Spatiotemporal variability of multifractal properties of fineresolution daily gridded rainfall fields over India

Sankaran, Adarsh; Chavan, Sagar Rohidas; Ali, Mumtaz; Sindhu, Archana Devarajan; Dharan, Drisya S.; Khan, Muhammad Ismail

doi:10.1007/s11069-021-04523-0

Cited by 9 publications

(1 citation statement)

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“…Kantelhardt et al [9] proposed an improved extension of DFA, by generalizing the concept for different moment orders and named it as Mul-tifractal DFA (MFDFA). The MFDFA method gained wide popularity in the multifractal characterization of hydro-meteorological time series [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. However, such studies on ET0 are relatively very less in literature and only few studies started appearing very recently on the datasets of USA by Adarsh et al [28], India by Patil and Sidnal [29], China, by Cun et al [30].…”

Section: Introductionmentioning

confidence: 99%

Multifractal characterization and cross correlations of reference evapotranspiration time series of India

Adarsh¹,

Nityanjaly²,

Pham

et al. 2021

Eur. Phys. J. Spec. Top.

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This study performs the multifractal characterization of reference evapotranspiration (ET0) and its controlling factors of five locations in India with climatic diversity. First, the ET0 and the predictor variables like minimum air temperature (Tmin), maximum air temperature (Tmax) and average wind speed (AW) of five stations are analysed using multifractal detrended fluctuation analysis (MFDFA). The investigation could detect long-term persistence and multifractality of different series, irrespective of the climatic condition and geographical location. Higher persistence (>0.8) is noted in the ET0 and Tmin series indicating higher predictability in all the stations and highest multifractality was noted for the highly complex wind speed time series. Further, the ET0 estimates by Hargreaves Samani (HS) and Droogers and Allen (DA) methods differs in their persistence and multifractal properties, controlled by the geographic location of the station. Subsequently, multifractal cross correlation analysis (MFCCA) is used to investigate the correlations between ET0 and other variables. MFCCA analysis showed that, for all the time series considered, the joint scaling exponent is roughly the average of individual scaling exponents, and base width of the joint spectra is lower than that of individual series, validating two universal properties of multifractal cross correlation studies for agro-meteorological datasets.

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