A Century of Australian natural disasters and how to reduce the toll from future events

This study reviews Australian experience of natural disasters over the last century and considers how to reduce this nation’s vulnerability to such events in the future. In line with global experience, the cost of Australian weather-related natural disasters has been increasing, while loss of life has decreased, with extreme heat events responsible for more fatalities than all other natural perils combined, baring epidemics. However when disaster costs arising from historical events are normalised to current day exposure, no long-term trend emerges. Moreover the frequency of these losses shows no sign of increasing since 1950. In other words, the rising cost of natural disasters can be firmly sheeted home to the fact that there are now more of us living in vulnerable places with more to lose. In view of the above, emergency management and government risk management and strategic planning should focus on plausible large event scenarios, whatever their cause. If we wish to reduce disaster losses, land-use planning has to become risk-informed and building codes need to consider potential economic impacts, rather than just life safety. Insurers can play a role by pricing risk correctly and sending clear signals to homeowners (and governments) to stimulate risk-reducing behaviours. The tools to achieve fine-grained risk assessments are increasingly available. The success of the regulated use of the building code in tropical cyclone-prone regions in Australia and the performance of modern seismic building codes in New Zealand, shows what can be achieved when there is a demonstrated need and political will.1 page(s

Catastrophe loss modeling and its application for enterprise risk management and land-use planning

The global increase in the cost of natural disasters can be explained by the increasing concentration of assets and wealth in hazard-prone areas, a situation unlikely to change for a very long time. This state of affairs is aggravated by poor land-use planning practices, with the 2009 Victorian bushfire and 2011 Brisbane flood events two recent Australian examples of such planning failures. This paper will argue the benefits of both stochastic and scenario loss modeling to help businesses and governments prepare for future large events. The use of such modeling is already well developed in the insurance sector, where, for example, Risk Frontiers' Multi-hazard platform is used to simulate the impact of hundreds of thousands of plausible natural hazard events on company portfolios, and estimate the annual probability of experiencing losses in excess of cer tain dollarthresholds. Perils include earthquake, tropical cyclone, hailstorm, flood and bushfire events. Model outputs are used to inform negotiations in regard to the purchase of reinsurance and, in conjunction with geo-spatial tools, risk-adjust premiums. Outside of the insurance sector the use of such modeling remains primitive although many businesses would benefit from its use and by exploring a number of large event scenarios could quickly ascer tain enterprise-specific vulnerabilities, including business interruption. In the government sector, the models could be used to under take cost-benefit analyses, prioritize mitigation measures, make risk-informed decisions in respect to land-use planning, whilst emergency managers could also them to plan and stress test evacuation operations.1 page(s

Analytical pork barreling?

Political decision-making often takes place in a soup of risk, uncertainty and conflicting expert opinion and inadequate information. This is not because of political failure but because of the inherent complexity of such decisions and the absence of complete information. In recent times, significant efforts have been devoted to quantifying the impact of natural hazards on communities. A preferred approach to risk-informed decision-making aims to exploit an ensemble of hazard-related exceedance probability (EP) curves. EP curves are commonly used by the insurance industry to price reinsurance contracts. But does the use of these tools improve our decision-making? Catastrophe insurance is an example where there is a clear market need, a regulatory requirement and mostly, but not always, a sophisticated understanding of risk as a financial liability. These EP curves tell us, within the limits and constraints of data inputs, the probability of the modelled loss exceeding a given amount. Impediments to extending this tool beyond insurance are complicated by the extreme skewness of the EP curves and, by inference, the skewness of any cost-benefit analysis that depends upon it. Using examples from natural hazards, bio-security and terrorism, this paper will consider these issues as well as complications of risk perception and other decision biases and the role of market mechanisms.2 page(s

Australian bushfire : quantifying and pricing the risk to residential properties

A new analysis of bushfire risk to residential properties shows that in 60% of years losses occur somewhere in Australia. The evident corollary to this is that in 40% of years no losses are experienced. This statistic has remained reasonably stable over the last century despite large increases in population and improvements in technology and firefighting resources. This stability was similarly demonstrated by the 40% probability of a major event, here arbitrarily defined as the loss of more than 25 homes within a period of 7 days, a time window of some relevance to reinsurance contracts. The annual average number of houses lost is estimated to be 83 homes and when this is combined with current asset values for home and contents, the Annual Average Damage is valued at $33.5 million. The 1 in 100 year event equates to a likely loss of AU$0.7 billion and the 1 in 250 year event, AU$1.1 billion. These figures are approximately equal to the present value of the insured losses from Tropical Cyclone Tracy and the Newcastle earthquake. When the Annual Average Damage is adjusted for the annual volatility of losses, as would typically be the case when risk is judged from a reinsurance perspective, the national bushfire risk premium amounts to$62.4 million. A complete costing for bushfire would need to include loss of life, the fixed cost of maintaining and supporting state fire fighting services, the opportunity cost of the volunteers engaged in firefighting activities as well as any contributions from Federal Government. This same general approach could be easily adapted to other perils in order to establish an objective ranking of the threat posed by the various natural hazards.9 page(s

Valuing new product development and analysing license agreements for intellectual property

11 page(s

Surface layer profiles of air temperature and humidity measured from unmanned aircraft

The atmospheric surface layer couples processes in the atmosphere with those at the land surface, but is not fully understood for heterogeneous land surfaces. A small remotely piloted aircraft was flown during the ReSeDA experiment to measure profiles of heat and humidity in the surface layer near boundaries between different land cover types to study atmospheric structure over heterogeneous terrain. The aircraft payload included sensors for temperature, humidity and position. Extensive data processing has been performed to check data quality and coordinate the datasets created (to common time and position references). Initial results show the expected general features of the surface layer profiles of temperature and humidity and also give details of structure near field boundaries, especially where there is strong contrast in surface properties and radiative forcing. The experiments also demonstrate that unmanned aircraft are practical tools for this form of atmospheric research

The Bushfire Threat in Urban Areas

3 page(s

A Sydney CAT bond: simply a matter of time?

3 page(s

Return on investment in flood risk ratings for managing flood exposure

2 page(s

High-resolution estimates of Australia's coastal population

Here we quantify Australian coastal population at a spatial resolution of 1 km and do this as a function of distance to shoreline and elevation above mean sea level. We also report comprehensive validations based on statistical and spatial relationships between very fineresolution Australian data sets and the recent high-resolution global data sets on ambient population distribution (LandScan2003), shorelines (GSHHS) and elevation (SRTM). Estimates are heavily dependent upon the resolutions of all input data, as well as the resolution of analysis. Our results show about 50% of Australian addresses or population are located within 7 km of the shore, and that population decreases very rapidly with increasing distance from the shoreline. About 6.0% of Australian addresses are situated within 3 km of shorelines in areas with elevations below 5 m.4 page(s