Evaluation of the subjective factors of the GLUE method and comparison with the formal Bayesian method in uncertainty assessment of hydrological models

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Hydrological uncertainty processor based on a copula function (2018, unknown): “To cite this article: Zhangjun Liu, Shenglian Guo, Lihua Xiong & Chong-Yu Xu (2018) Hydrological uncertainty processor based on a copula function, Hydrological Sciences Journal, 63:1, 74-86, DOI: 10.”
Improvement of hydrological model calibration by selecting multiple parameter ranges (2016, unknown): “In general, parameter variables follow some specific probability distributions within the given range after multiple independent calibrations (Viola et al., 2009;Jin et al., 2010;Li et al., 2010).”
Uncertainty estimates by Bayesian method with likelihood of AR (1) plus Normal model and AR (1) plus Multi-Normal model in different time-scales hydrological models (2011, background): “…Likelihood Uncertainty Estimation (GLUE) (Beven and Freer, 2001; Blasone et al., 2008a,b; Vogel et al., 2008; Xiong and O’Connor, 2008) and formal Bayesian methods (Montanari and Brath, 2004; Vrugt et al., 2003; Engeland et al., 2005; Yang et al., 2008; Jin et al., 2010; Li et al., 2010b).”
Copulas and Its Application in Hydrology and Water Resources (2018, background): “The TGR also plays a key role in the flood prevention of Yangtze River basin which is the richest area in China (Li et al. 2010).”