Hydrological modelling of the Chaohe Basin in China: Statistical model formulation and Bayesian inference

By Jing Yang, Peter Reichert, K. C. Abbaspour, Hong Yang. 2007

248 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/d1a4462af14975c238de9c229d435eaab980087b

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In Environmental Engineering

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis (2011, unknown): “Environmental models are becoming increasingly important in the decision making process as they can provide systematic and consistent information onwater availability, impacts of climate and land use changes, and analyses of non-point source pollution (Yang et al., 2007).”
Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors (2017, background): “…[e.g., Sorooshian and Dracup, 1980; Bates and Campbell, 2001; Schoups and Vrugt, 2010; Evin et al., 2014], seasonal and interannual trends [e.g., Ye et al., 1997; Yang et al., 2007; Vaze et al., 2010; Westra et al., 2014], the presence of zero flows [e.g., Smith et al., 2015], and others.”
Evaluation of the subjective factors of the GLUE method and comparison with the formal Bayesian method in uncertainty assessment of hydrological models (2010, unknown): “Two transform methods that have been widely used in uncertainty analyses of hydrological models include the Box-Cox method (Engeland et al., 2005; Yang et al., 2007; Wang et al., 2009; Jin et al., 2010) and the Normal Quantile Transform method (NQT) (Van der Waerden 1952, 1953a,b; Krzysztofowicz,…”
A log‐sinh transformation for data normalization and variance stabilization (2012, unknown): “The Box-Cox transformation has proven to be highly successful for many applications, including hydrological applications [e.g., Kuczera, 1983; Bates and Campbell, 2001; Thyer et al., 2002; Yang et al., 2007; Engeland et al., 2010].”
Calibration and uncertainty analysis of the SWAT model using Genetic Algorithms and Bayesian Model Averaging (2009, methodology): “…and uncertainty analysis methods have been developed and applied to improve the prediction reliability and quantify prediction uncertainty of SWAT simulations (Eckhardt and Arnold, 2001; Bekele and Nicklow, 2007; Yang et al., 2007; Harmel and Smith, 2007; Arabi et al., 2007; Kannan et al., 2008).”
Uncertainty and sensitivity assessments of an agricultural–hydrological model (RZWQM2) using the GLUE method (2016, methodology): “To account for these uncertainties, in the last two decades, many uncertainty analysis techniques, such as Bayesian inference based on MCMC (Markov Chain Monte Carlo) (Krzysztofowicz, 1999; Vrugt et al., 2003; Yang et al., 2007), LHS (Latin Hypercube Sampling) (Hossain et al., 2006; Rajabi et al., 2”
Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model (2010, methodology): “…a hydrological model in GLUE (Xiong and O’Connor, 2008), introducing adaptation into the MCMC method (Kuczera and Parent, 1998; Gilks et al., 1998; Yang et al., 2007, 2008), merging one of MCMC sampler with the SCE-UA global optimization algorithm (Vrugt et al., 2003), and revising GLUE based on…”
Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China (2008, methodology): “More details are given by Yang et al. (2007a).”
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, methodology): “One is the Box– Cox method (Engeland et al., 2005; Jin et al., 2010; Wang et al., 2009; Yang et al., 2007b) and the other one is the Normal Quantile Transform (NQT) (Krzysztofowicz, 1997, 1999; Todini, 2008; Van der Waerden, 1952, 1953a,b).”