An innovative method integrating run theory and DBSCAN for complete three-dimensional drought structures

作者：Jing Zhang, Min Zhang, Yang Yu, Ruide Yu · 发表于：The Science of The Total Environment · 年份：2024 · DOI：10.1016/j.scitotenv.2024.171901 · 被引用次数：18 · 研究领域：Hydrology and Drought Analysis、Hydrology and Watershed Management Studies、Tree-ring climate responses

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 ar...