Probabilistic bias adjustment of remotely sensed quantitative precipitation estimations to improve urban flood risk assessments in southern Canada

遥感定量降水估计的概率偏差调整以改进加拿大南部城市洪水风险评估

• 批准号: 538219-2019
• 负责人: Najafi, MohammadReza
• 金额: $ 1.82万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=681067
• 关键词: Probabilistic; bias; adjustment; remotely; sensed

Extreme precipitation events have significant social, economic and environmental consequences over Canada particularly in urban domains. This project, conducted in collaboration with industry-partner the Institute for Catastrophic Loss Reduction (ICLR), will develop a novel probabilistic framework to evaluate, bias-correct and blend recently generated remotely sensed (RS) precipitation products including Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG) and Multi-Radar Multi-Sensor (MRMS) to improve the robustness of urban flood risk assessments. We will analyze the intensity and duration of extreme precipitation events and the corresponding biases by comparing RS estimations with in-situ observations. The project will characterize the spatial dependencies of biases through the development of their marginal and joint distributions using Copulas. Monte Carlo samples from the joint distribution will be taken to bias correct RS for areas where in-situ observations do not exist. The probabilistic bias adjusted RS products will be merged based on a Bayesian approach which, combined with the insurance data, will be used for flood risk analysis. We will assess the reliability of the adjusted precipitation estimations by driving a hydrological model using raw and adjusted data and assessing streamflow simulations against observed records. The proposed probabilistic approach is a major step towards robust flood hazard and risk analysis and has considerable socio-economic and environmental benefits for municipalities, insurance industry and other stakeholders in Canada.

极端降水事件对加拿大尤其是城市地区产生重大的社会、经济和环境影响。该项目与行业合作伙伴灾难性损失减少研究所 (ICLR) 合作开展，将开发一种新颖的概率框架来评估、偏差校正和混合最近生成的遥感 (RS) 降水产品，包括 GPM 多卫星检索（全球降水测量）（IMERG）和多雷达多传感器（MRMS）可提高城市洪水风险评估的稳健性。我们将通过比较RS估计与现场观测来分析极端降水事件的强度和持续时间以及相应的偏差。该项目将通过使用 Copulas 开发偏差的边际分布和联合分布来表征偏差的空间依赖性。对于不存在现场观测的区域，将采用联合分布的蒙特卡罗样本来校正 RS 的偏差。概率偏差调整后的 RS 产品将基于贝叶斯方法进行合并，并与保险数据相结合，用于洪水风险分析。我们将通过使用原始数据和调整后的数据驱动水文模型并根据观测记录评估水流模拟来评估调整后的降水估算的可靠性。拟议的概率方法是迈向稳健的洪水灾害和风险分析的重要一步，并且为加拿大的市政当局、保险业和其他利益相关者带来了可观的社会经济和环境效益。