Rainfall zoning of Bahia State, Brazil: an update proposal

Santos

et al. 2022

Water

In Brazil, the Northeast region, particularly the state of Pernambuco, is prone to extreme hydrological events, especially episodes of heavy rain and long periods of drought. This study examined the spatiotemporal variability of rainfall in Pernambuco and the occurrence of extreme rainfall events. In this study, the following climate indices were used in daily rainfall data from the historical series from 1990 to 2020: (a) the Precipitation Concentration Index (PCI), (b) the Standardized Precipitation Index (SPI), (c) the Precipitation Concentration Degree (PCD), (d) the Precipitation Concentration Period (PCP), and (e) the Rainfall Anomaly Index (RAI), and the non-parametric Mann–Kendall and Sen’s slope tests, for the analysis of trends in the series of precipitation and the studied climate indices. The results obtained indicated that the east of the state presents more distributed precipitation throughout the year, being “moderately seasonal” in the mesoregions Metropolitana do Recife and Zona da Mata; “seasonal” in the Agreste Pernambucano mesoregion; and “strongly seasonal” in the Sertão Pernambucano and Sertão do São Francisco mesoregions. The SPI found that the most severe and extreme droughts occurred in almost all mesoregions during the 1990s, except in the Sertão do São Francisco mesoregion, where they were predominant in the 2010s. Furthermore, based on the RAI index, it was possible to have a greater occurrence of negative anomalies than positive anomalies, especially in the last decade (2010–2020), indicating a possible change in rainfall patterns. However, more in-depth research is required to determine the possible causes of this increased number of negative anomalies. Finally, the trend analysis indicated that only the SPI-12 presented trends in the Metropolitana do Recife, Agreste, and Sertão Pernambucano mesoregions. However, Sen’s slope test indicated that the magnitude of these trends is not significant.

Section: Discussionmentioning

confidence: 99%

Climate Indices-Based Analysis of Rainfall Spatiotemporal Variability in Pernambuco State, Brazil

Santos

et al. 2022

Water

“…Thus, Thornthwaite climate classi cation produced an overestimation of the arid climate in relation to reality. Similarly, according to this classi cation, the climate in the southeastern coast of Bahia fell into the moist subhumid category, but this area is actually known to have a humid climate (Sambuichi & Haridasan, 2007;Simões et al 2017;Mencia et al 2017;Mencia et al al. 2021).…”

Section: Climate Classi Cationmentioning

confidence: 99%

New climate classification for Northeast Brazil using reanalysis data

Sales

Matsunaga

Júnior

et al. 2023

Preprint

The Climate of the Northeast Region of Brazil (NEB) has been intensively studied and analyzed. Studies have used objective climate classification methods such as the aridity index of the United Nations Environment Programme (UNEP) (AIUNEP), however, without fully satisfactory results. The input variables needed for calculation of the AIUNEP are precipitation and reference potential evapotranspiration (ET0). However, although rainfall stations that record routine measurements of precipitation data are well distributed in the NEB, weather stations that provide the set of variables necessary for the estimation of ET0 are scarce. When weather stations are absent in a location, ET0 is interpolated, but this can lead to errors. Another objective climate classification approach is the Thornthwaite method, which is based on the determination of the moisture index (Im), whose calculation also requires weather stations data. Therefore, seeking improve the spatial distribution of information on meteorological variables in the NEB, the present work had as one of the objectives to validate reanalysis data from ERA5 of the European Center for Medium-range Weather Forecast (ECMWF) and the unified gauge-based analysis of global daily precipitation project of the Climate Prediction Center/National Oceanic and Atmospheric Administration (CPC/NOAA). After validation, climatic classifications were developed for the NEB using the AIUNEP and Im indices. It was observed that Thornthwaite climate classification tended to overestimate the aridity of the NEB, while the classification using AIUNEP tended to underestimate it. For these reasons, a new climate classification index, called absolute aridity index (Iab), yielding satisfactory results.

“…O objetivo de identificar regiões pluviometricamente homogêneas foi temática de vários trabalhos. Para o estado da Bahia autores definiram um novo zoneamento com 10 agrupamentos levando em consideração a principal característica climática da localidade, utilizando os parâmetros das altas variabilidades das precipitações, e as grandezas estatísticas das distribuições espacial e temporal das chuvas (Simões et al, 2018). No estado do Paraíba, o método de agrupamento de Ward foi utilizado para agrupar quatro e cinco regiões homogêneas, dos índices climáticos (umidade, aridez e hídrico), ajustando-se a realidade da climatologia da chuva (De Freitas et al, 2013).…”

Section: Variáveisunclassified

Contribuição de técnicas multivariadas a índices de seca na compreensão das otto-regiões da bacia do São Francisco

Santos

Barreto

Barbosa

et al. 2021

RSD

Objetivo: Utilizar as técnicas de análise multivariada, em particular a análise de componentes principais, para encontrar a melhor descrição dos índices de seca, e em seguida usar a análise de agrupamentos para determinar as regiões homogêneas do Rio São Francisco. Método: Foram calculados os índices para caracterizar a seca: Média anual de precipitação máxima diária (anualmax), média anual de precipitação acumulada (amount), média anual de dias sem chuva (<1 mm) (noprec), média anual de dias consecutivos sem chuva (<1 mm) (consecdry), média anual de dias consecutivos com chuva (>=1 mm) (consecwet) e a média anual de dias chuvosos com Precipitação excedendo o percentil 90% (prec90). Esses índices foram ortogonalizados por meio do método de componentes principais e posteriormente agrupados por meio do método de K-médias. Resultados: As variáveis amount e prec90 são as mais importantes, e juntas no primeiro componente são responsáveis por 40,56%, e as variáveis noprec e consecwet foram importantes para explicar 31,04% na segunda componente, e juntas explicam 71,60% da variabilidade total dos dados. Através da variabilidade de 86,40% nas três primeiras componentes principais retidas, a técnica de agrupamentos K-médias possibilitou a divisão de quatro áreas homogêneas na bacia do São Francisco. Conclusões: Foram observadas 4 regiões que são compostas pelas regiões do baixo e Sub-Médio São Francisco, Alto São Francisco e o Médio São Francisco, dividindo-se em duas partes que não ocorre uma perfeita correspondência com as otto-regiões estabelecidas.