Testing a Comprehensive Volcanic Risk Assessment of Tenerife by Volcanic Hazard Simulations and Social Vulnerability Analysis

Abstract

Volcanic activity remains highly detrimental to populations, property and activities in the range of its products. In order to reduce the impact of volcanic processes and products, it is critically important to conduct comprehensive volcanic risk assessments on volcanically active areas. This study tests a volcanic risk assessment methodology based on numerical simulations of volcanic hazards and quantitative analysis of social vulnerability in the Spanish island of Tenerife, a well-known tourist destination. We first simulated the most likely volcanic hazards in the two eruptive scenarios using the Volcanic Risk Information System (VORIS) tool and then evaluated the vulnerability using a total of 19 socio-economic indicators within the Vulnerability Scoping Diagram (VSD) framework by combining the analytic hierarchy process (AHP) and the entropy method. Our results show good agreement with previous assessments. In two eruptive scenarios, the north and northwest of the island were more exposed to volcanic hazards, and the east registered the highest vulnerability. Overall, the northern municipalities showed the highest volcanic risk in two scenarios. Our test indicates that disaster risk varies greatly across the island, and that risk reduction strategies should be prioritized on the north areas. While refinements to the model will produce more accurate results, the outputs will still be beneficial to the local authorities when designing policies for volcanic risk reduction policies in Tenerife. This study tests a comprehensive volcanic risk assessment for Tenerife, but it also provides a framework that is applicable to other regions threatened by volcanic hazards.

Assessing the Urban Eco-Environmental Quality by the Remote-Sensing Ecological Index: Application to Tianjin, North China

“…where X i and Y i are the original and normalized values of the measures respectively and Max i and Min i are the maximum and minimum values of the measures, respectively. This method was used in previous studies for normalizing different measures [22,52]. As Yang et al [53] have shown that greenness is both a positive contributor and the largest contributor to the urban eco-environment quality, the greenness measure was treated as a benchmark here and its correlation with the other measures helped to determine the nature of their contributions to the urban eco-environment quality.…”

Optimal Grain Size Based Landscape Pattern Analysis for Shanghai Using Landsat Images from 1998 to 2017

“…Random forest (RF) was selected for urban land use classification in this study as it is a superior classifier in producing accurate results for various purposes, e.g. mapping lava flow of different age [47], regional land use [48], urban vegetation types [49], and volcanic risk assessment [50]. The number of trees (ntree) and random features (mtry) are two important parameters in RF classification and usually determined by trial and error [51].…”

Spatiotemporal Evolution and Trend Prediction of Tourism Economic Vulnerability in China’s Major Tourist Cities

“…In the early stages, the vulnerability concept was mainly applied to the assessment of natural disasters such as floods and droughts or ecosystems such as forests and coasts [2][3][4][5]. With the gradual integration and penetration of the natural and social systems, the interaction between the natural environment and human social activities has become increasingly obvious [6], and the relevant research on vulnerability has gradually extended to the social and economic fields [7,8]. As one of the important components of the concept and connotation of vulnerability, economic vulnerability refers to the bearing capacity of the regional economy due to the impact of unexpected events in the process of 2 of 21 development [9,10].…”