Sub-daily runoff predictions using parameters calibrated on the basis of data with a daily temporal resolution

Concentration times in small and medium-sized basins (similar to 10-1000 km(2)) are commonly less than 24 h. Flood-forecasting models are thus required to provide simulations at high temporal resolutions (1 h-6 h), although time-series of input and runoff data with sufficient lengths are often only available at the daily temporal resolution, especially in developing countries. This has led to study the relationships of estimated parameter values at the temporal resolutions where they are needed from the temporal resolutions where they are available. This study presents a methodology to treat empirically model parameter dependencies on the temporal resolution of data in two small basins using a bucket-type hydrological model, HBV-light, and the generalised likelihood uncertainty estimation approach for selecting its parameters. To avoid artefacts due to the numerical resolution or numerical method of the differential equations within the model, the model was consistently run using modelling time steps of one-hour regardless of the temporal resolution of the rainfall-runoff data. The distribution of the parameters calibrated at several temporal resolutions in the two basins did not show model parameter dependencies on the temporal resolution of data and the direct transferability of calibrated parameter sets (e.g., daily) for runoff simulations at other temporal resolutions for which they were not calibrated (e.g., 3 h or 6 h) resulted in a moderate (if any) decrease in model performance, in terms of Nash-Sutcliffe and volume-error efficiencies. The results of this study indicate that if sub-daily forcing data can be secured, flood forecasting in basins with sub-daily concentration times may be possible with model-parameter values calibrated from long time series of daily data. Further studies using more models and basins are required to test the generality of these results. (C) 2017 The Author(s). Published by Elsevier B.V.