dt), is an important process boundary that has been used to classify and assess the resiliency of coastal landscapes, however, concerns with inconsistent methodologies have been raised in several recent works. This study provides the first comparative analysis of dt extraction methodologies, discusses the potential impacts on coastal research, and presents the Minimum Averaged Relative Relief (MARR) machine learning model aimed at improving the consistency of dt classifications. Results indicate that interannual horizontal (±29 m) and vertical positions (±1.5 m) of dt predictions are significantly different and the consistency in classifications range widely between approaches and study sites. Overall, MARR was the most consistent displaying its potential to provide an improved methodology for comparisons between sites. Over a period of decades, a disparity in the rates of change in the horizontal (±1 m/y) and vertical (±14 mm/y) dt position were also significantly different among methodologies, which can lead to a divergence in vulnerability of predicted dt elevations (−1 to +1.5 m) relative to a 1–100-year extreme sea level event by 2100. These findings suggest that studies focused on the dt may not be directly comparable, and there is a further need to improve the repeatability and transferability of all coastal landscape classifications and metrics to better inform coastal management and address uncertainties of foredune response to changing sea levels and storm climates.</div></div>\",\"PeriodicalId\":422,\"journal\":{\"name\":\"Science of the Total Environment\",\"volume\":\"979 \",\"pages\":\"Article 179277\"},\"PeriodicalIF\":8.2000,\"publicationDate\":\"2025-04-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Science of the Total Environment\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0048969725009131\",\"RegionNum\":1,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science of the Total Environment","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0048969725009131","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
A comparative analysis of dune toe extraction methodologies and implications for assessment of foredune dynamics
An acceleration in Sea Level Rise (SLR) and change in storm activity is likely to increase erosion and flood hazards over the coming decades, representing a significant economic burden and threat to coastal environments globally. Foredune systems may mitigate hazards by providing a natural flood protection to back barrier resources, but only if they can maintain their volume and elevation in pace with SLR and changing storm climates. The beach–dune interface, or dune toe (dt), is an important process boundary that has been used to classify and assess the resiliency of coastal landscapes, however, concerns with inconsistent methodologies have been raised in several recent works. This study provides the first comparative analysis of dt extraction methodologies, discusses the potential impacts on coastal research, and presents the Minimum Averaged Relative Relief (MARR) machine learning model aimed at improving the consistency of dt classifications. Results indicate that interannual horizontal (±29 m) and vertical positions (±1.5 m) of dt predictions are significantly different and the consistency in classifications range widely between approaches and study sites. Overall, MARR was the most consistent displaying its potential to provide an improved methodology for comparisons between sites. Over a period of decades, a disparity in the rates of change in the horizontal (±1 m/y) and vertical (±14 mm/y) dt position were also significantly different among methodologies, which can lead to a divergence in vulnerability of predicted dt elevations (−1 to +1.5 m) relative to a 1–100-year extreme sea level event by 2100. These findings suggest that studies focused on the dt may not be directly comparable, and there is a further need to improve the repeatability and transferability of all coastal landscape classifications and metrics to better inform coastal management and address uncertainties of foredune response to changing sea levels and storm climates.
期刊介绍:
The Science of the Total Environment is an international journal dedicated to scientific research on the environment and its interaction with humanity. It covers a wide range of disciplines and seeks to publish innovative, hypothesis-driven, and impactful research that explores the entire environment, including the atmosphere, lithosphere, hydrosphere, biosphere, and anthroposphere.
The journal's updated Aims & Scope emphasizes the importance of interdisciplinary environmental research with broad impact. Priority is given to studies that advance fundamental understanding and explore the interconnectedness of multiple environmental spheres. Field studies are preferred, while laboratory experiments must demonstrate significant methodological advancements or mechanistic insights with direct relevance to the environment.