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Application of generalized likelihood uncertainty estimation (GLUE) at different temporal scales to reduce the uncertainty level in modelled river flows

Ragab, Ragab ORCID: https://orcid.org/0000-0003-2887-7616; Kaelin, Alexandra; Afzal, Muhammad; Panagea, Ioanna. 2020 Application of generalized likelihood uncertainty estimation (GLUE) at different temporal scales to reduce the uncertainty level in modelled river flows. Hydrological Sciences Journal, 65 (11). 1856-1871. 10.1080/02626667.2020.1764961

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Abstract/Summary

In this study, the distributed catchment-scale model, DiCaSM, was applied on five catchments across the UK. Given its importance, river flow was selected to study the uncertainty in streamflow prediction using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology at different timescales (daily, monthly, seasonal and annual). The uncertainty analysis showed that the observed river flows were within the predicted bounds/envelope of 5% and 95% percentiles. These predicted river flow bounds contained most of the observed river flows, as expressed by the high containment ratio, CR. In addition to CR, other uncertainty indices – bandwidth B, relative bandwidth RB, degrees of asymmetry S and T, deviation amplitude D, relative deviation amplitude RD and the R factor – also indicated that the predicted river flows have acceptable uncertainty levels. The results show lower uncertainty in predicted river flows when increasing the timescale from daily to monthly to seasonal, with the lowest uncertainty associated with annual flows.

Item Type: Publication - Article
Digital Object Identifier (DOI): 10.1080/02626667.2020.1764961
UKCEH and CEH Sections/Science Areas: Water Resources (Science Area 2017-)
UKCEH Fellows
ISSN: 0262-6667
Additional Keywords: distributed catchment-scale model, DiCaSM, GLUE, model uncertainty, River Eden, River Don, River Ebbw, River Frome, River Pang, UK
NORA Subject Terms: Hydrology
Date made live: 22 Jul 2020 15:32 +0 (UTC)
URI: https://nora.nerc.ac.uk/id/eprint/528209

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