Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model
By Xiaoli Jin, Chong-yu Xu, Qi Zhang, Vijay P. Singh. 2010
341 citations recorded by the source corpus.
Subfields: Global and Planetary Change, Ocean Engineering, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/4023de220582fd2c4323e0cb5503a2a0eb1118e4
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Global and Planetary Change
In Ocean Engineering
In Water Science and Technology
Citations in the corpus
- Uncertainty and sensitivity assessments of an agricultural–hydrological model (RZWQM2) using the GLUE method (2016, methodology): “With increasing numbers of studies focused on uncertainty of hydrological model, the methods of uncertainty assessment were proposed, such as MCMC and SCEM-UA, and were compared to better understand model structure, errors of input data and output responses (Jin et al., 2010; Makowski et al., 2002; ”
- Improvement of hydrological model calibration by selecting multiple parameter ranges (2016, neutral): “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, 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).”
- Evaluation of the subjective factors of the GLUE method and comparison with the formal Bayesian method in uncertainty assessment of hydrological models (2010, neutral): “The prior probability density of the parameters is generally taken as non-informative multi-uniform distribution in hydrological applications (Engeland et al., 2005; Liu et al., 2005; Yang et al., 2008; Jin et al., 2010).”