Abstract
Based on a monthly drought index, the Standardized Precipitation Index (SPI), we investigate the multifractality of monthly drought areas in seven major regions in China from 1961 to 2012 using multifractal detrended fluctuation analysis. The results show that multifractality is evident in the monthly time series for all seven regions, but its strength varies between areas. From the numerical results, we further examine the stationarity and persistence of the time series in the seven drought areas. The characteristics of the variance of big and small fluctuations are also analyzed. The characteristics of multifractal spectra are used to distinguish the features of the singularity of all the data, as well as the large and small fluctuations and the spread of the changes of fractal patterns, and so on, in the monthly drought area time series for the different regions. Finally, we investigate the possible source(s) of multifractality in the drought series by random shuffling as well as surrogating the original series for each region.
Highlights
Droughts are among the most severe and frequently occurring natural hazards
Based on a monthly drought index, the Standardized Precipitation Index (SPI), we investigate the multifractality of monthly drought areas in seven major regions in China from 1961 to 2012 using multifractal detrended fluctuation analysis
This is an important research question, as the evidence of multifractality suggests that multifractal models could be built to allow variables of interest to be forecast, and the prediction study based on multifractality is a hot domain in last decades.We can examine the multifractal properties of a time series using multifractal detrended fluctuation analysis (MFDFA) and any models that contemplate the phenomena should be capable of reproducing the results, such as the relation of intensity to complexity, the sources of multifractality and the relation of small or large scale fluctuations with the increase of intensity
Summary
Droughts are among the most severe and frequently occurring natural hazards. They have substantial effects on economic, agricultural, ecological, and environmental activity worldwide (Begueria et al 2010; Li et al 2013; Vicente-Serrano et al 2010). Multifractals describe the dynamic characteristics of systems more carefully and comprehensively, and characterize their properties both locally and globally This is an important research question, as the evidence of multifractality suggests that multifractal models could be built to allow variables of interest to be forecast, and the prediction study based on multifractality is a hot domain in last decades (de Benicio et al 2013; Alvarez-Ramirez et al 2008; Grech and Mazur 2004; Lana et al 2001, 2010; Martınez et al 2007, 2010; Sun et al 2001; Wei and Wang 2008).We can examine the multifractal properties of a time series using MFDFA and any models that contemplate the phenomena should be capable of reproducing the results, such as the relation of intensity to complexity, the sources of multifractality and the relation of small or large scale fluctuations with the increase of intensity.
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