Premium
Identifying Sustained Drought Anomalies in Hydrological Records: A Wavelet Approach
Author(s) -
Rashid Md. Mamunur,
Johnson Fiona,
Sharma Ashish
Publication year - 2018
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1029/2018jd028455
Subject(s) - precipitation , environmental science , climatology , climate change , water resources , climatic variability , climate model , geography , geology , ecology , meteorology , oceanography , biology
Abstract Identification of climatic drivers that simulate the variability of year to multiyear sustained rainfall anomalies is important for water resource management. This study provides a comprehensive investigation of the relationship of drought and climatic variables filtered to represent specific frequency bands using a wavelet transform. The Standardized Precipitation Index (SPI) is used to represent drought periods and wet anomalies over Australia. Strong evidence is found that the year to multiyear SPI extremes are strongly related to the low‐frequency signal present in climate variables. Interannual variability (period of 3.73 and 7.4 years) of climate variables are associated with drought at the annual scale, whereas multiyear droughts are strongly influenced by the variability at interdecadal frequencies (period of 14.9 years). The strength of these relationships is found to vary with climate variables and regions. While significant negative correlation between SPI and surface air temperature at interannual and interdecadal frequencies exists over the eastern Australia, this relationship is positive for parts of Western Australia. Regional analysis for 13 river basins over Australia indicates that wavelet decomposed low frequencies of climate variables perform better at predicting drought and wet periods than the unfiltered climate variables. Identifying filtered climatic predictors has significant potential in reconstructing drought records in past climates as well as simulating likely drought for future climates. Additionally, the selection of filtered climatic predictor variables enables an assessment of the improvements needed in climate model simulations so as to improve our ability to better simulate drought or sustained wet extremes.