Hydrological processes such as evaporation, infiltration, and runoff are affected not only by natural climate change but also by land cover and soil conditions. The impact of urbanization on the key elements of the hydrological process is worth studying in context of rapid urbanization. This paper combines the soil-land use index grid and the GSSHA model to quantitatively study the impact of land use on urban hydrological processes under the background of the changing urbanization stage. The results show that with the increase in land development and utilization activities, the hydrological process will transform. When grassland and woodland are converted to construction land, the changes in runoff, infiltration, and evaporation are the largest. The runoff depth increased by 0.94×10-1~2.42×10-1mm/km², infiltration depth decreased by 0.80×10-1~ 2.18×10-1mm/km², evaporation decreased by 0.14×10-1~ 0.28×10-1mm/km². In the transition from forest land to grassland, from cultivated land to forest land, and from cultivated land to grassland, the increase of infiltration contributed over 80% to the decrease of runoff process. This provides a scientific basis for future urban planning and sponge city construction.

In order to realize the reproduction and simulation of urban rainstorm and waterlogging scenarios with complex underlying surfaces. Based on the Mike series models, we constructed an urban storm-flood coupling model considering one-dimensional river channels, two-dimensional ground and underground pipe networks. Luoyang City was used as a pilot to realize the construction of a one-dimensional and two-dimensional coupled urban flood model and flood simulation. where is located in the western part of Henan Province, China. The coupled model was calibrated and verified by the submerged water depths of 16 survey points in two historical storms flood events. The average relative error of the calibration simulated water depth was 22.65%, and the average absolute error was 13.93cm; the average relative error of the verified simulated water depth was 15.27%, The average absolute error is 7.54cm, and the simulation result is good. Finally, 28 rains with different return periods and different durations were designed to simulate and analyze the rainstorm inundation in the downtown area of Luoyang. The result shows that the R2 of rainfall and urban rainstorm inundation is 0.8776, and the R2 of rainfall duration and urban rainstorm inundation is 0.8141. Therefore, rainfall is the decisive factor in the formation of urban waterlogging disasters, which is actually the rainfall duration. The study results have important practical significance for urban flood prevention, disaster reduction and traffic emergency management.

The development of flood forecasting technology is crucial to flood control. Therefore, it is very essential to use the method of real-time error correction to improve the accuracy and reliability of the flood forecasting model. For flood forecasting, this study evaluated the performance of a single Excess Infiltration and Excess Storage (EIES) flood forecast model and the forecast model after error correction using the linear Auto Regressive, Auto Regressive Moving Average with exogenous inputs (ARMAX), and Long Short-term Memory Network (LSTM), and then compared the performance of each model forced with historical flood data in the upper reaches of Jingle station of the Fen River in China. These EIES-standalone, EIES-AR, EIES-ARMAX, and EIES-LSTM frameworks are field-tested for 1- to 6-hours lead-time flood forecasting with historical flood data. The capability of the four models are compared using the mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), Pearson’s correlation coefficients (r), and Percent error in volume (Evol). The evaluation measures analysis reveal that EIES-AR and EIES-ARMAX perform acceptable when the lead time is 1 hour(NSE>0.7), but poorly when the lead time is 2-6 hours; EIES-LSTM model performs well and is the best approach of these models for short to medium range flood forecasting with up to 6 hours lead-time(NSE≥0.75).