Rainfall variability over Alagoas under the influences of SST anomalies

Lyra, Guilherme Bastos; Júnior, José Francisco Oliveira; Góis, Givanildo de; Cunha-Zeri, Gisleine; Zeri, Marcelo

doi:10.1007/s00703-016-0461-1

Cited by 86 publications

(97 citation statements)

References 52 publications

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“…The climate has a strong influence on triggering extreme events such as prolonged and severe droughts, which aggravates environmental degradation as the episode occurred in 2005 in the Amazon biome (Marengo et al, 2016a;Panisset et al, 2018), and also in the Caatinga biome (Barbosa, Kumar, Paredes, Elliott, & Ayuga, 2019;Lyra, Oliveira-Júnior, Gois, Cunha-Zeri, & Zeri, 2017;Marengo et al, 2018;Marengo, Torres, & Alves, 2016b), which resulted in huge socio-economic and environmental damage.…”

Section: Introductionmentioning

confidence: 99%

Rainfall variability in the Brazilian northeast biomes and their interactions with meteorological systems and ENSO via CHELSA product

et al. 2019

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Brazilian biomes are home to a significant portion of the world's biodiversity, with a total of 14% of existing species and still concentrate 20% of the world's water resources. However, changes in biomes have a direct impact on rainfall patterns and water recycling. Based on this, the objective was to evaluate the variability of rainfall in the four existing biomes in the Northeast Brazil (NEB) and their interaction with the ENSO climate variability mode and regional scale meteorological systems via CHELSA product. For this, monthly rainfall data were used from 1979 to 2013, with a spatial resolution of 1 km × 1 km of the CHELSA product, and seasonal and annual rainfall patterns were extracted via boxplot. It was found that the rainy season in the Amazon, Caatinga and Cerrado biomes occurred between January and April, with varying intensities, except for the Atlantic Forest. Such seasonality patterns are associated with the NEB meteorological systems, with emphasis on ITCZ (all Biomes), UTCV (Amazon, Caatinga and Cerrado), Frontal Systems (extreme south of Caatinga, Cerrado and Atlantic Forest) and EWD/ TWD in the (Atlantic Forest). In the inter-annual scale, the remarkable influence of ENSO was verified,

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Section: Introductionmentioning

confidence: 99%

Rainfall variability in the Brazilian northeast biomes and their interactions with meteorological systems and ENSO via CHELSA product

et al. 2019

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“…The SPI was proposed by Mckee et al (1993) and has been widely used because it is normalized in time and is normalized to a location (Wu et al, 2007). While the normalized nature of the SPI gives it the ability to represent wet and dry events in a similar way (Wu et al, 2007;Lyra et al, 2017), the multi-scalar nature of this index facilitates the quantitative comparison of drought occurrence at different locations and over different timescales (Lloyd-Hughes and Saunders, 2002).…”

Section: Introductionmentioning

confidence: 99%

Using the normality assumption to calculate probability‐based standardized drought indices: selection criteria with emphases on typical events

Blain

Ávila

Pereira

2017

Intl Journal of Climatology

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Enhancing the capability of both standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) for quantifying wet and dry events under distinct climate conditions is of paramount importance. The different recommendations of recent studies regarding the best distribution to calculate the SPEI and the lack of studies addressing the effect of different parameters estimation methods on the SPI motivated us to apply and adapt distinct testing methodologies to select candidate models for calculating these standardized drought indices (SDI). The study is based on two data sets. The first represents a tropical–subtropical region of Brazil. The second comprises the same weather stations that were used for developing the original version of the SPEI. The study also emphasized the performance of the models within the range of typical SDI values [−2.0 : 2.0]. Along with goodness‐of‐fit tests, we calculated the mean absolute errors between the indices values estimated from the candidate distributions, and their corresponding theoretical values derived from the standard normal distribution. The two‐parameter gamma and the generalized extreme value distributions are, respectively, recommended for general use in SPI and SPEI algorithms (1–12‐month timescales). The unbiased probability weighted moments are recommended to estimate the distributions parameters. The study also described a trade‐off between choosing the best model for the central part and for the tails of the distributions. This trade‐off suggests that the methodologies used to select models for the SDI algorithms may have to decide which part of the distribution (central or tails) should be emphasized. The behaviour of the errors among different wet/dry categories showed that both indices were only capable of representing drought and floods in a similar probabilistic way within the range [−2.0 : 2.0]. This feature supports our decision to emphasize model performances within such range.

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“…Although most regions of Brazil have suffered extreme droughts, their impacts are significantly more complex in the semiarid region of Northeast Brazil (NEB) due to its high variability of precipitation in both time and space [8][9][10]. It is also the world's most densely populous dry land region [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the SMOS-Derived Soil Water Deficit Index as Agricultural Drought Index in Northeast of Brazil

Paredes‐Trejo

Barbosa

2017

Water

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Northeast Brazil (NEB) has recently experienced one of its worst droughts in the last decades, with large losses on rainfed agriculture. Soil moisture is the main variable to monitor agricultural drought. The remote sensing approach for drought monitoring has been enriched with the launch of the Soil Moisture and Ocean Salinity (SMOS) in November 2009 by European Space Agency (ESA). In this work, the Soil Water Deficit Index (SWDI) was calculated using the SMOS L2 soil moisture in the NEB. The SMOS-derived SWDI data (SWDIS) were evaluated against the atmospheric water deficit (AWD) calculated from in situ observations. Comparisons were made at seven-day and 0.25 • scales, over the time-span of June 2010 to December 2013. It was found that the SWDIS has a reasonably good overall performance in terms of the drought-weeks detection (skill = 0.986) and capture of the upper soil moisture temporal dynamic (r = 0.652), implying that the SWDIS could be used to track agricultural droughts. Furthermore, SWDIS shows poor performance at sites located in mountains regions affected by severe droughts (−0.10 ≤ r ≤ 0.10). It is also noted that the vegetal cover/use, climate regime, and soil texture have little influence on the AWD-SWDIS coupling.

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Rainfall variability over Alagoas under the influences of SST anomalies

Cited by 86 publications

References 52 publications

Rainfall variability in the Brazilian northeast biomes and their interactions with meteorological systems and ENSO via CHELSA product

Rainfall variability in the Brazilian northeast biomes and their interactions with meteorological systems and ENSO via CHELSA product

Using the normality assumption to calculate probability‐based standardized drought indices: selection criteria with emphases on typical events

Evaluation of the SMOS-Derived Soil Water Deficit Index as Agricultural Drought Index in Northeast of Brazil

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