Spatial Interpolation Techniques to Map Rainfall in Southeast Brazil

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.

Rainy season and crop calendars comparison between past (1950–2018) and future (2030–2100) in Madagascar

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A simple method for the estimation of minimum and maximum air temperature monthly mean maps using MODIS images in the region of Murcia, Spain

“…Traditionally global regressions are used which seek to understand the spatial behaviour of a variable through a unique equation, however, this equation's coefficients do not vary spatially (Aparecido et al, 2022;Lorençone et al, 2022). This search is done with a methodology named distance Weighted Least Squares (WLS); these weighted nonnegative constants being a function between each point and the rest (Fotheringham et al, 2002).…”

Climate zoning: identifying suitable regions for the occurrence of Alternaria Brown spot in tangerine trees in Brazil

“…33 The dissemination of the fungus is more significant in areas where the rainy season coincides with high temperatures in the summer, which is common in Southeast Brazil. 40,42 Dissemination also can occur under dry weather conditions when there is morning dew and presence of winds. 43 However, despite extensive knowledge about Alternaria in citrus, there is a lack of studies evaluating the influence of climate on Alternaria infection throughout Brazil in the international literature.…”

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