Inter-annual Rainfall Variability of Arid Australia: greater than elsewhere?

Etten, Eddie van

doi:10.1080/00049180802657075

Cited by 97 publications

(50 citation statements)

References 18 publications

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“…Actually, the classification of the four types of storm events could be made more quantitatively. Five widely applied indices (Van Etten, ; Bronikowski and Webb, ) are selected to measure the variability of the rainfall intensity in both spatial and temporal dimensions. The equations of the indices are described as follows:

Coefficient of variability (CV) = \sqrt{\frac{1}{N} \sum_{i = 1}^{N} {((), \frac{x_{i}}{\overset{x}{true}} - 1)}^{2}}

Variability index (VI) = \frac{90 P - 10 P}{50 P}

Shannon index = \frac{- \sum_{i = 1}^{N} p_{i} ln (p_{i})}{ln 12}

Simpson index = 1 - \sum_{i = 1}^{N} ln (p_{i}^{2})

Berger–Parker index = 1 - (x_{max} - \sum_{i = 1}^{N} x_{i})

where x i represents the cumulative rainfall of each rain gauge i (for the variability of the spatial rainfall distribution) or the average areal rainfall at each modelling time step i (for the variability of the temporal rainfall distribution),

\overset{x}{true}

and x max are the mean and maximum values of x i and p i is the ratio of x i to the total amount of rainfall.…”

Section: Results and Analysesmentioning

confidence: 99%

Sensitivity of the Weather Research and Forecasting (WRF) model to downscaling ratios and storm types in rainfall simulation

Jia

Bray

Han

2012

Hydrological Processes

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Abstract:Accurate information of rainfall is needed for sustainable water management and more reliable flood forecasting. The advances in mesoscale numerical weather modelling and modern computing technologies make it possible to provide rainfall simulations and forecasts at increasingly higher resolutions in space and time. However, being one of the most difficult variables to be modelled, the quality of the rainfall products from the numerical weather model remains unsatisfactory for hydrological applications. In this study, the sensitivity of the Weather Research and Forecasting (WRF) model is investigated using different domain settings and various storm types to improve the model performance of rainfall simulation. Eight 24-h storm events are selected from the Brue catchment, southwest England, with different spatial and temporal distributions of the rainfall intensity. Five domain configuration scenarios designed with gradually changing downscaling ratios are used to run the WRF model with the ECMWF 40-year reanalysis data for the periods of the eight events. A two-dimensional verification scheme is proposed to evaluate the amounts and distributions of simulated rainfall in both spatial and temporal dimensions. The verification scheme consists of both categorical and continuous indices for a first-level assessment and a more quantitative evaluation of the simulated rainfall. The results reveal a general improvement of the model performance as we downscale from the outermost to the innermost domain. Moderate downscaling ratios of 1:7, 1:5 and 1:3 are found to perform better with the WRF model in giving more reasonable results than smaller ratios. For the sensitivity study on different storm types, the model shows the best performance in reproducing the storm events with spatial and temporal evenness of the observed rainfall, whereas the type of events with highly concentrated rainfall in space and time are found to be the trickiest case for WRF to handle. Finally, the efficiencies of several variability indices are verified in categorising the storm events on the basis of the two-dimensional rainfall evenness, which could provide a more quantitative way for the event classification that facilitates further studies. It is important that similar studies with various storm events are carried out in other catchments with different geographic and climatic conditions, so that more general error patterns can be found and further improvements can be made to the rainfall products from mesoscale numerical weather models.

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Coefficient of variability (CV) = \sqrt{\frac{1}{N} \sum_{i = 1}^{N} {((), \frac{x_{i}}{\overset{x}{true}} - 1)}^{2}}

Variability index (VI) = \frac{90 P - 10 P}{50 P}

Shannon index = \frac{- \sum_{i = 1}^{N} p_{i} ln (p_{i})}{ln 12}

Simpson index = 1 - \sum_{i = 1}^{N} ln (p_{i}^{2})

Berger–Parker index = 1 - (x_{max} - \sum_{i = 1}^{N} x_{i})

\overset{x}{true}

and x max are the mean and maximum values of x i and p i is the ratio of x i to the total amount of rainfall.…”

Section: Results and Analysesmentioning

confidence: 99%

Sensitivity of the Weather Research and Forecasting (WRF) model to downscaling ratios and storm types in rainfall simulation

Jia

Bray

Han

2012

Hydrological Processes

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“…The Simpson Desert is classified as a hot, dry desert, with average annual rainfall between 100 mm and 150 mm. However, precipitation is highly variable by world standards [24]. Large, unpredictable rain events structure the environment and sustain high levels of biomass, creating relatively short ‘boom’ periods, immediately followed by relatively long ‘bust’ periods [25].…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Common Methods for Sampling Invertebrate Pollinator Assemblages: Net Sampling Out-Perform Pan Traps

2013

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Methods for sampling ecological assemblages strive to be efficient, repeatable, and representative. Unknowingly, common methods may be limited in terms of revealing species function and so of less value for comparative studies. The global decline in pollination services has stimulated surveys of flower-visiting invertebrates, using pan traps and net sampling. We explore the relative merits of these two methods in terms of species discovery, quantifying abundance, function, and composition, and responses of species to changing floral resources. Using a spatially-nested design we sampled across a 5000 km2 area of arid grasslands, including 432 hours of net sampling and 1296 pan trap-days, between June 2010 and July 2011. Net sampling yielded 22% more species and 30% higher abundance than pan traps, and better reflected the spatio-temporal variation of floral resources. Species composition differed significantly between methods; from 436 total species, 25% were sampled by both methods, 50% only by nets, and the remaining 25% only by pans. Apart from being less comprehensive, if pan traps do not sample flower-visitors, the link to pollination is questionable. By contrast, net sampling functionally linked species to pollination through behavioural observations of flower-visitation interaction frequency. Netted specimens are also necessary for evidence of pollen transport. Benefits of net-based sampling outweighed minor differences in overall sampling effort. As pan traps and net sampling methods are not equivalent for sampling invertebrate-flower interactions, we recommend net sampling of invertebrate pollinator assemblages, especially if datasets are intended to document declines in pollination and guide measures to retain this important ecosystem service.

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“…Rainfall across Australia exhibits high inter-annual variability and, over much of the continent, is not predictably seasonal (Hobbs 1998;Stafford Smith 2008;van Etten 2009). This high variability, together with generally mild winter temperatures and deeply weathered, infertile soils (McKenzie et al 2004), has led to woody vegetation that is predominantly evergreen (Bowman and Prior 2005).…”

Section: Introductionmentioning

confidence: 98%

Variation in stem radial growth of the Australian conifer, Callitris columellaris, across the world’s driest and least fertile vegetated continent

Prior

Grierson

McCaw³

et al. 2012

Trees

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Climate change could alter the biogeography of many tree species. However, there have been few studies of tree growth across climatic gradients at a continental scale. Callitris columellaris is a widespread conifer that spans many climates and landscape positions across Australia. Our aim was to determine how stem radial growth of C. columellaris varies with tree size and with the biogeographic factors of rainfall, temperature, soil fertility and inter-tree competition. We sampled cores from trees at 85 sites in biomes ranging from tropical savanna to arid desert and temperate forest, and measured widths of the 100 outermost growth rings. We analysed ring width in relation to changes in tree age and diameter, and also evaluated the influence of the biogeographic factors on the width of the ten most recently formed rings. The average width of outermost rings varied only slightly with stem diameter, because the decrease in ring width with age and diameter within trees is offset by an increase with diameter among trees. Our analyses thus explain the weak, inconsistent relationships often observed between stem diameter and growth rate amongst trees. The most important biogeographic factors were the climatic ones: across Australia, ring width increased with both mean annual rainfall and mean annual temperature. These relationships were largely driven by continental scale differences between the tropical and the southern (arid plus temperate) sites, while relationships within climate zones were comparatively weak. Ring width decreased with intense inter-tree competition but showed little correlation with available soil nitrogen or phosphorus.

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Inter-annual Rainfall Variability of Arid Australia: greater than elsewhere?

Cited by 97 publications

References 18 publications

Sensitivity of the Weather Research and Forecasting (WRF) model to downscaling ratios and storm types in rainfall simulation

Sensitivity of the Weather Research and Forecasting (WRF) model to downscaling ratios and storm types in rainfall simulation

Evaluation of Common Methods for Sampling Invertebrate Pollinator Assemblages: Net Sampling Out-Perform Pan Traps

Variation in stem radial growth of the Australian conifer, Callitris columellaris, across the world’s driest and least fertile vegetated continent

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