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Initial abstraction (Ia) is a sensitive parameter in hydrological models, and its value directly determines the amount of runoff. Ia, which is influenced by many factors related to antecedent watershed condition (AWC), is difficult to estimate due to lack of observed data. In the Soil Conservation Service curve number (SCS-CN) method, it is often assumed that Ia is 0.2 times the potential maximum retention S. Yet this assumption has frequently been questioned. In this paper, Ia/S and factors potentially influencing Ia were collected from rainfall–runoff events. Soil moisture and evaporation data were extracted from GLDAS-Noah datasets to represent AWC. Based on the driving factors of Ia, identified using the Pearson correlation coefficient and maximal information coefficient, artificial neural network (ANN)-estimated Ia was applied to simulate the selected flood events in the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model. The results indicated that Ia/S varies over different events and different watersheds. Over 75% of the Ia/S values are less than 0.2 in the two study areas. The driving factors affecting Ia vary over different watersheds, and the antecedent precipitation index appears to be the most influential factor. Flood simulation by the HEC-HMS model using statistical Ia gives the best fitness, whereas applying ANN-estimated Ia outperforms the simulation with median Ia/S. For over 60% of the flood events, ANN-estimated Ia provided better fitness in flood peak and depth, with an average Nash–Sutcliffe efficiency coefficient of 0.76 compared to 0.71 for median Ia/S. The proposed ANN-estimated Ia is physically based and can be applied without calibration, saving time in constructing hydrological models.

The main objectives of the present work are to investigate the hydraulic characteristics of the dam discharge flow and its impact downstream. Building information modeling technology is adopted to generate the terrain entity and hydraulic structures. The calculation of the dam discharge and flood routing simulation is achieved by employing Reynolds-Averaged Navier-Stokes equations with the RNG k- eddy viscosity model for its turbulence closure, as well as the Volume of Fluid method. An urban flood experiment and the field measurement records are utilized and validated the model accuracy. The flow field is obtained to assess the dam working conditions under different water levels. The results show that the maximum downstream flow depth, the maximum discharge capacity and the hydraulic jump length under normal water level is 18.6 m, 13,800 m3/s, and 108 m, respectively. The dam satisfies the safety demand under different water levels but close attention should be paid to the dam foundation, especially around the incident points of the discharge flow. Complex turbulent flow patterns, including collision, reflection, and vortices, are captured by three-dimensional simulation. The numerical simulation can assist the reservoir management vividly, so as to guarantee the stability of the dam operation.