Calil Juliano, Reguero Borja G, Zamora Ana R, Losada Iñigo J, Méndez Fernando J
Center for the Blue Economy, Middlebury Institute of International Studies, Monterey, California, United States of America.
Institute of Marine Sciences, University of California Santa Cruz, Santa Cruz, California, United States of America.
PLoS One. 2017 Nov 2;12(11):e0187011. doi: 10.1371/journal.pone.0187011. eCollection 2017.
As the world's population grows to a projected 11.2 billion by 2100, the number of people living in low-lying areas exposed to coastal hazards is projected to increase. Critical infrastructure and valuable assets continue to be placed in vulnerable areas, and in recent years, millions of people have been displaced by natural hazards. Impacts from coastal hazards depend on the number of people, value of assets, and presence of critical resources in harm's way. Risks related to natural hazards are determined by a complex interaction between physical hazards, the vulnerability of a society or social-ecological system and its exposure to such hazards. Moreover, these risks are amplified by challenging socioeconomic dynamics, including poorly planned urban development, income inequality, and poverty. This study employs a combination of machine learning clustering techniques (Self Organizing Maps and K-Means) and a spatial index, to assess coastal risks in Latin America and the Caribbean (LAC) on a comparative scale. The proposed method meets multiple objectives, including the identification of hotspots and key drivers of coastal risk, and the ability to process large-volume multidimensional and multivariate datasets, effectively reducing sixteen variables related to coastal hazards, geographic exposure, and socioeconomic vulnerability, into a single index. Our results demonstrate that in LAC, more than 500,000 people live in areas where coastal hazards, exposure (of people, assets and ecosystems) and poverty converge, creating the ideal conditions for a perfect storm. Hotspot locations of coastal risk, identified by the proposed Comparative Coastal Risk Index (CCRI), contain more than 300,00 people and include: El Oro, Ecuador; Sinaloa, Mexico; Usulutan, El Salvador; and Chiapas, Mexico. Our results provide important insights into potential adaptation alternatives that could reduce the impacts of future hazards. Effective adaptation options must not only focus on developing coastal defenses, but also on improving practices and policies related to urban development, agricultural land use, and conservation, as well as ameliorating socioeconomic conditions.
预计到2100年，世界人口将增长至112亿，生活在易受沿海灾害影响的低洼地区的人口数量预计也会增加。关键基础设施和宝贵资产仍不断被置于易受影响的地区，近年来，数以百万计的人因自然灾害而流离失所。沿海灾害的影响取决于受灾人口数量、资产价值以及关键资源是否处于危险之中。与自然灾害相关的风险由物理灾害、社会或社会生态系统的脆弱性及其遭受此类灾害的暴露程度之间的复杂相互作用所决定。此外，包括规划不善的城市发展、收入不平等和贫困在内的具有挑战性的社会经济动态加剧了这些风险。本研究采用机器学习聚类技术（自组织映射和K均值）与空间指数相结合的方法，在比较尺度上评估拉丁美洲和加勒比地区（LAC）的沿海风险。所提出的方法实现了多个目标，包括识别沿海风险的热点地区和关键驱动因素，以及处理大容量多维和多变量数据集的能力，有效地将与沿海灾害、地理暴露和社会经济脆弱性相关的16个变量整合为一个单一指数。我们的结果表明，在拉丁美洲和加勒比地区，超过50万人生活在沿海灾害、暴露（人员、资产和生态系统）与贫困相互交织的地区，为一场完美风暴创造了理想条件。通过所提出的比较沿海风险指数（CCRI）确定的沿海风险热点地区，居住着超过30万人，包括：厄瓜多尔的埃尔奥罗省；墨西哥的锡那罗亚州；萨尔瓦多的乌苏卢坦省；以及墨西哥的恰帕斯州。我们的结果为潜在的适应方案提供了重要见解，这些方案可以减少未来灾害的影响。有效的适应方案不仅要专注于发展沿海防御，还要改善与城市发展、农业土地利用和保护相关的做法和政策，以及改善社会经济状况。
