@Article{ZeriCunGoiLyrOli:2019:ExAsRa,
author = "Zeri, Marcelo and Cunha-Zeri, Gisleine da Silva and Gois,
Givanildo and Lyra, Gustavo B. and Oliveira J{\'u}nior, Jos{\'e}
Francisco",
affiliation = "{Centro Nacional de Monitoramento e Alertas de Desastres Naturais
(CEMADEN)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}
and {Universidade Federal Fluminense (UFF)} and {Universidade
Federal Rural do Rio de Janeiro (UFRRJ)} and {Universidade Federal
do Alagoas (UFAL)}",
title = "Exposure assessment of rainfall to interannual variability
usingthe wavelet transform",
journal = "International Journal of Climatology",
year = "2019",
volume = "39",
number = "1",
pages = "jan.",
abstract = "It is generally accepted that El NiņoSouthern Oscillation (ENSO)
is the main modulator of rainfall variability over Northern South
America in the interannual scale. Assuming that, an index is
proposed to quantify this expected interannual variability in time
series of rainfall. The result is the exposure assessment to the
effects of droughts, measured with the Standardized Precipitation
Index (SPI) for the monthly scale. The SPI is calculated from
rainfall series, and wavelet analysis is used to estimate the
variance for different frequencies present in the signal. The
Wavelet Interannual Variability Index (WIVI) is calculated as the
sum of the wavelet coefficients over a predetermined range of
modes of variability (with periods longer than 2 year and shorter
than 8 years). The index was tested using a dataset of rainfall
records from Tocantins state, Central Brazil. Most of the series
ranged from 1974 to 2012. On average, the series had 3.2% of gaps
which were not filled to avoid the effect of artificial trends on
the data. The state lies mostly over the Cerrado biome and is a
new frontier of agricultural development in Brazil. According to
the results, the Northern region is under higher exposure of
interannual variability, with higher values of WIVI. The
assessment is in agreement with large\‐scale features of
South American climate, specifically considering the influences of
the Pacific and Atlantic Oceans and their patterns of sea surface
temperature (SST).",
doi = "10.1002/joc.5812",
url = "http://dx.doi.org/10.1002/joc.5812",
issn = "0899-8418",
language = "en",
targetfile = "zeri_exposure.pdf",
urlaccessdate = "27 abr. 2024"
}