Reducing flood loss - A data-assimilation framework for improving forecasting capability in sparsely gauged regions

减少洪水损失 - 用于提高稀疏地区预报能力的数据同化框架

• 批准号: DP140102394
• 负责人: Prof Ashish Sharma
• 金额: $ 38.43万
• 依托单位: The University of New South Wales
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2014
• 资助国家: 澳大利亚
• 起止时间: 2014-03-01 至 2017-08-01
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP140102394
• 关键词: Reducing; flood; loss; data; assimilation

Floods are the biggest and severest natural disaster we face year after year. Furthermore, there has been little improvement in our capability to prevent flood damage over past decades. This research proposes a paradigm shift in the way flood forecasting, warning and evacuation proceeds, using 21st century technologies for collecting and incorporating flood related data into existing modelling platforms. It is argued that assimilating real-time satellite soil moisture data into flood models can increase accuracy manifold, even if the images are uncertain. The understanding gained in course of the proposed project has the potential to significantly reduce the damage caused year after year, especially in the data poor regions of the world.

洪水是我们年复一年面临的最大、最严重的自然灾害。此外，过去几十年来，我们防止洪水破坏的能力几乎没有改善。这项研究提出了洪水预报，预警和疏散进行的方式范式转变，使用21世纪的世纪技术收集和整合洪水相关的数据到现有的建模平台。有人认为，同化实时卫星土壤湿度数据到洪水模型可以提高精度的流形，即使图像是不确定的。在拟议的项目过程中获得的理解有可能大大减少年复一年造成的损害，特别是在世界上数据贫乏的地区。