{"title":"整合运行理论和 DBSCAN 的创新方法,用于完整的三维干旱结构。","authors":"Jing Zhang, Min Zhang, Yang Yu, Ruide Yu","doi":"10.1016/j.scitotenv.2024.171901","DOIUrl":null,"url":null,"abstract":"<p><p>Drought displays dynamic and uncertain spatiotemporal characteristics, thus it is typically not confined to fixed temporal-spatial boundaries. Existing drought clustering methods often involve spatially clustering drought points or grids into patches, subsequently connected over time to form three-dimensional structures. Despite this process being able to extract three-dimensional drought clusters, it is likely to overlook mild or relatively small, isolated drought patches. To overcome this limitation, this paper presented an effective method (named STD-CLUSTER) for identifying drought clusters with complete three-dimensional structures. The method initially employed run theory to extract drought events as \"lines\" and subsequently clustered these events using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. A case study on the 2006 flash drought in the Yangtze River Basin demonstrated that STD-CLUSTER successfully clustered drought events and ensured the integrity of drought clusters by considering small, isolated, or disconnected patches. Additionally, an in-depth analysis using STD-CLUSTER examined seasonal drought events in China from 1991 to 2022, identifying a total of 35 drought clusters. These clusters began and ended with small-area patches, exhibiting features of expansion, contraction, spread, merging, and splitting over time. Furthermore, seasonal changes significantly influenced the evolution of drought clusters, with affected area and severity increasing in spring and summer and decreasing in autumn and winter. The applicability of the proposed method extends beyond various geographical regions and time scales, providing effective support for comprehensively investigating the spatiotemporal evolution of drought.</p>","PeriodicalId":422,"journal":{"name":"Science of the Total Environment","volume":null,"pages":null},"PeriodicalIF":8.2000,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An innovative method integrating run theory and DBSCAN for complete three-dimensional drought structures.\",\"authors\":\"Jing Zhang, Min Zhang, Yang Yu, Ruide Yu\",\"doi\":\"10.1016/j.scitotenv.2024.171901\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Drought displays dynamic and uncertain spatiotemporal characteristics, thus it is typically not confined to fixed temporal-spatial boundaries. Existing drought clustering methods often involve spatially clustering drought points or grids into patches, subsequently connected over time to form three-dimensional structures. Despite this process being able to extract three-dimensional drought clusters, it is likely to overlook mild or relatively small, isolated drought patches. To overcome this limitation, this paper presented an effective method (named STD-CLUSTER) for identifying drought clusters with complete three-dimensional structures. The method initially employed run theory to extract drought events as \\\"lines\\\" and subsequently clustered these events using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. A case study on the 2006 flash drought in the Yangtze River Basin demonstrated that STD-CLUSTER successfully clustered drought events and ensured the integrity of drought clusters by considering small, isolated, or disconnected patches. Additionally, an in-depth analysis using STD-CLUSTER examined seasonal drought events in China from 1991 to 2022, identifying a total of 35 drought clusters. These clusters began and ended with small-area patches, exhibiting features of expansion, contraction, spread, merging, and splitting over time. Furthermore, seasonal changes significantly influenced the evolution of drought clusters, with affected area and severity increasing in spring and summer and decreasing in autumn and winter. The applicability of the proposed method extends beyond various geographical regions and time scales, providing effective support for comprehensively investigating the spatiotemporal evolution of drought.</p>\",\"PeriodicalId\":422,\"journal\":{\"name\":\"Science of the Total Environment\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.2000,\"publicationDate\":\"2024-05-20\",\"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://doi.org/10.1016/j.scitotenv.2024.171901\",\"RegionNum\":1,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/3/22 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science of the Total Environment","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1016/j.scitotenv.2024.171901","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/3/22 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
An innovative method integrating run theory and DBSCAN for complete three-dimensional drought structures.
Drought displays dynamic and uncertain spatiotemporal characteristics, thus it is typically not confined to fixed temporal-spatial boundaries. Existing drought clustering methods often involve spatially clustering drought points or grids into patches, subsequently connected over time to form three-dimensional structures. Despite this process being able to extract three-dimensional drought clusters, it is likely to overlook mild or relatively small, isolated drought patches. To overcome this limitation, this paper presented an effective method (named STD-CLUSTER) for identifying drought clusters with complete three-dimensional structures. The method initially employed run theory to extract drought events as "lines" and subsequently clustered these events using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. A case study on the 2006 flash drought in the Yangtze River Basin demonstrated that STD-CLUSTER successfully clustered drought events and ensured the integrity of drought clusters by considering small, isolated, or disconnected patches. Additionally, an in-depth analysis using STD-CLUSTER examined seasonal drought events in China from 1991 to 2022, identifying a total of 35 drought clusters. These clusters began and ended with small-area patches, exhibiting features of expansion, contraction, spread, merging, and splitting over time. Furthermore, seasonal changes significantly influenced the evolution of drought clusters, with affected area and severity increasing in spring and summer and decreasing in autumn and winter. The applicability of the proposed method extends beyond various geographical regions and time scales, providing effective support for comprehensively investigating the spatiotemporal evolution of drought.
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.