Christina Magill

When disasters occur, rapid impact assessments are required to prioritise response actions, support in-country efforts and inform the mobilisation of aid. The 15 January 2022 eruption of Hunga volcano, Tonga, and the resulting atmospheric shockwave, ashfall, underwater mass disturbance and tsunami, caused substantial impacts across the Kingdom of Tonga. Volcanic impacts on the scale observed after the eruption are rare, necessitating a reliance on international advice and assistance. The situation was complicated by the loss of Tonga’s international submarine fibreoptic cable (causing a complete loss of communications for approximately 20 days) along with border closures due to the COVID-19 pandemic. A need emerged for a rapid remote volcanic impact assessment and provision of specialist advice to help inform the response of international partners. Here we present a novel methodology for conducting rapid remote volcanic ashfall impact assessments, conducted over a 10-day period following the eruption. We used three different hazard models for ashfall thickness across the main island of Tongatapu and available asset information and vulnerability functions for buildings, agriculture, electricity networks, water supply and roads, to provide initial estimates of losses due to ashfall from the 15 January eruption. For buildings, we estimated losses both as total losses and as percentages of the total replacement cost of buildings on Tongatapu. For agriculture, we made probabilistic estimates of production losses for three different crop classes. For ashfall clean-up, we estimated ranges of ashfall volumes requiring clean-up from road surfaces and roofs. For water supply, electricity networks and roads, our analysis was limited to assessing the exposure of important assets to ashfall, as we had insufficient information on system configurations to take the analysis further. Key constraints on our analysis were the limited nature of critical infrastructure asset inventories and the lack of volcanic vulnerability models for tropical regions including Pacific Island nations. Key steps towards iteratively improving rapid remote impact assessments will include developing vulnerability functions for tropical environments as well as ground-truthing estimated losses from remote approaches against in-person impact assessment campaigns.

Developing approaches to assess the impact of tephra fall to agricultural and forestry systems is essential for informing effective disaster risk management strategies. Fragility functions are commonly used as the vulnerability model within a loss assessment framework and represent the relationship between a given hazard intensity measure (e.g., tephra thickness) and the probability of impacts occurring. Impacts are represented here using an impact state (IS), which categorises qualitative and quantitative statements into a numeric scale. This study presents IS schemes for pastoral, horticultural, and forestry systems, and a suite of fragility functions estimating the probability of each IS occurring for 13 sub-sectors. Temporal vulnerability is accounted for by a ‘seasonality coefficient,’ and a ‘chemical toxicity coefficient’ is included to incorporate the increased vulnerability of pastoral farming systems when tephra is high in fluoride. The fragility functions are then used to demonstrate a deterministic impact assessment with current New Zealand exposure.

Fire severity and vegetation indices are widely used to understand fire disturbance and vegetation recovery in heterogenous landscapes. Multispectral sensors are used to understand fire severity and post-fire vegetation dynamics. We utilised four fire severity (dNBR, RBR, RdNBR and MIRBI) and four vegetation indices (Chlorophyll Index of Red-Edge, dNDVI, EVI and NDRE) derived from Sentinel-2 images to understand the spatio-temporal variability of fire severity and vegetation recovery. The Masonite Road fire burned 2321.32 hectares of mixed Eucalyptus and wetland forests in the southwestern Telligerry State Conservation Area of New South Wales, Australia, in January 2018. Spectral and spatial analysis of different fire severity and vegetation indices revealed the blaze affected the landscape with moderate to high level of severity. Fire severity indices showed similar patterns in spatio-temporal analysis for a year, but Chlorophyll Index of Red-Edge and EVI did not show any pattern of fire impacts on vegetation. Among the analysed indices, two combinations of fire severity and vegetation indices (dNBR or RBR or RdNBR with NDVI and MIRBI withNDRE) were significantly correlated and can be effective in assessing fire severity, burned area and vegetation recovery.

Fire severity indices have been using to assess the level of fire severity and extent of burned areas. Three established fire severity indices - differenced Normalized Burn Ratio (dNBR), Relativized dNBR (RdNBR) and Relativized Burn Ratio (RBR) - from three optical satellite sensors (MODIS, Landsat 8 and Sentinel-2A) were compared in this study. Fire Severity Indices were derived for initial (immediately after the fire) and extended (a certain time after the fire) fire severity assessment. During the Kains Flat Fire, New South Wales, Australia, Callitris, Eucalypt Medium Woodland and Other Native Forest were the three-major fire affected forest types. Sentinel-2A derived fire indices showed better assessment of fire severity compared to the MODIS (Moderate Resolution Imaging Spectroradiometer) and Landsat 8 sensor. Landsat 8 and Sentinel-2A derived fire severity indices showed higher similarities in fire severity indices inter-comparisons. This study will facilitate the selection of appropriate fire indices and indices combinations for fire severity assessment in heterogeneous landscapes, burn severity mapping and monitoring.

Forest fire is one of the major environmental disturbances for the Australian continent. Identification of occurrence patterns of large fires, fire mapping, determination of fire spreading mechanisms, and fire effect modeling are some of the best measures to plan and mitigate fire effects. This chapter describes fire occurrence in New South Wales (Australia), the Australian National Bushfire Model Project (ANBMP), fire propagation modeling methods, the McArthur’s model and current forest fire modeling approaches in the state of New South Wales of Australia. Among the established fire models, PHOENIX Rapidfire predicts fire spread and facilitates loss and damage assessments as the model considers many environmental and social variables. Two fire spread models, SPARK and Amicus, have been developed and facilitated fire spread mapping and modeling in Australia.

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.

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 its source on Long Island in the Bismarck Sea, and linked to pyroclastic density currents in the Matapun Beds on the island. With a tephra fall volume >10 km³, this eruption was clearly one of the 10 largest globally in the past 600 years. Although almost certainly in the AD 17th or 18th centuries, determining just when this VEI 6 eruption occurred has proved difficult. Whether this eruption occurred before or after William Dampier sailed past, named, described and drew a profile of Long Island has been debated also. Dating the Long Island eruption has implications for assessing the rate of revegetation of the island, recolonisation of the island and caldera lake, Jared Diamond’s theory of ‘supertramp’ birds, correlations between major eruptions and ice core chronologies, the constant rate of supply model of ²¹⁰Pb concentration in lake sediments, reservoir effects in Lake Kutubu in the southern highlands of PNG, the timing of a prehistoric phase of agriculture in the Western Highlands and the longevity of oral histories recording a taim tudak (time of darkness) when sand fell from the sky, houses collapsed, crops were ruined and people died. This paper reviews early dates based on radiocarbon, ²¹⁰Pb and paleosecular magnetic variation, historical reports and genealogical dates from oral histories, and speculation based on tree ring and ice core evidence. Analyses of 10 new radiocarbon dates from the Matapun Beds on Long Island are reported. Our best estimate places the eruption between 1651 and 1671 AD with a 95.4% probability and between 1655 and 1665 AD with a 68.2% probability.