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The Case of Samoa GeoHazards Article Multiscale Quantification of Tsunami Hazard Exposure in a Pacific Small Island Developing State: The Case of Samoa Shaun Williams 1,* , Ryan Paulik 1 , Rebecca Weaving 2,3, Cyprien Bosserelle 1 , Josephina Chan Ting 4, Kieron Wall 1 , Titimanu Simi 5 and Finn Scheele 6 1 National Institute of Water and Atmospheric Research (NIWA), Christchurch 8011, New Zealand; [email protected] (R.P.); [email protected] (C.B.); [email protected] (K.W.) 2 School of Geography, Environment and Geosciences, University of Portsmouth, Portsmouth, Hampshire PO1 2UP, UK; [email protected] 3 Ascot Underwriting Ltd., London EC3M 3BY, UK 4 Disaster Management Office, Ministry of Natural Resources and Environment, Apia WS1339, Western Samoa; [email protected] 5 Project Unit, Samoa Green Climate Fund Project, Ministry of Finance, Apia WS1339, Western Samoa; [email protected] 6 GNS Science, Lower Hutt 5011, New Zealand; [email protected] * Correspondence: [email protected] Abstract: This study presents a scenario-based approach for identifying and comparing tsunami exposure across different sociopolitical scales. In Samoa, a country with a high threat to local tsunamis, we apply scenarios for the 2009 South Pacific tsunami inundation at different grid resolutions (50 and 10 m) to quantify building and road exposure at the national, district and village levels. We Citation: Williams, S.; Paulik, R.; show that while the coarser 50 m model is adequate for use in the rapid identification of exposure Weaving, R.; Bosserelle, C.; Chan at the national and district levels, it can overestimate exposure by up to three times more at the Ting, J.; Wall, K.; Simi, T.; Scheele, F. village level. Overestimation typically occurs in areas characterized by flat, low-lying, gentle-rising Multiscale Quantification of Tsunami terrain. Overall, a 35% increase in buildings exposed to the 50 m model is observed compared with Hazard Exposure in a Pacific Small the 10 m scenario on southeast Upolu island. Similarly, a 31% increase in road exposure is observed Island Developing State: The Case of for the 50 m scenario. These observations are discussed within the context of tsunami evacuation Samoa. GeoHazards 2021, 2, 63–79. planning and logistics. Notwithstanding the variability in exposure, a precautionary approach leads https://doi.org/10.3390/ us to conclude that while higher-resolution models are recommended where available data and/or geohazards2020004 financial resources permit, the absence of such datasets should not preclude the use of coarser hazard datasets in risk assessments. Finer-resolution models provide more credence in detailed local-level Academic Editor: Jorge Macías exposure evaluation. While the results of this study are specific to the Samoan context, the results Received: 9 April 2021 can be applied to the multiscale assessment of tsunami risk exposure in similar hazard contexts. Accepted: 10 May 2021 Published: 11 May 2021 Keywords: tsunami inundation; BG-Flood; RiskScape; samoa; 2009 South Pacific tsunami; risk man- agement Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. 1. Introduction Tsunami hazard and risk assessments provide crucial decision-support tools to inform disaster resilience in coastal locations, particularly in the context of preparedness, evac- uation and land-use planning [1–6]. Indeed, with global tsunami-related casualties and Copyright: © 2021 by the authors. damages estimated to be one hundred times greater between 1998 and 2017 compared to Licensee MDPI, Basel, Switzerland. the preceding 20 year period, due largely to the 2004 Indian Ocean and 2011 Tohoku-oki This article is an open access article tsunamis [7], the need for hazard risk information in tsunami-prone regions is essential distributed under the terms and (e.g., [8,9]). conditions of the Creative Commons In the Small Island Developing States (SIDS) of the Pacific, tsunami resilience planning Attribution (CC BY) license (https:// is guided by the Framework for Resilient Development in the Pacific (FRDP) [10]. Aligned creativecommons.org/licenses/by/ with the Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR) [3], the FRDP 4.0/). GeoHazards 2021, 2, 63–79. https://doi.org/10.3390/geohazards2020004 https://www.mdpi.com/journal/geohazards GeoHazards 2021, 2, x FOR PEER REVIEW 2 of 18 GeoHazards 2021, 2 64 Aligned with the Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR) [3], the FRDP provides recommendations on integrative approaches to address climate provideschange and recommendations disaster risk management on integrative within approaches Pacific Island to address country climate contexts. change This and in- disastercludes enhancing risk management adaptative within capacity Pacific and Island resilience country to contexts.tsunami hazard This includess. enhancing adaptativeA significant capacity proportion and resilience of SIDS to tsunami populations hazards. and business hubs in the central Pacific are inA coastal significant low- proportionlying areas ofwhich SIDS are populations vulnerableand to the business impacts hubs of tsunamis in the central [10]. This Pa- cificwas areclearly in coastal demonstrated low-lying by areas the 2009 which South are vulnerablePacific tsunami to the (SPT) impacts which of tsunamis impacted [ 10the]. ThisIndependent was clearly State demonstrated of Samoa (herein by the Samoa) 2009 South [11–14 Pacific], causing tsunami widespread (SPT) which loss of impacted life and theinfrastructure Independent damage State ofalong Samoa the southern (herein Samoa) shores [of11 Upolu–14], causing and Savaii widespread islands [15 loss–19 of] (Fig- life andure 1). infrastructure While planning damage tools along to support the southern tsunami shores resilience of Upolu in Samoa and Savaiihave b islandseen developed, [15–19] (Figure1 ). While planning tools to support tsunami resilience in Samoa have been devel- such as identifying evacuation and hazard mitigation zones (e.g., [20,21]), these were pur- oped, such as identifying evacuation and hazard mitigation zones (e.g., [20,21]), these were posefully designed to be conservative and derived using relatively coarse datasets. Con- purposefully designed to be conservative and derived using relatively coarse datasets. Con- sequently, they may not necessarily be representative of onshore hazard risk characteris- sequently, they may not necessarily be representative of onshore hazard risk characteristics tics at local village levels. at local village levels. FigureFigure 1.1. SamoanSamoan IslandsIslands showingshowing districtdistrict andand villagevillage boundaries,boundaries, asas wellwell asas thethe extentsextents ofof thethe red,red, orange,orange, andand yellowyellow tsunamitsunami evacuationevacuation zoneszones [[20].20]. RedRed zonezone denotesdenotes areasareas atat potentialpotential threatthreat toto smallsmall tsunamitsunami whichwhich posepose onlyonly aa marinemarine threat.threat. OrangeOrange zonezone encompassesencompasses allall areasareas at at potential potential threat threat to to tsunami tsunami inundation inundation from from regional/distant regional/distant earthquakesearthquakes whichwhich areare notnot locallylocally felt.felt. YellowYellow zonezone denotesdenotes allall areasareas atat potentialpotential threatthreat toto tsunamitsunami inundationinundation followingfollowing aa longlong andand stronglystrongly feltfelt earthquake.earthquake. DistrictsDistricts locatedlocated alongalong southeastsoutheast UpoluUpolu experiencedexperienced thethe greatestgreatest losseslosses inin termsterms ofof casualties,casualties, damagedamage toto propertyproperty and and the the built built environment environment from from the the 2009 2009 tsunami. tsunami. Districts Districts cited cited in in text: text: 1 =1 = Palauli Palauli West; West; 2 =2 Palauli= Palauli I leI le Falefa; Falefa; 3 3 = = Satupaitea; Satupaitea; 4 4 = = PalauliPalauli East;East; 55 == Faasale Faasaleleaga-I;leaga-I; 6 6 = = Aiga Aiga I Ile le Tai; Tai; 7 7 = =Aana Aana Alofi Alofi-III;-III; 8 8= =Falelatai/Samatau; Falelatai/Samatau; 9 = Lefaga/Faleseela; 10 = Siumu; 11 = Falealili; 12 = Lotofaga; 13 = Lepa; 14 = Aleipata Itupa I Luga; 15 = Aleipata Itupa I Lalo. 9 = Lefaga/Faleseela; 10 = Siumu; 11 = Falealili; 12 = Lotofaga; 13 = Lepa; 14 = Aleipata Itupa I Luga; 15 = Aleipata Itupa I Villages cited in text: a = Saleapaga; b = Lalomanu; c = Satitoa; d = Malaela. Star = Apia city. Lalo. Villages cited in text: a = Saleapaga; b = Lalomanu; c = Satitoa; d = Malaela. Star = Apia city. Similarly, detailed exposure data representing built environments is limited in Pacific SIDS. The distribution of built elements, particularly buildings and roads for example, pro- vide useful indicators for population clusters (e.g., [19]). Building and road exposure to the GeoHazards 2021, 2 65 physical characteristics of tsunamis (e.g., flow depth, extent, distance from coast, shielding) can support the identification of vulnerable clusters or ‘risk hotspots’ (e.g., [8]), whereby mitigation and risk reduction strategies could be targeted (e.g., identifying compromised structures and routes where evacuation might be impeded). In general, there is a dearth in country specific tsunami hazard and exposure infor- mation to inform coastal risk management and preparedness planning in Pacific SIDS. However, tsunami inundation and runup characteristics along with exposure
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