Análise espaço-temporal da precipitação na região central do estado de São Paulo utilizando dados CHIRPS

Abstract

A climatologia tropical e as oscilações das chuvas colide nas mais diversas regiões do continentes. Com o avanço das tecnologias, dados pluviométricos vêm sendo utilizados e aplicados para realização de análises ao longo em séries temporais utilizando produtos de sensoriamento remoto orbital. Com isso, o estudo objetivou analisar à distribuição espacial em escala multiespacial e multitemporal de 1981 até 2020, usando com os dados pluviométricos CHIRPS, para a região central do estado de São Paulo. Analisou 40 anos de dados mensais de chuvas, em 31 postos pluviométricos. Utilizou-se das relações entre as estimativas por satélite e os os coletados pelas estações de superfície apenas dos valores observados dentro do recorte temporal. Para isso, foram utilizadas duas análises de erro absoluto médio e erro médio quadrático e aliadas ao coeficiente de determinação. Para análise temporal, aplicou-se o Índice de Precipitação Normalizada (SPI) e tendências por regressão linear. As análises global dos dados evidenciaram que o CHIRPS apresentou-se uma tendência abrupta à subestimar os valores de precipitação (74%), através dos valores médios de ME; valores médios de RMSE variando entre 36,4 mm a 49,3 mm; e a regressão linear de R² acerca de 0.81. E, para as tendências com espacialidade uma distribuição de redução para áreas a sudoeste e norte e de aumento para áreas a sudeste. Essa diferença pode estar associada aos efeitos orográficos. Em tempo, o CHIRPS, proporcionalizou-se uma análise completa de dados séries temporalmente e favoreceu-se com o desenvolvimento do comportamento da atmosfera nas diversas porções da superfície terrestre. Space-time analysis of precipitation, in the central region of the state of São Paulo using CHIRPS dataA B S T R A C TTropical climatology and rainfall oscillations colide in the most diverse regions of the continentes. With the advancement of Technologies, rainfall data have been used and Applied to perform analyzes over time series using orbital remote sensing products. Thus, the study aimed to analyze the spatial distribution on a multitemporal scale from 1981 to 2020, using CHIRPS rainfall data, for the central region of the state of São Paulo. It analyzed 40 years of monthyl rainfall data, in 31 pluviometric stations. The relationships between the satellite estimates and those colleted by surface stations were used Only from the values observed within the time frame. For this, analyzes of mean absolute error mean squared error were used, together with the coeffcient of determination. For temporal analysis, the Normalized Precipitation Index (SPI) and trends by linear regression were applied. The gblobal analysis of the data showed that the CHIRPS presented and abrupt tendency to underestimate the precipitation values (74%), through the average values of ME; mean RMSE values ranging from 36.4 mm to 49.3 mm; and the linear regression of R2 about 0.81. And, for the trens with spatiality, a distribution of reduction for areas to the southeast. This difference may be associated with orographic effects. In time, CHIRPS, a complete analysis of time series data was proportionally and favored with the development of the behavior of the atmosphere in the different portions of the Earth’s surface.

Precipitation Anomalies and Trends Estimated via Satellite Rainfall Products in the Cananeia–Iguape Coastal System, Southeast Region of Brazil

“…With 99% significance, ID7 recorded a trend towards a greater decrease in rainfall of 2.71 mm/year, which corresponds to a decrease of 113.82 mm at the end of the historical series (42 years). The trend of decreasing rainfall in the central region of the state of São Paulo for CHIRPS products is also observed in other studies [16]. Legend: ** = 99% significance; * = 95% significance; = 90% significance; TAU = trend curvature magnitude; P.VA = significance level; K = value that indicates the possibility of locating the point where the ruptures occurred in the series; t = value that indicates the position of ruptures; T0 and Q = critical values of the SNHT and Buishand's tests.…”

“…These values are in line with the occurrence of La Niña during the last years of the series [85]. In recent years, in the central region, the state of São Paulo [16] has also recorded that dry years are more recurrent.…”

“…Considering the annual analysis (SPI-12), of the 42 years analyzed (1981-2022), 1983 was considered an extremely rainy year in practically all rainfall stations, with the exception of ID1 (Figure 4), as also demonstrated by research carried out in the state of São Paulo [16,38,69,71].…”

“…The CHIRPS data allow a complete analysis of the historical series [11]. In addition, compared to surface data, they have higher compatibility, as other research has also demonstrated [16,17,23,24,61,83].…”

“…In this region, the absence of surface rainfall data makes these analyses difficult locally. To meet this demand, these rainfall values from reanalysis are being widely used in climatological research in the search for more complete analyzes in certain regions [13][14][15][16][17]. Rainfall products from satellite rainfall are tools with great potential for analyzing rainfall time series for locations where there is a shortage of historical data from surface rainfall stations [16].…”