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Multifractal Detrended Fluctuation Analysis of Regional Precipitation Sequences Based on the CEEMDAN-WPT

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Abstract

In this paper, the complete ensemble empirical mode decomposition with the adaptive noise (CEEMDAN) algorithm is introduced into the complexity research of precipitation systems to improve the traditional complexity measure method specific to the mode mixing of the Empirical Mode Decomposition (EMD) and incomplete decomposition of the ensemble empirical mode decomposition (EEMD). We combined the CEEMDAN with the wavelet packet transform (WPT) and multifractal detrended fluctuation analysis (MF-DFA) to create the CEEMDAN-WPT-MFDFA, and used it to measure the complexity of the monthly precipitation sequence of 12 sub-regions in Harbin, Heilongjiang Province, China. The results show that there are significant differences in the monthly precipitation complexity of each sub-region in Harbin. The complexity of the northwest area of Harbin is the lowest and its predictability is the best. The complexity and predictability of the middle and Midwest areas of Harbin are about average. The complexity of the southeast area of Harbin is higher than that of the northwest, middle, and Midwest areas of Harbin and its predictability is worse. The complexity of Shuangcheng is the highest and its predictability is the worst of all the studied sub-regions. We used terrain and human activity as factors to analyze the causes of the complexity of the local precipitation. The results showed that the correlations between the precipitation complexity and terrain are obvious, and the correlations between the precipitation complexity and human influence factors vary. The distribution of the precipitation complexity in this area may be generated by the superposition effect of human activities and natural factors such as terrain, general atmospheric circulation, land and sea location, and ocean currents. To evaluate the stability of the algorithm, the CEEMDAN-WPT-MFDFA was compared with the equal probability coarse graining LZC algorithm, fuzzy entropy, and wavelet entropy. The results show that the CEEMDAN-WPT-MFDFA was more stable than 3 contrast methods under the influence of white noise and colored noise, which proves that the CEEMDAN-WPT-MFDFA has a strong robustness under the influence of noise.

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (no. 51579044, no. 41071053, and no. 51479032), the Specialized Research Fund for Innovative Talents of Harbin (Excellent Academic Leader) (no. 2013RFXXJ001), and the Science and Technology Program of the Water Conservancy of Heilongjiang Province (no. 201319, no. 201501, and no. 201503).

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Authors and Affiliations

  1. School of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin, 150030, Heilongjiang, China

    Dong Liu, Chen Cheng, Qiang Fu, Chunlei Liu, Mo Li, Muhammad Abrar Faiz, Tianxiao Li, Muhammad Imran Khan & Song Cui

  2. Key Laboratory of Effective Utilization of Agricultural Water Resources of Ministry of Agriculture, Northeast Agricultural University, Harbin, 150030, Heilongjiang, China

    Dong Liu, Qiang Fu & Mo Li

  3. Heilongjiang Provincial Collaborative Innovation Center of Grain Production Capacity Improvement, Northeast Agricultural University, Harbin, 150030, Heilongjiang, China

    Dong Liu, Qiang Fu & Mo Li

  4. Key Laboratory of Water-Saving Agriculture of Ordinary University in Heilongjiang Province, Northeast Agricultural University, Harbin, 150030, Heilongjiang, China

    Dong Liu, Qiang Fu & Mo Li

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  1. Dong Liu
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  2. Chen Cheng
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  3. Qiang Fu
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Correspondence to Qiang Fu.

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Liu, D., Cheng, C., Fu, Q. et al. Multifractal Detrended Fluctuation Analysis of Regional Precipitation Sequences Based on the CEEMDAN-WPT. Pure Appl. Geophys. 175, 3069–3084 (2018). https://doi.org/10.1007/s00024-018-1820-2

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