Tsunami evacuation times and routes to safe zones: a GIS-based approach to tsunami evacuation planning on the island of Stromboli, Italy

Abstract

While a landslide at the volcanic island of Stromboli (Aeolian Islands, Italy) in December 2002 created a tsunami with a run-up of 10.9 m, two paroxysmal eruptions in the summer of 2019 caused a tsunami with an amplitude of 40 to 20 cm. All three events required rapid, spontaneous emergency evacuations of the beach zone as the time between tsunami generation and impact is around 4 min. These conditions thus require a special consideration of the issue of evacuation capabilities on the island in the event of a volcanogenic tsunami. The purpose of this paper is thus to (i) determine pedestrian evacuation times from high-risk coastal areas to safe zones, (ii) to assess building evacuation ease, and (iii) determine emergency evacuation plans (for buildings and coastal zones). For this purpose, we created a GIS-based risk analysis/mapping tool that also allowed macroscopic evacuation modelling. In our case, the high-risk zone to be evacuated involves an area extending to 10 m a.s.l. and involving 123 individual buildings over an area of 0.18 km2. The results show that 33% of the buildings can be evacuated in 4 min, and that a 10-min warning time is required for a complete and well-distributed evacuation whereby the population is evenly distributed between all evacuation exits to avoid the potential for congestion. Initial interviews of residents in the at-risk zone reveal a high level of awareness and a desire for personalized evacuation scenarios.

A GIS-Based Evacuation Route Planning in Flood-Susceptible Area of Siraha Municipality, Nepal

“…Another issue is the use of factors that affect the speed of evacuation during the time of emergency. R. Dewi [73] used the same speed for all the evacuees while Bonilauri et al [71] and González-Riancho et al [72] added the effect of slope on the speed of the evacuees. Similarly, Zhu et al [69] took into account road factors such as road width, slope, pedestrian density, etc., while Watik et al [81] used the risk zone as a barrier and avoided the road that falls within this region to find the safest and shortest path.…”

“…The safe public buildings are assigned as the emergency shelter for this case as they host a large number of evacuees. Government offices, school buildings, sports centers, medical facilities, etc., were identified and digitized to obtain the points of these locations and it was ensured that they are in the safe zone and have access to the health centers nearby [69][70][71].…”

Inundation and evacuation of shoreline populations during landslide-triggered tsunami: An integrated numerical and statistical hazard assessment

“…For every simulated scenario, we applied an evacuation model to each inundated pixel, by using the approach of Bonilauri et al (2021). This is a macroscopic model, i.e., a model for global evacuation and not an agent-based approach, which determines the fastest pedestrian evacuation paths from a danger point to a safe point, and compares the escape time with the wave arrival time.…”

“…The risk associated with tsunamis is particularly high on Stromboli, due the high density of population on the coasts, and where travel time for the tsunami from the source is minutes (Bonilauri et al, 2021). This is enhanced at Stromboli where, with the development of global tourism, vacation rentals, restaurants, shops, and hotels have been built close to the beaches.…”

“…The second is based on the interpretation of observations and instrumental data (Bonaccorso et al, 2003;Pino et al, 2004;Boldini et al, 2005;Calvari et al, 2005;Maramai et al, 2005a,b;Tinti et al, 2005;Tommasi et al, 2005;Acocella et al, 2006;Tinti et al, 2006a,b;Chiocci et al, 2008a,b;Fornaciai et al, 2022). The third comprises model-based scenarios through data-driven simulations (Fornaciai et al, 2019;Bonilauri et al, 2021;Esposti Ongaro et al, 2021).…”