Spatial Interpolation Techniques to Map Rainfall in Southeast Brazil

• Published in: Revista Brasileira de Meteorologia 2022-03, Vol.37 (1), p.141-155
• Main Authors: Aparecido, Lucas Eduardo de Oliveira, Moraes, Jose Reinaldo da Silva Cabral de, Lima, Rafael Fausto de, Torsoni, Guilherme Botega
• Format: Article
• Language: English
• Subjects: big data; climate modeling; geostatistics; spatial prediction
• ISSN: 0102-7786; 1982-4351
• DOI: 10.1590/0102-77863710015

Abstract The prediction, as well as the estimation of precipitation, is one of the challenges of the scientific community in the world, due to the high spatial and seasonal variability of this meteorological element. For this purpose, methodologies that allow the accurate interpolation of these elements have fundamental importance. Thus, we seek to evaluate the efficiency of the interpolation methods in the mapping of rainfall and compare it with multiple linear regression in tropical regions. The interpolation methods studied were inverse distance weighted (IDW) and Kriging. Monthly meteorological data rainfall from 1961 to 1990 was obtained from 1505 rainfall stations in the Southeast region of Brazil, provided by the National Institute of Meteorology. The comparison between the interpolated data and the real precipitation data of the surface meteorological stations was performed through the following analyzes: accuracy, presicion and tendency. The mean PYEAR, for summer, autumn, winter, and spring are 596 mm seasons−1 (s= ±118 mm), 254 mm seasons−1 (s= ±52 mm), 114 mm seasons−1 (s= ±54 mm) and 393 (s= ± 58 mm) mm seasons−1, respectively. The Kriging highlight accuracy slightly high in relation to IDW. Since the MAPEKRIGING was of 2% while the MAPEIDW was of 3%. The IDW and Kriging methods were accurate and, with low trends in precipitation estimation. While multiple linear regression showed low accuracy when compared with interpolation methods. Despite the lower accuracy the regression linear is more practical and easy to use, as it estimates the rain with only altitude, latitude and longitude, input variables that commonly known input variables. The largest errors in estimating the spatial distribution of precipitation occurred in Winter for all interpolation methods. Resumo A previsão, assim como a estimativa de precipitação, é um dos desafios da comunidade científica no mundo, devido à alta variabilidade espacial e sazonal deste elemento meteorológico. Para tanto, metodologias que permitam a interpolação precisa desses elementos são de fundamental importância. Assim, buscamos avaliar a eficiência dos métodos de interpolação no mapeamento de chuvas e compará-la com regressão linear múltipla em regiões tropicais. Os métodos de interpolação estudados foram distância inversa ponderada (IDW) e Krigagem. Dados meteorológicos mensais de chuva de 1961 a 1990 foram obtidos de 1.505 estações pluviométricas da região Sudeste do Brasil, fornecidos pelo Instituto