Quantification of the uncertainty associated with stormwater models should be analyzed before using modelling results to make decisions on urban stormwater control and management programs. In this study, the InfoWorks Integrated Catchment Modelling (ICM) rainfall-runoff model was used to simulate hydrographs at the outfall of a catchment (drainage area 8.3 ha, with 95% pervious areas) in Shenzhen, China. The model was calibrated and validated for two rainfall events with Nash-Sutcliffe efficiency >0.81. The influence of rainfall, model parameters and routing methods on outflow hydrograph of the catchment was systematically studied. The influence of rainfall was analyzed using generated rainfall distributions with random errors and systematic errors (± 30% offsets). Random errors had less influence than systematic errors on peak flow and runoff volume, especially for two rainfall events with larger depths and longer durations. The Monte Carlo simulations using 500 parameter sets were used to verify the equifinality of the nine model parameters and determine the prediction uncertainty. Most of the monitored flows were within the uncertainty range. The influence of two routing methods from rainfall excess to hydrograph was studied. The InfoWorks ICM model incorporating double quasilinear reservoir routing was found to have a larger effect on the simulated hydrographs for rainfall events having larger depths and longer durations than using the U.S. EPA’s Storm Water Management Model nonlinear reservoir routing method did.
Flash flood disaster ranks top among all the natural hazards across the world due to its high frequency, severity and fatality. However, flash flood simulation is still challenging in small and medium-sized catchments with complex orography, flashy hydrological responses and poor observations. Three distributed hydrological models, i.e., TOPModel, HEC and CNFF, are selected to simulate flash floods in seven humid and six semi-humid catchments in China, with consideration of water balance (RER), peak flow rate (REQ) and its occurrence time (TP), hydrograph variation (SNSE) and model uncertainty. Influences of five catchment attributes are further investigated on individual model performances. All three models perform satisfactorily in humid catchments, but less satisfactorily in semi-humid catchments. Water balance is well obtained by CNFF, followed by HEC and TOPModel. Peak flow rate and its occurrence time are most accurately captured by CNFF and HEC, respectively. Hydrograph variations are well reproduced by HEC and CNFF. TOPModel performs well for picking peak flow and hydrograph variation in humid catchments. Uncertainty interval is narrowest for HEC with average relative interval length at 95% confidence level being 0.78?~?2.53. Most observations are bracketed by uncertainty intervals for TOPModel (64.79%?~?91.91% of total). Three model performance indices (i.e., RER, REQ, and SNSE) are mainly affected by drainage area and forest ratio across humid and semi-humid catchments, while TP performance is mainly affected by mean slope in humid catchments.
