An Integrated Approach for a Real-Time Forecasting and Risk Assessment of the Cascading Extreme Storm Triggered Flood Inundation

As the global climate change will continue to cause the unevenly distributed precipitation and the water resources, hydrologic models can be the helpful tools to analyze water storage and forecast and to support the water security. The remote sensing flood extent mapping provided solid ground observations as the ground truth to validate and calibrate the model simulations. This study focuses coupling a 2D hydraulic model with an existing global flood early warning system to simulate the forecast the riverine flood and stormwater accumulation at a large scale with high resolution (e.g. =<10m grid cells) using high performance computing techniques and the machine learning modules. The characteristics of two different coupling schemes is studied and the high resolution 2D simulation of flood extent can better facilitate the public to understand the impact and scientist to quantify the risk and damage. The model was applied to the 2017 Hurricane Harvey over the Northern Houston area which include the entire Spring Creek and Cypress Creek channel networks. The case study of the Hurricane Harvey showed the promising results of the stream flows, surface water extents and water depths with the very efficient computation speed to meet the real-time simulation demand. Since the model is capable to simulate in real-time over large area, the model is driven by the real time QPE (Quantitative Precipitation Estimation) and the real time QPF (Quantitative Precipitation Forecast) over the same region with the 10 m resolution overall, which will be further studied with the advanced logistic optimization projects. The model is integrated with a FEMA adapted national hydrological simulating frame work, EF5, where to support the water resource management in the national scale.