In this paper, a copula-based method was proposed for stochastic simulation of the daily suspended sediment concentration (SSC). In this method, bivariate copula functions were used to describe the dependence structures of the SSCs between adjacent days. To reduce the difficulties of determining the daily marginal distributions of the SSC, the observed daily SSC data were normalized by using the normal quantile transform method. The proposed method was applied to generate the long-term daily SSC data of the Pingshan Station on the Jinsha River, and was compared with the autoregressive (AR) model. The results show that the proposed method can better preserve the statistical properties of observed daily SSC data with high accuracy. In addition, the skewness and nonlinear correlation of the SSCs simulated by the proposed method were better than the AR model. This study can provide a new tool for stochastic simulation of the long-term daily SSCs.
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