Drought characterization for the state of Rio de Janeiro based on the annual SPI index: trends, statistical tests and its relation with ENSO

Sobral, Bruno Serafini; Oliveira‐Júnior, José Francisco de; Góis, Givanildo de; Pereira-Júnior, Edson Rodrigues; Terassi, Paulo Miguel de Bodas; Muniz-Júnior, João Gualberto Rodrigues; Lyra, Gustavo Bastos; Zeri, Marcelo

doi:10.1016/j.atmosres.2019.01.003

Cited by 82 publications

(46 citation statements)

References 55 publications

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“…Over the decades, several indices have been developed with research or operational bias (Heim Jr., 2002; Mishra and Singh, 2010; Zargar et al, 2011). Recently, the Drought Indices Calculator (DrinC) software was created aiming to unify traditional and new drought indices in the literature, such as reconnaissance drought index (RDI), streamflow drought index (SDI), standardized precipitation index (SPI), precipitation deciles (PD), agricultural standardized precipitation index (aSPI), and effective reconnaissance drought index (eRDI) (Tigkas and Tsakiris, 2015; Sobral et al, 2018; Sobral et al, 2019; Macedo et al, 2010). It is worth mentioning that the DrinC software can be applied to various types of drought (agricultural, hydrological, and meteorological) in different sites around the world.…”

Section: Introductionmentioning

confidence: 99%

Rainfall extremes and drought in Northeast Brazil and its relationship with El Niño–Southern Oscillation

Costa

Oliveira‐Júnior

Santos

et al. 2020

Intl Journal of Climatology

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The objective of this study was to evaluate the annual standardized precipitation index (SPI) obtained from the DrinC software based on multivariate analysis in the identification of rainfall and drought extremes in the State of Alagoas and its relationship with El Niño-Southern Oscillation. Monthly rainfall data from 1960 to 2016 from National Water Agency were analysed. Annual SPI (SPI-12) has been designed for comparison with ENSO phases via Oceanic Niño Index for 3.4 region and in identifying climate extremes in the State of Alagoas. The principal component analysis and cluster analysis techniques were applied to the rainfall series of SPI-12. Extreme events were identified in both rainy and drought periods according to SPI-12, and were associated with the ENSO phases (El Niño, La Niña, and Neutral). The first four principal components explained 46.68% of the variance. Our findings are crucial for agriculture and civil defence since northeastern Brazil has several areas of risk and social vulnerability.

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Section: Introductionmentioning

confidence: 99%

Rainfall extremes and drought in Northeast Brazil and its relationship with El Niño–Southern Oscillation

Costa

Oliveira‐Júnior

Santos

et al. 2020

Intl Journal of Climatology

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“…Labedzki et al [29] used SPI to verify the precipitation condition forecast and found that the 10-day SPI forecast was reliable no matter the SPI classification forecast or the SPI value forecast, and the 20-day forecast should be reserved, accepted, and used cautiously. Sobral et al [30] found that SPI could better reflect the drought situation in Rio de Janeiro in Brazil. SPI plays an important role in the analysis of drought characteristics because the income of precipitation does have a direct impact on the occurrence and evolution of drought.…”

Section: Introductionmentioning

confidence: 99%

Drought Trends and the Extreme Drought Frequency and Characteristics under Climate Change Based on SPI and HI in the Upper and Middle Reaches of the Huai River Basin, China

Hou

Wang

Fan

et al. 2020

Water

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The Huai River Basin (HRB) is an important grain and industrial production area in eastern China with frequent droughts. Under the background of current climate change, the hydrological and meteorological characteristics of the basin may be changed, which may lead to the changes of regional drought characteristics. It should be paid more attention on the drought research under climate change and the difference between different drought indices. Coupled Model Intercomparison Project phase 5 (CMIP5) as an important tool for climate change research has been used in the study and the study chosen three global circulation models (GCMs)—such as CNRM-CM5 (CNR), HadGEM2-ES (Had), and MIROC5 (MIR)—to gather an ensemble model (EnM) for providing the future climate information. The Standardized Precipitation Index (SPI) and Humidity Index (HI) were used to evaluate and compare the drought situations in the past and the future periods with two representative concentration path scenarios (RCP4.5 & RCP8.5). Some sequence statistics methods, such as Mann–Kendall test and run theory, were carried out to analyze the trend and the changes of extreme drought frequency and characteristics values. The research showed that the simulation accuracy of the EnM would better. SPI and HI take different factors into count and thus lead to differences in describing drought trend, extreme drought frequency, and characteristic values, such as drought severity, drought duration, mean drought severity, and max drought intensity. The research showed that both SPI and HI showed the same wetting or drying trend in the same timescales (except winter) as in the historical data. However, the future annual and seasonal drought trend reflected by SPI shows a wetting trend while HI shows a drying trend. Both in the past and the future, extreme drought frequency and characteristic values reflected by HI are higher than SPI. The drought trend is greater, and the extreme drought frequency and characteristics tend to be strengthened under RCP8.5. Low precipitation and high potential evapotranspiration (PET), especially the PET caused by temperature rise, are the main influencing factors of drought in the future. Therefore, the influence of the PET should not be ignored in drought analysis and we should strengthen the comparative study of different drought indices in future drought analysis under climate change.

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“…SPI trends can be evidenced through the innovative Sen's method, as pointed out by Yilmaz (2019). Moreover, other future works should use our strategy concerning splitting state territory in regional areas with different agricultural uses to carry out correlation analysis between SPI values and corresponding regional yields or El Niño Southern Oscillation Index values or fluviometric levels or water reservoir levels or piezometric levels (for instance, see Sobral et al,2019).…”

Section: Standardized Precipitation Indexmentioning

confidence: 99%

Negative regional Standardized Precipitation Index trends prevail in the Mexico’s state of Zacatecas.

Magallanes‐Quintanar

Blanco‐Macías

Galván-Tejada

et al. 2019

Terra

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Drought characterization for the state of Rio de Janeiro based on the annual SPI index: trends, statistical tests and its relation with ENSO

Cited by 82 publications

References 55 publications

Rainfall extremes and drought in Northeast Brazil and its relationship with El Niño–Southern Oscillation

Rainfall extremes and drought in Northeast Brazil and its relationship with El Niño–Southern Oscillation

Drought Trends and the Extreme Drought Frequency and Characteristics under Climate Change Based on SPI and HI in the Upper and Middle Reaches of the Huai River Basin, China

Negative regional Standardized Precipitation Index trends prevail in the Mexico’s state of Zacatecas.

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