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.

中文翻译：

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在不同时间尺度应用广义似然不确定性估计 (GLUE) 以降低模拟河流流量的不确定性水平

摘要 在这项研究中，分布式流域尺度模型 DiCaSM 应用于英国的五个流域。鉴于其重要性，选择河流流量来研究使用不同时间尺度（每日、每月、季节性和每年）的广义似然不确定性估计 (GLUE) 方法的流量预测中的不确定性。不确定性分析表明，观察到的河流流量在 5% 和 95% 百分位数的预测界限/包络内。这些预测的河流流量边界包含了大部分观察到的河流流量，如高遏制率 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，当时间尺度从每日增加到每月增加到季节性时，预测河流流量的不确定性较低，与年流量相关的不确定性最低。