141 - Enhancing Near Real-Time Flood Forecasting Using Spatial Distributed PERSIANN Datea in an Automatic H&H Framework

Rainfall plays a significant role in determining runoff quantity and flood magnitude within a watershed. Applying the forecasted rainfall as the input dataset for the hydrological simulation is the key to achieve the near real time flood forecasting. However, the forecasted rainfall data, which drive hydrological simulations, often possess inherent data accuracy uncertainty, undermining the reliability of the forecasted flood inundation. With the advance of remote sensing technologies, satellite-based precipitation data provides an alternative to enhance and expedite the rainfall-runoff simulation process, which offers a significant advantage over ground-based rain gauge and radar-based precipitation data. One of the satellite-based precipitation datasets found to be useful for near real time flood forecasting is named Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN) family products. This study presents an automatic Hydrological & Hydraulic (H&H) simulation framework that has the potential to be applied for the near real time flood forecasting by using spatial distributed PERSIANN data. The HEC-HMS and HEC-RAS will be used to duplicate the rainfall-runoff process and simulate the flood inundation. The H&H framework is calibrated by using historical PERSIANN rainfall data and validate by comparing the 100-y 24-h flood inundation area to the FEMA Flood Insurance Study A zone. The results shows that the spatial distributed PERSIAAN data has significant suitability for near-real time flood forecasting.