An approach for runoff and sediment nexus analysis under multi-flow conditions in a hyper-concentrated sediment river, Southwest China

Joint probability analysis of streamflow and sediment load based on hybrid copula

Statistical analysis of streamflow and sediment is very important for integrated watershed management and the design of water infrastructure, especially in silt-rich rivers. Here, we propose a bivariate joint distribution framework based on nonparametric kernel density estimation (KDE) and a hybrid copula function to describe the complex streamflow-sediment dependent structure. In this framework, the non-parametric KDE is used to fit the marginal distribution function of streamflow and sediment variables, and then the hybrid copula function is constructed by using the linear combination of Clayton, Frank, and Gumbel copulas, and compared with five commonly used single copulas (Clayton, Frank, Gumbel, Gaussian, and t). We use the Jinsha River Basin (JRB) in the Yangtze River's (JR) upper reaches to verify the proposed method. The results show the following: (1) Compared with the gamma distribution (Gamma) and generalized extreme value (GEV) distribution of parameters, the marginal distribution function of streamflow and sediment variables can be effectively obtained based on nonparametric KDE. (2) Compared with the single copula, the hybrid copula function more fully reflects the complex dependent structure of streamflow and sediment variables. (3) Compared with the best single copula, the precision of return period based on hybrid copula can be increased by 7.41%. In addition, the synchronous probability of streamflow and sediment in JRB is 0.553, and the asynchronous probability of streamflow and sediment is 0.447. This study can not only improve the accuracy of streamflow and sediment statistical analysis in JRB, but also provide a useful framework for other bivariate joint probability analysis.

Analysis and Forecasting of Wetness-Dryness Encountering of a Multi-Water System Based on a Vine Copula Function-Bayesian Network

The analysis and forecasting of wetness-dryness encountering is the basis of joint operation of a multi-water system, which is important for water management of intake areas of water transfer projects. On the basis of a vine copula function-Bayesian network, this study developed an analysis and forecasting of a wetness-dryness encountering model. The model consists of two modules: firstly, the joint distribution among multi-inflows is established based on the vine copula function, and the obtained historical laws of wetness-dryness encountering; then, a Bayesian network is established in order to forecast wetness-dryness encountering in the future, using the forecasting information of some water systems. The model was applied to the water receiving areas inside Jiangsu Province of the South-to-North Water Transfer East Route Project in China. The results revealed the following: (1) Compared with conventional copulas, the probability values of wetness-dryness encountering obtained by the vine copula function were closer to the observed values. (2) The wetness-dryness encountering in 2017–2019 was forecasted, and the results were consistent with reality. These results demonstrate that the proposed model improves the accuracy of the obtained historical laws of wetness-dryness encountering, and that it can forecast wetness-dryness encountering in the future.