Exploring the Role of Multivariate Observations in Urban Flood Prediction: A Data Assimilation Approach

In this study, we present a probabilistic urban inundation modeling framework that combines high-resolution process-based modeling with observed information via ensemble data assimilation (DA). We investigate the impact of multivariate flood observations on improving urban inundation prediction accuracy through synthetic experiments. The framework leverages diverse flood observations from both the urban surface and sewer system, integrating them into the modeling process using non-Gaussian sequential DA methods, such as particle filtering. The modeling framework employs the H12 model for integrated 1D sewer network and 2D surface inundation analyses, with synthetic experiments conducted in an urban catchment in Osaka, Japan. Prior to implementing DA, a sensitivity analysis is performed to assess the effects of uncertainties in inundation modeling. Major uncertainty components, such as input forcings and storm drain box efficiency, are perturbed to evaluate their influence. The synthetic DA experiments analyze the influence of various types of flood observations, including urban surface inundation depths and sewer water levels, on the posterior distributions of the model ensemble. Additionally, the impact of observation location and density on DA performance is evaluated. This study demonstrates the potential of utilizing diverse flood observations in data assimilation to enhance the accuracy and reliability of urban flood predictions.