Development of Real-Time Assimilation Model for Prediction of Inundation on Urbanized Coastal Lowland

Abstract

Inundation hazard is one of the severest natural hazards nowadays, especially in the urbanized coastal lowland area. Real-time predication of urban inundation is highly demanded for disaster preparedness due to the increasing frequency of extreme weather events. In this study, an assimilation model and a databank-based assimilation method were developed for improving the accuracy of an integrated inundation model (Seamless model) for real-time prediction of urban inundation. The assimilation model is able to account for the effect of flow acceleration when conducting data assimilation. Numerical experiment was carried out to validate the assimilation model and results showed that it is able to determine the initial condition well after the data assimilation under unsteady, and non-uniform conditions. The assimilation model can be used both for river flow model and sewerage network model. The databank-based assimilation method employed real-time measurement of water level at sewerage pipes to determine the most similar scenario in a previously generated database. Water level distribution in the scenario is then used to optimize the numerical result. While the proposed method is based on a simple concept, it is demonstrated that improvement of both stability and accuracy of the real-time prediction of the Seamless model was achieved.