Application of Generalized Likelihood Uncertainty Estimation (GLUE) at different temporal scales to reduce the uncertainty level in modelled river flows期刊界 All Journals 搜尽天下杂志传播学术成果专业期刊搜索期刊信息化学术搜索

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Application of Generalized Likelihood Uncertainty Estimation (GLUE) at different temporal scales to reduce the uncertainty level in modelled river flows

Authors:	Ragab Ragab Alexandra Kaelin Muhammad Afzal Ioanna Panagea

Institution:	1. Water Resources Department, UK Centre for Ecology &2. Hydrology , Wallingford, Oxfordshire, UK rag@ceh.ac.ukhttps://orcid.org/0000-0003-2887-7616;4. Hydrology , Wallingford, Oxfordshire, UK;5. School of Earth and Ocean Sciences, Cardiff University , Cardiff, UK;6. Division of Soil and Water Management, Katholieke Universiteit Leuven , Leuven, Belgium

Abstract:	ABSTRACT 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.

Keywords:	distributed catchment-scale model DiCaSM GLUE model uncertainty River Eden River Don River Ebbw River Frome River Pang UK

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