ABSTRACT

Identifying situations where a hydrological model yields poor performance is useful for improving its predictive capability. Here we applied an evaluation methodology to diagnose the weaknesses of a parsimonious rainfall-runoff model for flood simulation. The GR5H-I hourly lumped model was evaluated over a large set of 229 French catchments and 2990 flood events. Model bias was calculated considering different streamflow time windows, from calculations using all observations to analyses of individual flood events. We then analysed bias across seasons and against several flood characteristics. Our results show that although GR5H-I had good overall performance, most of the summer floods were underestimated. In summer and autumn, compensations between flood and recession periods were identified. The largest underestimations of flood volumes were identified when high-intensity precipitation events occurred, especially under low soil moisture conditions.

摘要

识别水文模型性能不佳的情况有助于提高其预测能力。在这里，我们应用了一种评估方法来诊断用于洪水模拟的简约降雨径流模型的弱点。GR5H-I 每小时集总模型在包含 229 个法国集水区和 2990 个洪水事件的大型集合中进行了评估。模型偏差的计算考虑了不同的水流时间窗口，从使用所有观测的计算到单个洪水事件的分析。然后，我们分析了跨季节和几种洪水特征的偏差。我们的结果表明，虽然 GR5H-I 具有良好的整体性能，但大部分夏季洪水都被低估了。在夏季和秋季，确定了洪水期和衰退期之间的补偿。