Rainfall in Brazilian Northeast via in situ data and CHELSA product: mapping, trends, and socio-environmental implications

Oliveira‐Júnior, José Francisco de; Filho, Washington Luiz Félix Correia; Santiago, Dimas de Barros; Góis, Givanildo de; Costa, Marcelo Viana da; Silva, Carlos Antônio da; Teodoro, Paulo Eduardo; Freire, Felipe Machado

doi:10.1007/s10661-021-09043-9

Cited by 19 publications

(33 citation statements)

References 61 publications

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“…CHIRPS provided the best estimate of rainfall magnitude both inside and outside forest, increasing linearly with observed values and explaining around half the variation for in situ rain gauges. Other studies have found CHIRPS to be the best option in tropical regions (Beck et al ., 2017), although WorldClim and Chelsa explained >70% of the variation for higher density stations in Brazil and Colombia (Bastidas Osejo et al ., 2019; de Oliveira‐Júnior et al ., 2021). In northern Peru, CHIRPS predicted rainfall greater than 150 mm·month −1 and 1,000 mm·year −1 poorly.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal climate variability in the Andes of northern Peru: Evaluation of gridded datasets to describe cloud forest microclimate and local rainfall

Newell

Ausprey

Robinson

2022

Intl Journal of Climatology

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Tropical montane cloud forest may be especially sensitive to climate change. However, our ability to understand effects of climate on montane biodiversity remains limited by the resolution of climate data. We compared 5 years of in situ weather data from cloud forests in northern Peru, regional weather stations, and gridded datasets to examine how climatologies reflect (a) forest microclimate buffering and (b) local rainfall in a sparse data region; we also examined spatiotemporal variability and regional trends. Across a 1,700–3,100 m gradient in which temperature did not covary with relative humidity (RH), in situ data showed interactions between climate and land‐use. Forest humidity buffered warming‐induced evaporative drying across elevations, and inside forest maximum vapour pressure deficit (VPDmax) did not change with elevation, whereas with a 22% reduction in RHmin at stations, VPDmax increased >10‐fold from high to low elevations. Cloud forest dried out on sunny days after 3 days without rain, especially during ENSO‐related drought concurrent with peak solar insolation. Climatologies were twice as precise for temperature as rainfall. Chelsa captured a 3.9°C reduction in maximum temperatures inside forest (MAE 1.6°C, R2 = 0.95) whereas WorldClim reflected drier lapse rates and higher Tmax outside forest. CHIRPS provided the best fit for monthly rainfall (MAE 23 mm, R2 = 48), capturing regional drought but underestimating rainfall >150 mm·month−1. Consistent with stations, CHIRPS showed strong support for regional increases in wet‐season rainfall. Reduced variability and more regular dry seasons were only detected by montane stations, especially south of 6°S, where rainfall seasonality shifted to earlier wet‐season peaks and reduced dry‐season rainfall as part of a transition from the Northern to Central Andes. Our results show that cloud forests may be partly buffered from warming but are likely to become extremely vulnerable under reduced humidity either through forest loss or drought.

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Section: Discussionmentioning

confidence: 99%

Spatiotemporal climate variability in the Andes of northern Peru: Evaluation of gridded datasets to describe cloud forest microclimate and local rainfall

Newell

Ausprey

Robinson

2022

Intl Journal of Climatology

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“…Este conjunto de dados foi processado a partir da técnica estatística de downscaling para refinamento de grade de 50 km para 25 km, utilizando o método de interpolação B-Spline com a ajuda de dados de precipitação da Era-Interim. No caso das chuvas, há a inclusão dos efeitos da topografia, padrão de vento e altura de Camada Limite Atmosférica (ABL), ao qual são refinados de 25 km para 1 km (Karger et al, 2017;Correia Filho et al, 2019;Oliveira-Júnior et al, 2021).…”

Section: Dadosunclassified

Evaluation of environmental degradation on Biomes in Brazilian Northeastern

Batista

Filho²,

Santos

et al. 2022

Jour. Hypers. Rem. Sensg.

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In recent years, due to irregular rainfall, several regions across the globe suffer from some type of environmental degradation. Depending on the degree of intensity, they can lead to a series of socioeconomic problems, both regional and national. One of the most emblematic types of degradation today is the desertification development process, resulting in a combination of factors: anthropic, climatic, and/or environmental. In an attempt to understand the behavior of these impacts, this work evaluated the degradation process based on the Aridity Index (AI) on biomes located in the Brazilian Northeast (Caatinga, Atlantic Forest, Amazon, and Cerrado). The AI consists of the relationship between the use of evapotranspiration (in this case, the Thornthwaite estimation method) and precipitation. The data used correspond to the product Climatologies at high resolution for the earth's land surface areas (CHELSA), which have a spatial resolution of 1km x 1km and temporal resolution on a monthly scale, from 1979 to 2013. In addition, it was found that the AI behavior during years of the occurrence of the El Niño-Southern Oscillation (ENSO) warm phase, the El Niños. The identification of El Niño episodes was based on the Oceanic Niño Index (ONI) climate proxy, characterized by the Equatorial Pacific region known as the Niño 3.4 region, from which the years 1983, 1993, 1998, and 2012 were selected. AI results point out different behaviors between the biomes, mainly over the south of the northeastern Cerrado and the Caatinga, motivated by the strong variability of rainfall in the respective regions. The Caatinga stands out, which exhibited a large part of its area was classified as arid (AI between 0.05 -0.20) and hyper-arid (AI < 0.05), mainly in 2012. The Amazon and biomes, on the other hand. Atlantic Forest has areas classified as semi-arid (AI < 0.5). El Niño had a reduction in the AI values, motivated by the long periods of drought and irregular rainfall that negatively impacted the semi-arid of the Northeast region.

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“…Os meses de janeiro, novembro e dezembro (estação seca), os meses de abril e julho (estação chuvosa) e os meses de agosto e setembro (estação transição) - (Bomfim e Januário, 2018;Oliveira Júnior et al, 2021). Os dados de casos de dengue em Maceió foram assimétricos positivos na série temporal.…”

Section: Distribuição Mensal Dos Casos De Dengueunclassified

“…A sazonalidade da chuva é peça chave na variabilidade dos casos de dengue em Arapiraca (Correia Filho, 2017). No município de Marechal Deodoro (Figura 3c), os meses com os valores próximos do 1° quartil foram março, abril e maio, novamente os dados foram assimétricos positivos similares a Maceió, tal similaridade se deve ambas fazer parte da Região Metropolitana de Maceió (RMM) - (Oliveira Júnior et al, 2020;Oliveira Júnior et al, 2021).…”

Section: Distribuição Mensal Dos Casos De Dengueunclassified

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Dynamics of notified cases of dengue in Alagoas: Geospatialization and Applied Statistics

Silva

Júnior

Filho

et al. 2021

RSD

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The aim of the study was to evaluate the reported cases of dengue in the State of Alagoas via applied statistics and geospatialization.Dengue data were obtained from the DATASUS system between 2000 and 2015. The time series was submitted to descriptive and exploratory statistics and the Pettitt test. In the dengue case maps, the Spline method via QGis version 3.4 was used. In descriptive statistics, only 10 municipalities were evaluated based on the largest case records. All municipalities were above the average of dengue cases in 2010, the year of the biggest recent flood in the state. The boxplot pointed out that all 10 municipalities were positive asymmetric in a few months for dengue cases, the exception was Delmiro Gouveia. The Pettitt test identified biannual cycles (2006/2007 and 2009/2010) associated with the phases of ENSO (El Niño and La Niña) in the moderate category, followed by the months of January, February, April and December, corresponding to the performance of some systems synoptics. In the mapping of annual cases of dengue, the highest records occurred in the Agreste and East Alagoas. Geospatialization and applied statistics are efficient in the spatiotemporal evaluation of dengue cases in Alagoas.

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Rainfall in Brazilian Northeast via in situ data and CHELSA product: mapping, trends, and socio-environmental implications

Cited by 19 publications

References 61 publications

Spatiotemporal climate variability in the Andes of northern Peru: Evaluation of gridded datasets to describe cloud forest microclimate and local rainfall

Spatiotemporal climate variability in the Andes of northern Peru: Evaluation of gridded datasets to describe cloud forest microclimate and local rainfall

Evaluation of environmental degradation on Biomes in Brazilian Northeastern

Dynamics of notified cases of dengue in Alagoas: Geospatialization and Applied Statistics

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