scholarly journals The Australian Great Flood of 1954: Estimating the Cost of a Similar Event in 2011

2013 ◽  
Vol 5 (3) ◽  
pp. 199-209 ◽  
Author(s):  
Kevin M. Roche ◽  
K. John McAneney ◽  
Keping Chen ◽  
Ryan P. Crompton

Abstract As in many other parts of the globe, migration to the coast and rapid regional development in Australia is resulting in large concentrations of population and insured assets. One of the most rapidly growing regions is southeastern Queensland and northern New South Wales, an area prone to flooding. This study reexamines the Great Flood of 1954 and develops a deterministic methodology to estimate the likely cost if a similar event had occurred in 2011. This cost is estimated using council flood maps, census information, historical observations, and Risk Frontiers' proprietary flood vulnerability functions. The 1954 flood arose from heavy rainfall caused by the passage of a tropical cyclone that made landfall on 20 February near the Queensland–New South Wales border, before heading south. Responsible for some of the largest floods on record for many northern New South Wales' river catchments, it occurred prior to the availability of reliable insurance statistics and the recent escalation in property values. The lower-bound estimate of the insurance loss using current exposure and assuming 100% insurance penetration for residential buildings and contents is AU$3.5 billion, a cost that would make it the third-highest ranked insured loss due to an extreme weather event since 1967. The corresponding normalized economic loss is AU$7.6 billion but the uncertainty about this figure is high. The magnitude of these losses reflects the accumulation of exposure on the floodplains. Risk-informed land-use planning practices and improved building regulations hold the key to reducing future losses.

2020 ◽  
Vol 33 (3) ◽  
pp. 286-293
Author(s):  
Vanessa L. Scarf ◽  
Serena Yu ◽  
Rosalie Viney ◽  
Laura Lavis ◽  
Hannah Dahlen ◽  
...  

2003 ◽  
Vol 43 (1) ◽  
pp. 71 ◽  
Author(s):  
M. K. Conyers ◽  
C. L. Mullen ◽  
B. J. Scott ◽  
G. J. Poile ◽  
B. D. Braysher

The cost of buying, carting and spreading limestone, relative to the value of broadacre crops, makes investment in liming a questionable proposition for many farmers. The longer the beneficial effects of limestone persist, however, the more the investment in liming becomes economically favourable. We re-established previous lime trials with the aim of measuring the long-term effects of limestone on surface acidity (pH run-down), subsurface acidity (lime movement) and grain yield. The study made use of experiments where there was adequate early data on soil chemical properties and cereal yields. We report data from 6 trials located at 4 sites between Dubbo and Albury in New South Wales. The rate of surface soil (0–10 cm) pH decline after liming was proportional to the pH attained 1 year after liming. That is, the higher the pH achieved, the more rapid the rate of subsequent pH decline. Since yields (product removal) and nitrification (also acid producing) may both vary with pH, the post-liming pH acts as a surrogate for the productivity and acid-generating rate of the soil–plant system. The apparent lime loss rate of the surface soils ranged from the equivalent of nearly 500 kg limestone/ha.year at pH approaching 7, to almost zero at pH approaching 4. At commercial application rates of 2–2.5 t/ha, the movement of alkali below the layer of application was restricted. However, significant calcium (Ca) movement sometimes occurred to below 20 cm depth. At rates of limestone application exceeding the typical commercial rate of 2.5 t/ha, or at surface pH greater than about 5.5, alkali and Ca movement into acidic subsurface soil was clearly observed. It is therefore technically feasible to ameliorate subsurface soil acidity by applying heavy rates of limestone to the soil surface. However, the cost and risks of this option should be weighed against the use of acid-tolerant cultivars in combination with more moderate limestone rates worked into the surface soil.There was a positive residual benefit of limestone on cereal grain yield (either barley, wheat, triticale, or oats) at all sites in both the 1992 and 1993 seasons. While acid-tolerant cultivars were less lime responsive than acid-sensitive ones, the best yields were generally obtained using a combination of liming and acid-tolerant cultivars.The long-term residual benefits of limestone were shown to extend for beyond 8–12 years and indicate that liming should be profitable in the long term.


1991 ◽  
Vol 155 (8) ◽  
pp. 522-528 ◽  
Author(s):  
Craig M Mellis ◽  
Jennifer K Peat ◽  
Adrian E Bauman ◽  
Ann J Woolcock

1994 ◽  
Vol 19 (4) ◽  
pp. 375-384 ◽  
Author(s):  
R. L. PRESSEY ◽  
S. L. TULLY

Soil Research ◽  
2015 ◽  
Vol 53 (2) ◽  
pp. 216 ◽  
Author(s):  
Xihua Yang

The Universal Soil Loss Equation (USLE) and its main derivate, the Revised Universal Soil Loss Equation (RUSLE), are widely used in estimating hillslope erosion. The effects of topography on hillslope erosion are estimated through the product of slope length (L) and slope steepness (S) subfactors, or LS factor, which often contains the highest detail and plays the most influential role in RUSLE. However, current LS maps in New South Wales (NSW) are either incomplete (e.g. point-based) or too coarse (e.g. 250 m), limiting RUSLE-based applications. The aim of this study was to develop automated procedures in a geographic information system (GIS) to estimate and map the LS factor across NSW. The method was based on RUSLE specifications and it incorporated a variable cutoff slope angle, which improves the detection of the beginning and end of each slope length. An overland-flow length algorithm for L subfactor calculation was applied through iterative slope-length cumulation and maximum downhill slope angle. Automated GIS scripts have been developed for LS factor calculation so that the only required input data are digital elevation models (DEMs). Hydrologically corrected DEMs were used for LS factor calculation on a catchment basis, then merged to form a seamless LS-factor digital map for NSW with a spatial resolution ~30 m (or 1 s). The modelled LS values were compared with the reference LS values, and the coefficient of efficiency reached 0.97. The high-resolution digital LS map produced is now being used along with other RUSLE factors in hillslope erosion modelling and land-use planning at local and regional scales across NSW.


2021 ◽  
Vol 13 (14) ◽  
pp. 7828
Author(s):  
Sigma Dolins ◽  
Helena Strömberg ◽  
Yale Z. Wong ◽  
MariAnne Karlsson

As connected, electric, and autonomous vehicle (AV) services are developed for cities, the research is conclusive that the use of these services must be shared to achieve maximum efficiency. Yet, few agencies have prioritised designing an AV system that focuses on dynamic ridepooling, and there remains a gap in the understanding of what makes people willing to share their rides. However, in 2017, the Australian transport authority Transport for New South Wales launched over a dozen trials for on-demand, shared public transport, including AVs. In this paper, we investigate the user willingness-to-share, based on experiences from one of these trials. Four focus groups (19 participants in total) were held in New South Wales with active users of either the trialled on-demand dynamic ridepooling service (Keoride) or commercial ridepooling (UberPool). Through thematic analysis of the focus group conversations, the cost, comfort, convenience, safety, community culture, and trust in authority emerged as factors that influenced the willingness-to-share. When presented with driverless scenarios, the focus group participants had significant concerns about the unknown behaviour of their co-passengers, revealing sharing anxiety as a significant barrier to the adoption of shared AVs. This paper identifies previously disregarded factors that influence the adoption of AVs and dynamic ridepooling and offers insights on how potential users’ sharing anxiety can be mitigated.


2002 ◽  
Vol 13 (5) ◽  
pp. 110
Author(s):  
Serrie Kamara ◽  
Indira de Silva ◽  
Tilak Kuruppuarachchi

2011 ◽  
Vol 51 (9) ◽  
pp. 821 ◽  
Author(s):  
J. M. Young ◽  
A. N. Thompson ◽  
M. Curnow ◽  
C. M. Oldham

Profitability of sheep production systems in southern Australia is optimised at a stocking rate that provides adequate nutrition for breeding ewes and enables efficient utilisation of grown pasture and supplements. In this paper we used bio-economic modelling to develop optimum liveweight1 profiles for spring-lambing Merino ewes in different environments. The modelling included the impacts of the ewe liveweight profile on the production of the ewe and the survival and lifetime wool production of her progeny. Fifteen ewe liveweight profiles were analysed for each region to determine the profitability of varying ewe liveweight at joining, varying rate of loss of liveweight after joining and the rate of gain in liveweight from the minimum to lambing. The analyses support the hypotheses that whole-farm profitability is sensitive to the liveweight profile of Merino ewe flocks and that there is a liveweight profile that maximises whole-farm profit. The variation between the most and least profitable ewe liveweight profile was $69 0002 per farm ($14.30/ewe) for south-west Victoria, $51 000 per farm ($8.70/ewe) for Great Southern Western Australia and $33 300 per farm ($9.70/ewe) for southern New South Wales. The changes in profit were due to differences in costs of feeding to achieve the ewe liveweight profile and its influence on the production of both the ewes and their progeny. Failure to include the impacts of liveweight profile on progeny survival and lifetime wool production incorrectly identifies the optimum ewe liveweight profile and provided inaccurate estimates of profitability. The optimum liveweight profiles for ewes lambing in spring were similar for all three regions and insensitive to changing commodity prices, pasture productivity and management. The optimum profile was to join ewes at ~90% of the standard reference weight of the genotype, lose a small amount of weight after joining and regain weight in late pregnancy to return to the joining weight by lambing. Regaining the liveweight lost in early pregnancy by lambing is the most important target to achieve. The cost per farm of missing this liveweight target by 1 kg was $13 000 ($2.60/ewe) for south-west Victoria, $8900 ($1.45/ewe) for Great Southern Western Australia and $5500 ($1.65/ewe) for southern New South Wales. By contrast, the cost per farm of missing the joining target by 1 kg was $5500 for south-west Victoria and less than $2000 across the other two regions. Whole-farm profit increased with increasing stocking rate up to an optimum and regardless of stocking rate there is an additional opportunity to increase whole-farm profit by up to 15% by managing ewes to achieve the optimum liveweight profile. This indicates that the optimum liveweight profile should be achieved by increasing the level of grain feeding and altering the timing of utilising the farm feed resources rather than manipulating stocking rate.


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