Multifractal characterization of meteorological drought in India using detrended fluctuation analysis

Abstract

This study presents multifractal detrended fluctuation analysis (MF-DFA) to describe the multifractality of Standardized Precipitation Index (SPI) series from 30 meteorological subdivisions of India estimated at different aggregation timescales (3, 6 and 12 months) based on long-term monthly rainfall data sets of 1871-2016 period. The plots of fluctuation function and generalized Hurst exponents confirmed that multifractality is evident in most of the SPI series; however, it is found that its strength and persistency differ with timescale and space. The Hurst exponents of long-term (SPI-12) and medium-term (SPI-6) drought series of all subdivisions showed long-term persistence irrespective of the climatic conditions of the region, while the short-term droughts (SPI-3) of six subdivisions displayed short-term persistence. Further, the analysis by partitioning the data to four time spells

Assessing Nonlinear Dynamics and Trends in Precipitation by Ensemble Empirical Mode Decomposition (EEMD) and Fractal Approach in Benin Republic (West Africa)

“…e selection of the scale range for computing the fluctuation function F q (s) and the type of polynomial chosen (detrending) is one of the major issues in applying the MFDFA method [10,40]. Indeed, firstly, if there is a trend in a real-time series, the functional form of this trend is usually unknown.…”

“…According to Kantelhardt et al [27], Li et al [39], Krzyszczak et al [26], Adarsh et al [40], and Baranowski et al [13], the different steps involved in MFDFA computational procedure can be described as follows:…”

Dynamics of meteorological time series on the base of ground measurements and retrospective data from MERRA‐2 for Poland

“…Reanalysis products have been successfully used for climate monitoring and atmospheric research, including the creation and verification of a high spatio‐temporal resolution global precipitation dataset designed for hydrological modelling (Beck et al ., 2017), capturing half‐century trends in global air temperatures (Compo et al ., 2013), evaluating the dynamics of the warming hiatus (Huang et al ., 2017), modelling the response of plant diseases to climate change (Bebber et al ., 2016), reconstruction of Atlantic tropical cyclone activity for the last millennium (Burn and Palmer, 2015), changes and interannual variability in daily scale temperature and precipitation extremes (Donat et al ., 2016), analysis of the precipitation data properties for different land regions (Reichle et al ., 2017), selection of the regions vulnerable to the occurrence of extreme events (García‐Marín et al ., 2013; Baranowski et al ., 2019), estimation of temperature–mortality associations (Royé et al ., 2020), modelling of a global streamflow for the world's medium‐to‐large river basins (Candogan Yossef et al ., 2017; Alfieri et al ., 2020), analysing distributions of long‐term pan evaporation across China (Wang et al ., 2019), assessing cloud cover over the Arabian Peninsula (Yousef et al ., 2020), assessing the impact of volcanic eruptions on climate extremes (Paik and Min, 2018), and the characterization of meteorological drought in India (Adarsh et al ., 2019).…”

Multifractal Cross Correlation Analysis of Agro-meteorological Datasets (Including Reference Evapotranspiration) of California, United States

“…Multifractal Detrended Fluctuation analysis (MFDFA) propounded by Kantelhardt et al [24], is one of the most popular approach for the multifractal characterization of time series. The method was extensively applied for different hydrological like streamflow [28][29][30][31], meteorological time series such as rainfall, drought index, temperature, solar radiation and relative air humidity [32][33][34][35][36][37][38][39][40][41][42]. Even though some alternative methods like joint-multifractal analysis was proposed recently for the multifractal characterization of reference evapotranspiration (ET0) time series [35], no comprehensive study of global recognition was reported for the characterization of ET0 time series employing MFDFA and its extended cross correlation variants.…”