Tsunami evacuation modelling as a tool for risk management: application to the coastal area of El Salvador

Abstract

Abstract. Advances in the understanding and prediction of tsunami impacts allow the development of risk reduction strategies for tsunami-prone areas. This paper presents an integral framework for the formulation of tsunami evacuation plans based on tsunami vulnerability assessment and evacuation modelling. This methodology considers (i) the hazard aspects (tsunami flooding characteristics and arrival time), (ii) the characteristics of the exposed area (people, shelters and road network), (iii) the current tsunami risk management situation (time needed by the responsible authorities to warn the local communities), (iv) the time needed for evacuation of the population, and (v) the identification of measures to improve the evacuation. The proposed methodological framework aims to fill the gap between risk assessment and management in terms of tsunami evacuation, as it allows for an estimation of the degree of evacuation success of specific management options, as well as for the classification and prioritization of the gathered information, in order to formulate an optimal evacuation plan.

An accessibility graph-based model to optimize tsunami evacuation sites and routes in Martinique, France

“…Its use has been diverted to calculate evacuation time and optimized route to ensure safety of people on foot in case of a tsunami. Similar approaches have been developed in tsunami risk assessment, especially in Indonesia to help for evacuation routes planning (González-Riancho et al, 2013;Dewi, 2010;Mück, 2008).…”

“…Confronted with tsunami scenarios, this information allows us to define the maximum time available for the evacuation of exposed populations and to identify critical areas which are far from evacuation sites (González-Riancho et al, 2013;Scheer et al, 2012).…”

“…This factor was defined by means of a formula used by Dewi (2010) which relies on the width of the escape route and the surface type. In the case of stairs, an additional average value of 0.5 m s −1 is assumed, suggested by Fruin (1971). The network has been divided into 10 classes of slopes extracted from Litto3D with a range of 3 %.…”

“…Pedestrians' travel speeds were identified from available bibliographic references (Moussaïd, 2010;Laghi et al, 2007;Daamen, 2004;Lee et al, 2004Lee et al, , 2003Fruin, 1971) and some in situ tests, taking into account the slope and the road type, but without consideration of the potential network congestion and the physical and psychological profile of people. These effects could be taken into account by a complementary method using a macro-simulation tool such as SimWalk ® , similar to what we have already done but at a limited scale .…”