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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.

Abstract. This study aimed to understand the water quality circumstance of Sifeng Reservoir thoroughly, according to the national environmental quality standard of surface water. Due to the comprehensive artificial lake scenic spot, combined with flood control, irrigation, tourism and was used in a variety of ways. Regularly measure the water temperature, pH and turbidity. BOD and COD, total phosphorus, potassium permanganate index and total number of E. coli were measured in different sections. All measurement data are based on time-space distribution compared with national standards. According to the corresponding standard conditions, time and activities of the reservoir water environment to detect, taking into account climate factors. Discussing the problem of the different time and space variation of the reservoir, and on the basis of combining with the testing data, the location in spatial and temporal change of water quality were analyzed. It is concluded that the present excessive pollution factor and more than Ⅴ class standard was COD, other indicators satisfied the Ⅲ according to the Chinese standard for surface water.

Implementing real-time prediction and warning systems is an effective approach for mitigating flash flood disasters. However, there is still a challenge in improving the accuracy and reliability of flood prediction models. This study develops a hydrological prediction model named SCE-GUH, which combines the Shuffled Complex Evolution-University of Arizona optimization algorithm with the general unit hydrograph routing method. Our aims were to investigate the applicability of the general unit hydrograph in runoff calculations and its performance in predicting flash flood events. Furthermore, we examined the influence of parameter variations in the general unit hydrograph on flood simulations and conducted a comparative analysis with the conventional Nash unit hydrograph. The research findings demonstrate that the utilization of the general unit hydrograph method can considerably decrease computational errors and enhance prediction accuracy. The flood peak detection rate was found to be 100% in all four study watersheds. The average Nash–Sutcliffe efficiency coefficients were 0.83, 0.83, 0.84, and 0.87, while the corresponding coefficients of determination were 0.86, 0.85, 0.86, and 0.94, and the absolute errors of peak present time were 0.19 h, 0.40 h, 0.91 h, and 0.82 h, respectively. Moreover, the utilization of the general unit hydrograph method was found to significantly reduce the peak-to-current time difference, thereby enhancing simulation accuracy. Parameter variations have a substantial influence on peak flow characteristics. The SCE-GUH model, which incorporates the topographic and geomorphological features of the watershed along with the optimization algorithm, is capable of effectively characterizing the catchment properties of the watershed and offers valuable insights for enhancing the early warning and prediction of hydrological forecasting.