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We examined the radiocarbon (14C) reservoir effect in Lake Kutubu using tephrochronology and terrestrial plant material to deliver a precise age-depth profile and sedimentation rates for this lake. Based on the presence of two tephra horizons (Tibito and Olgaboli), we found a reservoir age offset in sediments of between 1490 and 228014C yr using the sediment ages derived from the lead-210 (210Pb) dating method. The live submerged biological samples collected exhibited a higher reservoir age offset than the sediment. This is most likely a result of delayed transport of “bomb”14C from the atmosphere to aquatic and sedimentary system. The14C reservoir effect increased with distance from the lake inlet and also decreased with depth. Dissolution of14C-depleted carbon from surrounding limestone and direct in-wash of old soil or vegetation remnants from the catchment are the most likely causes of the14C reservoir effect. Based on limestone areas mapped in Papua New Guinea, we indicate lakes which may be subject to a significant14C reservoir effect. The results of this study demonstrate the magnitude of the14C reservoir effect in lakes and provide insights to the correct interpretation of past environmental and archaeological events in PNG.

Tibito Tephra was first recorded in the central highlands of Papua New Guinea (PNG) in 1971. By the late 1970s, the tephra had been mapped across tens of thousands of square kilometres, traced to i...

Sediment cores were retrieved from Lake Kutubu, the largest upland lake in Papua New Guinea, to assess palaeoenvironmental baselines. Two prominent tephra layers were encountered within the cores. Using a combination of core stratigraphy, sediment properties and geochemical characterization (electron microprobe and laser ablation inductively coupled-plasma mass spectrometry techniques) we were able to correlate these tephras with Tibito and Olgaboli tephras, both previously described at adjacent distal localities. Glass shard compositional data of Tibito tephra from Lake Kutubu, Kuk swamp and from proximal volcaniclastic successions from Long Island are indistinguishable and this similarity strongly supports Long Island as the eruptive source. For Olgaboli tephra, the location of its eruptive source is less certain but the glass shard compositional data indicate a Karkar Island source. The occurrence of Tibito and Olgaboli tephras in Lake Kubutu sediments extends their known distributions further south-west into the southern Highlands region and affirms their importance as inter-regional stratigraphic markers, of late Holocene age, that have considerable potential for use in future palaeoenvironmental, archaeological and volcanic hazard and/or impact studies.

William Dampier, one of England’s greatest navigators, named, described, and drew a profile of Long Island, Papua New Guinea in 1700. As this island produced a very large volcanic eruption with a disputed seventeenth- or eighteenth-century date, his observations assist in determining whether the eruption occurred before or after his visit. This contribution uses Dampier’s recorded observations to elucidate where the Roebuck was when the Long Island profile was drawn so that an appropriate comparison can be made with a modern profile of the island. This comparison confirms that the most recent major eruption of Long Island occurred before Dampier’s observations were made.

Estimates of the age of the widespread Tibito Tephra and the VEI 6 eruption of Long Island, Papua New Guinea, have been based on genealogical records derived from oral histories of a ‘time of darkness’, the voyage of William Dampier who named Long Island in 1700 CE, radiocarbon, palaeomagnetic and 210Pb dating, and efforts from around the world to associate the eruption with a number of sulphur spikes in Greenland and Antarctic ice cores that were not linked to better-known eruptions. In total, there have been more than one hundred estimates of the timing of the eruption, with appraisals ranging from the late 16th century to the early 20th century. Some of these estimates have been based on solid science, some have ignored or been unaware of constraints on the possible eruption date imposed by the need to make an allowance for revegetation of the island before or after the observations of Dampier and other navigators, and some have tried to relate the eruption to the misdirected observation of sunsets over London in 1665. This account describes, more or less in chronological order, the narrowing of the supposed eruption date to the latter half of the 17th century, an early record suggesting a possible link to a 1667 acidity peak in the Crete, Greenland ice core, further constraints imposed by a renewed round of radiocarbon assays, and a final suggestion of a likely but not proven 1665–1668 eruption based on recent Antarctic and Greenland ice cores. We also elucidate missteps along the way introduced by the reliance on Royal Society of London records from the late 19th and 20th centuries and the role of the Victorian novelist William Harrison Ainsworth and Daniel Defoe's early-17th century accounts of bubonic plague in London in 1665 in leading us down dead-end pathways.

With increasing population densities and expanding urban boundaries, the potential for explosive volcanic eruptions to have adverse impacts upon urban areas is on the rise. This is particularly true for volcanoes along subduction zones, because they are almost exclusively explosive and often coincident with large populations. Explosive eruption hazards such as tephra fall have the potential to affect very large areas and numbers of people; populations in volcanic areas may therefore be exposed to tephra falls from more than one volcano. In this study we have simulated large numbers of plausible explosive eruptions of Volcanic Explosivity Index (VEI) 4 or greater for each of 141 volcanoes in the Asia-Pacific region. Tephra fall footprints are aggregated for 16 major cities, according to their probability of occurrence. This addresses an emerging need for international agencies and organizations to conduct regional-scale assessments, where at-risk areas can be compared on a like-for-like basis. Hazard in cities near subduction zones is two to three orders of magnitude greater than for those farther away. By combining our hazard estimates with indicators describing the exposure and vulnerability of people and infrastructure, tephra fall risk scores were calculated for each city. Risk is evaluated separately for human populations and potential economic impact, with the highest human risk scores calculated for Manila and the highest economic scores for Tokyo. The volcanoes with the greatest hazard contribution in the very populous cities of Manila, Tokyo, and Jakarta were identified as Taal (Manila), Fujisan (Tokyo), and six volcanoes equally (Jakarta). While this study provides a transparent and consistent method for assessing regional volcanic hazard and risk, there are challenges associated with the data-poor setting and we conclude by discussing what is required in order to improve regional tephra fall hazard and risk assessments. This study provides a regional-scale assessment that cannot replace hazard and risk information provided locally by official organizations. Published version

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Although probabilistic insurance loss models, particularly for ash fall, are currently being developed volcanic risk has been widely ignored by insurers and policy holders alike. Volcanic eruption cover is often grouped in insurance and reinsurance policies with earthquake and tsunami cover. Many volcanic eruptions include several perils occurring in different spaces around the volcano, with widely varying intensities and consequences, sometimes all at once, sometimes sequentially, and sometimes repeatedly. Given the possibly large differences in hazard characteristics, event durations and potential losses the policy alignment with earthquake and tsunami covers can be unfortunate. Does ‘volcanic activity’ have the same meaning as ‘volcanic eruption’? Do the terms ‘ash fall’ and ‘pyroclastic fall’ have identical meanings to an insurer—or to a volcanologist? Some policies cover all volcanic perils while others include only named volcanic perils such as pyroclastic flows, ash falls, and/or lava flows. Often the intent of the coverage is not clear—were some volcanic perils missing from a list excluded by accident or design? Does a policy that covers damage occasioned by a fall of volcanic ash also cover the cost of clean-up, removal, transport and appropriate storage of the ash—even if the fall of 5–10 mm of ash causes almost no property damage? Clear communication between the insurance sector and policy holders (and the media) is dependent upon informed understanding of the nature of volcanic perils and volcanic eruptions, insurance wordings, and the potential losses to property and business interruption covers. This chapter explores these issues using examples of policy wordings, evidence from past eruptions, insurance case law, and potential losses in future eruptions.

Volcanic ash falls are one of the most widespread and frequent volcanic hazards, and are produced by all explosive volcanic eruptions. Ash falls are arguably the most disruptive volcanic hazard because of their ability to affect large areas and to impact a wide range of assets, even at relatively small thicknesses. From an insurance perspective, the most valuable insured assets are buildings. Ash fall vulnerability curves or functions, which relate the magnitude of ash fall to likely damage, are the most developed for buildings, although there have been important recent advances for agriculture and infrastructure. In this paper, we focus on existing vulnerability functions developed for volcanic ash fall impact on buildings, and apply them to a hypothetical building portfolio impacted by a modern-day Tambora 1815 eruption scenario. We compare and contrast the different developed functions and discuss some of the issues surrounding estimation of potential building damage following a volcanic eruption. We found substantial variability in the different vulnerability estimates, which contribute to large uncertainties when estimating potential building damage and loss. Given the lack of detailed and published studies of building damage resulting from ash fall this is not surprising, although it also appears to be the case for other natural hazards for which there are far more empirical damage data. Notwithstanding the potential limitations of some empirical data in constraining vulnerability functions, efforts are required to improve our estimates of building damage under ash fall loading through the collection of damage data, experimental testing and perhaps theoretical failure analysis. For insurance purposes, the current building typologies provided for use with vulnerability functions are too detailed to map to the relatively limited information on building types that is typically available to insurers. Thus, efforts to provide vulnerability functions that can be used where only limited information is available regarding building types would also be valuable, both for insurers and for at-risk areas that have not been subject to detailed building vulnerability surveys. Published version

Volcanic ash is one of the farthest-reaching volcanic hazards and ash produced by large magnitude explosive eruptions has the potential to affect communities over thousands of kilometres. Quantifying the hazard from ash fall is problematic, in part because of data limitations that make eruption characteristics uncertain but also because, given an eruption, the distribution of ash is then controlled by time and altitude-varying wind conditions. Any one location may potentially be affected by ash falls from one, or a number of, volcanoes so that volcano-specific studies may not fully capture the ash fall hazard for communities in volcanically active areas. In an attempt to deal with these uncertainties, this paper outlines a probabilistic framework for assessing ash fall hazard on a regional scale. The methodology employs stochastic simulation techniques and is based upon generic principles that could be applied to any area, but is here applied to the Asia-Pacific region. Average recurrence intervals for eruptions greater than or equal to Volcanic Explosivity Index 4 were established for 190 volcanoes in the region, based upon the eruption history of each volcano and, where data were lacking, the averaged eruptive behaviour of global analogous volcanoes. Eruption histories are drawn from the Smithsonian Institution’s Global Volcanism Program catalogue of Holocene events and unpublished data, with global analogues taken from volcanoes of the same type category: Caldera, Large Cone, Shield, Lava dome or Small Cone. Simulated are 190,000 plausible eruption scenarios, with ash dispersal for each determined using an advection–diffusion model and local wind conditions. Key uncertainties are described by probability distributions. Modelled results include the annual probability of exceeding given ash thicknesses, summed over all eruption scenarios and volcanoes. A companion paper describes the results obtained for the Asia-Pacific region

In a companion paper (this volume), the authors propose a methodology for assessing ash fall hazard on a regional scale. In this study, the methodology is applied to the Asia-Pacific region, determining the hazard from 190 volcanoes to over one million square kilometre of urban area. Ash fall hazard is quantified for each square kilometre grid cell of urban area in terms of the annual exceedance probability (AEP), and its inverse, the average recurrence interval (ARI), for ash falls exceeding 1, 10 and 100 mm. A surrogate risk variable, the Population-Weighted Hazard Score: the product of AEP and population density, approximates the relative risk for each grid cell. Within the Asia-Pacific region, urban areas in Indonesia are found to have the highest levels of hazard and risk, while Australia has the lowest. A clear demarcation emerges between the hazard in countries close to and farther from major subduction plate boundaries, with the latter having ARIs at least 2 orders of magnitude longer for the same thickness thresholds. Countries with no volcanoes, such as North Korea and Malaysia, also face ash falls from volcanoes in neighbouring countries. Ash falls exceeding 1 mm are expected to affect more than one million people living in urban areas within the study region; in Indonesia, Japan and the Philippines, this situation could occur with ARIs less than 40 years.

Correspondence from a long-established sugar mill provided the opportunity to construct the longest flood series for a river in Fiji—the Ba River in northwest Viti Levu—from 1892 to 2002. Flood waters reached the mill floor every four years on average. Contrary to common lore, this study could detect no increase in the frequency of major floods over the course of the 20th century, despite intensification of land use and siltation of the river channel over that time. Large, slow-moving tropical cyclones situated northwest of the valley have generated large floods, but so too have tropical rainstorms. Major floods have occurred in months when the Southern Oscillation Index (SOI) was both negative and positive, suggesting that the SOI is a poor indicator of flood potential for the Ba River.

In a volcanic eruption crisis, robust, scientifically informed risk advice can assist individuals to adopt suitable policies for personal protection and societal decision makers in the implementation of rational mitigation strategies for communities. We outline a number of risk assessment procedures that inform volcanic risk management, recognising that it is difficult to construct a single uniform framework suitable for all situations and all economic circumstances. We list a number of post-1950 landmark eruptions that have prompted development of volcanic risk assessment techniques, and briefly consider the range of volcanic hazards involved, the assets at risk and associated vulnerabilities. Then, we focus on risk metrics, tolerable and intolerable risks, as low as reasonably practicable (ALARP) principles, and casualty scale aversion, using as examples some volcanic disasters embedded in an industrial health and safety framework. Risk matrices, and their limitations, and F-N curves, often used to express relative risk levels for communication, are described in some detail before exploring the role of cost–benefit analysis in characterizing societal and individual risks objectively. Attention is also paid to the enumeration of relative risks from a range of volcanic hazards and, for illustration, we outline a specific risk assessment approach that combines jointly measures of ash fall hazard, exposure, and vulnerability. Because we recognise that some of the inputs to volcanic risk management are subjective, there is a strong emphasis on the value of using structured expert elicitation to quantify volcanic hazard uncertainties as informatively as possible. We conclude with a commentary on public risk communication, and end by noting that information relating to “near-miss” and very rare events can be included in risk assessment through counterfactual analysis.

Hailstorms are by far the costliest insured natural hazard in Australia. Major metropolitan areas such as Sydney and Brisbane, located in mid-latitude coastal Eastern Australia, are especially vulnerable due to building exposure and geographical location. Results are presented using data from metropolitan Sydney and Brisbane for seven recent severe storms, including the April 1999 Sydney hailstorm. The focus of this study is on comparisons between the radar-derived pattern of hail kinetic energy and damage to residential buildings in the form of insurance claims. CAPPI (Constant Altitude Plan Position Indicators) reflectivities were generated from S-band radar at low storm levels (1.5 km) and the hail kinetic energy was calculated. High reflectivity hail cells (≥ 55 dBZ) were investigated during the time they passed over occupied urban areas causing damage on the ground to insured assets. The preferred area for hail damage was found to be located to the left side and towards the rear left flank with respect to the storm movement. Higher claim costs were identified within storms that produced “follow up cells.” Two damage variables were derived from insurance claims on a 2 × 2 km grid: average cost and spatial average loss ratio. The radar fields were shifted horizontally in order to maximise correlations between the damage on the ground and the radar signal aloft. Correlation coefficients improved on average from 0.09 before shift to 0.47 after optimal shift for average cost; corresponding values for spatial average loss ratio improved from 0.06 to 0.43. These improvements in correlation are gratifying, especially considering the limitations of the data. Nonetheless, our general conclusion, when looking at all storms, is that hail kinetic energy as calculated in this study cannot by itself provide a robust, consistent and reliable predictor of insured losses.

The Auckland Region, New Zealand is at significant risk from tephra falls originating both from the local Auckland Volcanic Field (AVF) and several distant, large-volume centres. We use geological data and observations of historical eruptions to develop a catalogue of simulated tephra dispersal patterns for the Auckland Region, using the ASHFALL model. Simulated patterns relate to individual eruptive phases and can be combined to simulate events that vary in volume and duration. AVF eruption parameters were chosen to replicate fine tephra fall dispersed from small-volume phreatomagmatic eruptions. Modelling of eruptions from distant volcanoes was carried out in two stages. First, the sensitivity of modelled tephra thickness to various model parameters was tested. By far the largest thickness variations were the result of varying wind conditions, followed by eruption volume. The second stage involved modelling volumes that best replicated thicknesses of preserved events within the Auckland Region, allowing the catalogue to be created. Only about 1% of simulations from distant volcanoes reached the Auckland Region, suggesting that preserved tephra layers are either the result of reasonably long-lived eruptions or a number of eruptive phases. For eruptions originating within the AVF, mean modelled tephra thickness on land is small—approximately 1.6 mm near the coast and thinning rapidly inland. Mean andesitic thicknesses are dominated by eruptions from Egmont volcano, which typically deposit tephra over a narrow section of the Auckland Region with a maximum thickness of approximately 1.7 mm. Rhyolitic eruptions are more widespread, with mean thickness varying from 30 mm in the south of the Region to 9 mm in the north. When all centres are combined and weighted by their conditional likelihood of occurrence, mean thickness ranges from 7 mm in the south to 1.5 mm in the north. However, maximum thicknesses of 150 mm for the AVF, 12 mm for andesitic centres and 830 mm for rhyolitic centres all have the potential to cause much greater damage within parts of the Auckland Region.

VolcaNZ is a probabilistic volcanic loss model developed for the Auckland Region in New Zealand that currently considers tephra fall hazards from the Auckland Volcanic Field (AVF), Tuhua volcano, Okataina volcanic centre, Taupo volcano, Tongariro volcanic centre and Egmont volcano. In this first version of the model, structural and non-structural damage to residential building envelopes and associated cleanup costs are calculated using Monte Carlo simulation. VolcaNZ assigns a Minimum and Maximum Damage Value to groups of buildings for every simulation, dependent on tephra thickness. A Central Damage Value, representing loss as a percentage of total replacement cost, is then randomly selected between these limits. Even with small-thickness falls, non-structural damage is expected to roof and wall coatings, air-conditioning units, aerials and satellite dishes due to the corrosive and abrasive properties of tephra. An average loss of $583, attributed to non-structural damage, was assigned to all residential buildings impacted by any thickness of tephra greater than 0.1 mm. The costs of tephra removal from buildings, cleaning of building exteriors and tephra transport and disposal are also calculated within the model, assuming much of the cleanup process will be carried out by homeowners. Losses from all simulations are plotted against calculated Average Recurrence Intervals (ARIs) to produce loss curves. Structural damage does not become apparent until ARIs of approximately 8000 years. $1 billion losses, due to structural damage, occur at about 35,000 years and this increases to about $26 billion at 1 million years. Loss due to non-structural damage is constant at approximately $160 million for ARIs above about 600 years. Between 600 and 3000 years, cleanup loss is approximately $50 million, increasing to over $450 million at a return period of 1 million years. At ARIs between 600 and 3000 years, total loss is approximately $210 million, increasing to $10 billion at 100,000 years and over $26 billion at 1 million years. Because we only consider residential building damage and associated cleanup, these values greatly underestimate total loss from the next volcanic event to impact Auckland. Loss calculations will be improved by adding additional hazard and loss modules to VolcaNZ , resulting in a complete catastrophe loss model.

In an analysis of the French episode of heat wave in 2003, this article highlights how heat wave dangers result from the intricate association of natural and social factors. Unusually high temperatures, as well as socioeconomic vulnerability, along with social attenuation of hazards, in a general context where the anthropogenic contribution to climate change is becoming more plausible, led to an excess of 14,947 deaths in France, between August 4 and 18, 2003. The greatest increase in mortality was due to causes directly attributable to heat: dehydration, hyperthermia, heat stroke. In addition to age and gender, combinatorial factors included preexisting disease, medication, urban residence, isolation, poverty, and, probably, air pollution. Although diversely impacted or reported, many parts of Europe suffered human and other losses, such as farming and forestry through drought and fires. Summer 2003 was the hottest in Europe since 1500, very likely due in part to anthropogenic climate change. The French experience confirms research establishing that heat waves are a major mortal risk, number one among so-called natural hazards in postindustrial societies. Yet France had no policy in place, as if dangerous climate were restricted to a distant or uncertain future of climate change, or to preindustrial countries. We analyze the heat wave's profile as a strongly attenuated risk in the French context, as well as the causes and the effects of its sudden shift into amplification. Research and preparedness needs are highlighted.

Hailstorms occur frequently in metropolitan Sydney, in the eastern Australian State of New South Wales, which is especially vulnerable due to its building exposure and geographical location. Hailstorms challenge disaster response agencies and pose a great risk for insurance companies. This study focuses on the Sydney hailstorm of 14 April 1999 – Australia's most expensive insured natural disaster, with supporting information from two other storms. Comparisons are drawn between observed hailstone sizes, radar-derived reflectivity and damage data in the form of insurance claims and emergency calls. The "emergency response intensity" (defined by the number of emergency calls as a proportion of the total number of dwellings in a Census Collection District) is a useful new measure of the storm intensity or severity experienced. The area defined by a radar reflectivity ≥55 dBZ appears to be a good approximation of the damage swath on ground. A preferred area for hail damage is located to the left side of storm paths and corresponds well with larger hailstone sizes. Merging hail cells appear to cause a substantially higher emergency response intensity, which also corresponds well to maximum hailstone sizes. A damage threshold could be identified for hailstone sizes around 2.5 cm (1 cm), based on the emergency response intensity (insurance claims). Emergency response intensity and claims costs both correlate positively with hailstone sizes. Higher claim costs also occurred in areas that experienced higher emergency response intensities.

The first in-depth hail climatology for the state of New South Wales (NSW), Australia, based on reports of hailstones from 1791 to 2003, is described. The Australian Bureau of Meteorology's severe weather database is extended with a detailed compilation of scientific and newspaper accounts of hail fall for the greater Sydney area for the period 1805 to 1998. Owing to its high exposure to thunderstorm and hail by virtue of population and building density, the greater Sydney area is the focus of the database. Comparisons are drawn between Sydney and the rest of NSW, and between coastal and inland areas of the state. Over the study period, a total of 1570 thunderstorms produced hail. On average, 10 hailstorms per year were recorded in Sydney in the last 50 years. However, there is a statistically significant decrease in the hailstorm frequency during the last 14 years compared with the preceding 36 years. The magnitude of hailstorms, as measured by reports of estimated hailstone size, revealed an average maximum hailstone size per storm that ranged from 3.8 to 4.0 cm for different regions. Hailstorms occur most frequently between October and February (Australian spring and summer) in NSW, with peak activity in November and December. The hail season in Sydney begins 2 months earlier (August to February). However, during the last 14 years there has been a shift to November to March. The majority of NSW hailstorms occur during the late afternoon between 3pm and 7pm, with hailstorms in Sydney occurring about 1 h earlier. The most active hail fall regions are located in the Bureau of Meteorology's northern NSW weather forecast districts of the Northern Tablelands, Northern Rivers and parts of the Northwest Slopes. In Sydney, the most hail-prone suburbs are concentrated over the most densely populated areas and corridors. Copyright © 2005 Royal Meteorological Society

Risk decision-making in natural hazards encompasses a plethora of environmental, socio-economic and management-related factors, and benefits greatly from exploring possible patterns and relations among these multivariate factors. Artificial neural networks, capable of general pattern classifications, are potentially well suited for risk decision support in natural hazards. This paper reports an example that assesses the risk patterns or probabilities of house survival from bushfires using artificial neural networks, with a simulation data set based on the empirical study by Wilson and Ferguson (Predicting the probability of house survival during bushfires, Journal of Environmental Management 23 (1986) 259–270). The aim of this study was to re-model and predict the relationship between risk patterns of house survival and a series of independent variables. Various configurations for input and output variables were tested using neural networks. An approach for converting linguistic terms into crisp numbers was used to incorporate linguistic variables into the quantitative neural network analysis. After a series of tests, results show that neural networks are capable of predicting risk patterns under all tested configurations of input and output variables, with a great deal of flexibility. Risk-based mathematical functions, be they linear or non-linear, can be re-modelled using neural networks. Finally, the paper concludes that the artificial neural networks serve as a promising risk decision support tool in natural hazards.

The most important natural perils in Australia are tropical cyclones, earthquakes, bushfires, thunderstorms, floods, landslides and tsunamis. However, as far as residential buildings are concerned, the correct relative order of importance depends on the frames of reference used. Certainly, meteorological perils are more significant than geological hazards. Residential building damage produced by the most important natural hazards is assessed. Governance is shown to be poorly related to actual risk. Tropical cyclone wind-loading codes are amongst the best in the world, but the more limited potential for storm surge damage is largely ignored. While land-use regulations are strong in some states, almost no attention has been paid to appropriate building materials for flood-prone properties. Hail is probably the most important peril along the populated south-eastern seaboard, but no regulations govern roofing materials. Other issues relating to the present understanding of damage to buildings are raised.

In a companion paper, a methodology for ranking volcanic hazards and events in terms of risk was presented, and the likelihood and extent of potential hazards in the Auckland Region, New Zealand investigated. In this paper, the effects of each hazard are considered and the risk ranking completed. Values for effect are proportions of total loss and, as with likelihood and extent, are based on order of magnitude. Two outcomes were considered – building damage and loss of human life. In terms of building damage, tephra produces the highest risk by an order of magnitude, followed by lava flows and base surge. For loss of human life, risk from base surge is highest. The risks from pyroclastic flows and tsunami are an order of magnitude smaller. When combined, tephra fall followed by base surge produces the highest risk. The risks from lava flows and pyroclastic flows are an order of magnitude smaller. For building damage, the risk from Mt. Taranaki volcano, 280 km from the Auckland CBD, is largest, followed by Okataina volcanic centre, an Auckland volcanic field eruption centred on land, then Tongariro volcanic centre. In terms of human loss, the greatest risk is from an Auckland eruption centred on land. The risks from an Auckland eruption centred in the ocean, Okataina volcanic centre, and Taupo volcano are more than an order of magnitude smaller. When combined, the risk from Mt. Taranaki remains highest, followed by an Auckland eruption centred on land. The next largest risks are from the Okataina and Tongariro volcanic centres, followed by Taupo volcano. Three alternative situations were investigated. As multiple eruptions may occur from the Auckland volcanic field, it was assumed that a local event would involve two eruptions. This increased risk of a local eruption occurring on land so that it was equal to that of an eruption from Mt. Taranaki. It is possible that a future eruption may be of a similar, or larger size, to the previous Rangitoto eruption. Risk was re-calculated for local eruptions based on the extent of hazards from Rangitoto. This increased the risk of lava flow to greater than that of base surge, and the risk from an Auckland land eruption became greatest. The relative probabilities used for Mt. Taranaki volcano and the Auckland volcanic field may only be minimum values. When the probability of these occurring was increased by 50%, there was no change in either ranking.

Volcanic eruptions typically produce a number of hazards, and many regions are at risk from more than one volcano or volcanic field. So that detailed risk assessments can be carried out, it is necessary to rank potential volcanic hazards and events in terms of risk. As it is often difficult to make accurate predictions regarding the characteristics of future eruptions, a method for ranking hazards and events has been developed that does not rely on precise values. Risk is calculated individually for each hazard from each source as the product of likelihood, extent and effect, based on the parameters’ order of magnitude. So that multiple events and outcomes can be considered, risk is further multiplied by the relative probability of the event occurring (probabilitye) and the relative importance of the outcome (importanceo). By adding the values obtained, total risk is calculated and a ranking can be carried out. This method was used to rank volcanic hazards and events that may impact the Auckland Region, New Zealand. Auckland is at risk from the Auckland volcanic field, Okataina volcanic centre, Taupo volcano, Tuhua volcano, Tongariro volcanic centre, and Mt. Taranaki volcano. Relative probabilities were determined for each event, with the highest given to Mt. Taranaki. Hazards considered were, for local events: tephra fall, scoria fall and ballistic impacts, lava flow, base surge and associated shock waves, tsunami, volcanic gases and acid rain, earthquakes and ground deformation, mudflows and mudfills, lightning and flooding; and for distal events: tephra fall, pyroclastic flows, poisonous gases and acid rain, mudflows and mudfills, climate variations and earthquakes. Hazards from each source were assigned values for likelihood, with the largest for tephra fall from all sources, earthquakes and ground deformation, lava flows, scoria fall and base surge for an Auckland eruption on land, and earthquakes and ground deformation from an Auckland eruption in the ocean. The largest values for extent were for tephra fall and climate variation from each of the distal centres. However, these parameters do not give a true indication of risk. In a companion paper the effect of each hazard is fully investigated and the risk ranking completed.

Flooding in the business district of Kempsey, New South Wales, Australia, in 2001 allowed the collection and analysis of commercial flood damage data. Analysis indicated that direct losses were significant, totalling A$2.5 million. Data were variable owing to differences in the vulnerability of businesses to flood damage, differences in the impacts of the hazard upon businesses and survey uncertainty. Little direct relationship was found between direct commercial damage and over-floor water depth. Simple averaging and stage-damage curve loss estimation methods ignore the large variability present and result in inaccurate estimation of direct commercial damage. Probability loss estimation methods account for the variability present by assessing the chance of loss values occurring at specified depths of over-floor flooding.

Catastrophe loss estimation for natural hazards combines both hazard and exposure data. While hazard attributes such as intensity distributions are usually represented at a spatially explicit raster (or pixel) level, exposure data such as population, dwellings and insurance portfolios are usually only available at spatially lumped census tracts. In current loss estimation studies, this spatial incompatibility is often inadequately addressed and a uniform distribution of exposure data within an areal unit assumed. As a result, loss estimation models overlook a great deal of spatial disparity. This paper defines occupied residential area as the area at risk and uses a dasymetric mapping approach to obtain this from areal census tracts. Using Sydney (Australia) as an example, residential areas at risk were produced through street buffers. The effect of incorporating area at risk in loss estimation models in this manner was then tested for two hazards (earthquakes and hailstorms) that impose very different-sized damage footprints. Total numbers of separate houses (as exposure data) were represented at two hierarchically nested areal unit forms (postcode and census collection district—CCD) and their corresponding residential area forms. The spatial distribution of calculated losses for these different forms was then pairwise compared. For earthquakes, estimated losses were insensitive to the manner of delineating area at risk and the use of finer-resolution exposure data. This follows because the affected zone is much larger than the areal unit to which exposure data are attached. Hailstorms, on the other hand, have relatively small affected zones, and loss estimates at a coarse postcode level were considerably different to those at postcode-based residential, CCD and CCD-based residential levels. Differences between CCD and CCD-based residential area levels were relatively small in both cases because the distribution of fine CCD units closely reflects the underlying residential areas. Our empirical findings suggest that improved delineation of the area at risk and employing exposure data based on finer areal units are important for improving loss estimation from catastrophic events, particularly those that affect only a small proportion of the area under consideration. The results also have significance for other multidisciplinary studies concerned with the integration of spatially explicit environmental data and spatially lumped socioeconomic data.

Understanding the spatial structure of fine spatial resolution images is instrumental for either pixel- or object-based image analysis. In this Letter, the characteristic scale of scene variation in images is evaluated using statistics of sub-images produced by a wavelet transform. Six statistics, namely mean, variance, standard deviation (SD), coefficient of variation, skewness and kurtosis, were calculated for three directional sub-images and their derivative energy signature image at a sequence of wavelet decomposition levels. A simulated image, an aerial AUSIMAGE image (spatial resolution 0.2 ml and a recent Systeme Probatoire de l'Observation de la Terre (SPOT)-5 High Resolution Geometric (HRG) panchromatic image (spatial resolution 2.5 m) were analysed. It was found that with energy signature images, the change rate of SD over spatial resolution ranges between two successive decomposition levels (ASD/AR) suggested a synoptic and approximate description for the characteristic scale of scene variation. However, by comparing the result with the ranges of geostatistical variograms, it is suggested that the geostatistical method can correctly identify the characteristic scales of scene variation: semivariances can be calculated at any lag and orientation, while standard wavelet transforms are decomposed at only limited spatial resolution and orientation levels.

A new damage index to estimate damage to buildings relies on construction costs per square metre, and a replacement ratio which approximates costs relative to the cost of replacing a median-sized family home. Building damage is estimated against a five-point scale with Central Damage Values at 0.02, 0.1, 0.4, 0.75 and 1.0 of the replacement cost. Damage is expressed as damage in House Equivalents (HE) = Replacement Ratio x Central Damage Value. The Damage Index = log(2) ( HE) provides a simple 0 - 20 scale covering total damage of less than 1 HE to gt 1 million HE. For all natural hazard impacts in Australia DI is less than 12. Where the only damage data available are of lesser quality Generic or Qualitative Damage Indices (GDI and QDI) can be used. The various advantages and limitations of the Damage Index are discussed. Language: en

A wide range of scales and indices are used to describe natural hazards and theirimpacts. Some scales infer damage levels from hazard characteristics while othersuse damage levels to estimate a physical characteristic. Damage scales may relyon raw dollar values, percent loss estimates, damage states, normalized values ormacrodamage categories. Whatever the basis of the scale it should tell the truth.However, scales are compromises between the need for detailed information andbeing simple enough to use. Damage scales may be nominal (categorical), ordinal, interval or ratio scales. Frequencywords such as ``few'', ``many'' can be dealt with in a range of ways to produce contiguous,widely separated, broadly overlapping or narrow overlapping values. Most scales rely onmaximum values but some focus on minimum or threshold values. The number of levelson damage scales commonly ranges from five to 13. Some long-lived damage scales haveevolved through several editions, changing to reflect the new or additional uses to whichthey have been put and as buildings and the nature of damage to those structures has changed. Few scales state precisely the purpose of the scale, deal clearly with ambiguities or provideguidelines for the use of qualitative information.

Damage information on 173 buildings was collected in the immediate aftermath of the September 1994 eruption which destroyed large sections of Rabaul town, Papua New Guinea. The extent of damage is presented on a five-point scale and related to construction characteristics and tephra load. Total roof loads in Rabaul town ranged from 2 to 16 kN m–2 (about 100- to 950-mm tephra thickness). Most buildings which collapsed completely experienced loads >7.5 kN m–2, but many buildings sustained half this load with little more than cosmetic damage. Timber-framed buildings suffered more than buildings with at least some concrete block walls. However, some old, steel-framed buildings experienced severe damage with tephra loads as low as 2 kN m–2. Comparisons with tephra loads and building damage elsewhere are limited but suggest that Rabaul buildings experiencing roof loads of 2–5 kN m–2 generally survived reasonably well. However, the comparisons are unsatisfactory because of inadequate data on tephra unit weights, roof design, building ages, the quality of workmanship, and the natural variability of construction materials, particularly timber. The Rabaul data suggest that concrete block walls increase building resistance to tephra loads but it is doubtful that conventional residential buildings can be designed to sustain tephra loads exceeding 7 kN m–2. The Rabaul experience also indicates that building codes for volcanic areas need to consider mudfills and post-eruption corrosion of sheet metal roofs and wall cladding. Most importantly, had the majority of buildings in Rabaul survived the September 1994 tephra falls, wet season mudflows a few months later would still have made most of the town unsafe and uninhabitable.

During the past two decades there have been a wide range of applications for decision-making linking multicriteria evaluation (MCE) and geographic information systems (GIS). However, limited literature reports the development of MCE-GIS software, and the comparison of various MCE-GIS approaches. This paper introduces an MCE-GIS program called MCE-RISK for risk-based decision-making. It consists of a series of modules for data standardisation, weighting, MCE-GIS methods, and sensitivity analysis. The program incorporates different MCE-GIS methods, including weighted linear combination (WLC), the technique for order preference by similarity to ideal solution (TOPSIS), and compromise programming (CP), enabling comparisons between different methods for the same decision problem to be made. An example of decision-making for determining priority areas for a bushfire hazard reduction burning is examined. After implementing the alternative MCE-GIS methods, and comparing final outputs and the computational difficulty involved in the analysis, WLC is recommended. Some caveats on using MCE-GIS methods are also discussed. Although the development of MCE-RISK and its application reported in this paper are specific to risk-based decision-making in natural hazards, the program can be used for other environmental decision applications, such as environmental impact assessment and land-use planning.

Earthquakes, tropical cyclones and floods are the most important natural perils in terms of human deaths on a global basis. In Australia, at least 4300 deaths in the last 200 years have been produced by heatwaves; about 2000–2200 each by tropical cyclones and floods; and bushfires and lightning strikes have each killed at least 650 people. On a global basis it appears that floods, tropical storms, droughts and earthquakes are the most damaging natural perils. In Australia, in terms of median damage per event, hailstorms are the most expensive insured natural peril, while three events—the 1989 Newcastle earthquake, 1974's Cyclone Tracy, and the 1990 Sydney hailstorm—produced 36 per cent of the total insured damage in the period since 1967. The Newcastle earthquake and the Sydney hailstorm have provided opportunities for new understandings of these perils and their consequences. While much has been learnt from the devastation of Rabaul town by the 1994 eruption, a rare opportunity for a detailed st...

Witori and Dakataua caldera volcanoes have been very active in the middle to late Holocene. Using tephrochronology, this paper establishes the chronostratigraphy of these eruptions and their magnitude, and the frequency of explosive volcanism at Witori and Dakataua. After a long dormancy, Witori started explosive activity at ca. 5600 conventional radiocarbon years BP, producing in the next 4500 years five major tephra layers (W-K1 to W-K4, W-G) with VEIs of 5 to 6. After the W-G eruption at around 1200 BP, the activity decreased in magnitude but increased in frequency, with some eruptions forming central cones. The major eruption of Dakataua began with alternating ejections of phreatomagmatic ashfalls and plinian deposits followed by the cataclysmic eruption resulting in lithic-rich pyroclastic flows ca. 1100–1200 BP. The major tephra layers cover extensive areas in West New Britain due to their large volumes and the prevailing easterly winds, providing valuable time markers for establishing Holocene chronology. The largest eruption, the W-K2 event of ca. 3300 BP, shaped much of the present landscape, with an extensive area significantly devastated by tephra falls and pyroclastic flows. Obsidian and other artefacts buried by the tephras indicate that the area was repeatedly occupied. The major tephra events formed new coastal plains favourable for human occupation.

Information on fatalities from lightning strikes has been extracted from a specially compiled database on natural hazards in Australia. Records dating from 1803–1991 indicate that at least 650 persons have been killed by lightning strikes. Maps and charts show the percentages of victims with respect to age, sex, locality, activity, and other circumstances of the strike. The majority of the 650 fatalities recorded have occurred along the more populous southeastern coast. The overall death rate, from 1910–1989, is 0.08 per 100 000 population. The annual number of lightning fatalities has decreased with time, from a death rate of 0.21 in 1910–1919 to 0.01 in 1980–1989. This trend is more pronounced when population figures are taken into account. The group that has been most at risk in Australia is that of males aged 15–19, followed by males aged 20–34. The male:female ratio of victims has decreased with time but is not approaching equality, being 11.6 in 1910–1919 and 5.3 in 1980–1989. The diurnal and monthly occurrences of lightning fatalities peak at 12.00–18.00 hours and November-February respectively. About 86% of fatalities have occurred outdoors and 14% have occurred indoors. Approximately three-fifths of fatalities have been work-related, and the group of workers that has traditionally been most at risk is that of land-workers. Approximately one-fifth of fatalities have been recreation-related, although this proportion has been increasing with time. The recreational activities of water sports, golf, and cricket have had the greatest number of lightning fatalities. Comparisons are made between these data and the results of other similar studies, both in Australia and overseas.

Archival and library search techniques have been used to establish extensive databases on deaths and damage resulting from natural hazards in the Solomon Islands. Although the records of fatalities are certainly incomplete, volcanic eruptions, tropical cyclones, landslides, tsunami and earthquakes appear to have been the most important. Only 22 per cent of the recorded deaths have resulted from meteorological hazards but a single event could change this proportion significantly. Five events in the fatality database account for 88 per cent of the recorded deaths. Future death tolls are also likely to be dominated by a small number of events. While the expected number of deaths in a given period is dependent upon the length of record considered, it is clear that a disaster which kills one hundred or more people in the Solomons can be expected more frequently than once in a hundred years.

In an analysis of the French episode of heat wave in 2003, this article highlights how heat wave dangers result from the intricate association of natural and social factors. Unusually high temperatures, as well as socioeconomic vulnerability, along with social attenuation of hazards, in a general context where the anthropogenic contribution to climate change is becoming more plausible, led to an excess of 14,947 deaths in France, between August 4 and 18, 2003. The greatest increase in mortality was due to causes directly attributable to heat: dehydration, hyperthermia, heat stroke. In addition to age and gender, combinatorial factors included preexisting disease, medication, urban residence, isolation, poverty, and, probably, air pollution. Although diversely impacted or reported, many parts of Europe suffered human and other losses, such as farming and forestry through drought and fires. Summer 2003 was the hottest in Europe since 1500, very likely due in part to anthropogenic climate change. The French experience confirms research establishing that heat waves are a major mortal risk, number one among so-called natural hazards in postindustrial societies. Yet France had no policy in place, as if dangerous climate were restricted to a distant or uncertain future of climate change, or to preindustrial countries. We analyze the heat wave's profile as a strongly attenuated risk in the French context, as well as the causes and the effects of its sudden shift into amplification. Research and preparedness needs are highlighted.

This paper develops a GIS-based integrated approach to risk assessment in natural hazards, with reference to bushfires. The challenges for undertaking this approach have three components: data integration, risk assessment tasks, and risk decision-making. First, data integration in GIS is a fundamental step for subsequent risk assessment tasks and risk decision-making. A series of spatial data integration issues within GIS such as geographical scales and data models are addressed. Particularly, the integration of both physical environmental data and socioeconomic data is examined with an example linking remotely sensed data and areal census data in GIS. Second, specific risk assessment tasks, such as hazard behavior simulation and vulnerability assessment, should be undertaken in order to understand complex hazard risks and provide support for risk decision-making. For risk assessment tasks involving heterogeneous data sources, the selection of spatial analysis units is important. Third, risk decision-making concerns spatial preferences and/or patterns, and a multicriteria evaluation (MCE)-GIS typology for risk decision-making is presented that incorporates three perspectives: spatial data types, data models, and methods development. Both conventional MCE methods and artificial intelligence-based methods with GIS are identified to facilitate spatial risk decision-making in a rational and interpretable way. Finally, the paper concludes that the integrated approach can be used to assist risk management of natural hazards, in theory and in practice.