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GLUE based assessment on the overall pbkp_redictions of a MIKE SHE application

Abstract:

The generalised likelihood uncertainty estimation (GLUE) approach was applied to assess the performance of a distributed catchment model and to estimate pbkp_rediction limits after conditioning based on observed catchment-wide streamflow. Pbkp_rediction 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 m3 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 m3 s-1 before the Bayesian updating of the pbkp_rediction limits). Similarly, wide pbkp_rediction 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 pbkp_rediction limits, respectively. Consequently, the results suggest that, in average terms, the model of the catchment pbkp_redicts overall outputs within the limitations of the errors in the input variables. © Springer Science+Business Media B.V. 2008.