Multifractal analysis of rainfall in coastal area in Pernambuco, Brazil

Authors

DOI:

https://doi.org/10.33448/rsd-v10i2.12424

Keywords:

Rainfall; Climate change; Multifractal.

Abstract

Global warming and climate change are the mayor concerns of scientists, engineers and policy makers because they affect every aspect of nature and human life. Rainfall and air temperature are the most important variables used to detect climate change, through the statistical analysis of set of indices that describe temperature and rainfall extremes. Over the last decades concepts and methods from complex system science were applied in analysis of hydrological data to describe variability of hydrological processes on multiple temporal and spatial scales. In this work we analyzed daily rainfall temporal series in Recife, Brazil (during the period from 1962 to 2019) using Multifractal Detrended Fluctuation analysis (MFDFA) in order to study long term correlations in subsets of small and large rainfall fluctuations. We calculated multifractal parameters (that quantify position of maximum, width and asymmetry of multifractal spectrum) which are related to different properties of rainfall fluctuations. By comparing the values of these parameters for two subperiods of 29 years, we found that after 1990, rainfall dynamics changed towards stronger persistency, weaker multifractality and decreased dominance of small fluctuations.

References

Adarsh, S., Nourani, V., Archana, D. S., & Dharan, D. S. (2020). Multifractal description of daily rainfall fields over India. Journal of Hydrology, 586, 124913. https://doi.org/10.1016/j.jhydrol.2020.124913

Arvor, D., Dubreuil, V., Ronchail, J., Simões, M., & Funatsu, B. M. (2014). Spatial patterns of rainfall regimes related to levels of double cropping agriculture systems in Mato Grosso (Brazil). International Journal of Climatology, 34(8), 2622–2633. https://doi.org/10.1002/joc.3863

da Silva, H. S., Silva, J. R. S., & Stosic, T. (2020). Multifractal analysis of air temperature in Brazil. Physica A: Statistical Mechanics and Its Applications, 549, 124333. https://doi.org/10.1016/j.physa.2020.124333

De Benicio, R. B., Stošić, T., De Figueirêdo, P. H., & Stošić, B. D. (2013). Multifractal behavior of wild-land and forest fire time series in Brazil. Physica A: Statistical Mechanics and Its Applications, 392(24), 6367–6374. https://doi.org/10.1016/j.physa.2013.08.012

Douglas, E. M., & Barros, A. P. (2003a). Probable maximum precipitation estimation using multifractals: Application in the eastern United States. Journal of Hydrometeorology, 4(6), 1012–1024. https://doi.org/10.1175/1525-7541(2003)004<1012:PMPEUM>2.0.CO;2

Douglas, E. M., & Barros, A. P. (2003b). Probable maximum precipitation estimation using multifractals: Application in the eastern United States. Journal of Hydrometeorology, 4(6), 1012–1024. https://doi.org/10.1175/1525-7541(2003)004<1012:PMPEUM>2.0.CO;2

Fuwape, I. A., Ogunjo, S. T., Oluyamo, S. S., & Rabiu, A. B. (2017). Spatial variation of deterministic chaos in mean daily temperature and rainfall over Nigeria. Theoretical and Applied Climatology, 130(1–2), 119–132. https://doi.org/10.1007/s00704-016-1867-x

García-Marín, A. P., Estévez, J., Medina-Cobo, M. T., & Ayuso-Muñoz, J. L. (2015). Delimiting homogeneous regions using the multifractal properties of validated rainfall data series. Journal of Hydrology, 529(P1), 106–119. https://doi.org/10.1016/j.jhydrol.2015.07.021

García-Marín, Amanda P., Jiménez-Hornero, F. J., & Ayuso-Muñoz, J. L. (2008). Multifractal analysis as a tool for validating a rainfall model. Hydrological Processes, 22(14), 2672–2688. https://doi.org/10.1002/hyp.6864

Haines, A., Kovats, R. S., Campbell-Lendrum, D., & Corvalan, C. (2006). Climate change and human health: Impacts, vulnerability and public health. Public Health, 120(7), 585–596. https://doi.org/10.1016/j.puhe.2006.01.002

Herr, H. D., & Krzysztofowicz, R. (2005). Generic probability distribution of rainfall in space: The bivariate model. Journal of Hydrology, 306(1–4), 234–263. https://doi.org/10.1016/j.jhydrol.2004.09.011

Jha, S. K., & Sivakumar, B. (2017a). Complex networks for rainfall modeling: Spatial connections, temporal scale, and network size. Journal of Hydrology, 554, 482–489. https://doi.org/10.1016/j.jhydrol.2017.09.030

Jha, S. K., & Sivakumar, B. (2017b). Complex networks for rainfall modeling: Spatial connections, temporal scale, and network size. Journal of Hydrology, 554(9), 482–489. https://doi.org/10.1016/j.jhydrol.2017.09.030

Kabo-Bah, A. T., Diji, C. J., Nokoe, K., Mulugetta, Y., Obeng-Ofori, D., & Akpoti, K. (2016). Multiyear rainfall and temperature trends in the Volta River basin and their potential impact on hydropower generation in Ghana. Climate, 4(4), 49. https://doi.org/10.3390/cli4040049

Kantelhardt, J. W., Zschiegner, S. A., Koscielny-Bunde, E., Havlin, S., Bunde, A., & Stanley, H. E. (2002). Multifractal detrended fluctuation analysis of nonstationary time series. Physica A: Statistical Mechanics and Its Applications, 316(1–4), 87–114. https://doi.org/10.1016/S0378-4371(02)01383-3

Krzyszczak, J., Baranowski, P., Zubik, M., Kazandjiev, V., Georgieva, V., Sławiński, C., Siwek, K., Kozyra, J., & Nieróbca, A. (2019). Multifractal characterization and comparison of meteorological time series from two climatic zones. Theoretical and Applied Climatology, 137(3–4), 1811–1824. https://doi.org/10.1007/s00704-018-2705-0

Langousis, A., Veneziano, D., Furcolo, P., & Lepore, C. (2009). Multifractal rainfall extremes: Theoretical analysis and practical estimation. Chaos, Solitons and Fractals, 39(3), 1182–1194. https://doi.org/10.1016/j.chaos.2007.06.004

Movahed, M. S., Jafari, G. R., Ghasemi, F., Rahvar, S., & Tabar, M. R. R. (2006). Multifractal detrended fluctuation analysis of sunspot time series. Journal of Statistical Mechanics: Theory and Experiment, 316(2), 87–114. https://doi.org/10.1088/1742-5468/2006/02/P02003

Oliveira, P. T., Santos e Silva, C. M., & Lima, K. C. (2017). Climatology and trend analysis of extreme precipitation in subregions of Northeast Brazil. Theoretical and Applied Climatology, 130(1–2), 77–90. https://doi.org/10.1007/s00704-016-1865-z

Park, J., Kang, H., Lee, Y. S., & Kim, M. (2011). Changes in the extreme daily rainfall in South Korea. International Journal of Climatology, 31(15), 2290–2299.

Park, J. S., Kang, H. S., Lee, Y. S., & Kim, M. K. (2011). Changes in the extreme daily rainfall in South Korea. International Journal of Climatology, 31(15), 2290–2299. https://doi.org/10.1002/joc.2236

Peng, C. K., Buldyrev, S. V., Havlin, S., Simons, M., Stanley, H. E., & Goldberger, A. L. (1994). Mosaic organization of DNA nucleotides. Physical Review E, 49(2), 1685–1689. https://doi.org/10.1103/PhysRevE.49.1685

Sa’adi, Z., Shahid, S., Ismail, T., Chung, E. S., & Wang, X. J. (2019). Trends analysis of rainfall and rainfall extremes in Sarawak, Malaysia using modified Mann–Kendall test. Meteorology and Atmospheric Physics, 131(3), 263–277. https://doi.org/10.1007/s00703-017-0564-3

Shimizu, Y. U., Thurner, S., & Ehrenberger, K. (2002). Multifractal spectra as a measure of complexity in human posture. Fractals, 10(1), 103–116. https://doi.org/10.1142/S0218348X02001130

Stošić, D., Stošić, D., Stošić, T., & Stanley, H. E. (2015). Multifractal analysis of managed and independent float exchange rates. Physica A: Statistical Mechanics and Its Applications, 428, 13–18. https://doi.org/10.1016/j.physa.2015.02.055

Svensson, C., Olsson, J., & Berndtsson, R. (1996). Multifractal properties of daily rainfall in two different climates. Water Resources Research, 32(8), 2463–2472. https://doi.org/10.1029/96WR01099

Tan, X., & Gan, T. Y. (2017). Multifractality of Canadian precipitation and streamflow. International Journal of Climatology, 37, 1221–1236. https://doi.org/10.1002/joc.5078

Telesca, L., & Toth, L. (2016). Multifractal detrended fluctuation analysis of Pannonian earthquake magnitude series. Physica A: Statistical Mechanics and Its Applications, 448, 21–29. https://doi.org/10.1016/j.physa.2015.12.095

Weltzin, J. F., Loik, M. E., Schwinning, S., Williams, D. G., Fay, P. A., Haddad, B. M., Harte, J., Huxman, T. E., Knapp, A. K., Lin, G., Pockman, W. T., Shaw, M. R., Small, E. E., Smith, M. D., Smith, S. D., Tissue, D. T., & Zak, J. C. (2003). Assessing the Response of Terrestrial Ecosystems to Potential Changes in Precipitation. In BioScience (Vol. 53, Issue 10, pp. 941–952). American Institute of Biological Sciences. https://doi.org/10.1641/0006-3568(2003)053[0941:ATROTE]2.0.CO;2

Xavier, S. F. A., da Silva Jale, J., Stosic, T., dos Santos, C. A. C., & Singh, V. P. (2019). An application of sample entropy to precipitation in Paraíba State, Brazil. Theoretical and Applied Climatology, 136(1–2), 429–440. https://doi.org/10.1007/s00704-018-2496-3

Zorick, T., & Mandelkern, M. A. (2013). Multifractal Detrended Fluctuation Analysis of Human EEG: Preliminary Investigation and Comparison with the Wavelet Transform Modulus Maxima Technique. PLoS ONE, 8(7), e68360. https://doi.org/10.1371/journal.pone.0068360

Zunino, L., Tabak, B. M., Figliola, A., Pérez, D. G., Garavaglia, M., & Rosso, O. A. (2008). A multifractal approach for stock market inefficiency. Physica A: Statistical Mechanics and Its Applications, 387(26), 6558–6566. https://doi.org/10.1016/j.physa.2008.08.028

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Published

08/02/2021

How to Cite

BARRETO, I. D. de C.; STOSIC, . T. . Multifractal analysis of rainfall in coastal area in Pernambuco, Brazil. Research, Society and Development, [S. l.], v. 10, n. 2, p. e15410212424, 2021. DOI: 10.33448/rsd-v10i2.12424. Disponível em: https://www.rsdjournal.org/index.php/rsd/article/view/12424. Acesso em: 23 apr. 2024.

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Section

Exact and Earth Sciences