Investigation to the relation between meteorological drought and hydrological drought in the upper Shaying River Basin using wavelet analysis

Li, Qiongfang; He, Pengfei; He, Yiming; Han, Xingye; Zeng, Tianshan; Lu, Guoping; Wang, Hongjie

doi:10.1016/j.atmosres.2019.104743

Cited by 105 publications

(73 citation statements)

References 48 publications

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“…CWT is more suitable for signal feature extraction. As a tool for feature extraction, it has been widely used in geophysical research (S. Z. Huang et al., 2015; Q. F. Li, He, et al., 2020). Therefore, this study uses CWT to investigate the periodicities of SPIn and SRI1 for better understanding of the response relationship between meteorological and hydrological drought.…”

Section: Methodsmentioning

confidence: 99%

Characteristics of Propagation From Meteorological Drought to Hydrological Drought in the Pearl River Basin

et al. 2021

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Section: Methodsmentioning

confidence: 99%

Characteristics of Propagation From Meteorological Drought to Hydrological Drought in the Pearl River Basin

et al. 2021

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“…The continuous wavelet transform (CWT) (Torrence et al, 1998) is widely used for analysing the frequency domain of hydrometeorological time series (Fang et al, 2018;Li et al, 2020). The spectral and temporal features of the time series can be projected onto a time-frequency plane by CWT, where the dominant cycle period and its duration can be identified (Grinsted et al, 2004).…”

Section: Wavelet Analysismentioning

confidence: 99%

Comment on nhess-2020-416

2021

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Drought disasters, such as water scarcity and wildfires, are serious natural disasters in Japan that are also affected by climate change. However, as drought generally has widespread impacts and the duration of drought can vary considerably, it is difficult to assess the spatiotemporal characteristics and the climatic causes of drought. Therefore, to identify the drought homogeneous regions and understand climatic causes of regional drought over Japan, this study provides a spatiotemporal analysis for historical droughts patterns and teleconnections associated with global climatic drivers. The trends of meteorological elements, which are the basis of drought index calculation, was first assessed. Then, drought characterized by the Self-calibrating Palmer Drought Severity Index (scPDSI) was investigated. Trends and patterns of drought were identified through the trend-free pre-whitening Mann-Kendall test and distinct empirical orthogonal function. The continuous wavelet transform and cross wavelet transform together with wavelet coherence were utilized to depict the links between drought and global climatic drivers. The results are described as follows: (1) the trends of precipitation were insignificant. However, temperature and potential evapotranspiration increasing trends were detected over Japan; (2) the drought trend over Japan varied seasonally, increasing in spring and summer and decreasing in autumn and winter; (3) two major subregions of drought variability-the western Japan (W region) and most of the northernmost Japan near the Pacific (N region) were identified; (4) wildfires with large burned area were more likely to occur when the scPDSI was less than -1; and (5) the North Atlantic Index (NAOI)

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“…On the one hand, according to different disciplines, current correlation analysis research methods can be divided into data mining methods, statistical methods, wavelet correlation analysis (WCA), correlation analysis based on mutual information [4][5][6][7][8][9], and so forth. WCA is based on the wavelet transform with a finite length of attenuation [10][11][12][13][14][15]. In this way, not only the frequency can be obtained, but also the time can be located; that is, the time-frequency analysis of the signal can be carried out [16,17].…”

Section: Introductionmentioning

confidence: 99%

Crack Prediction Based on Wavelet Correlation Analysis Least Squares Support Vector Machine for Stone Cultural Relics

Liu

et al. 2021

Mathematical Problems in Engineering

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Preventive protection of cultural relics is to make use of all the science and technology beneficial to the research and protection of archaeological heritage to predict the disease of cultural relics. The existing preventive cultural relics protection system has made some achievements in environmental monitoring, but the analysis and utilization of large data of cultural relics are still insufficient. In this paper, under the idea of multisource information fusion, a least squares support vector machine regression method based on multivariate time series wavelet correlation analysis is proposed to achieve accurate crack prediction of stone cultural relics. Firstly, the correlation of multivariate time series of stone cultural relics are quantitatively analyzed and the validity of characteristic variables of the crack is discriminated by wavelet correlation analysis; then, a least squares support vector machine prediction model is constructed based on the correlation obtained from the analysis; finally, the good performance of the method is verified by using the environmental monitoring data of the rock mass fracture in the North Qianfo Cliff of Dafo Temple in Binzhou City of Shaanxi Province. The experimental results show that the proposed method is more effective than the traditional backpropagation neural network, support vector machine, and relevance vector machine regression methods. This method is universal and easy to implement for multisource data prediction of nonmovable cultural relics diseases. It provides a scientific theoretical reference for the preventive protection of cultural relics.

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Investigation to the relation between meteorological drought and hydrological drought in the upper Shaying River Basin using wavelet analysis

Cited by 105 publications

References 48 publications

Characteristics of Propagation From Meteorological Drought to Hydrological Drought in the Pearl River Basin

Characteristics of Propagation From Meteorological Drought to Hydrological Drought in the Pearl River Basin

Comment on nhess-2020-416

Crack Prediction Based on Wavelet Correlation Analysis Least Squares Support Vector Machine for Stone Cultural Relics

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