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Watershed Planning within a Quantitative Scenario Analysis Framework
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An innovative method integrating run theory and DBSCAN for complete three-dimensional drought structures.

Jing Zhang1, Min Zhang1, Yang Yu1

  • 1Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China.

The Science of the Total Environment
|March 23, 2024
PubMed
Summary

This study introduces STD-CLUSTER, a novel method for identifying complete 3D drought clusters, overcoming limitations of existing approaches by including small, isolated patches. The method effectively captures the dynamic evolution of drought events across China.

Keywords:
Drought identificationDynamic evolutionMigration trajectoriesSpatiotemporal characteristicsThree-dimensional clustering

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Area of Science:

  • Hydrology
  • Climate Science
  • Environmental Science

Background:

  • Droughts are complex phenomena with dynamic spatiotemporal characteristics, often lacking fixed boundaries.
  • Current drought clustering methods may miss small or isolated drought patches, limiting the understanding of their full structure.
  • Identifying comprehensive three-dimensional drought structures is crucial for accurate impact assessment.

Purpose of the Study:

  • To present an effective method, STD-CLUSTER, for identifying drought clusters with complete three-dimensional structures.
  • To overcome the limitations of existing methods in capturing small, isolated, or disconnected drought patches.
  • To analyze the spatiotemporal evolution of seasonal drought events in China.

Main Methods:

  • Employed run theory to extract drought events as temporal "lines".
  • Utilized the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for clustering drought events.
  • Applied the method to a 2006 flash drought case study in the Yangtze River Basin and seasonal drought data in China (1991-2022).

Main Results:

  • STD-CLUSTER successfully identified and clustered drought events, ensuring the integrity of three-dimensional drought clusters, including small and isolated patches.
  • Analysis of seasonal drought events in China (1991-2022) revealed 35 distinct drought clusters.
  • Drought clusters exhibited dynamic evolution, including expansion, contraction, merging, and splitting, with seasonal variations significantly impacting affected area and severity.

Conclusions:

  • STD-CLUSTER provides an effective approach for comprehensive drought cluster identification and analysis.
  • Seasonal factors play a significant role in the evolution and severity of drought clusters.
  • The method's applicability across diverse regions and timescales supports robust investigation of drought spatiotemporal dynamics.