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首页> 外文期刊>Hydrological sciences journal >Application of Generalized Likelihood Uncertainty Estimation (GLUE) at different temporal scales to reduce the uncertainty level in modelled river flows
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Application of Generalized Likelihood Uncertainty Estimation (GLUE) at different temporal scales to reduce the uncertainty level in modelled river flows

机译：广义似然性不确定性估计（胶水）在不同时间尺度下的应用降低建模河流中的不确定性水平

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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 - bandwidthB, relative bandwidth RB, degrees of asymmetrySandT, deviation amplitudeD, relative deviation amplitude RD and theRfactor - 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.
机译：在这项研究中，分布式集水量表模型DICASM在英国的五个集水区应用。鉴于其重要性，选择河流流动在不同时间尺度（每日，每月，季节性和年度）的广义似然不确定性估计（胶水）方法中，研究流流程中的不确定性。不确定性分析表明，观察到的河流流动在预测的边界/包络中为5％和95％百分位。这些预测的河流流量界定了大多数观察到的河流流动，如高容积率Cr的表达。除了CR，其他不确定性指数 - 带宽，相对带宽RB，非对称度和偏差，偏差，相对偏差幅度RD和其切材也表明预测河流具有可接受的不确定性水平。结果表明，在每天向季节性到季节性增加时，预测河流流动的不确定性较低，与年度流动相关的不确定性最低。

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