Assessment of Multifractal Fingerprints of Reference Evapotranspiration Based on Multivariate Empirical Mode Decomposition
This study analyzed the multifractal characteristics of daily reference evapotranspiration (ET<sub>o</sub>) time series of the Tabriz and Urmia stations of northwestern Iran and its cross-correlation with five other meteorological variables. The results of multifractal detrended fluctuat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/14/8/1219 |
| _version_ | 1827730403944824832 |
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| author | Adarsh Sankaran Thomas Plocoste Vahid Nourani Shamseena Vahab Aayisha Salim |
| author_facet | Adarsh Sankaran Thomas Plocoste Vahid Nourani Shamseena Vahab Aayisha Salim |
| author_sort | Adarsh Sankaran |
| collection | DOAJ |
| description | This study analyzed the multifractal characteristics of daily reference evapotranspiration (ET<sub>o</sub>) time series of the Tabriz and Urmia stations of northwestern Iran and its cross-correlation with five other meteorological variables. The results of multifractal detrended fluctuation analysis (MFDFA) of ET<sub>o</sub>, temperature, pressure, relative humidity solar radiation, and wind velocity showed that all the time series of both stations exhibited multifractality and long memory persistence with higher persistence and complexity in the datasets of Urmia station. Then, a multivariate empirical mode decomposition (MEMD)-(MFDFA) coupled framework was proposed to identify the dominant modes suitable for the forecasting of the different variables. The examination of reconstructed time series consistently displayed an increase in persistence and multifractality. The cross-correlation between different candidate variables and ET<sub>o</sub> was examined using a recently proposed multifractal cross-correlation analysis (MFCCA) method. The results showed that in each pair-wise cross-correlation analysis, the joint persistence is approximately half of the persistence of an individual time series, reinforcing the universality in the fractal cross-correlation analysis. The cross-correlation properties displayed diverse patterns in different pair-wise combinations of cross-correlation analysis despite the similarity of patterns among the data of the two stations. |
| first_indexed | 2024-03-11T00:08:46Z |
| format | Article |
| id | doaj.art-b175f461f0174e53837c440496412d5e |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-11T00:08:46Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-b175f461f0174e53837c440496412d5e2023-11-19T00:12:07ZengMDPI AGAtmosphere2073-44332023-07-01148121910.3390/atmos14081219Assessment of Multifractal Fingerprints of Reference Evapotranspiration Based on Multivariate Empirical Mode DecompositionAdarsh Sankaran0Thomas Plocoste1Vahid Nourani2Shamseena Vahab3Aayisha Salim4TKM College of Engineering, Kollam 691005, IndiaDepartment of Research in Geoscience, KaruSphère SASU, 97139 Abymes, Guadeloupe, FranceFaculty of Civil Engineering, University of Tabriz, Tabriz 5166616471, IranTKM College of Engineering, Kollam 691005, IndiaTKM College of Engineering, Kollam 691005, IndiaThis study analyzed the multifractal characteristics of daily reference evapotranspiration (ET<sub>o</sub>) time series of the Tabriz and Urmia stations of northwestern Iran and its cross-correlation with five other meteorological variables. The results of multifractal detrended fluctuation analysis (MFDFA) of ET<sub>o</sub>, temperature, pressure, relative humidity solar radiation, and wind velocity showed that all the time series of both stations exhibited multifractality and long memory persistence with higher persistence and complexity in the datasets of Urmia station. Then, a multivariate empirical mode decomposition (MEMD)-(MFDFA) coupled framework was proposed to identify the dominant modes suitable for the forecasting of the different variables. The examination of reconstructed time series consistently displayed an increase in persistence and multifractality. The cross-correlation between different candidate variables and ET<sub>o</sub> was examined using a recently proposed multifractal cross-correlation analysis (MFCCA) method. The results showed that in each pair-wise cross-correlation analysis, the joint persistence is approximately half of the persistence of an individual time series, reinforcing the universality in the fractal cross-correlation analysis. The cross-correlation properties displayed diverse patterns in different pair-wise combinations of cross-correlation analysis despite the similarity of patterns among the data of the two stations.https://www.mdpi.com/2073-4433/14/8/1219evapotranspirationmultifractaldecompositionIrancorrelationscaling |
| spellingShingle | Adarsh Sankaran Thomas Plocoste Vahid Nourani Shamseena Vahab Aayisha Salim Assessment of Multifractal Fingerprints of Reference Evapotranspiration Based on Multivariate Empirical Mode Decomposition Atmosphere evapotranspiration multifractal decomposition Iran correlation scaling |
| title | Assessment of Multifractal Fingerprints of Reference Evapotranspiration Based on Multivariate Empirical Mode Decomposition |
| title_full | Assessment of Multifractal Fingerprints of Reference Evapotranspiration Based on Multivariate Empirical Mode Decomposition |
| title_fullStr | Assessment of Multifractal Fingerprints of Reference Evapotranspiration Based on Multivariate Empirical Mode Decomposition |
| title_full_unstemmed | Assessment of Multifractal Fingerprints of Reference Evapotranspiration Based on Multivariate Empirical Mode Decomposition |
| title_short | Assessment of Multifractal Fingerprints of Reference Evapotranspiration Based on Multivariate Empirical Mode Decomposition |
| title_sort | assessment of multifractal fingerprints of reference evapotranspiration based on multivariate empirical mode decomposition |
| topic | evapotranspiration multifractal decomposition Iran correlation scaling |
| url | https://www.mdpi.com/2073-4433/14/8/1219 |
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