Uncertainty-based multi-criteria calibration of rainfall-runoff models: a comparative study
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- 作者：Mahyar Shafii (1)
Bryan Tolson (1)
Loren Shawn Matott (2) - 关键词：Hydrologic modelling ; Multi ; criteria calibration ; Uncertainty analysis ; Bayesian inference ; GLUE
- 刊名：Stochastic Environmental Research and Risk Assessment (SERRA)
- 出版年：2014
- 出版时间：August 2014
- 年：2014
- 卷：28
- 期：6
- 页码：1493-1510
- 全文大小：2,453 KB
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Bryan Tolson (1)
Loren Shawn Matott (2)
1. Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON, Canada
2. Center for Computational Research, University at Buffalo, Buffalo, NY, USA - ISSN：1436-3259
文摘
This study compares formal Bayesian inference to the informal generalized likelihood uncertainty estimation (GLUE) approach for uncertainty-based calibration of rainfall-runoff models in a multi-criteria context. Bayesian inference is accomplished through Markov Chain Monte Carlo (MCMC) sampling based on an auto-regressive multi-criteria likelihood formulation. Non-converged MCMC sampling is also considered as an alternative method. These methods are compared along multiple comparative measures calculated over the calibration and validation periods of two case studies. Results demonstrate that there can be considerable differences in hydrograph prediction intervals generated by formal and informal strategies for uncertainty-based multi-criteria calibration. Also, the formal approach generates definitely preferable validation period results compared to GLUE (i.e., tighter prediction intervals that show higher reliability) considering identical computational budgets. Moreover, non-converged MCMC (based on the standard Gelman–Rubin metric) performance is reasonably consistent with those given by a formal and fully-converged Bayesian approach even though fully-converged results requires significantly larger number of samples (model evaluations) for the two case studies. Therefore, research to define alternative and more practical convergence criteria for MCMC applications to computationally intensive hydrologic models may be warranted.