Taiwan faces intense rainfall during typhoon seasons, leading to rapid increases in water level in rivers. Accurate flood forecasting in rivers is essential for protecting lives and property. The objective of this study is to develop a river flood forecasting model combining multiple additive regression trees (MART) and ensemble Kalman filtering (EnKF). MART, a machine learning technique, predicts water levels for internal boundary conditions, correcting a one-dimensional (1D) unsteady flow model. EnKF further refines these predictions, enabling precise real-time forecasts of water levels in the Danshui River system for up to three hours lead time. The model was calibrated and validated using observed data from four historical typhoons to evaluate its accuracy. For the present time at three water level stations in the Danshui River system, the root mean square error (RMSE) ranged from 0.088 to 0.343 m, while the coefficient of determination (R²) ranged from 0.954 to 0.999. The validated model (module 1) was divided into two additional modules: module 2, which combined the ensemble unsteady flow model with inner boundary correction and MART, and module 3, which featured an ensemble 1D unsteady flow model without inner boundary correction. These modules were employed to forecast water levels at three stations from the present time to 3 h lead time during Typhoon Muifa in 2022. The study revealed that the Tu-Ti-Kung-Pi station was less affected by inner boundaries due to significant tidal influences. Consequently, excluding the upstream and downstream boundaries, Tu-Ti-Kung-Pi station showed a superior RMSE trend from present time to 3 h lead time across all three modules. Conversely, the Taipei Bridge and Bailing Bridge stations began using inner boundary forecast values for correction from 1 h to 3 h lead times. This increased the uncertainty of the inner boundary, resulting in higher RMSE values for these locations in modules 1 and 2 compared to module 3.

Evaluating land and runoff variations caused by urbanization is crucial to ensure the safety of people living in highly developed areas. Based on spatial scales, runoff analysis involves different methods associated with the interpretation of land cover and land use, the application of hydrological models, and the consideration of flood mitigation measures. Most studies have focused on analyzing the phenomenon on a certain scale by using a single data source and a specific model without discussing mutual influences. In this study, the runoff changes caused by urbanization are assessed and cross-analyzed on three sizes of study areas in the Zhuoshui River Basin in Taiwan, including basin (large), watershed (medium), and city (small) scales. The results demonstrate that, on the basin scale, land-cover changes interpreted from satellite images are very helpful for identifying the watersheds with urbanization hotspots that might have larger runoff outputs. However, on the watershed scale, the resolution of the land-cover data is too low, and land-cover data should be replaced by investigated land-use data for sophisticated hydrological modeling. The mixed usage of land-cover and land-use data is not recommended because large discrepancies occur when determining hydrological parameters for runoff simulation. According to present and future land-use scenarios, the influence of urbanization on runoff is simulated by HEC-1 and SWMM on watershed and city scales, respectively. The results of both models are in agreement and show that runoff peaks will obviously increase as a result of urbanization from 2008 to 2030. For low return periods, the increase in runoff as a result of urbanization is more significant and the city’s contribution to runoff is much larger than its area. Through statistical regression, the watershed runoff simulated by HEC-1 can be perfectly predicted by the city runoff simulated by SWMM in combination with other land/rainfall parameters. On the city scale, the installation of LID satisfactorily reduces the runoff peaks to pre-urbanization levels for low return periods, but the effects of LID are not as positive and are debatable for higher return periods. These findings can be used to realize the applicability and limitations of different approaches for analyzing and mitigating urbanization-induced runoff in the process of constructing a sponge city.

Typhoon is the most frequent natural disaster that causes widespread damage during summer and autumn in Taiwan. On average, each year the island suffers four typhoons, which result in disastrous flash floods and losses in a short time because of steep terrains and intense rainfall. The Tamsui River Basin is located in northern Taiwan about 2,726 square kilometers and inhabited by eight million people. During flooding events, the emergency managers rely on accurate flood forecasting to take proper actions for damage reductions. The flood forecasting and warning system based on hydraulic models play an important role in flood risk management. This study first establishes river stage routing model based on dynamic wave theory. Then, both the real-time observed river stages and the least squares method are used to adjust the model currently flow conditions as the data assimilation. Finally, The Ensemble Kalman Filter method carries out the data correction with the computation of minimum error-covariance between the model prediction and the observation. The simulation results found the root-mean-square error of forecasted river stage using the data assimilation at the gauged stations of Taipei Bridge and Tudi-Gong-Bi for 1-3 hours lead time is 0.862m, 0.892m, 0.903m, and 0.281m, 0.326m, 0.345m, respectively. When the Ensemble Kalmen Filter is added in the model, the root-mean-square error reduces to 0.191m, 0.375m, 0.612m, and 0.062m, 0.090m, 0.145m at described gauged stations. It is found that the data assimilation and the Ensemble Kalmen Filter give reliable forecast water stages with a small root-mean-square error which successfully corrects the forecasted river stage at each time step of the flood routing process. The results reveal that the integrated model gains a better accuracy of the water-stage profiles with probabilistic uncertainties. The model provides reliable forecasts of the water-stage profiles for 1–3 hours lead time along the Tamsui River for specific locations in emergency response for flood risk management.