A numerical model is developed in this study with various components for simulating the complex flow phenomena in urban drainage basins. The model integrates the HEC-1 model, a 1-D dynamic channel-flow model, a 2-D non-inertia overland-flow model and the SWMM model to reflect the hydraulic processes in areas with different characteristics. The inundation of underground infrastructure during flood is also considered in the model. The typhoon Nari event in 2001, which resulted in severe flood in downtown Taipei, is simulated by the model. The result is compared with the survey records of flooded areas, which reveals the storage effect of underground infrastrucures is significant to the simulation results of highly developed urban areas.

Precise predictions of storm surges during typhoon events have the necessity for disaster prevention in coastal seas. This paper explores an artificial neural network (ANN) model, including the back propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS) algorithms used to correct poor calculations with a two-dimensional hydrodynamic model in predicting storm surge height during typhoon events. The two-dimensional model has a fine horizontal resolution and considers the interaction between storm surges and astronomical tides, which can be applied for describing the complicated physical properties of storm surges along the east coast of Taiwan. The model is driven by the tidal elevation at the open boundaries using a global ocean tidal model and is forced by the meteorological conditions using a cyclone model. The simulated results of the hydrodynamic model indicate that this model fails to predict storm surge height during the model calibration and verification phases as typhoons approached the east coast of Taiwan. The BPNN model can reproduce the astronomical tide level but fails to modify the prediction of the storm surge tide level. The ANFIS model satisfactorily predicts both the astronomical tide level and the storm surge height during the training and verification phases and exhibits the lowest values of mean absolute error and root-mean-square error compared to the simulated results at the different stations using the hydrodynamic model and the BPNN model. Comparison results showed that the ANFIS techniques could be successfully applied in predicting water levels along the east coastal of Taiwan during typhoon events.

A flood routing model with time-varying roughness updating was developed to simulate flows through natural channels based on the dynamic wave theory. Taking observed stages as the targets, a roughness updating technique was developed using the Gauss-Newton method to update the Manning n in each time step of the routing processes. The technique provides a reasonable roughness coefficient estimate and reliable initialization of stage profile for the forecast. The examinations including the initialization of stage profile, conservation of mass, iteration convergence, effectiveness evaluation, and convergence with different initial values were conducted to verify the predictive capability of the roughness updating technique. The forecasting results show that the stage recalculated by updating the Manning n in current time has a good agreement with the observed stage. The presented model can improve the forecast for a lead time up to 6 h in the Tanshui River of northern Taiwan.

Due to the special hydrographic and physiographic conditions in Taiwan, flooding is likely to occur in the middle and lower reaches of a plain whenever serious rainstorm events occurred. Note worthily, the loss of lives and property caused by flooding are always most considerable in a metropolitan area, and the densely distributed buildings would, not only increase the impervious area, but also decrease the water storage area. Furthermore, a large number of intensive buildings have changed the original land flow conditions, resulting in a beam shrinking flow and the additional form drag phenomenon, which makes the flooding phenomenon more serious. The main purpose of this research is to find the correlation between building coverage and the Manning’s coefficient n through a water flume model experiment. To probe into this issue, the Manning’s roughness adjustment is further divided into a part caused by the surface impedance and a part caused by the building impedance. Thus, building coverage can be added to the general computing grid to reflect the flooding situation with buildings. The two-dimensional inundation model, based on this research, was applied to Taichung City for an actual case simulation. The simulation result of Typhoon Kalmaegi showed that the presented model can obtain a more accurate flooding situation in urban area by considering the blockage effects of buildings and adjusting the surface roughness.