A Modified Multifractal Detrended Fluctuation Analysis (MFDFA) Approach for Multifractal Analysis of Precipitation in Dongting Lake Basin, China

• Published in: Water (Basel) Vol. 11; no. 5; p. 891
• Main Authors: Zhang, Xike, Zhang, Gui, Qiu, Luo, Zhang, Bo, Sun, Yurong, Gui, Zifan, Zhang, Qiuwen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2019
• Subjects: Climate change; Decomposition; Dongting Lake Basin; ensemble empirical mode decomposition (EEMD); Fractal analysis; Fractal geometry; Hydrologic cycle; Hydrology; Lake basins; Lakes; Methods; multifractal detrend fluctuation analysis (MFDFA); overlap moving window (OMW); Performance evaluation; Polynomials; Precipitation; Probability density functions; Resource management; Runoff; Stream flow; Time series; Trends; Water management; Water resources; Water resources management; Dongting Lake; China; Yangtze River
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w11050891

Multifractal detrended fluctuation analysis (MFDFA) method can examine higher-dimensional fractal and multifractal characteristics hidden in time series. However, removal of local trends in MFDFA is based on discontinuous polynomial fitting, resulting in pseudo-fluctuation errors. In this paper, we propose a two-stage modified MFDFA for multifractal analysis. First, an overlap moving window (OMW) algorithm is introduced to divide time series of the classic MFDFA method. Second, detrending by polynomial fitting local trend in traditional MFDFA is replaced by ensemble empirical mode decomposition (EEMD)-based local trends. The modified MFDFA is named OMW-EEMD-MFDFA. Then, the performance of the OMW-EEMD-MFDFA method is assessed by extensive numeric simulation experiments based on a p-model of multiplicative cascading process. The results show that the modified OMW-EEMD-MFDFA method performs better than conventional MFDFA and OMW-MFDFA methods. Lastly, the modified OMW-EEMD-MFDFA method is applied to explore multifractal characteristics and multifractal sources of daily precipitation time series data at the Mapoling and Zhijiang stations in Dongting Lake Basin. Our results showed that the scaling properties of the daily precipitation time series at the two stations presented a long-range correlation, showing a long-term persistence of the previous state. The strong q-dependence of H ( q ) and τ ( q ) indicated strong multifractal characteristics in daily precipitation time series data at the two stations. Positive Δ f values demonstrate that precipitation may have a local increasing trend. Comparing the generalized Hurst exponent and the multifractal strength of the original precipitation time series data with its shuffled and surrogate time series data, we found that the multifractal characteristics of the daily precipitation time series data were caused by both long-range correlations between small and large fluctuations and broad probability density function, but the broad probability density function was dominant. This study may be of practical and scientific importance in regional precipitation forecasting, extreme precipitation regulation, and water resource management in Dongting Lake Basin.