RAPID: Fast Reconstruction of Flood Hydrographs in the Houston Metropolitan Area during Hurricane Harvey Based on Image Processing and In-situ Measurements

RAPID：基于图像处理和现场测量快速重建飓风“哈维”期间休斯顿都会区洪水过程线

• 批准号: 1760582
• 负责人: Navid Jafari
• 金额: $ 6.12万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1760582&HistoricalAwards=false
• 关键词: RAPID; Fast; Reconstruction; Flood; Hydrographs

The ability to construct flood hydrographs in urban areas in real-time during flash floods, hurricanes, and other extreme weather events is difficult because of the low spatial density of water level measurements and the complex interactions of built infrastructure, ground topography, and natural landscape with flowing water. The goal of this RAPID project is to leverage perishable images and video footage from traffic intersection and interstate highway cameras, major news media outlets, and social media along with reference objects/points. Subsequent photo image processing, scaled to the reference objects, will enable development of a more continuous, accurate hydrograph in the Houston metropolitan area. By reconstructing the flood hydrographs at a large number of locations in flooded highways, streets and residential subdivisions, high-resolution, process-based urban inundation modeling from hurricane-generated surge and rainfall will become significantly more accurate. For example, it will facilitate a better understanding of transport of sediments and pollutants in and out of Houston during Hurricane Harvey. Such a model validated by the reconstructed hydrographs will also aid state and local governments in making timely evacuation decisions for low-lying areas to mitigate the impact of similar hurricane-induced hazards.This RAPID project based on reconstruction of flood hydrographs in Houston using image processing and in-situ measurements has significant intellectual merit: (1) The proposed methodology is innovative and creative because it does not employ any traditional stream gages. Instead, it relies on a unique form of existing data employed for a new application. (2) The reconstructed flood hydrographs will significantly improve the understanding of the hydrological processes of this unprecedented flood event caused by the extreme rainfall of Hurricane Harvey. (3) The data will benefit the development of a new flood model for Houston. (4) The developed algorithms and software for processing the traffic image data, which will be available on the NHERI DesignSafe-CI platform, can be readily applied to many other flood-prone urban centers, such as New York City, New Orleans, and Miami.

由于水位测量的空间密度较低，以及已建基础设施、地面地形和自然景观与流动水的复杂相互作用，在山洪暴发、飓风和其他极端天气事件期间实时构建城市地区的洪水过程线的能力是困难的。这个快速项目的目标是利用交通路口和州际高速公路摄像机、主要新闻媒体和社交媒体以及参考对象/点的易腐烂图像和视频片段。随后的照片图像处理，按比例调整到参考对象，将使休斯敦大都市区能够开发出更连续、更准确的过程线。通过重建被洪水淹没的高速公路、街道和居民区中大量地点的洪水过程线，高分辨率、基于过程的城市洪水模型将变得更加准确。例如，这将有助于更好地了解飓风哈维期间沉积物和污染物进出休斯顿的运输情况。通过重建的水文线验证的这种模型还将帮助州和地方政府及时做出低洼地区的疏散决策，以减轻类似飓风引发的危险的影响。这个快速项目基于图像处理和现场测量重建休斯敦的洪水水文线，具有显著的智力优势：(1)所提出的方法具有创新性和创造性，因为它不使用任何传统的水流计。相反，它依赖于用于新应用程序的现有数据的唯一形式。(2)重建的洪水过程图将显著提高对这场由飓风哈维的极端降雨造成的史无前例的洪水过程的理解。(3)这些数据将有助于开发休斯敦新的洪水模型。(4)开发的处理交通图像数据的算法和软件将在NHERI DesignSafe-CI平台上提供，可以很容易地应用于许多其他易发洪水的城市中心，如纽约市、新奥尔良和迈阿密。