Mapping coastal resilience: a Gis-based Bayesian network approach to coastal hazard identification for Queensland’s dynamic shorelines

IF 1.6 Q4 ENVIRONMENTAL SCIENCES Anthropocene Coasts Pub Date : 2024-10-09 DOI:10.1007/s44218-024-00060-y
Ahmet Durap
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Abstract

Coastal regions worldwide face increasing threats from climate change-induced hazards, necessitating more accurate and comprehensive vulnerability assessment tools. This study introduces an innovative approach to coastal vulnerability assessment by integrating Bayesian Networks (BN) with the modern coastal vulnerability (CV) framework. The resulting BN-CV model was applied to Queensland's coastal regions, with a particular focus on tide-modified and tide-dominated beaches, which constitute over 85% of the studied area. The research methodology involved beach classification based on morphodynamic characteristics, spatial subdivision of Queensland's coast into 78 sections, and the application of the BN-CV model to analyze interactions between geomorphological features and oceanic dynamics. This approach achieved over 90% accuracy in correlating beach types with vulnerability factors, significantly outperforming traditional CVI applications. Key findings include the identification of vulnerability hotspots and the creation of detailed exposure and sensitivity maps for Gold Coast City, Redland City, Brisbane City, and the Sunshine Coast Regional area. The study revealed spatial variability in coastal vulnerability, providing crucial insights for targeted management strategies. The BN-CV model demonstrates superior precision and customization capabilities, offering a more nuanced understanding of coastal vulnerability in regions with diverse beach typologies. This research advocates for the adoption of the BN-CV approach to inform tailored coastal planning and management strategies, emphasizing the need for regular reassessments and sustained stakeholder engagement to build resilience against climate change impacts.

Recommendations include prioritizing adaptive infrastructure in high-exposure areas like the Gold Coast, enhancing flood management in Brisbane, improving socio-economic adaptive capacity in Redland, and maintaining natural defences in Moreton Bay. This study contributes significantly to the field of coastal risk management, providing a robust tool for policymakers and coastal managers to develop more effective strategies for building coastal resilience in the face of climate change.

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绘制海岸复原力图:昆士兰动态海岸线沿海灾害识别的基于 Gis 的贝叶斯网络方法
全球沿海地区面临着气候变化引起的灾害所带来的日益严重的威胁,因此需要更准确、更全面的脆弱性评估工具。本研究通过将贝叶斯网络(BN)与现代海岸脆弱性(CV)框架相结合,引入了一种创新的海岸脆弱性评估方法。由此产生的 BN-CV 模型被应用于昆士兰的沿海地区,尤其侧重于潮汐改良和潮汐主导的海滩,这些海滩占研究区域的 85% 以上。研究方法包括根据形态动力学特征对海滩进行分类,将昆士兰海岸在空间上划分为 78 个区段,并应用 BN-CV 模型分析地貌特征与海洋动力学之间的相互作用。这种方法在将海滩类型与脆弱性因素相关联方面达到了 90% 以上的准确率,明显优于传统的 CVI 应用。研究的主要发现包括确定了脆弱性热点,并为黄金海岸市、红土地市、布里斯班市和阳光海岸地区绘制了详细的暴露和敏感性地图。该研究揭示了海岸脆弱性的空间变异性,为制定有针对性的管理战略提供了重要启示。BN-CV 模型具有卓越的精确性和定制能力,可以更细致地了解具有不同海滩类型的地区的海岸脆弱性。这项研究提倡采用 BN-CV 方法,为量身定制的海岸规划和管理战略提供信息,强调需要定期重新评估和利益相关者的持续参与,以建立抵御气候变化影响的能力。建议包括优先考虑黄金海岸等高风险地区的适应性基础设施,加强布里斯班的洪水管理,提高雷德兰的社会经济适应能力,以及维护莫尔顿湾的自然防御工事。这项研究为沿海风险管理领域做出了重要贡献,为政策制定者和沿海管理者提供了一个强有力的工具,以制定更有效的战略,提高沿海地区面对气候变化的适应能力。
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