The hazards-of-place model posits that vulnerability to environmental hazards depends on both biophysical and social factors. Biophysical factors determine where wildfire potential is elevated, whereas social factors determine where and how people are affected by wildfire. We evaluated place vulnerability to wildfire hazards in the coterminous US. We developed a social vulnerability index using principal component analysis and evaluated it against existing measures of wildfire potential and wildland-urban interface designations. We created maps showing the coincidence of social vulnerability and wildfire potential to identify places according to their vulnerability to wildfire. We found that places with high wildfire potential have, on average, lower social vulnerability than other places, but nearly 10% of all housing in places with high wildfire potential also exhibits high social vulnerability. We summarised our data by states to evaluate trends at a subnational level. Although some regions, such as the South-east, had more housing in places with high wildfire vulnerability, other regions, such as the upper Midwest, exhibited higher rates of vulnerability than expected. Our results can help to inform wildfire prevention, mitigation and recovery planning, as well as reduce wildfire hazards affecting vulnerable places and populations.

Autonomous underwater vehicles (AUVs) are playing an ever-growing role in modern subocean operations, generating a demand for faster, more manoeuvrable designs capable of deployments of increasingly longer durations. In order to meet these demands, vehicle developers have been looking to biological aquatic animals for inspiration. After evolving for millions of years, fish and cetaceans have developed fast efficient locomotion techniques, with levels of manoeuvrability that far outperform conventional engineered marine locomotion systems. This paper aims to give a brief introduction into some of the biologically inspired propulsion mechanisms that have been developed, to explain their strengths, their weaknesses, and the motivation behind them, and then finally to predict future trends in biomimetic AUV propulsion design.

Terrain-aided navigation (TAN) is a localisation method which uses bathymetric measurements for bounding the growth in inertial navigation error. The minimisation of navigation errors is of particular importance for long-endurance autonomous underwater vehicles (AUVs). This type of AUV requires simple and effective on-board navigation solutions to undertake long-range missions, operating for months rather than hours or days, without reliance on external support systems. Consequently, a suitable navigation solution has to fulfil two main requirements: (a) bounding the navigation error, and (b) conforming to energy constraints and conserving on-board power. This study proposes a low-complexity particle filter-based TAN algorithm for Autosub Long Range, a long-endurance deep-rated AUV. This is a light and tractable filter that can be implemented on-board in real time. The potential of the algorithm is investigated by evaluating its performance using field data from three deep (up to 3,700 m) and long-range (up to 195 km in 77 hr) missions performed in the Southern Ocean during April 2017. The results obtained using TAN are compared to on-board estimates, computed via dead reckoning, and ultrashort baseline (USBL) measurements, treated as baseline locations, sporadically recorded by a support ship. Results obtained through postprocessing demonstrate that TAN has the potential to prolong underwater missions to a range of hundreds of kilometres without the need for intermittent surfacing to obtain global positioning system fixes. During each of the missions, the system performed 20 Monte Carlo runs. Throughout each run, the algorithm maintained convergence and bounded error, with high estimation repeatability achieved between all runs, despite the limited suite of localisation sensors. K E Y W O R D S long-range AUV navigation, marine robotics, nonlinear filtering, terrain-aided navigation

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