Quantitative assessment of climatic and environmental health risks is necessary because changes in climate are expected. We therefore aimed to quantify the relationship between climatic extremes and mortality in the 5 largest Australian cities during the period [1979][1980][1981][1982][1983][1984][1985][1986][1987][1988][1989][1990]. We then applied the relationship determined between recent climatic conditions and mortality to scenarios for climate and demographic change, to predict potential impacts on public health in the cities in the year 2030. Data on mortality, denominator population and climate were obtained. The expected numbers of deaths per day in each city were calculated. Observed daily deaths were compared with expected rates according to temperature thresholds. Mortality was also examined in association with temporal synoptic indices (TSI) of climate, developed by principal component and cluster analysis. According to observed-expected threshold analyses, for the 5 cities combined, the annual mean excess of deaths attributable to temperature over the period [1979][1980][1981][1982][1983][1984][1985][1986][1987][1988][1989][1990] was 175 for the 28°C threshold. This sum of statistically significant differences from the 5 cities was the greatest excess found in association with any threshold considered in the range of temperatures that occur. Excess mortality for the hottest days in summer was greater than for the coldest days in winter. Temperature-mortality relationships were little modified by socio-economic status. TSI analyses produced similar results: using this method, the climate-attributable mortality in the 5 cities was approximately 160 deaths yr -1 , although this number was evenly distributed across summer and winter. Persons in the group aged 65 yr and older were the most vulnerable. After allowing for increases in population, and combining all age groups, the synoptic method showed a 10% reduction in mortality in the year 2030. We conclude that the 5 largest Australian cities exhibit climate-attributable mortality in both summer and winter. Given the scenarios of regional warming during the next 3 decades, the expected changes in mortality due to direct climatic effects in these major coastal Australian cities are minor.

Storm tide (combination of storm surge and the astronomical tide) flooding is a natural hazard with significant global social and economic consequences. For this reason, government agencies and stakeholders need storm tide flood maps to determine population and infrastructure at risk to present and future levels of inundation. Computer models of varying complexity are able to produce regional scale storm tide flood maps and current model types are either static or dynamic in their implementation. Static models of storm tide utilize storm tide heights to inundate locations hydrologically connected to the coast, whilst dynamic models simulate physical processes that cause flooding. Static models have been used in regional scale storm tide flood impact assessments but model limitations and coarse spatial resolutions contribute to uncertain impact estimates. Dynamic models are better at estimating flooding and impact but are computationally expensive. In this study we have developed a dynamic reducedcomplexity model of storm tide flooding that is computationally efficient and is applied at hyper-resolutions (< 100 m cell size) over regional scales. We test the performance of this dynamic reduced-complexity model and a separate static model at three test sites where storm tide observational data is available. Additionally, we perform a flood impact assessment at each site using the dynamic reduced-complexity and static model outputs. Our results show that static models can overestimate observed flood areas up to 204% and estimate more than twice the number of cleanCopyManuscript Click here to view linked References people, infrastructure, and agricultural land affected by flooding. Overall we find that that a reduced-complexity dynamic model of storm tide provides more conservative estimates of coastal flooding and impact.

The evaluation and verification of landscape evolution models (LEMs) has long been limited by a lack of suitable observational data and statistical measures which can fully capture the complexity of landscape changes. This lack of data limits the use of objective function based evaluation prolific in other modelling fields, and restricts the application of sensitivity analyses in the models and the consequent assessment of model uncertainties. To overcome this deficiency, a novel model function approach has been developed, with each model function representing an aspect of model behaviour, which allows for the application of sensitivity analyses. The model function approach is used to assess the relative sensitivity of the CAESAR-Lisflood LEM to a set of model parameters by applying the Morris method sensitivity analysis for two contrasting catchments. The test revealed that the model was most sensitive to the choice of the sediment transport formula for both catchments, and that each parameter influenced model behaviours differently, with model functions relating to internal geomorphic changes responding in a different way to those relating to the sediment yields from the catchment outlet. The model functions proved useful for providing a way of evaluating the sensitivity of LEMs in the absence of data and methods for an objective function approach.

Purpose -The purpose of this paper is to examine aspects of corporate social investment (CSI) in the Southern African context. Design/methodology/approach -The paper looks at current practice in Southern Africa against the historical development of corporate social responsibility (CSR) and CSI. It looks at the impact of new legislation in South Africa. Findings -The paper concludes by reflecting on the contribution that African public relations practice may have on the development challenges of Africa. Originality/value -The paper adds insights into CSR in South Africa, focusing for energising trends.

a b s t r a c tThe hydraulic modelling of tidal estuarine environments has been largely limited to complex 3D models that are computationally expensive. This makes them unsuitable for applications which make use of live data to make real/near time forecasts, such as the modelling of storm surge propagation and associated flood inundation risks. To address this requirement for a computationally efficient method a reduced complexity, depth-integrated 2D storage cell model (Lisflood-FP) has been applied to the Humber Estuary, UK. The capability of Lisflood-FP to reproduce the tidal heights of the Humber Estuary has been shown by comparing modelled and observed tidal stage heights over a period of a week. The feasibility of using the Lisflood-FP model to forecast flood inundation risk from a storm surge is demonstrated by reproducing the major storm surge that struck the UK East Coast and Humber Estuary on 5 December 2013. Results show that even for this 2013 extreme event the model is capable of reproducing the hydraulics and tidal levels of the estuary. Using present day flood defences and observed flooding extents, the modelled flood inundation areas produced by the model were compared, showing agreement in most areas and illustrating the model's potential as a now-casting early warning system when driven by publically available data, and in near real-time. The Lisflood-FP model used was incorporated into the CAESAR-Lisflood GUI, with the calibration and verification of the estuarine hydraulics reported herein being a key step in creating an estuary evolution model, capable of operating in the decadal to century timescales that are presently underrepresented in estuarine predictive capability, and ultimately developing a model to predict the evolution of flood risk over the longer term.