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Exposure assessment of rainfall to interannual variability using the wavelet transform
Author(s) -
Zeri Marcelo,
CunhaZeri Gisleine,
Gois Givanildo,
Lyra Gustavo B.,
OliveiraJúnior José Francisco
Publication year - 2019
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.5812
Subject(s) - climatology , environmental science , precipitation , series (stratigraphy) , multivariate enso index , el niño southern oscillation , scale (ratio) , range (aeronautics) , wavelet , index (typography) , pacific decadal oscillation , la niña , geography , geology , meteorology , paleontology , materials science , cartography , artificial intelligence , computer science , world wide web , composite material
It is generally accepted that El Niño–Southern 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).