Climate conditions in the eastern amazon: Rainfall variability in Belem and indicative of soil water deficit

Abstract

The objective of this study was to evaluate the regimes of temperature and rainfall in Belém, PA, Brazil, with emphasis on the start of the dry season in order to provide planning support for agricultural activities during years of climatic anomolies in the region. An initial analysis was done of the metropolitan region of Belém comparing it to the typology of Amazonian climates using rainfall data from 1971 to 2014 and creating an annual index of precipitation anomolies (AIPA). The temperature regime was described using a homogeneous rainfall dataset from 1990 to 2014. The hydrological balance was estimated for the period 1990 to 2014 using an index of capacity of soil water availability equal to 100 mm to identify the months with deficit or excess of soil water. Box plots were analyzed by decade and maximums of daily rainfall for the month of August were used. The Pareto principle was applied to 9 indices to assess the effects of rainfall quantity in relation to anomalous years. Although the metropolitan region of Belém is for the most part categorized by the Af2 climate type it is possible to have prolonged soil water deficit from August through November, an effect that is intensified by the El Niño phenomenon. Furthermore, in the month of August there were years with extreme rainfall events, such as that of August 7th, 2010 where 72.4 mm of rainfall occurred representing 53% of total monthly rainfall. This event can be explained by the intense waves of humidity coming from the East that amplified local rainfall. During the last two decades extreme daily rainfall events have become more frequent, and rainfall reductions in the region have tended to intensify in areas that historically receive less rainfall such as the transition between the Amazonian and Savannah biomes. Therefore, in La Niña or El Niño years, the month of August can be considered to be the signal for meso-and large-scale atmospheric mechanisms that influence precipitation regimes and that can have a negative effect of the region's agricultural productivity.

Dynamics of Space and Time of the Production Chain of the Ceramic Industry Production Center of Iranduba, Amazonas, Brazil

“…In order to identify the spatial variability of climate conditions in the study area, information from Martorano [19] was used, including an adaptation of the Köppen method proposed [20]. Data from the National Institute of Meteorology (INMET) and the National Water Agency (ANA) were also used, together with matrix data from the WorldClim Global Climate Data platform, with interpolated data at a spatial resolution of 1 km [21], in agreement with the data extraction assumptions described [22,23]. In the municipality of Iranduba, in the State of Amazonas, the predominant climate type is Af 3 , principally in the northeast of the municipality, and transitions to Am 3 which predominates in the rest of the area, and these classifications have monthly rainfall above and below 60 mm, respectively, during the months with the lowest amount of rainfall in the Amazon region ( Figure 2).…”

Occurrence of anti-Toxoplasma gondii and anti-Neospora caninum antibodies in pigs in the State of Pará, Brazil

“…The study was conducted in the Northeast of Pará state, Northern Brazil, located in the Amazon Biome (Figure 1). The climate of the region is characterized by an annual mean temperature of 25.9 °C and an annual mean rainfall of 2,150 mm, with a strong concentration from January to June, and more rarely, from July to December (Martorano et al, 2017).…”

Análise observacional e de precipitação em uma fazenda experimental na Amazônia Oriental entre 1914 e 1981

“…O estado do Pará é caracterizado por grande variabilidade nas chuvas devido às influências de grande escala que acabamos de discutir, principalmente entre os meses de dezembro a maio (MARTORANO et al, 2017). Estudos avaliando regiões homogêneas, têm mostrado que os regimes de chuva possuem características locais e regionais específicas (HOFFMANN et al, 2018).…”