Advancing flood risk mapping from global modelling systems in small islands

该项目旨在推动绘制小岛屿洪水灾害和风险的地图，并评估新出现的数据集在确定广泛灾害风险热点方面的潜力，以及如何将这些数据与社区一级的信息结合起来使用。这将通过三个步骤实现：1)利用新兴的高分辨率数据集改善小岛屿发展中国家从岛屿到社区尺度的洪水风险估计；2)在社区尺度上调查风险积累和日常灾害/广泛灾害风险；3)中间会议：一些新兴的高分辨率海拔和人口数据集正在出现，这些数据集可能使社区能够在小岛屿发展中国家等数据稀疏但高风险的地区解决洪水风险建模问题。识别和理解这些数据集如何在洪水建模测试案例中使用，以及这些数据集是否可以比当前可用的数据集改进洪水风险估计，这一点很重要。在我们需要自下而上的社区层面的信息（例如，社区对洪水地区、排水口位置的当地知识、关键参与者的专家知识、关于渗透或经常被洪水淹没地点的地面真实信息）之前，“自上而下”的遥感数据能走多远？我们如何结合本地知识来告知我们的模型，这样我们就可以更进一步地进行建模，而不仅仅是自上而下的推断？自顶向下和自底向上之间的交集在哪里？它们是否可以协同使用？改进的风险评估技术对包括保险在内的小型集水区的应用有什么影响？为解决这些问题而开发的技能应该为与减少灾害风险、风险评估和灾难风险建模有关的广泛职业提供培训。

This project aims to advance the mapping of flood hazard and risk in small islands and assess the potential of emerging data sets to identify hotspots of extensive disaster risk and how such data might be used in conjunction with community level information. This will be achieved via three steps: 1) Utilising emerging high-resolution datasets to improve flood risk estimation in SIDS from island to community scales 2) Investigating risk accumulation and everyday hazards/extensive disaster risk at community scale 3) Meeting in the middle: assessing the utility of emerging risk modelling methods for the identification of everyday hazards in SIDS Several emerging high-resolution elevation and population datasets are becoming available that might enable community resolving flood risk modelling in data-sparse but high-risk locations such as SIDS. It is important to identify and understand how these datasets can be used in a flood modelling test case, and whether these datasets can improve flood risk estimates over currently-available datasets. How far can we go with the 'top down' remotely sensed data before we need information from the bottom up community level (e.g. community local knowledge on areas of flooding, drain location, expert knowledge from key actors, ground truth information on infiltration or frequently flooded locations)? How could we incorporate local knowledge to inform our models so that we can take the modelling further than just top-down inference? Where is this intersection between the top-down and the bottom-up and can they be used in synergy? What are the implications of improved risk estimation technology for applications in small catchments including insurance? Skills developed addressing these problems should provide training for a wide range of careers related to disaster risk reduction, risk assessment and catastrophe risk modelling.