Comparison of homogeneous regions of precipitation from two distinct data source for the state of Pará-Brazil
DOI:
https://doi.org/10.55761/abclima.v32i19.16424Resumo
The aim of this study was to compare the regionalization of precipitation carried out using the Fuzzy C-Means grouping technique, with two distinct data sources, one provided by the National Water Agency (ANA) and the other obtained through the Global Precipitation Climatology Centre (GPCC) meteorological satellite provided by the German National Meteorological Service (DWD), for 30 years (1986 to 2015), with the aim of verifying through statistical techniques, what will be the representativeness and differences of the regions formed by traditional and satellite. The non-hierarchical technique of Fuzzy C-Means was applied to the formation of the regions for the two data, in order to group the stations, later owned by the groupings, validation techniques (Dunn, Silhouette and PBM) were applied, with the aim of forming the best cluster for data analysis. Performance analyses were also performed, using statistical methods. As results, 2 homogeneous rainfall regions were found after the calculations of the validation indices, in which they were specialized in GIS environment. The southwestern portion of the state was where there was the greatest divergence between the analyzed data, in such a way that, in the homogeneous rainfall region formed by GPCC, there was a greater concentration of region 2, while in the analysis formed by ANA data, there were fragments of region 2. The results of the statistical tests showed that comparisons between the two regions are acceptable, with small differences, but of great value for hydrological studies in the region.
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Referências
AGUADO, A. G.; CANTANHEDE, Marco André. Lógica Fuzzy. 2010. Available at: < http://www.sysrad.com.br/redmine/attachments/1843/Artigo_logicaFuzzi.pdf >.
AHANI, A.; NADOUSHANI, S. M. Assessment of some combinations of hard and fuzzy clustering techniques for regionalization of catchments in Sefidroud basin. Journal of Hydroinformatics, v. 18, n. 6, p. 1033–1054. 2016.
de ALBUQUERQUE, M. F.; DE SOUZA, E. B.; DE OLIVEIRA, M. D. C. F.; DE SOUZA JÚNIOR, J. A. Precipitação nas mesorregiões do estado do Pará: climatologia, variabilidade e tendências nas últimas décadas (1978-2008). Revista Brasileira de Climatologia, n. 6. 2010.
AMANAJÁS, J. C.; BRAGA, C. C. Spatio-temporal rainfall patterns in eastern Amazônia using multivariate analysis. Brazilian Journal of Meteorology, v. 27, n. 4, p.423 – 434. 2012.
ARELLANO-LARA, F.; ESCALANTE-SANDOVAL, C. A. Multivariate delineation of rainfall homogeneous regions for estimating quantiles of maximum daily rainfall: a case study of northwestern Mexico. Atmosphere, v. 27, n. 1, p. 47-60. 2014.
ASONG, Z. E.; KHALIQ, M. N.; WHEATER, H. S. Regionalization of precipitation characteristics in the Canadian Prairie Provinces using large-scale atmospheric covariates and geophysical atributes. Stochastic Env. Res. and Risk Asses., v. 29, n. 3, p. 875-892. 2015.
ÁVILA, P. L. R.; DE SOUZA, E. B.; PINHEIRO, A. N.; FIGUEIRA, W. S. Analysis of simulated seasonal precipitation using regcm4 over the state of Pará in years of climatic extremes. Brazilian Journal of Climatology, n. 14. 2014.
BEZDEK, James C. Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York. 1981.
CAI, Y.; JIN, C.; WANG, A.; GUAN, D.; WU, J.; YUAN, F.; XU, L. Spatiotemporal analysis of tropical multisatellite precipitation analysis accuracy 3B42 precipitation data at high mid-latitudes in China. PloS One, v. 10, n. 4, e0120026. 2015.
COSTA, J. C.; PEREIRA, G.; SIQUEIRA, M. E.; DA SILVA CARDOZO, F.; DA SILVA, V. V. Validation of rainfall data estimated by CHIRPS for Brazil. Brazilian Journal of Climatology, v. 24, p. 228-243. 2019.
COSTA, M. H.; PIRES, G. F. Effects of Amazon and Central Brazil deforestation scenarios on the duration of the dry season in the arc of deforestation. International Journal of Climatology, v. 30, n. 13, p. 1970-1979. 2010.
DAVIDSON, E. A.; DE ARAUJO, A. C.; ARTAXO, P.; BATCH, J. K.; BROWN, I. F.; BUSTAMANTE, M. M.; et al. The Amazon basin in transition. Nature, v. 481, p. 321-328. 2012.
DEZFULI, A. K. Spatio-temporal variability of seasonal rainfall in western equatorial Africa. Theoretical and applied climatology, v. 104, n. 1-2, p. 57-69. 2011.
DINKU, T.; CONNOR, S. J.; CECCATO, P.; ROPELEWSKI, C.F. Comparison of global gridded precipitation products over a mountainous region of Africa. International Climatology, v. 11, p. 2960–2979. 2008.
Do AMARAL, M. A. C. M.; JOSÉ, J. V.; FOLEGATTI, M. V.; COELHO, R. D.; BARROS, T. H. S. Spatial distribution of rainfall in relation to the topography in the state of Pará. Irriga, v.1, n. 1, p.1-10. 2016.
DUNN, J. C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Cybernetics and Systems, v. 3, p. 32-5. 1973.
FAZEL, N.; BERNDTSSON, R.; UVO, C. B.; MADANI, K.; KLØVE, B. Regionalization of precipitation characteristics in Iran’s Lake Urmia basin. Theoretical and Applied Climatology, v. 132, n. 1-2, p. 363-373. 2018.
FERREIRA FILHO, D. F.; BEZERRA, P. E. S..; SILVA, M. de N. A. da; RODRIGUES, R. S. S. .; de FIGUEIREDO, N. M. Application of interpolation techniques for spatialization of rainfall in the hydrographic region of Calha Norte, Pará. Brazilian Journal of Climatology, v. 24, p. 341-363. 2019.
FERREIRA FILHO, D. F.; LIRA, B. R. P. .; CRISPIM, D. L.; PESSOA, F. C. L. .; FERNANDES, L. L. Rainfall analysis in the state of Pará: comparison between data obtained from rainfall stations and the GPCC satellite. Brazilian Journal of Climatology, v. 26. 2020.
FITZJARRALD, D. R; SAKAI, R. K.; MORAES, O. L.; COSME DE OLIVEIRA, R.; ACEVEDO, O. C.; CZIKOWSKY, M. J.; BELDINI, T. Spatial and temporal rainfall variability near the Amazon‐Tapajós confluence. Journal of Geophys. Res., v. 113. 2008.
De OLIVEIRA GIL, V.; FERRARI, F.; EMMENDORFER, L. Research on the application of clustering algorithms for the astrophysical problem of classification galaxies. Brazilian Journal of Applied Computing, v. 7, n. 2, p. 52-61. 2015.
GONÇALVES, M. F.; BLANCO, C. J. C.; DOS SANTOS, V. C.; DOS SANTOS OLIVEIRA, L. L.; PESSOA, F. C. L. Identification of Rainfall Homogenous Regions taking into account El Niño and La Niña and Rainfall Decrease in the state of Pará, Brazilian Amazon. Acta Scientiarum. Technology, v. 38, n. 2, p. 209-216. 2016.
HALKIDI, M.; BATISTAKIS, Y.; VARGIANNIS, M. Cluster validity methods: Part. I. ACM SIGMOD Record, v. 31, n. 2. 2002.
IBGE (2010). Census 2010. https://cidades.ibge.gov.br. Access on 10 August 2022.
ISHIHARA, J. H.; FERNANDES, L. L.; DUARTE, A. A. A. M.; DUARTE, A. R. C. L. M.; PONTE, M. X.; LOUREIRO, G. E. Quantitative and Spatial Assessment of Precipitation in the Brazilian Amazon (Legal Amazon) - (1978 to 2007). Brazilian Journal of Water Resources., Porto Alegre, v. 19, p. 29-39. 2014.
KÖPPEN, W.; GEIGER, R. Klimate der Erde. Gotha: Verlagcondicionadas. Justus Perthes, 1928.
LEMOS, A. L. F.; SILVA, J. de A. Deforestation in the Legal Amazon: evolution, causes, monitoring and mitigation possibilities through the Amazon Fund. Forest and Environment, v. 18, n. 1, p. 98-108. 2012.
LIMBERGER, L.; SILVA, M. E. S. Observed precipitation in Brazilian Amazonia: conventional networks and data from Reanalysis I of NCEP/NCAR, CRU and GPCC. Brazilian Journal of Climatology, v. 22, ed. Jan/Jun. 2018.
MENEZES, F. P.; FERNANDES, L. L.; DA ROCHA, E. J. P.. The Use of Statistics for Precipitation Regionalization in the State of Pará, Brazil. Brazilian Journal of Climatology, v. 16, p. 64-71. 2015.
MENEZES, F.; FERNANDES, L. Analysis of trend and variability of precipitation in the State of Pará. Biosphere Encyclopedia, v. 13, no. 24, 2016.
NADOUSHANI, S. S. M.; DEHGHANIAN, N.; SAGHAFIAN, B. A fuzzy hybrid clustering method for identifying hydrologic homogeneous regions. Journal of Hydroinformatics, v. 20, n.6. 2018.
NASH, J. E.; SUTCLIFFE, J. V. River flow forecasting through conceptual models part I – a discusssion of principles. Journal of Hydrology (Amsterdam), v. 10, n. 3, p. 282-290. 1970.
NOBRE, C.; YOUNG, A. F.; SALDIVA, P. H. N.; ORSINI, J. A. M.; NOBRE, A. D.; OGURA, A. T.; et al. Vulnerability of Brazilian Megacities to Climate Change: the São Paulo Metropolitan Region (RMSP). Climate Change in Brazil: economic, social and regulatory aspects. Brasilia: IPEA., v., p. 197-219. 2011.
PAKHIRA, M. K.; BANDYOPADHYAY, S.; MAULIK, U. Validity index for crisp and fuzzy clusters, Pattern Recognition, n. 37, p.481-501.2004.
PARCHURE, A. S.; GEDAM, S. K. Homogeneous regionalization via L-moments for Mumbai City, India. Meteorology Hydrology and Water Management, v. 7, n. 2, p. 73 – 83. 2019.
PEDRYCZ, W.; VUKOVICH, G. Fuzzy clustering with supervision. Pattern Recognition. The Journal of the Pattern Recognition Society, v.37, p. 1339-1349. 2004.
PEREIRA, D. D. R.; ULIANA, E. M.; MARTINEZ, M. A; DA SILVA, D. D. Performance of a concentrated hydrologic model and a semidistributed in the prediction of daily flows. Irriga, Botucatu, v. 21, n. 2, p.409-424. 2016.
PESSOA, F. C. L.; BLANCO, C. J. C.; MARTINS, J. R. Regionalization of flow permanence curves in the region of Calha Norte in the State of Pará. Brazilian Journal of Water Resources, v. 16, p. 65-74. 2011.
RAZIEI, T.; DARYABARI, J.; BORDI, I.; PEREIRA, L. S. Spatial patterns and temporal trends of precipitation in Iran. Theor Appl Climatol, v. 115, p. 531–540. 2014.
RENCHER, A. C.; CHRISTENSEN, W. F. Methods of multivariate analysis. New Jersey: John Wiley and Sons, 2012. 768 p.
RIBEIRO, A.; VICTORIA, R. L.; PEREIRA, A. R.; VILLA NOVA, N. A.; MARTINELLI, L. A.; MORTATTI, J. Analysis of the Rainfall Regime of the Amazon Region from Data from Eleven Locations. Brazilian Journal of Meteorology, v. 11, p. 25 – 35. 1996.
ROUSSEEUW, P. J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, v. 20, p. 53–65. 1987.
SARMADI, F.; SHOKOOHI, A. Regionalizing precipitation in Iran using GPCC gridded data via multivariate analysis and L-moment methods. Theor Appl Climatol, p. 122:121–128. 2014.
SCHNEIDER, U.; FUCHS, T.; MEYER-CHRISTOFFER, A.; RUDOLF, B. Global Precipitation Analysis Products of the GPCC. Global Precipitation Climatology Centre (GPCC) Deutscher Wetterdienst, Offenbach a. M., 2011, Germany.
SHI, W.; YU, X.; LIAO, W.; WANG, Y.; JIA, B. Spatial and temporal variability of daily precipitation concentration in the Lancang River basin, China. Journal of Hydrology, v. 495, p. 197–207. 2013.
SILVA, M. D. N. A. D.; PESSOA, F. C. L.; SILVEIRA, R. N. P. D. O.; ROCHA, G. S.; MESQUITA, D. A. Determination of Homogeneity and Trend of Precipitations in the Tapajós River Basin. Brazilian Journal of Meteorology, v. 33, v. 4, p. 665 675. 2018.
SILVEIRA, C. D. S.; SOUZA FILHO, F. D. A. D.; MARTINS, E. S. P. R.; OLIVEIRA, J. L.; COSTA, A. C.; NÓBREGA, M. T.; et al. Climatic changes in the basin of the São Francisco River: An analysis for precipitation and temperature. Brazilian Journal of Water Resources, v. 21, 416-428. 2016.
SULOCHANA, Y.; CHANDRIKA, P.; BHASKARA RAO, S. V. Rainrate and rain attenuation statistics for different homogeneous regions of India. Indian Journal of Radio & Space Physics, v. 43, p. 301-314. 2014.
TAN, P. N.; STEINBACH, M.; KUMAR, V. Introduction to Data Mining. Addison Wesley. 2005.
WWO. World Meteorological Organization. Commission on Instruments and Methods of Observation. International Organizing Committee for the WMO Solid Precipitation Measurement Intercomparison, final report of the first session, 31 pp., Geneva, 1985.
YAMANA, T. K.; ELTAHIR, E. A. B. On the use of satellite‐based estimates of rainfall temporal distribution to simulate the potential for malaria transmission in rural Africa. Water Resour. Res., v. 47. 2011.
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