Application of Generalized Likelihood Uncertainty Estimation (GLUE) at different temporal scales to reduce the uncertainty level in modelled river flows
Application of Generalized Likelihood Uncertainty Estimation (GLUE) at different temporal scales to reduce the uncertainty level in modelled river flows
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DOI：
10.1080/02626667.2020.1764961
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发表时间：
2020-06
影响因子：
3.5
通讯作者：
R. Ragab;Alexandra Kaelin;M. Afzal;I. Panagea
中科院分区：
文献类型：
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作者：
R. Ragab;Alexandra Kaelin;M. Afzal;I. Panagea
摘要在这项研究中，分布式流域尺度模型，DiCaSM，在英国的五个集水区。鉴于其重要性，河流流量被选择来研究在不同的时间尺度（每日，每月，季节和年度）的径流预测使用广义似然不确定性估计（GLUE）方法的不确定性。不确定性分析表明，观测到的河流流量在5%和95%的预测范围内。这些预测的河流流量边界包含了大部分观测到的河流流量，如高包容比CR所示。除CR外，其他不确定性指标--带宽B、相对带宽RB、不对称度S和T、偏差幅度D、相对偏差幅度RD和R因子--也表明预测的河流流量具有可接受的不确定性水平。结果表明，较低的不确定性预测河流流量时，增加时间尺度从每日每月到季节，与最低的不确定性与年流量。
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.