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<front>
<journal-meta>
<journal-id journal-id-type="publisher">EGUsphere</journal-id>
<journal-title-group>
<journal-title>EGUsphere</journal-title>
<abbrev-journal-title abbrev-type="publisher">EGUsphere</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">EGUsphere</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub"></issn>
<publisher><publisher-name>Copernicus Publications</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/egusphere-2026-5468</article-id>
<title-group>
<article-title>A disruption scoring framework for critical infrastructure exposed to multiple hazard sources: tephra fall on Japan&apos;s electricity network</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Teng</surname>
<given-names>Rui Xue Natalie</given-names>
<ext-link>https://orcid.org/0009-0006-6175-8829</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Hayes</surname>
<given-names>Josh L.</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Jenkins</surname>
<given-names>Susanna F.</given-names>
<ext-link>https://orcid.org/0000-0002-7523-1423</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Lallemant</surname>
<given-names>David</given-names>
<ext-link>https://orcid.org/0000-0001-5759-9972</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Joffrain</surname>
<given-names>Mathis</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Fujita</surname>
<given-names>Eisuke</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Wardman</surname>
<given-names>John</given-names>
</name>
<xref ref-type="aff" rid="aff5">
<sup>5</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Miyagi</surname>
<given-names>Yousuke</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Kubo</surname>
<given-names>Tomohiro</given-names>
</name>
<xref ref-type="aff" rid="aff6">
<sup>6</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>Earth Observatory of Singapore, Asian School of the Environment, Nanyang Technological University, Singapore, 639798,  Singapore</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>Earth Sciences New Zealand, Lower Hutt, 5040, New Zealand</addr-line>
</aff>
<aff id="aff3">
<label>3</label>
<addr-line>AXA, Paris, 75008, France</addr-line>
</aff>
<aff id="aff4">
<label>4</label>
<addr-line>National Research Institute for Earth Science and Disaster Resilience, Tsukuba, 305-0006, Japan</addr-line>
</aff>
<aff id="aff5">
<label>5</label>
<addr-line>Maximum Information, London, EC4R 0AN, United Kingdom</addr-line>
</aff>
<aff id="aff6">
<label>6</label>
<addr-line>Mount Fuji Research Institute, Fujiyoshida, 403-0005, Japan</addr-line>
</aff>
<pub-date pub-type="epub">
<day>11</day>
<month>09</month>
<year>2026</year>
</pub-date>
<volume>2026</volume>
<fpage>1</fpage>
<lpage>36</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2026 Rui Xue Natalie Teng et al.</copyright-statement>
<copyright-year>2026</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri"  xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p>
</license>
</permissions>
<self-uri xlink:href="https://egusphere.copernicus.org/preprints/2026/egusphere-2026-5468/">This article is available from https://egusphere.copernicus.org/preprints/2026/egusphere-2026-5468/</self-uri>
<self-uri xlink:href="https://egusphere.copernicus.org/preprints/2026/egusphere-2026-5468/egusphere-2026-5468.pdf">The full text article is available as a PDF file from https://egusphere.copernicus.org/preprints/2026/egusphere-2026-5468/egusphere-2026-5468.pdf</self-uri>
<abstract>
<p>Assessing how hazards disrupt critical infrastructure systems is challenging, as these systems often comprise spatially distributed assets whose importance to service functionality varies across the network. This challenge is particularly acute in volcanic regions, where infrastructure may be exposed to hazards from multiple volcanoes with different eruption frequencies and impact footprints. This makes it hard to know where to focus resources for response, recovery and resilience planning ahead of an event. Here we present a probabilistic disruption scoring framework that combines hazard intensity, spatial extent, and occurrence probability with infrastructure exposure, asset criticality, and vulnerability to quantify the relative disruption potential of different hazard sources and locations. Asset criticality is represented by the number of people served, enabling impacts on individual infrastructure assets to be translated into a common disruption metric. We apply the framework to mainland Japan to assess potential electricity disruptions from tephra fall, considering impacts on power plants, transmission towers and substations exposed to simulated tephra fall events from 62 active volcanoes. A disruption score was calculated for each hazard footprint and used to rank the disruption potential of different tephra fall events and source volcanoes. We also quantified the contribution of different spatial areas to the annual expected electricity disruption across mainland Japan to identify locations with high disruption potential and disruption hotspots. Conditional on an eruption occurring, Fujisan is the volcano with the highest disruption potential because of its proximity to Tokyo and high criticality electricity infrastructure in central Japan. When eruption probabilities are incorporated, Asamayama emerges as the largest contributor to annual expected electricity disruption. Disruption hotspots are concentrated in major metropolitan centres, including Tokyo, Osaka, and Nagoya, as well as regions containing high criticality electricity generation facilities such as Tohoku. These findings provide a quantitative basis for prioritising volcanic risk reduction and infrastructure resilience measures. As the framework uses open-source globally available datasets and models, it can be readily applied to other hazards, infrastructure sectors, and geographic regions, providing a scalable screening tool for identifying priority hazard sources, locations, and infrastructure assets where more detailed risk and resilience investigations are likely to be most beneficial.</p>
</abstract>
<counts><page-count count="36"/></counts>
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