The geomorphologic instantaneous unit hydrograph (GIUH) is an applicable approach that simulates the runoff for the ungauged basins. The nash model is an efficient tool to derive the unit hydrograph (UH), which only requires two items, including the indices n and k. Theoretically, the GIUH method describes the process of a droplet flowing from which it falls on to the basin outlet, only covering the flow concentration process. The traditional technique for flood estimation using GIUH method always uses the effective rainfall, which is empirically obtained and scant of accuracy, and then calculates the convolution of the effective rainfall and GIUH. To improve the predictive capability of the GIUH model, the Xin’anjiang (XAJ) model, which is a conceptual model with clear physical meaning, is applied to simulate the runoff yielding and the slope flow concentration, integrating with the GIUH derived based on Nash model to compute the river network flow convergence, forming a modified GIUH model for flood simulation. The average flow velocity is the key to obtain the indices k, and two methods to calculate the flow velocity were compared in this study. 10 flood events in three catchments in Fujian, China are selected to calibrate the model, and six for validation. Four criteria, including the time-to-peak error, the relative peak flow error, the relative runoff depth error, and the Nash–Sutcliff efficiency coefficient are computed for the model performance evaluation. The observed runoff value and simulated series in validation stage is also presented in the scatter plots to analyze the fitting degree. The analysis results show the modified model with a convenient calculation and a high fitting and illustrates that the model is reliable for the flood estimation and has potential for practical flood forecasting.

The issue of regional design flood composition should be considered when it comes to the analysis of multiple sections. However, the uncertainty accompanied in the process of regional design flood composition point identification is often overlooked in the literature. The purpose of this paper, therefore, is to uncover the sensibility of marginal distribution selection and the impact of sampling uncertainty caused by the limited records on two copula-based conditional regional design flood composition methods, i.e., the conditional expectation regional design flood composition (CEC) method and the conditional most likely regional design flood composition (CMLC) method, which are developed to derive the combinations of maximum 30-day flood volumes at the two sub-basins above Bengbu hydrological station for given univariate return periods. An experiment combing different marginal distributions was conducted to explore the former uncertainty source, while a conditional copula-based parametric bootstrapping (CC-PB) procedure together with five metrics (i.e., horizontal standard deviation, vertical standard deviation, area of 25%, 50%, 75% BCIs (bivariate confidence intervals)) were designed and employed subsequently to evaluate the latter uncertainty source. The results indicated that the CEC and CMLC point identification was closely bound up with the different combinations of univariate distributions in spite of the comparatively tiny difference of the fitting performances of seven candidate univariate distributions, and was greatly affected by the sampling uncertainty due to the limited observations, which should arouse critical attention. Both of the analyzed sources of uncertainty increased with the growing T (univariate return period). As for the comparison of the two proposed methods, it seemed that the uncertainty due to the marginal selection had a slight larger impact on the CEC scheme than the CMLC scheme; but in terms of sampling uncertainty, the CMLC method performed slightly stable for large floods, while when considering moderate and small floods, the CEC method performed better.