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Sci Total Environ. 2022 Jan 10;803:149947. doi: 10.1016/j.scitotenv.2021.149947. Epub 2021 Aug 27.
The popular concept of wellbeing has added multiple dimensions to the current socio-economic measures of vulnerability from natural hazards. Due to the wellbeing concept's relevance in various policy agendas, there is a need for a stronger integration of what is predominantly a socio-economic concept into the natural hazards space. Graphical methods have been used as transdisciplinary engagement tools to translate verbal descriptions of socio-ecological systems into simulation models able to test hypotheses. The purpose of this article is to identify the graphical methods that have been used in the literature to graphically represent, structure and model different segments of the hazard risk chain. A thorough review of the literature on natural hazards was performed using a set of keywords and filters that resulted in a total of 94 articles, which were then categorised based on the graphical methods used, broad families, properties, hazard types, and segments along the risk chain considered. A case study on volcanic hazards in Mount Taranaki, New Zealand showcased ways forward by conceptually combining methods to link hazards to impacts on wellbeing. Out of the review it was identified that the most widely used methodologies in the natural hazards space are probabilistic graphs (e.g. Bayesian networks) representing the random nature of hazards while mapping methods based on System Dynamic principles (SD) (e.g. causal loop diagrams) are used to characterise the dynamically emergent behaviours of socio-economic agents. While studies linking hazards to wellbeing using graphs are scarce, there is a nascent literature on the characterisation of wellbeing's multi-dimensionality using networks and SD diagrams. Hence, the possibilities to use common methods, or combinations of these, are numerous potentially enabling the creation of graph-based, distilled simulation models that can be used by experts from different backgrounds to quantitatively model the wellbeing impacts exerted by natural hazards.
幸福的概念为当前自然灾害脆弱性的社会经济衡量标准增添了多个维度。由于幸福概念在各种政策议程中的相关性，需要将这一主要是社会经济概念更有力地纳入自然灾害领域。图形方法已被用作跨学科参与工具，将社会生态系统的口头描述转化为能够检验假设的模拟模型。本文的目的是确定文献中用于图形化表示、构建和模拟灾害风险链不同部分的图形方法。使用一组关键词和筛选器对自然灾害文献进行了彻底审查，共产生了 94 篇文章，然后根据使用的图形方法、广泛的类别、属性、灾害类型以及风险链上考虑的各个部分对其进行了分类。以新西兰塔拉纳基山火山灾害为例，展示了通过概念上结合方法将灾害与幸福感影响联系起来的前进方向。从审查中可以看出，在自然灾害领域最广泛使用的方法是概率图（例如贝叶斯网络），用于表示灾害的随机性，而基于系统动态原理（SD）的映射方法（例如因果循环图）用于描述社会经济主体的动态涌现行为。虽然将灾害与幸福感联系起来的研究很少，但关于使用网络和 SD 图来描述幸福感多维性的新兴文献很多。因此，使用通用方法或这些方法的组合的可能性很多，这可能使创建基于图形的简化模拟模型成为可能，这些模型可以供来自不同背景的专家使用，以便对自然灾害产生的幸福感影响进行定量建模。
