A Bayesian-Model-Averaging Copula Method for Bivariate Hydrologic Correlation Analysis - Wen, Yizhuo; Yang, Aili; Kong, Xiangming; Su, Yueyu (Frontiers in Environmental Science, 2022)
A Bayesian-model-averaging Copula (i.e. BMAC) approach was proposed for correlation analysis of monthly rainfall and runoff in Xiangxi River watershed, China. The MBAC approach was formulated by incorporating existing Bayesian model averaging (i.e. BMA) method and Archimedean Copula techniques (e.g. Gumbel-Hougaard, Clayton and Frank Copulas) within a general bivariate hydrologic correlation analysis framework. In this paper, BMA method was applied to determine the marginal distribution functions of variables, and Archimedean Copula method was used to analyze the correlation. Results showed that: (1) the BMA method could improve the representation of the marginal distribution of hydrological variables with smaller corresponding errors; (2) the predictive joint distributions of monthly rainfall and runoff was much better calibrated by the Frank Copula according to criteria of the root mean square error (i.e. RMSE), Akaike Information Criterion (i.e. AIC) values, Anderson-Darling test (i.e. AD test) and Cramer-von Mises test (i.e. CM test ); (3) the monthly rainfall and runoff presented a strong positive correlation based on Kendall and Spearman's rank correlation coefficients. Therefore, the BMAC approach performed reasonably well and can be further used to simulate runoff values according to the historical and predicted rainfall data.