Advantages of calibrating a daily rainfall-runoff model to monthly streamflow data

Abstract

<p>Development of robust approaches for calibrating daily rainfall-runoff models to monthly streamflow data enable modelling platforms that operate at daily time step to be applied in practical situations. Here 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 the daily GR4J hydrological model when calibrated against (1) daily and (2) monthly streamflow data. The performance comparison relies on a wide range of metrics and is undertaken for 508 Australian catchments. Two evaluation periods (1975–1992 and 1992–2015) and four objective functions (including sum-of-squared-errors of Box-Cox transformed streamflow and the Kling-Gupta efficiency) were tested.</p><p>Monthly calibration performs similar to or better than daily calibration in most sites and both periods in terms of bias and fit of the flow duration curve. This result remains the same when the flow duration curve is computed at the daily time step, which constitutes a significant finding of this study.</p><p>However, the performance of monthly calibration is worse than daily calibration for daily pattern metrics such as Nash-Sutcliffe efficiency in most sites and both periods. Significant improvement can be achieved if the flow-timing parameter of GR4J is regionalised, effectively reducing the number of calibrated parameters. Similar results are obtained for other pattern metrics and all objective functions.</p><p>These findings suggest that monthly calibration of rainfall-runoff models using daily-rainfall and monthly-streamflow data is a viable alternative to daily calibration when no daily streamflow data are available.</p>