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Multiscale Analysis of Runoff Complexity in the Yanhe Watershed.
Liu, Xintong; Zhao, Hongrui.
Affiliation
  • Liu X; Institute of Transportation Engineering and Geomatics, Department of Civil Engineering, Tsinghua University, Beijing 100084, China.
  • Zhao H; 3S Center, Tsinghua University, Beijing 100084, China.
Entropy (Basel) ; 24(8)2022 Aug 07.
Article de En | MEDLINE | ID: mdl-36010752
ABSTRACT
Runoff complexity is an important indicator reflecting the sustainability of a watershed ecosystem. In order to explore the multiscale characteristics of runoff complexity and analyze its variation and influencing factors in the Yanhe watershed in China during the period 1991-2020, we established a new analysis method for watershed runoff complexity based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method for the decomposition of multiscale characteristics and the refined composite multiscale entropy (RCMSE) method for the quantification of the system complexity. The results show that runoff and its components all present multiscale complexity characteristics that are different from random signals, and the intermediate frequency modes contribute the most to runoff complexity. The runoff complexity of the Yanhe watershed has decreased gradually since 1991, and 2010 was a turning point of runoff complexity, when it changed from a decline to an increase, indicating that the ecological sustainability of this basin has improved since 2010, which was mainly related to the ecological restoration measures of the Grain for Green Project. This study expands the research perspective for analyzing the variation characteristics of runoff at the multiscale, and provides a reference for the study of watershed ecological sustainability and ecological management.
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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Langue: En Journal: Entropy (Basel) Année: 2022 Type de document: Article Pays d'affiliation: Chine

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Langue: En Journal: Entropy (Basel) Année: 2022 Type de document: Article Pays d'affiliation: Chine