Calibrating hourly rainfall-runoff models with daily forcings for streamflow forecasting applications in meso-scale catchments. (February 2016) - Bennett, James C.; Robertson, David E.; Ward, Phillip G.D.; Hapuarachchi, H.A. Prasantha; Wang, Q.J.
Abstract: The absence of long sub-daily rainfall records can hamper development of continuous streamflow forecasting systems run at sub-daily time steps. We test the hypothesis that simple disaggregation of daily rainfall data to hourly data, combined with hourly streamflow data, can be used to establish efficient hourly rainfall-runoff models. The approach is tested on four rainfall-runoff models and a range of meso-scale catchments (150–3500 km 2 ). We also compare our disaggregation approach to a method of parameter scaling that attains an hourly parameter-set from daily data. Simple disaggregation of daily rainfall produces hourly streamflow models that perform almost as well as those developed from hourly rainfall data. Rainfall disaggregation performs at least as well as parameter scaling, and often better. For the catchments and models we test, simple disaggregation is a very straightforward and effective way to establish hydrological models for continuous sub-daily streamflow forecasting systems when sub-daily rainfall data are unavailable. Highlights: Daily rainfall is disaggregated to hourly to calibrate hourly hydrological models. Models perform almost as well as models calibrated with observed hourly rainfall. Disaggregation performed at least as well as parameter scaling. A way to develop hourly river forecast systems with daily rainfall.