Abstract:Road networks in volcanically active regions can be exposed to various volcanic hazards from multiple volcanoes. Exposure assessments are often used in these environments to prioritise risk management and mitigation efforts towards volcanoes or hazards that present the greatest threat. Typically, road exposure has been assessed by quantifying the amount of road network affected by different hazards and/or hazard intensity. Whilst this approach is computationally efficient, it largely fails to consider the rela… Show more
“…motorway, primary and residential. We consolidated the 16 OSM road classifications into four distinct hierarchies -motorway (hierarchy 4), arterial (hierarchy 3), collector (hierarchy 2) and local (hierarchy 1) -on the basis that road hierarchy is an indicator of the scale of disruption experienced by the road network from hazardous impacts (Hayes et al, 2022b).…”
Section: Exposure Assessmentmentioning
confidence: 99%
“…2. Eruption frequency-magnitude estimates: https://doi.org/10.21979/N9/CGKS6C (Jenkins et al, 2022b); 3. Exposure results: https://doi.org/10.21979/N9/OUJPZQ (Jenkins et al, 2022c).…”
Abstract. Regional volcanic threat assessments provide a large-scale comparable vision of the threat posed by multiple volcanoes. They are useful for prioritising risk-mitigation actions and are required by local through international agencies, industries and governments to prioritise where further study and support could be focussed. Most regional volcanic threat studies have oversimplified volcanic hazards and their associated impacts by relying on concentric radii as proxies for hazard footprints and by focussing only on population exposure. We have developed and applied a new approach that quantifies and ranks exposure to multiple volcanic hazards for 40 high-threat volcanoes in Southeast Asia. For each of our 40 volcanoes, hazard spatial extent, and intensity where appropriate, was probabilistically modelled for four volcanic hazards across three eruption
scenarios, giving 697 080 individual hazard footprints plus 15 240 probabilistic hazard outputs. These outputs were overlain with open-access datasets across five exposure categories using an open-source Python geographic information system (GIS) framework developed for this study (https://github.com/vharg/VolcGIS, last access: 5 April 2022). All study outputs – more than 6500 GeoTIFF files and 70 independent estimates of exposure to volcanic hazards across 40 volcanoes – are provided in the “Data availability” section in user-friendly format. Calculated exposure values were used to rank each of the 40 volcanoes in terms of the threat they pose to surrounding communities. Results highlight that the island of Java in Indonesia has the highest median exposure to volcanic hazards, with Merapi consistently ranking as the highest-threat volcano. Hazard seasonality, as a result of varying wind conditions affecting tephra dispersal, leads to increased exposure values during the peak rainy season (January, February) in Java but the dry season (January through April) in the Philippines. A key aim of our study was to highlight volcanoes that may have been overlooked perhaps because they have not been frequently or recently active but that have the potential to affect large numbers of people and assets. It is not intended to replace official hazard and risk information provided by the individual country or volcano organisations. Rather, this study and the tools developed provide a road map for future multi-source regional volcanic exposure assessments with the possibility to extend the assessment to other geographic regions and/or towards impact and loss.
“…motorway, primary and residential. We consolidated the 16 OSM road classifications into four distinct hierarchies -motorway (hierarchy 4), arterial (hierarchy 3), collector (hierarchy 2) and local (hierarchy 1) -on the basis that road hierarchy is an indicator of the scale of disruption experienced by the road network from hazardous impacts (Hayes et al, 2022b).…”
Section: Exposure Assessmentmentioning
confidence: 99%
“…2. Eruption frequency-magnitude estimates: https://doi.org/10.21979/N9/CGKS6C (Jenkins et al, 2022b); 3. Exposure results: https://doi.org/10.21979/N9/OUJPZQ (Jenkins et al, 2022c).…”
Abstract. Regional volcanic threat assessments provide a large-scale comparable vision of the threat posed by multiple volcanoes. They are useful for prioritising risk-mitigation actions and are required by local through international agencies, industries and governments to prioritise where further study and support could be focussed. Most regional volcanic threat studies have oversimplified volcanic hazards and their associated impacts by relying on concentric radii as proxies for hazard footprints and by focussing only on population exposure. We have developed and applied a new approach that quantifies and ranks exposure to multiple volcanic hazards for 40 high-threat volcanoes in Southeast Asia. For each of our 40 volcanoes, hazard spatial extent, and intensity where appropriate, was probabilistically modelled for four volcanic hazards across three eruption
scenarios, giving 697 080 individual hazard footprints plus 15 240 probabilistic hazard outputs. These outputs were overlain with open-access datasets across five exposure categories using an open-source Python geographic information system (GIS) framework developed for this study (https://github.com/vharg/VolcGIS, last access: 5 April 2022). All study outputs – more than 6500 GeoTIFF files and 70 independent estimates of exposure to volcanic hazards across 40 volcanoes – are provided in the “Data availability” section in user-friendly format. Calculated exposure values were used to rank each of the 40 volcanoes in terms of the threat they pose to surrounding communities. Results highlight that the island of Java in Indonesia has the highest median exposure to volcanic hazards, with Merapi consistently ranking as the highest-threat volcano. Hazard seasonality, as a result of varying wind conditions affecting tephra dispersal, leads to increased exposure values during the peak rainy season (January, February) in Java but the dry season (January through April) in the Philippines. A key aim of our study was to highlight volcanoes that may have been overlooked perhaps because they have not been frequently or recently active but that have the potential to affect large numbers of people and assets. It is not intended to replace official hazard and risk information provided by the individual country or volcano organisations. Rather, this study and the tools developed provide a road map for future multi-source regional volcanic exposure assessments with the possibility to extend the assessment to other geographic regions and/or towards impact and loss.
“…In addition to the development of global hazard modelling methodologies, we identify three future research directions for global volcanic hazard, exposure and impact assessment: The development of global PVHA that accounts for the spatiotemporal probability of eruptions (e.g., Deligne et al 2010 ; Hayes et al 2022a ; Rougier et al 2018 ; Sheldrake et al 2020 ) and systematically estimate uncertainties (Marzocchi et al 2010 ). Exposure analyses that consider more assets than only population (Biass et al 2017 ; Hayes et al 2022b ) , which is made possible by crowdsourcing, modern spatial data infrastructures and machine learning applied to big Earth Observation data (Biass et al 2022a , b ; Buchhorn et al 2020 ; Giuliani et al 2019 ; Gorelick et al 2017 ).…”
Section: Discussionmentioning
confidence: 99%
“…Finally, Cereme, ranking 4 when using a 100 km buffer, is the volcano with the highest exposure to tephra fallout ≥ 1 kg/m 2 and PDC inundation for column collapse for VEI ≥ 4, capturing the exposure of Cirebon (0.33 MM people) and Bandung to large eruptions, respectively. It is however interesting to notice that when weighting the exposure from hazard footprints by their long-term probabilities of occurrences (Hayes et al 2022a ), its relatively low eruptive frequency results in Cereme ranking > 10, whereas Merapi ranks 1 st for all hazards except inundation from column collapse PDC. Fig.…”
Section: Comparing Model- and Radii-derived Exposure Estimatesmentioning
confidence: 99%
“…PDC inundation from dome collapse considers an overspill buffer of 990 m. Ranks increase with decreasing exposure. The column A represents the rank when the exposure for each VEI is weighted by the median annual probability of occurrence of each VEI scenario obtained using the method of Hayes et al ( 2022a ) and summed. Since dome collapse PDCs are defined independently from the VEI scale, no long-term absolute exposure is computed VEI 3 4 5 A Population Rank Population Rank Population Rank Rank Population exposed to tephra load ≥ 1 kg/m 2 Merapi 1,467,147 1 7,809,433 3 33,456,111 4 1 Gede-Pangrango 829,163 5 5,098,633 7 33,903,642 3 8 Cereme 678,746 6 9,620,476 1 56,177,908 1 12 Population exposed to large clast impacts ≥ 30 J Merapi 974 1 110,53 1 333,268 2 3 Gede-Pangrango 2 30 340 ...…”
Section: Comparing Model- and Radii-derived Exposure Estimatesmentioning
Effective risk management requires accurate assessment of population exposure to volcanic hazards. Assessment of this exposure at the large-scale has often relied on circular footprints of various sizes around a volcano to simplify challenges associated with estimating the directionality and distribution of the intensity of volcanic hazards. However, to date, exposure values obtained from circular footprints have never been compared with modelled hazard footprints. Here, we compare hazard and population exposure estimates calculated from concentric radii of 10, 30 and 100 km with those calculated from the simulation of dome- and column-collapse pyroclastic density currents (PDCs), large clasts, and tephra fall across Volcanic Explosivity Index (VEI) 3, 4 and 5 scenarios for 40 volcanoes in Indonesia and the Philippines. We found that a 10 km radius—considered by previous studies to capture hazard footprints and populations exposed for VEI ≤ 3 eruptions—generally overestimates the extent for most simulated hazards, except for column collapse PDCs. A 30 km radius – considered representative of life-threatening VEI ≤ 4 hazards—overestimates the extent of PDCs and large clasts but underestimates the extent of tephra fall. A 100 km radius encapsulates most simulated life-threatening hazards, although there are exceptions for certain combinations of scenario, source parameters, and volcano. In general, we observed a positive correlation between radii- and model-derived population exposure estimates in southeast Asia for all hazards except dome collapse PDC, which is very dependent upon topography. This study shows, for the first time, how and why concentric radii under- or over-estimate hazard extent and population exposure, providing a benchmark for interpreting radii-derived hazard and exposure estimates.
Proximal to the source, tephra fall can cause severe disruption, and populations of small volcanically active islands can be particularly susceptible. Volcanic hazard assessments draw on data from past events generated from historical observations and the geological record. However, on small volcanic islands, many eruptive deposits are under-represented or missing due to the bulk of tephra being deposited offshore and high erosion rates from weather and landslides. Ascension Island is such an island located in the South Atlantic, with geological evidence of mafic and felsic explosive volcanism. Limited tephra preservation makes it difficult to correlate explosive eruption deposits and constrains the frequency or magnitude of past eruptions. We therefore combined knowledge from the geological record together with eruptions from the analogous São Miguel island, Azores, to probabilistically model a range of possible future explosive eruption scenarios. We simulated felsic events from a single vent in the east of the island, and, as mafic volcanism has largely occurred from monogenetic vents, we accounted for uncertainty in future vent location by using a grid of equally probable source locations within the areas of most recent eruptive activity. We investigated the hazards and some potential impacts of short-lived explosive events where tephra fall deposits could cause significant damage and our results provide probabilities of tephra fall loads from modelled events exceeding threshold values for potential damage. For basaltic events with 6–10 km plume heights, we found a 50% probability that tephra fallout across the west side of the island would impact roads and the airport during a single explosive event, and if roofs cannot be cleared, three modelled explosive phases produced tephra loads that may be sufficient to cause roof collapse (≥ 100 kg m−2). For trachytic events, our results show a 50% probability of loads of 2–12 kg m−2 for a plume height of 6 km increasing to 898–3167 kg m−2 for a plume height of 19 km. Our results can assist in raising awareness of the potential impacts of tephra fall from short-lived explosive events on small islands.
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