scholarly journals From the 1990s climate change has decreased cool season catchment precipitation reducing river heights in Australia’s southern Murray-Darling Basin

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Milton S. Speer ◽  
L. M. Leslie ◽  
S. MacNamara ◽  
J. Hartigan
Keyword(s):  
Permutation Tests ◽  
Southern Annular Mode ◽  
Significant Decline ◽  
Sea Surface ◽  
Wagga Wagga ◽  
Cool Season ◽  
P Values ◽  
Late Autumn ◽  
Murray Darling Basin ◽  
Catchment Runoff

AbstractThe Murray-Darling Basin (MDB) is Australia’s major agricultural region. The southern MDB receives most of its annual catchment runoff during the cool season (April–September). Focusing on the Murrumbidgee River measurements at Wagga Wagga and further downstream at Hay, cool season river heights are available year to year. The 27-year period April–September Hay and Wagga Wagga river heights exhibit decreases between 1965 and 1991 and 1992–2018 not matched by declining April-September catchment rainfall. However, permutation tests of means and variances of late autumn (April–May) dam catchment precipitation and net inflows, produced p-values indicating a highly significant decline since the early 1990s. Consequently, dry catchments in late autumn, even with average cool season rainfall, have reduced dam inflows and decreased river heights downstream from Wagga Wagga, before water extraction for irrigation. It is concluded that lower April–September mean river heights at Wagga Wagga and decreased river height variability at Hay, since the mid-1990s, are due to combined lower April–May catchment precipitation and increased mean temperatures. Machine learning attribute detection revealed the southern MDB drivers as the southern annular mode (SAM), inter-decadal Pacific oscillation (IPO), Indian Ocean dipole (IOD) and global sea-surface temperature (GlobalSST). Continued catchment drying and warming will drastically reduce future water availability.

scholarly journals Inflows and river heights in Australia’s Murray Darling Basin: impacts of decreased catchment rainfall on water availability.

2021 ◽  
Author(s):  
Milton Speer ◽  
Lance Leslie ◽  
Shev MacNamara ◽  
Joshua Hartigan
Keyword(s):  
Water Availability ◽  
Permutation Tests ◽  
Significant Decline ◽  
Wagga Wagga ◽  
Cool Season ◽  
P Values ◽  
River Catchment ◽  
Late Autumn ◽  
Murray Darling Basin ◽  
Catchment Runoff

Abstract The Murray Darling Basin (MDB) is Australia’s most important agricultural region. The southern MDB receives most of its annual catchment runoff during the cool season (April-September). Focusing on the Murrumbidgee River measurements at Wagga Wagga and further downstream at Hay, river heights in the cool season decreased markedly in variability over a century prior to 1991. Box and whisker plots of 27-year April-September Hay and Wagga Wagga river heights reveal decreases not matched by declining April-September catchment rainfall. However, permutation tests of both means and variances of late autumn (April-May) dam catchment rainfall and inflows, produced p-values indicating a highly significant decline since the early 1990s. Consequently, dry catchments in late autumn, even with average cool season rainfall, have reduced dam inflows and decreased river heights downstream from Wagga Wagga, before water extraction for irrigation. It is concluded that lower April-September mean river heights at Wagga Wagga and decreased river height variability at Hay, since the mid-1990s, are due to combined lesser April-May catchment rainfall and increased mean temperatures. If these drying and warming trends continue, they will drastically reduce water availability for the Murrumbidgee River catchment and, consequently, for a vital part of the southern MDB.

The impact of pasture development and grazing on water-yielding catchments in the Murray - Darling Basin in south-eastern Australia

2003 ◽  
Vol 43 (8) ◽  
pp. 817 ◽  
Author(s):  
W. H. Johnston ◽  
D. L. Garden ◽  
A. Rančić ◽  
T. B. Koen ◽  
K. B. Dassanayake ◽  
...  
Keyword(s):  
Perennial Grasses ◽  
Wagga Wagga ◽  
Deep Drainage ◽  
Nitrogen And Phosphorus ◽  
South Wales ◽  
Murray Darling Basin ◽  
Productivity Gain ◽  
Eastern Australia ◽  
Annual Grasses ◽  
The Impact

Experiments conducted from November 1996 to June 2002 in adjacent small catchments near Wagga Wagga, New South Wales, compared the productivity and hydrology of a heavily fertilised (about 30 kg phosphorus/ha.year) Phalaris aquatica (phalaris) pasture with that of a lightly fertilised (about 14 kg phosphorus/ha every second year) native grassland that contained a mixture of C3 and C4 perennial grasses, dominantly C4 Bothriochloa macra (redgrass).In summer, the native catchment was dominated by C4 perennial grasses while the phalaris catchment was dominated by annual C4 weedy species. During the cooler months, the phalaris pasture contained higher proportions of Vulpia spp., and other less-desirable annual grasses. Throughout the experiment, the native catchment was dominated by redgrass, whereas in the phalaris catchment the persistence of phalaris declined. Redgrass became prominent on the more arid aspects of the phalaris catchment as the experiment progressed.Pasture production in the phalaris catchment was higher in most seasons than the native catchment, which resulted in an overall stocking rate advantage of about 80%. The productivity gain per unit of P input was 0.4 for the phalaris catchment compared with 1 for the native catchment, implying that phosphorus was applied to the phalaris catchment at an excessive rate.During wet periods the native catchment produced substantially more runoff than the phalaris catchment, while in dry times it developed substantially larger soil water deficits. Runoff from the phalaris catchment was higher in suspended and dissolved nitrogen and phosphorus than for the native catchment. Higher runoff from the native catchment combined with its drier soil profile in summer indicated that its deep drainage potential was less than in the phalaris catchment.

scholarly journals Air-Sea Interactions Influence Major Southern Wind Belt

Eos ◽  
2016 ◽  
Vol 97 ◽  
Author(s):  
Sarah Stanley
Keyword(s):  
Southern Annular Mode ◽  
Sea Surface ◽  
Sea Surface Temperatures ◽  
Annular Mode ◽  
Surface Temperatures ◽  
Atmospheric Data

Ocean and atmospheric data provide evidence for how sea surface temperatures affect the Southern Annular Mode.

scholarly journals Common Permutation Methods in Animal Social Network Analysis Do Not Control for Non-independence

2021 ◽  
Author(s):  
Jordan D. A. Hart ◽  
Michael N. Weiss ◽  
Lauren J. N. Brent ◽  
Daniel W. Franks
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Regression Models ◽  
Permutation Tests ◽  
Network Data ◽  
P Values ◽  
Parametric Regression ◽  
Node Label ◽  
Parametric Regression Models

The non-independence of social network data is a cause for concern among behavioural ecologists conducting social network analysis. This has led to the adoption of several permutation-based methods for testing common hypotheses. One of the most common types of analysis is nodal regression, where the relationships between node-level network metrics and nodal covariates are analysed using a permutation technique known as node-label permutation. We show that, contrary to accepted wisdom, node-label permutations do not account for the types of non-independence assumed to exist in network data, because regression-based permutation tests still assume exchangeability of residuals. The same theoretical condition also applies to the quadratic assignment procedure (QAP), a permutation-based method often used for conducting dyadic regression. We highlight that node-label permutations produce the same p-values as equivalent parametric regression models, but that in the presence of confounds, parametric regression models produce more accurate effect size estimates. We also note that QAP only controls for a specific type of non-independence between edges that are connected to the same nodes, and that appropriate parametric regression models are also able to account for this type of non-independence. Based on this, we advocate the retirement of permutation tests for regression analyses, in favour of well-specified parametric models. Moving away from permutation-based methods will reduce over-reliance on p-values, generate more reliable estimates of effect sizes, and facilitate the adoption of more powerful types of statistical analysis.

scholarly journals Statistical Analysis and Forecasting of Price and Mileage Correlation for Second-hand Cars in Australia

2019 ◽  
Author(s):  
Chinedu G. Agokei ◽  
Bomonyo J. Afa
Keyword(s):  
Permutation Tests ◽  
Developed Countries ◽  
Daily Activities ◽  
P Values ◽  
Business Studies ◽  
Parametric Bootstrapping ◽  
The Usa ◽  
Used Cars ◽  
Relationship Of ◽  
The Relationship

In developed countries, especially the big-sized ones like Australia and the USA, a car is almost an inevitable necessity to carry out daily activities. Due to this, used cars have become a great alternative to brand new cars because of their cost effectiveness. In this work, estimation of prices of used cars based on numerous factors is studied statistically. Data is based on prices of used cars sold across Australia. Statistical methods like correlation and permutation tests using linear regression model, exact tests and non-parametric bootstrapping is implemented to study the relationship of price with mileage and year of manufacture of the car using p-values and null hypothesis. Predictions are also made on the price by calculating a 95% confidence interval (CI) of median prices in small portions of the dataset. The study presents potential ideas for understanding correlation between variables and parameters in business studies.

scholarly journals Visualization Strategies for Regression Estimates with Randomization Inference

2019 ◽  
Author(s):  
Marshall A. Taylor
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Regression Models ◽  
Statistical Significance ◽  
Permutation Tests ◽  
P Value ◽  
P Values ◽  
Alpha Level ◽  
Significance Levels ◽  
Nonprobability Sample

Coefficient plots are a popular tool for visualizing regression estimates. The appeal of these plots is that they visualize confidence intervals around the estimates and generally center the plot around zero, meaning that any estimate that crosses zero is statistically non-significant at at least the alpha-level around which the confidence intervals are constructed. For models with statistical significance levels determined via randomization models of inference and for which there is no standard error or confidence intervals for the estimate itself, these plots appear less useful. In this paper, I illustrate a variant of the coefficient plot for regression models with p-values constructed using permutation tests. These visualizations plot each estimate's p-value and its associated confidence interval in relation to a specified alpha-level. These plots can help the analyst interpret and report both the statistical and substantive significance of their models. Illustrations are provided using a nonprobability sample of activists and participants at a 1962 anti-Communism school.

scholarly journals Using Indicators of ENSO, IOD, and SAM to Improve Lead Time and Accuracy of Tropical Cyclone Outlooks for Australia

2020 ◽  
Vol 59 (11) ◽  
pp. 1901-1917
Author(s):  
Andrew D. Magee ◽  
Anthony S. Kiem
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Lead Time ◽  
Southern Oscillation ◽  
Pearson Correlation ◽  
Southern Annular Mode ◽  
Sea Surface ◽  
Selection Algorithm ◽  
Statistical Framework ◽  
The Indian Ocean

AbstractCatastrophic impacts associated with tropical cyclone (TC) activity mean that the accurate and timely provision of TC outlooks are important to people, places, and numerous sectors in Australia and beyond. In this study, we apply a Poisson regression statistical framework to predict TC counts in the Australian region (AR; 5°–40°S, 90°–160°E) and its four subregions. We test 10 unique covariate models, each using different representations of the influence of El Niño–Southern Oscillation (ENSO), Indian Ocean dipole (IOD), and southern annular mode (SAM) and use an automated covariate selection algorithm to select the optimum combination of predictors. The performance of preseason TC count outlooks generated between April and October for the AR TC season (November–April) and in-season TC count outlooks generated between November and January for the remaining AR TC season are tested. Results demonstrate that skillful TC count outlooks can be generated in April (i.e., 7 months prior to the start of the AR TC season), with Pearson correlation coefficient values between r = 0.59 and 0.78 and covariates explaining between 35% and 60% of the variance in TC counts. The dependence of models on indices representing Indian Ocean sea surface temperature highlights the importance of the Indian Ocean for TC occurrence in this region. Importantly, generating rolling monthly preseason and in-season outlooks for the AR TC season enables the continuous refinement of expected TC counts in a given season.

Comments on a Note on Permutation Tests of Significance for Multiple Regression Coefficients by Long, et al.

2007 ◽  
Vol 101 (3_suppl) ◽  
pp. 1041-1042
Author(s):  
Bryan F. J. Manly
Keyword(s):  
Multiple Regression ◽  
Permutation Tests ◽  
Regression Coefficients ◽  
Standard Theory ◽  
Standard Errors ◽  
P Value ◽  
Randomization Tests ◽  
P Values ◽  
Regression Methods ◽  
Classical Regression

It is noted that a recent paper in the Journal may give a misleading impression of the robustness of classical regression methods. First, the authors show that p-values based on the randomization distributions of regression coefficients may be quite different from p-values based on t-distributions. However, there is no reason why these two types of p-value should be exactly the same, and p-values based on the randomization distributions of t-statistics are usually similar to p-values obtained from t-distributions. These latter p-values are perfectly valid for the purposes of randomization tests. Second, the authors imply that estimating the standard errors of regression coefficients by randomizing the order of the Y values gives better estimates of standard errors than standard theory. In fact these standard errors based on randomization will tend to be too large unless the regression equation accounts for none of the variation in the Y values.

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