Long-term variability of the leading seasonal modes of rainfall in south-eastern Australia

Montazerolghaem, Maryam; Vervoort, W.; Minasny, Budiman; McBratney, Alex B.

doi:10.1016/j.wace.2016.04.001

Cited by 34 publications

(17 citation statements)

References 48 publications

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“…The use of multivariate techniques such as the principal component analysis (PCA, e.g., 38) to characterise drought evolutions was pioneered by Karl and Koscielny (39) and has since resulted in several applications of PCA in the spatio-temporal analysis of drought patterns globally (see, e.g., 3,37,9,86,12). Not only is it useful in the analysis of meteorological and geophysical observations (e.g., 59,85,55), it is now an effective tool in diagnosing the impacts of large scale oceanic atmospheric predictors, atmospheric blocking, and climate teleconnection patterns on hydro-meteorological patterns (e.g., 63,56,45,73). Consider a matrix of SPI and SPEI values as…”

Section: Statistical Analyses 321 Spatio-temporal Drought Patternsmentioning

confidence: 99%

Evolutionary drought patterns over the Sahel and their teleconnections with low frequency climate oscillations

Ndehedehe

Agutu

Ferreira

et al. 2020

Atmospheric Research

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The need for ideal drought metrics to explore the impacts of climate variability drivers on drought intensity and characteristics is essential to provide support that leads to optimizing existing templates on risk mitigation in drought-prone regions. The main aim of this study therefore is to improve contemporary understanding on the evolutionary patterns of historical drought over the Sahel (1901−2014) and the large-scale processes that drive its variability using the SPEI (standardized precipitation evapotranspiration index) and SPI (standardised precipitation index). Historically, the distributions of SPEI and SPI are generally quite similar with fairly strong correlations ranging from 0.80 to 1.0 around the central Sahel. The dissimilarity between SPI and SPEI in some regions however, suggest the influence of changing land surface conditions and the need to incorporate biophysical indicators to support the accounting process that aid the understanding of drought impacts and its cascading effects. Furthermore, SPEI and SPI confirm that the worst drought during the 20th century over the Sahel occurred between 1982 and 1985, affecting more than 90% of the region while the 1950s remained the wettest on record. However, between 2005 and 2015, drought episodes and their intensities have diminished over the Sahel. In the assessment of the coupled relationship of seven climate modes with drought evolutions, SPEI was more associated with Atlantic Multi-decadal Oscillation-AMO (r = 0.66) and Pacific Decadal Oscillation (r = −0.53) unlike the SPI. A predictive multivariate model confirms the AMO is a strong driver of drought events and in addition, highlights the multi-scale climate influence in the Sahel.

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Section: Statistical Analyses 321 Spatio-temporal Drought Patternsmentioning

confidence: 99%

Evolutionary drought patterns over the Sahel and their teleconnections with low frequency climate oscillations

Ndehedehe

Agutu

Ferreira

et al. 2020

Atmospheric Research

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“…Information on variance estimation, geometric and statistical properties of PCA can be found, e.g., in Preisendorfer (1988), Jolliffe (2002), and Martinez and Martinez (2005) and is only summarised here. Also, additional implementation details and applications of this technique in exploring the influence of large scale oceanic atmospheric predictors on droughts (e.g., Ndehedehe et al, 2019;Agutu et al, 2017;Montazerolghaem et al, 2016), and analyses of meteorological and geophysical data (e.g., Ndehedehe et al, 2016a;Sanogo et al, 2015;Rangelova et al, 2007) have been heavily documented.…”

Section: Statistical Analyses 281 Spatial and Temporal Dynamics Ofmentioning

confidence: 99%

Predicting hot spots of aquatic plant biomass in a large floodplain river catchment in the Australian wet-dry tropics

Ndehedehe

Stewart‐Koster

Burford

et al. 2020

Ecological Indicators

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“…ENSO Modoki and Madden Julian Oscillation (MJO) affects north-western and northern Australian rainfall (Marshall and Hendon, 2014;Taschetto and England, 2009). Among all these indices, ENSO grouped indices were found as the major climate indices for all over Australia (Montazerolghaem et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Many researchers have tried to evaluate the relationship among climate indices and rainfall in Australia (Kirono et al, 2010;Montazerolghaem et al, 2016). Most of these studies were mainly focused on concurrent relationship between rainfall and climate indices.…”

Section: Introductionmentioning

confidence: 99%

“…From the previous literature, it was apparent that ENSO is the most influential climate index for Australia (Montazerolghaem et al, 2016;Risbey et al, 2009) and IOD mostly influences Western Australian rainfall (Fierro and Leslie, 2013;Ummenhofer et al, 2008). So, these climate indices can be used as potential predictors for autumn rainfall.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of lagged climate indices for forecasting autumn rainfall in South Coast region of Western Australia using ARIMA model

2017

Syme, G., Hatton MacDonald, D., Fulton, B. And Piantadosi, J. (Eds) MODSIM2017, 22nd International Congress on Modelling and Si

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Climate indices are very effective predictors for forecasting seasonal rainfall. For any rainfall forecasting approach, it is necessary to understand the behavior of potential climate indices with rainfall variability. At present, rainfall forecasting using climate indices is one of the most reliable method to predict rainfall variability in many parts of the world. But, most of the studies have concentration on concurrent relationship between climate indices and seasonal rainfall. While a very few studies were conducted on concentration of lagged relationship between climate indices and seasonal rainfall. This study explores the significant correlation among lagged climate indices with autumn rainfall for south coast of Western Australia. As several climate indices such as Indian Ocean Dipole (IOD), El Nino Southern Oscillation (ENSO), Southern Oscillation Index (SOI), Southern Annular Mode (SAM), Blocking highs, Enso Modoki Index (EMI) are responsible for rainfall variability in Australia; therefore, this study evaluates the major climate indices and their interaction in generating rainfall variability in south coast of Western Australia. In the south coast part of Western Australia, two stations (Albany and Mount Barker) were considered for this study. From the single statistical correlation analysis, it was found that DMI (IOD indicator), SOI, Nino3.4, Nino3 and Nino4 (ENSO indicators) have significant correlation with autumn rainfall in Albany and Mount Barker. However, for these two stations EMI did not show any significant correlation with autumn rainfall. A time series analysis approach (Auto Regressive Integrated Moving Average-ARIMA) was conducted using climate indices, which showed significant correlation with autumn rainfall. In ARIMA model, lagged DMI-Nino3; lagged DMI-Nino4; Lagged DMI-Nino3.4 and Lagged DMI-SOI were chosen as independent variables (predictors) as it has showed significant correlation. From the ARIMA model analysis, it was evident that lagged DMI-Nino3 models showed highest predictability that is 56% and 33% for Albany and Mount Barker respectively. Finally, among those models, statistically significant models that showed high performance in predictability were suggested to forecast long-term rainfall for this region.

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Long-term variability of the leading seasonal modes of rainfall in south-eastern Australia

Cited by 34 publications

References 48 publications

Evolutionary drought patterns over the Sahel and their teleconnections with low frequency climate oscillations

Evolutionary drought patterns over the Sahel and their teleconnections with low frequency climate oscillations

Predicting hot spots of aquatic plant biomass in a large floodplain river catchment in the Australian wet-dry tropics

Application of lagged climate indices for forecasting autumn rainfall in South Coast region of Western Australia using ARIMA model

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