Abstract Development of robust approaches for calibrating daily rainfall-runoff models to monthly streamflow data is of major practical interest. Such approaches would enable widely used hydrological modelling platforms that operate at daily time step to be applied in practical situations where precipitation is available at the daily scale, but observed streamflow is available only at the monthly scale (e.g. predicting inflows into large dams). This study compares the performance of a hydrological model running at daily time step (GR4J) that is calibrated against daily and monthly streamflow data using a wide range of metrics: fit of the daily and monthly flow duration curve, daily and monthly pattern metrics, and long-term bias. The comparison is undertaken for 508 Australian catchments, two evaluation periods and four objective functions (including sum-of-squared-errors of Box-Cox transformed streamflow and the Kling-Gupta efficiency). Monthly calibration performs similar or better than daily calibration in a majority of sites and periods in terms of bias and fit of the flow duration curve. This result holds even when the flow duration curve is computed at the daily time step, which constitutes a major finding of this study. However, performance of monthly calibration is worse than daily calibration for daily pattern metrics such as Nash-Sutcliffe efficiency in a majority of sites and periods. This performance loss can be reduced significantly by using regionalised values for the flow-timing parameter of GR4J. Similar results are obtained for other pattern metrics and all objective functions. These findings suggest that monthly calibration of rainfall-runoff models to daily-rainfall/monthly-streamflow is a viable alternative to daily calibration when no daily streamflow data are available.

中文翻译：

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将日降雨径流模型校准为月流量数据的稳健方法

摘要 开发根据月流量数据校准日降雨径流模型的稳健方法具有重要的实际意义。此类方法将使在每日时间步长运行的广泛使用的水文建模平台能够应用于实际情况，即在每日尺度上可以获得降水，但观测到的流量仅在月尺度上可用（例如预测流入大坝的流量）。本研究比较了在每日时间步长 (GR4J) 运行的水文模型的性能，该模型使用各种指标针对每日和每月流量数据进行校准：每日和每月流量持续时间曲线的拟合、每日和每月模式指标以及长期偏见。对澳大利亚的 508 个流域进行了比较，两个评估期和四个目标函数（包括 Box-Cox 变换的流的平方和误差和 Kling-Gupta 效率）。就流量持续时间曲线的偏差和拟合而言，在大多数地点和时期，每月校准的性能类似于或优于每日校准。即使在每日时间步长计算流动持续时间曲线时，该结果也成立，这是本研究的一个主要发现。然而，在大多数站点和时期，每月校准的性能比每日模式指标（如 Nash-Sutcliffe 效率）的每日校准差。通过使用 GR4J 的流动时间参数的区域化值，可以显着减少这种性能损失。对于其他模式度量和所有目标函数，都获得了类似的结果。