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GLUE Based Assessment on the Overall Predictions of a MIKE SHE Application

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  • R. Vázquez
  • K. Beven
  • J. Feyen

Abstract

The generalised likelihood uncertainty estimation (GLUE) approach was applied to assess the performance of a distributed catchment model and to estimate prediction limits after conditioning based on observed catchment-wide streamflow. Prediction limits were derived not only for daily streamflow but also for piezometric levels and for extreme events. The latter analysis was carried out considering independent partial duration time series (PDS) obtained from the observed daily streamflow hydrograph. Important data uncertainties were identified. For streamflow the stage-discharge data analysis led to estimate an average data uncertainty of about 3 m 3 s − 1 . For piezometric levels, data errors were estimated to be in the order of 5 m in average and 10 m at most. The GLUE analysis showed that most of the inspected parameters are insensitive to model performance, except the horizontal and vertical components of the hydraulic conductivity of one of the geological layers that have the most influence on the streamflow model performance in the application catchment. The study revealed a considerable uncertainty attached to the simulation of both high flows and low flows (i.e., in average terms 5 m 3 s − 1 before the Bayesian updating of the prediction limits). Similarly, wide prediction intervals were obtained for the piezometric levels in relevant wells, in the order of 3.3 and 1.5 m before and after the Bayesian updating of the prediction limits, respectively. Consequently, the results suggest that, in average terms, the model of the catchment predicts overall outputs within the limitations of the errors in the input variables. Copyright Springer Science+Business Media B.V. 2009

Suggested Citation

  • R. Vázquez & K. Beven & J. Feyen, 2009. "GLUE Based Assessment on the Overall Predictions of a MIKE SHE Application," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(7), pages 1325-1349, May.
    • Handle: RePEc:spr:waterr:v:23:y:2009:i:7:p:1325-1349
    DOI: 10.1007/s11269-008-9329-6
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    References listed on IDEAS

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    1. R. Vázquez Z & J. Feyen, 2002. "Assessment of the Performance of a Distributed Code in Relation to the ET p Estimates," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 16(4), pages 329-350, August.
    2. E. XEVI & K. Christiaens & A. Espino & W. Sewnandan & D. Mallants & H. Sørensen & J. Feyen, 1997. "Calibration, Validation and Sensitivity Analysis of the MIKE-SHE Model Using the Neuenkirchen Catchment as Case Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 11(3), pages 219-242, June.
    3. Stuart Coles, 2002. "Models and inference for uncertainty in extremal dependence," Biometrika, Biometrika Trust, vol. 89(1), pages 183-196, March.
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    Cited by:

    1. Xi Chen & Tao Yang & Xiaoyan Wang & Chong-Yu Xu & Zhongbo Yu, 2013. "Uncertainty Intercomparison of Different Hydrological Models in Simulating Extreme Flows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1393-1409, March.
    2. Hairong Zhang & Jianzhong Zhou & Lei Ye & Xiaofan Zeng & Yufan Chen, 2015. "Lower Upper Bound Estimation Method Considering Symmetry for Construction of Prediction Intervals in Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5505-5519, December.
    3. Yuyin Liang & Shuguang Liu & Yiping Guo & Hong Hua, 2017. "L-Moment-Based Regional Frequency Analysis of Annual Extreme Precipitation and its Uncertainty Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 3899-3919, September.
    4. Carlos Llopis-Albert & José Merigó & Daniel Palacios-Marqués, 2015. "Structure Adaptation in Stochastic Inverse Methods for Integrating Information," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(1), pages 95-107, January.
    5. Wei Zhang & Tian Li, 2015. "The Influence of Objective Function and Acceptability Threshold on Uncertainty Assessment of an Urban Drainage Hydraulic Model with Generalized Likelihood Uncertainty Estimation Methodology," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 2059-2072, April.

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