Introduction to the special issue: historical and projected climatic changes to Australian natural hazards

Westra, Seth; White, Christopher J.; Kiem, Anthony S.

doi:10.1007/s10584-016-1826-7

Cited by 21 publications

(9 citation statements)

References 79 publications

(67 reference statements)

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“…As indicated, we explore the issue of the human response to disasters via studies of selected catastrophic bushfires in Australia. The size of Australia and its varied climates mean that it is affected by a range of weather‐related natural hazards, including tropical and extratropical storms and associated extreme wind and hail, coastal and inland floods, heatwaves, and bushfires (Westra, White, & Kiem, ). Bushfires are one of the most frequent natural hazards experienced in Australia (Sharples et al., ).…”

Section: Research Contextmentioning

confidence: 99%

Human error during the multilevel responses to three Australian bushfire disasters

Brooks

Curnin

Bearman

et al. 2018

Contingencies & Crisis Mgmt

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The scale and complexity associated with the coordinated response to natural disasters inevitably produce human errors. However, little is known about the frequency and distribution of human error at different levels of coordination during disasters. The purpose of this research was to explore this phenomenon for selected catastrophic bushfires in Australia. To accomplish this, we used the Human Factors Analysis and Classification System that has been widely applied to accidents but is untested with respect to the complexity and temporality of disasters. The results identified that decision errors made during these disasters differed depending upon the level of coordination but were associated with information uncertainty, fatigue, coordination complexities, procedural violations, and degraded personal interactions.

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Section: Research Contextmentioning

confidence: 99%

Human error during the multilevel responses to three Australian bushfire disasters

Brooks

Curnin

Bearman

et al. 2018

Contingencies & Crisis Mgmt

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“…However, the Australian climate is highly variable (e.g., Herold et al, 2018;Westra et al, 2016), making the analysis of extremes over different subregions more complex. The climate over Australia is related to a variety of physical mechanisms and teleconnections to modes of internal climate variability.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Extreme Temperatures Over Australia in the Historical Simulations of CMIP5 and CMIP6 Models

et al. 2021

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 The assessment on the probability distributions of daily maximum and minimum temperature makes the evaluation of extremes more robust  Temperature extremes over Australia are broadly similar in CMIP5 and CMIP6 There are differences in estimating internal variability across multiple CMIP6 models Accepted ArticleThis article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

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“…This is because 48‐month SPI time series is smoother than 12‐month SPI time series as it is estimated by aggregating 48 months previous rainfall and thus most closely resembles with the variability of filtered SLP time series at interdecadal frequency band. For these longer time scale droughts, the influence of SLP is strongest in eastern Australia (EA) where the multidecadal Interdecadal Pacific Oscillation (IPO) is known to be influential in water resources systems and droughts specifically (Kiem et al, ; Westra et al, ).…”

Section: Resultsmentioning

confidence: 99%

Identifying Sustained Drought Anomalies in Hydrological Records: A Wavelet Approach

Rashid

Johnson

2018

JGR Atmospheres

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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.

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Introduction to the special issue: historical and projected climatic changes to Australian natural hazards

Cited by 21 publications

References 79 publications

Human error during the multilevel responses to three Australian bushfire disasters

Human error during the multilevel responses to three Australian bushfire disasters

Evaluation of Extreme Temperatures Over Australia in the Historical Simulations of CMIP5 and CMIP6 Models

Identifying Sustained Drought Anomalies in Hydrological Records: A Wavelet Approach

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