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

极端降水事件对加拿大产生了重大的社会、经济和环境影响，特别是在城市地区。该项目与行业合作伙伴减少灾难性损失研究所（ICLR）合作开展，将开发一个新的概率框架来评估、校正和混合最近生成的遥感（RS）降水产品，包括全球降水测量（IMERG）和多雷达多传感器（MRMS）的多卫星检索，以提高城市洪水风险评估的稳健性。我们将通过与现场观测的比较，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copula开发其边际分布和联合分布来描述偏差的空间依赖性。来自联合分布的蒙特卡罗样本将被用于对不存在原位观测的区域进行偏差校正RS。将基于贝叶斯方法的概率偏差调整后的RS产品与保险数据相结合，用于洪水风险分析。我们将通过使用原始和调整后的数据驱动水文模型，并根据观测记录评估径流模拟，来评估调整后降水估计的可靠性。拟议的概率方法是朝着强有力的洪水灾害和风险分析迈出的重要一步，对加拿大的市政当局、保险业和其他利益攸关方具有相当大的社会经济和环境效益。

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.