Abstract
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.
Highlights
Design flood analysis provides reasonable hydrological design values for water conservancy and wading projects [1,2]
Dung et al [25] developed a non-parametric bootstrapping procedure for investigating the uncertainty of the parameter estimation method, model selection, and sampling, and the results further revealed that compared with sampling uncertainty, the other two sources of uncertainty were of less importance
Generalized (GP), gamma (GUM), generalized logistic (GLO), were picked in order to construct the bivariate model for W30d at each sub-basin, the first step is to choose appropriate marginal distributions
Summary
Design flood analysis provides reasonable hydrological design values for water conservancy and wading projects [1,2]. When it comes to the analysis and calculation of multiple sections, it is necessary to deal with the issue of the regional design flood composition. The search for proper combinations of natural flood variables that occurred at different sub-basins above the study section. Regional design flood composition is a spatially stochastic issue, and the most scientific and rational method to describe this law of nature is to build the joint probability distribution of flood variables in each sub-basin
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
