Abstract
Parameter uncertainty in hydrologic modeling is crucial to the flood simulation and forecasting. The Bayesian approach allows one to estimate parameters according to prior expert knowledge as well as observational data about model parameter values. This study assesses the performance of two popular uncertainty analysis (UA) techniques, i.e., generalized likelihood uncertainty estimation (GLUE) and Bayesian method implemented with the Markov chain Monte Carlo sampling algorithm, in evaluating model parameter uncertainty in flood simulations. These two methods were applied to the semi-distributed Topographic hydrologic model (TOPMODEL) that includes five parameters. A case study was carried out for a small humid catchment in the southeastern China. The performance assessment of the GLUE and Bayesian methods were conducted with advanced tools suited for probabilistic simulations of continuous variables such as streamflow. Graphical tools and scalar metrics were used to test several attributes of the simulation quality of selected flood events: deterministic accuracy and the accuracy of 95 % prediction probability uncertainty band (95PPU). Sensitivity analysis was conducted to identify sensitive parameters that largely affect the model output results. Subsequently, the GLUE and Bayesian methods were used to analyze the uncertainty of sensitive parameters and further to produce their posterior distributions. Based on their posterior parameter samples, TOPMODEL’s simulations and the corresponding UA results were conducted. Results show that the form of exponential decline in conductivity and the overland flow routing velocity were sensitive parameters in TOPMODEL in our case. Small changes in these two parameters would lead to large differences in flood simulation results. Results also suggest that, for both UA techniques, most of streamflow observations were bracketed by 95PPU with the containing ratio value larger than 80 %. In comparison, GLUE gave narrower prediction uncertainty bands than the Bayesian method. It was found that the mode estimates of parameter posterior distributions are suitable to result in better performance of deterministic outputs than the 50 % percentiles for both the GLUE and Bayesian analyses. In addition, the simulation results calibrated with Rosenbrock optimization algorithm show a better agreement with the observations than the UA’s 50 % percentiles but slightly worse than the hydrographs from the mode estimates. The results clearly emphasize the importance of using model uncertainty diagnostic approaches in flood simulations.
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Acknowledgments
This work was supported by the National Key R&D Program of China (Nos. 2016YFC0402706, 2016YFC0402707, 2016YFC0402709), National Natural Science Foundation of China (No. 51509067), Special Fund for Public Welfare Industry of the Ministry of Water Resources of China (No. 201501004), the Fundamental Research Funds for the Central Universities of China (No. 2015B00114) and the Project Funded by China Postdoctoral Science Foundation (No. 2015M580450). We would like to thank Zhongbo Yu, Jiapeng Hua and Jintao Liu for their comments on MCMC implementation, Qingbo Cheng for his constructive suggestions and Keith Beven (Lancaster University) for providing the program code of HSY GSA and GLUE at http://www.uncertain-future.org.uk. We also thank the anonymous reviewers as their comments have largely improved this work.
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Li, B., Liang, Z., He, Y. et al. Comparison of parameter uncertainty analysis techniques for a TOPMODEL application. Stoch Environ Res Risk Assess 31, 1045–1059 (2017). https://doi.org/10.1007/s00477-016-1319-2
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DOI: https://doi.org/10.1007/s00477-016-1319-2