Chih-Tsung Hsu

Accurate real-time forecasts of inundation depth and area during typhoon flooding is crucial to disaster emergency response. The development of an inundation forecasting model has been recognized as essential to manage disaster risk. In the past, most researchers used multiple single-point forecasts to obtain surface flooding depth forecasts with spatial interpolation. In this study, a forecasting model (QPF-RIF) integrating a hydrodynamic model (SOBEK), support vector machine–multi-step forecast (SVM-MSF), and a self-organizing map (SOM) were proposed. The task of this model was divided into four parts: hydrodynamic simulation, point forecasting, inundation database clustering, and spatial expansion. First, the SOBEK model was used in simulating inundation hydrodynamics to construct the flooding maps database. Second, the SVM-MSF yields water level (inundation volume) forecasted with a 1 to 72 h lead time. Third, the SOM clustered the previous flooding maps database into several groups representing different flooding characteristics. Finally, a spatial expansion module produced inundation maps based on forecasting information from forecasting flood volume and flood causative factors. To demonstrate the effectiveness of the proposed forecasting model, we presented an application to the Yilan River basin in Taiwan. Our forecasting results indicated that the proposed model yields accurate flood inundation maps (less than 1 cm error) for a 1 h lead time. For long-term forecasting (46 h to 72 h ahead), the model controlled the error of the forecast results within 7 cm. In the testing events, the model forecasted an average of 83% of the flooding area in the long term. This flood inundation forecasting model is expected to be useful in providing early flood warning information for disaster emergency response.

Coupled 1D–2D hydrodynamic models are widely utilized in flood hazard mapping. Previous studies adopted conceptual hydrological models or 1D hydrodynamic models to evaluate the impact of drainage density on river flow. However, the drainage density affects not only river flow, but also the flooded area and location. Therefore, this work adopts the 1D–2D model SOBEK to investigate the impact of drainage density on river flow. The uncertainty of drainage density in flood hazard mapping is assessed by a designed case and a real case, Yanshuixi Drainage in Tainan, Taiwan. Analytical results indicate that under the same return period rainfall, reduction in tributary drainages in a model (indicating a lower drainage density) results in an underestimate of the flooded area in tributary drainages. This underestimate causes higher peak discharges and total volume of discharges in the drainages, leading to flooding in certain downstream reaches, thereby overestimating the flooded area. The uncertainty of drainage density decreases with increased rainfall. We suggest that modeling flood hazard mapping with low return period rainfalls requires tributary drainages. For extreme rainfall events, a lower drainage density could be selected, but the drainage density of local key areas should be raised.

An operational two-dimensional real-time flood forecasting system has been developed to prevent urban inundation in Taiwan, and it uses the Delft-FEWS (Flood Early Warning System) platform to integrate the SOBEK models to forecast flooding. A new generation of smart water level gauges, integrating power charging, recording, transmission, and sensing into one, can monitor the inundation on roads in real-time. This study took Annan District in Tainan City, Taiwan, as the study object and presented the application of the flood forecasting system and smart water level gauges in the urban environment. The collected data from Storm 0611 and Typhoon Megi in 2016 were used to assess the accuracy of the flood forecasting system. The analysis of three water level stations in the drainage showed that the one-dimensional simulation results were fairly accurate. According to the 18 observed and simulated inundation depths on the roads, the true positive, false positive, false negative, and true negative values were 8, 0, 2, and 8, respectively. The accuracy, sensitivity, and precision of the flood forecasting system were 0.889, 0.800, and 1.000, respectively, indicating that the two-dimensional simulation results were highly accurate.

An operational two-dimensional real-time flood forecasting system has been developed in Taiwan to prevent urban inundation. This system takes an hour to come up with forecasts for the next three hours, and the resolution of the forecasts is 40 × 40 m. This study used a large urban area of 126 km² in downtown Taichung City for the case study and adopted the active-cell and multi-grid methods to meet the target by computing from data of a 12-h rainfall within one hour at 20 m × 20 m spatial resolution to provide faster forecasting and more hours for flood preparation. With the active-cell method, the Central Processing Unit (CPU) time was reduced by 65.04% from 659 m 29 s to 230 m 33 s under the 200-year return period storm. Further, with multi-grid methods, the CUP time was reduced by 73.98% from 230 m 33 s to 60 m 0 s. In general, the computing time of this model has been reduced 11-fold. The error validation coefficients of inundation areas were between 89.39~97.45% with an average error of depth between 1.06~3.22 cm.