Advancing flood risk mapping from global modelling systems in small islands

利用全球建模系统推进小岛屿洪水风险测绘

• 批准号: 2279970
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2279970
• 关键词: Advancing; flood; risk; mapping; global

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.

该项目旨在推进小岛屿洪水危害和风险的绘图工作，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使用。这将通过三个步骤实现：1）利用新出现的高分辨率数据集，从岛屿到社区各级改进小岛屿发展中国家的洪水风险估计; 2）在社区一级调查风险积累和日常危害/广泛灾害风险; 3）中间会议：评估新出现的风险建模方法在查明小岛屿发展中国家日常危害方面的效用高分辨率的海拔和人口数据集正在变得可用，这可能使社区能够在小岛屿发展中国家等数据稀少但风险高的地点解决洪水风险建模问题。重要的是要识别和理解这些数据集如何用于洪水建模测试用例，以及这些数据集是否可以改善当前可用数据集的洪水风险估计。在我们需要自下而上的社区一级的信息（例如，社区关于洪水地区、排水沟位置的当地知识、关键行为者的专家知识、关于渗透或经常发生洪水的地点的地面实况信息）之前，我们可以在“自上而下”的遥感数据方面走多远？我们如何将本地知识融入到我们的模型中，以便我们能够进一步进行建模，而不仅仅是自上而下的推理？自上而下和自下而上之间的交叉点在哪里？改进的风险估计技术对小流域的应用，包括保险，有什么影响？为解决这些问题而开发的技能应能为与减少灾害风险、风险评估和灾难风险建模有关的各种职业提供培训。