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Multifractal analyses of daily rainfall time series in Pearl River basin of China

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  • Yu, Zu-Guo
  • Leung, Yee
  • Chen, Yongqin David
  • Zhang, Qiang
  • Anh, Vo
  • Zhou, Yu

Abstract

The multifractal properties of daily rainfall time series at the stations in Pearl River basin of China over periods of up to 45 years are examined using the universal multifractal approach based on the multiplicative cascade model and the multifractal detrended fluctuation analysis (MF-DFA). The results from these two kinds of multifractal analyses show that the daily rainfall time series in this basin have multifractal behavior in two different time scale ranges. It is found that the empirical multifractal moment function K(q) of the daily rainfall time series can be fitted very well by the universal multifractal model (UMM). The estimated values of the conservation parameter H from UMM for these daily rainfall data are close to zero indicating that they correspond to conserved fields. After removing the seasonal trend in the rainfall data, the estimated values of the exponent h(2) from MF-DFA indicate that the daily rainfall time series in Pearl River basin exhibit no long-term correlations. It is also found that K(2) and elevation series are negatively correlated. It shows a relationship between topography and rainfall variability.

Suggested Citation

  • Yu, Zu-Guo & Leung, Yee & Chen, Yongqin David & Zhang, Qiang & Anh, Vo & Zhou, Yu, 2014. "Multifractal analyses of daily rainfall time series in Pearl River basin of China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 193-202.
    • Handle: RePEc:eee:phsmap:v:405:y:2014:i:c:p:193-202
    DOI: 10.1016/j.physa.2014.02.047
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    Cited by:

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    3. Morales Martínez, Jorge Luis & Segovia-Domínguez, Ignacio & Rodríguez, Israel Quiros & Horta-Rangel, Francisco Antonio & Sosa-Gómez, Guillermo, 2021. "A modified Multifractal Detrended Fluctuation Analysis (MFDFA) approach for multifractal analysis of precipitation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    4. Méndez-Gordillo, Alma Rosa & Campos-Amezcua, Rafael & Cadenas, Erasmo, 2022. "Wind speed forecasting using a hybrid model considering the turbulence of the airflow," Renewable Energy, Elsevier, vol. 196(C), pages 422-431.
    5. Braga, A.C. & Alves, L.G.A. & Costa, L.S. & Ribeiro, A.A. & de Jesus, M.M.A. & Tateishi, A.A. & Ribeiro, H.V., 2016. "Characterization of river flow fluctuations via horizontal visibility graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 1003-1011.
    6. Adarsh Sankaran & Sagar Rohidas Chavan & Mumtaz Ali & Archana Devarajan Sindhu & Drisya Sasi Dharan & Muhammad Ismail Khan, 2021. "Spatiotemporal variability of multifractal properties of fineresolution daily gridded rainfall fields over India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 106(3), pages 1951-1979, April.

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