Recurrence quantification analysis of monthly rainfall time series in Pernambuco, Brazil
DOI:
https://doi.org/10.33448/rsd-v9i9.7737Keywords:
Rainfall; Recurrence plot; Recurrence quantification analysisAbstract
Precipitation is the main climatic variable that is used for modeling risks indices for natural disasters. We investigated nonlinear dynamics of monthly rainfall temporal series recorded from 1962 to 2012, at three stations in Pernambuco state, Brazil, located in regions with different rainfall regime (Zona da Mata, Agreste and Sertão), provided by the Meteorological Laboratory of the Institute of Technology of Pernambuco (Laboratório de Meteorologia do Instituto de Tecnologia de Pernambuco – LAMEP/ITEP). The objective of this work is to contribute to a better understanding of the spatiotemporal distribution of rainfall in the state of Pernambuco. We use the methodology from nonlinear dynamics theory, Recurrence plot (RP) that allows to distinguish between different types of underlying processes. The results showed that rainfall regime in deep inland semiarid Sertão region is characterized by weaker and less complex deterministic behavior, comparing to Zona da Mata and Agreste, where we identified transitions between chaotic and nonstationary type of dynamics. For transitional Agreste region rainfall dynamics showed stronger memory with longer mean prediction time, while for sub humid Zona da Mata rainfall dynamics is characterized by laminar (slowly changing) states.
References
Alvares, C. A., Stape, J. L., Sentelhas, P. C., de Moraes, G., Leonardo, J., & Sparovek, G. (2013). Köppen's climate classification map for Brazil. Meteorologische Zeitschrift, 22(6), 711-728.
Afsar, O., Tirnakli, U., & Marwan, N. (2018). Recurrence Quantification Analysis at work: Quasi-periodicity based interpretation of gait force profiles for patients with Parkinson disease. Scientific reports, 8(1), 9102.
Bastos, J. A., & Caiado, J. (2011). Recurrence quantification analysis of global stock markets. Physica A: Statistical Mechanics and its Applications, 390(7), 1315-1325.
Buytaert, W., Celleri, R., Willems, P., De Bievre, B., & Wyseure, G. (2006). Spatial and temporal rainfall variability in mountainous areas: A case study from the south Ecuadorian Andes. Journal of hydrology, 329(3-4), 413-421.
Chadwick, R., Good, P., Martin, G., & Rowell, D. P. (2016). Large rainfall changes consistently projected over substantial areas of tropical land. Nature Climate Change, 6(2), 177-181.
Debortoli, N. S., Camarinha, P. I. M., Marengo, J. A., & Rodrigues, R. R. (2017). An index of Brazil’s vulnerability to expected increases in natural flash flooding and landslide disasters in the context of climate change. Natural hazards, 86(2), 557-582.
Donner, R. V., Balasis, G., Stolbova, V., Georgiou, M., Wiedermann, M., & Kurths, J. (2019). Recurrence‐Based Quantification of Dynamical Complexity in the Earth's Magnetosphere at Geospace Storm Timescales. Journal of Geophysical Research: Space Physics, 124(1), 90-108.
Eckmann, J. P., Kamphorst, S. O., & Ruelle, D. (1987). Recurrence Plots of Dynamical Systems. EPL (Europhysics Letters), 4(9), 973.
Hastenrath, S. (2012). Exploring the climate problems of Brazil’s Nordeste: a review. Climatic Change, 112(2), 243-251.
Jha, S. K., & Sivakumar, B. (2017). Complex networks for rainfall modeling: spatial connections, temporal scale, and network size. Journal of Hydrology, 554, 482-489.
Kantz, H., & Schreiber, T. (2004). Nonlinear time series analysis (Vol. 7). Cambridge university press.
Longobardi, A., & Villani, P. (2010). Trend analysis of annual and seasonal rainfall time series in the Mediterranean area. International journal of Climatology, 30(10), 1538-1546.
Lyra, G. B., Oliveira‐Júnior, J. F., & Zeri, M. (2014). Cluster analysis applied to the spatial and temporal variability of monthly rainfall in Alagoas state, Northeast of Brazil. International Journal of Climatology, 34(13), 3546-3558.
Marengo, J. A., & Bernasconi, M. (2015). Regional differences in aridity/drought conditions over Northeast Brazil: present state and future projections. Climatic Change, 129(1-2), 103-115.
Marwan, N., Romano, M. C., Thiel, M., & Kurths, J. (2007). Recurrence plots for the analysis of complex systems. Physics reports, 438(5-6), 237-329.
Maslin, M., & Austin, P. (2012). Uncertainty: Climate models at their limit?. Nature, 486(7402), 183.
Medeiros, E. S. D., Lima, R. R. D., Olinda, R. A. D., & Santos, C. A. C. D. (2019). Modeling Spatiotemporal Rainfall Variability in Paraíba, Brazil. Water, 11(9), 1843.
Melo Santos, A. M., Cavalcanti, D. R., Silva, J. M. C. D., & Tabarelli, M. (2007). Biogeographical relationships among tropical forests in north‐eastern Brazil. Journal of Biogeography, 34(3), 437-446.
Oliveira, P. T., e Silva, C. S., & 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.
Panagoulia, D., & Vlahogianni, E. I. (2014). Nonlinear dynamics and recurrence analysis of extreme precipitation for observed and general circulation model generated climates. Hydrological Processes, 28(4), 2281-2292.
Pereira, A. S., Shitsuka, D. M., Parreira, F. J., & Shitsuka, R. (2018). Metodologia do trabalho científico.[e-Book]. Santa Maria. Ed. UAB/NTE/UFSM. Available at: https://repositorio. ufsm. br/bitstream/handle/1/15824/Lic_Computacao_Metodologia-Pesquisa-Cientifica. pdf.
R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
Robertson, A. W., Kirshner, S., & Smyth, P. (2004). Downscaling of daily rainfall occurrence over northeast Brazil using a hidden Markov model. Journal of climate, 17(22), 4407-4424.
Silva, A. S. A., Stosic, B., Menezes, R. S. C., & Singh, V. P. (2019). Comparison of interpolation methods for spatial distribution of monthly precipitation in the state of Pernambuco, Brazil. Journal of Hydrologic Engineering, 24(3), 04018068.
Stosic, T., Telesca, L., de Souza Ferreira, D. V., & Stosic, B. (2016). Investigating anthropically induced effects in streamflow dynamics by using permutation entropy and statistical complexity analysis: A case study. Journal of Hydrology, 540, 1136-1145.
Tan, X., & Gan, T. Y. (2017). Multifractality of Canadian precipitation and streamflow. International Journal of Climatology, 37, 1221-1236.
Webber Jr, C. L., & Zbilut, J. P. (2005). Recurrence quantification analysis of nonlinear dynamical systems. Tutorials in contemporary nonlinear methods for the behavioral sciences, 94(2005), 26-94.
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Copyright (c) 2020 Leika Irabele Tenório de Santana; Antonio Samuel Alves da Silva; Rômulo Simões Cezar Menezes; Tatijana Stosic
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