Exploring Flood Predictability in Taiwan through Coupled Atmospheric–Hydrological and High-Performance Hydrodynamic Models

Effective flood simulation capabilities can tremendously support early warning and disaster prevention. To examine the applicability of a fully physics-based and high-performance flood simulation and forecasting modeling framework for a flood-prone region in Taiwan, we conduct a numerical experiment that couples the Weather Research and Forecasting (WRF) Model, WRF-Hydrological modeling system (WRF-Hydro), and the Two-Dimensional Runoff Inundation Toolkit for Operational Needs (TRITON) to perform integrated rainfall, streamflow, and flood simulations. We first use the coupled WRF and WRF-Hydro (WWH) to predict rainfall and streamflow and then drive TRITON with the predicted streamflow hydrographs to simulate flood depth and inundation area. With the refined spatial resolution and parameterization, this framework can better predict rainfall with reasonable spatial patterns. Although WWH could overestimate the amount of rainfall in some areas, the uncertain rainfall–streamflow predictions produce reasonable flood maps able to pinpoint regions at risk of flooding. In terms of model efficiency, the graphics processing unit–based computation can yield a speed-up factor as high as ∼13 compared to the central processing unit–based computation, promoting the efficacy of the coupled modeling framework in practical real-time flood forecasting. Significance Statement The purpose of this study is to develop and assess a modeling framework that integrates a coupled atmospheric–hydrological model with a high-performance hydrodynamic model for rainfall, streamflow, and flood simulations in a holistic manner. By integrating these state-of-the-art models, we aim to advance our understanding of regional flood predictability subject to the chaotic nature of hydrometeorological forecasts. Based on our case study in Taiwan, we demonstrate that the proposed modeling framework can identify inundation hotspots despite the found uncertainties in rainfall–streamflow predictions. We also demonstrate that using the high-performance hydrodynamic model can significantly enhance simulation efficiency, suggesting the need for a modeling framework like the one proposed in this study for real-time flood forecasting.