Toward a novel approach for assimilation of SAR-based flood observations in a fully coupled hydrological model

探索一种在全耦合水文模型中同化基于 SAR 的洪水观测的新方法

• 批准号: 520222-2017
• 负责人: Scott, Andrea
• 金额: $ 1.82万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=639869
• 关键词: Toward; novel; approach; assimilation; SAR

Flooding poses a significant risk to humanity. Thousands of people are displaced each year due to flooding, inaddition to destruction of infrastructure and agriculture. Satellite observations are increasingly being used forflood monitoring. Satellite-borne synthetic aperture radars (SARs) are a particularly appealing source ofobservations due to their ability to return meaningful information of the earth's surface during all weatherconditions. With increasing numbers of SAR systems in orbit, and growing data volumes, it is of interest todevelop methods to use these data in automated processes.One such process, known as data assimilation, combines observational data with output from a forecast modelto provide an improved initial condition for the forecast model. This is the method used to generate weatherforecasts. For flood forecasting, assimilation of water level observations (WLOs) from SAR imagery is arelatively new area. Previous studies have obtained the WLOs from SAR using a prescribed geometry of theground surface (bathymetry). The proposed research will use a fully coupled hydrological model in which thebathymetry evolves with the flood to develop a novel approach to retrieve and assimilate WLOs from SARdata. The long-term goal is to improve flood forecasts.

洪水对人类构成重大风险。除了基础设施和农业遭到破坏外，每年还有数千人因洪水而流离失所。卫星观测越来越多地用于洪水监测。星载合成孔径雷达（SAR）是一个特别有吸引力的观测来源，因为它们能够在所有天气条件下返回地球表面的有意义的信息。随着在轨合成孔径雷达系统数量的增加和数据量的增加，人们有兴趣开发在自动化过程中使用这些数据的方法，其中一个过程称为数据同化，它将观测数据与预测模型的输出相结合，为预测模型提供改进的初始条件。这是用于生成天气预报的方法。对于洪水预报，同化SAR图像的水位观测（WLOs）是一个相对较新的领域。以前的研究已经获得了WLO从SAR使用一个规定的几何形状的thesground表面（bathymetry）。该研究将使用一个完全耦合的水文模型，在该模型中，水深随洪水而演变，以开发一种新的方法来检索和同化SAR数据中的WLO。长期目标是改善洪水预报。