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Spatio-temporal variations of watershed evolution, a process under rainfall erosion, soil erosion, and kinetic energy variation, are diverse, complex, and dynamic phenomena. In this study, a clear evolution process of the loess watershed under an artificial rainfall experiment was monitored with close-range photogrammetry. Via complex network theory, we constructed the watershed weighted complex network (WWCN) to simulate watershed spatial structures in nine evolution stages. On this basis, we fully analyzed and discussed the watershed structural variation during the watershed evolution process from the aspect of node, edges, polygon, and overall spatial structure. The quantitative indexes showed a regular variation following the loess watershed evolution, with a drastic trend in the early and active evolution stages, whilst a slow trend in the stable evolution stages. From the perspective of watershed spatial structure, fresh insights into the watershed structure (such as the closeness, community effect, connectivity, stability, and stability of the watershed network) are introduced to comprehensively explain the watershed evolution process. Besides, we found watershed structural indexes are closely related to the typical terrain indexes, which may suggest that watershed structures have responses to characteristics of watershed morphologies during the watershed evolution process. This study is beneficial to deepen further understanding of the formation genesis and variation laws of the watershed evolution process as well as present a potential method for landform quantitative analysis. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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Earth Science Informatics
ISSN: 1865-0473
Year: 2023
Issue: 2
Volume: 16
Page: 1779-1796
2 . 7
JCR@2023
2 . 7 0 0
JCR@2023
ESI HC Threshold:26
JCR Journal Grade:2
CAS Journal Grade:4
Cited Count:
WoS CC Cited Count: 0
SCOPUS Cited Count: 1
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 2
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