Traditional reasoning formula methods and instantaneous unit hydrograph methods are difficult to reflect geomorphological features and land use changes in design flood calculation. To address this issue, a new design flood flow concentration calculation method, namely the runoff curve number (SCS) geomorphologic general unit hydrograph method, was proposed by considering the influence of geomorphological features and land use changes on the time of flow concentration. This method was coupled with the SCS model to establish a complete flow generation and concentration model, and the Shangbu River Watershed in Hangzhou City was taken as an example to verify the method. The verification results show that compared to traditional reasoning formula method and instantaneous unit hydrograph methods, the SCS geomorphologic general unit hydrograph method is more reasonable for calculating design floods in small watersheds within urbanized areas, as it better reflects the watershed’s geomorphological features and adapts to land use changes. Scenario analysis further reveals that the changes in underlying surface conditions caused by urbanization will lead to an increase in design flood volume and flood peak.

Flood process simulation in karst basins is challenging due to complex runoff generation and concentration mechanisms, often resulting in low accuracy. This study investigated two typical karst basins (the Maiweng and Liudong river basins) in Guizhou Province, China, and developed two hydrological models for flood simulation: the karst-Xin'anjiang (Karst-XAJ) model, a modified Xin'anjiang (XAJ) hydrological model adapted for karst runoff characteristics, and the long short-term memory (LSTM) deep learning model. Their performances were compared, and their results were integrated using Bayesian model averaging (BMA). The Karst-XAJ model accurately simulated flood peak time and runoff depth but showed limited peak flow accuracy. The LSTM model performed well within a 2-h computational window, with accuracy declining for longer computational windows (3—4 h) yet maintaining a Nash—Sutcliffe model efficiency coefficient above 0.7. The BMA approach further enhanced simulation accuracy beyond individual models. Overall, both models effectively captured flood dynamics in karst basins, with the LSTM model achieving superior precision. This study offers a novel framework for simulating flood processes in karst regions with complex runoff processes.