Efficient and accurate streamflow predictions are important for urban water management. Data-driven models, especially neural network (NN) models can predict streamflow fast, while the results are uncertain in some complex river systems. Physically based models can reveal the underlying physics, but it is relatively slow and computationally costly. This work focuses on evaluating the reliability of three NN models (artificial neural networks (ANN), long short-term memory networks (LSTM), adaptive neuro-fuzzy inference system (ANFIS)) and one physically based model (SOBEK) in terms of efficiency and accuracy for average and peak streamflow simulation. All the models are applied for a tidal river and a mountainous river in Shenzhen. The results show that, the ANN model calculates fastest since the hidden layer's structure is simple. The LSTM model is reliable in average streamflow simulation in tidal river with the lowest bias while the ANFIS model has the best accuracy for peak streamflow simulation. Furthermore, the SOBEK model shows reliability in simulating average and peak streamflow in mountainous river due to its ability to capture uneven spatial rainfall in the area. Overall, the results indicate that the LSTM model can be a helpful supplementary to physically based models in streamflow simulation of complex urban river systems, by giving fast streamflow predictions with usually acceptable accuracy. Our results can provide helpful information for hydrological engineers in the application of flooding early warning and emergency preparedness in the context of flooding risk management.

Climate change leads to sea level rise worldwide, as well as increases in the intensity and frequency of tropical cyclones (TCs). Storm surge induced by TC’s, together with spring tides, threatens to cause failure of flood defenses, resulting in massive flooding in low-lying coastal areas. However, limited research has been done on the combined effects of the increasing intensity of TCs and sea level rise on the characteristics of coastal flooding due to the failure of sea dikes. This paper investigates the spatial variation of coastal flooding due to the failure of sea dikes subject to past and future TC climatology and sea level rise, via a case study of a low-lying deltaic city- Shanghai, China. Using a hydrodynamic model and a spectral wave model, storm tide and wave parameters were calculated as input for an empirical model of overtopping discharge rate. The results show that the change of storm climatology together with relative sea level rise (RSLR) largely exacerbates the coastal hazard for Shanghai in the future, in which RSLR is likely to have a larger effect than the TC climatology change on future coastal flooding in Shanghai. In addition, the coastal flood hazard will increase to a large extent in terms of the flood water volume for each corresponding given return period. The approach developed in this paper can also be utilized to investigate future flood risk for other low-lying coastal regions.

Tide is influenced due to not only mainly tide generating force but also local wind and weather patterns. The East Asian monsoons cause strong seasonal climatic variations in the Mekong Delta. A two-dimensional, barotropic numerical model was employed to investigate the dynamics of tidal wave propagation in the South China Sea with a particular interest for its characteristics along the Mekong deltaic coast under wind monsoon climate. The results reveal that wind monsoon climate could causes damped or amplified tidal amplitudes around Mekong deltaic coast approximately 2–3 cm due to the changing atmospheric pressure, the tangential stress of wind over the water surface, and wind enhanced bottom friction. The monsoon climate influences rather strongly on the M₂ semidiurnal tide system in the eastern Mekong deltaic coast, meanwhile the monsoon climate controls K₁ diurnal tide in the western region of Mekong delta.

As the third largest fresh water lake in China, Taihu Lake is suffering from serious eutrophication, where nutrient loading from tributary and surrounding river networks is one of the main contributors. In this study, water age is used to investigate the impacts of tributary discharge and wind influence on nutrient status in Taihu Lake, quantitatively. On the base of sub-basins of upstream catchments and boundary conditions of the lake, multiple inflow tributaries are categorized into three groups. For each group, the water age has been computed accordingly. A well-calibrated and validated three-dimensional Delft3D model is used to investigate both spatial and temporal heterogeneity of water age. Changes in wind direction lead to changes in both the average value and spatial pattern of water age, while the impact of wind speed differs in each tributary group. Water age decreases with higher inflow discharge from tributaries; however, discharge effects are less significant than that of wind. Wind speed decline, such as that induced by climate change, has negative effects on both internal and external nutrient source release, and results in water quality deterioration. Water age is proved to be an effective indicator of water exchange efficiency, which may help decision-makers to carry out integrated water management at a complex basin scale.