Dynamic streamflow forecasts within an uncertainty framework for 100 catchments in Australia

The Bureau of Meteorology recently released a new streamflow forecast product based on a dynamic approach where the forecasts are generated with a lumped hydrological model (GR4J) that is forced by statistically downscaled rainfall forecasts obtained from the Bureau's Predictive Ocean Atmosphere Model for Australia (POAMA). This registered user service provides ensemble forecasts of the 1 month and 3 month streamflow volume at 100 locations across Australia. The forecast system is composed of three different components: (1) downscaling of gridded outputs from POAMA version M2.4 to catchment scale rainfall forecasts; (2) hydrological model calibrated with a statistical tool that accounts for predictive uncertainty (BATEA) and forced with downscaled rainfall forecasts; and (3) post processed streamflow forecasts to correct for residual biases and adjust the ensemble spread. In this paper, we describe the complexities involved and challenges faced in operationalising the dynamic approach. Performance of this system is reviewed by computing performance metrics for historical reforecast in a cross-validation framework. The forecast performance exceeds the one obtained with a climatological forecast for a vast majority of sites, for all metrics, and for both monthly and seasonal forecasts. In addition, it reaches a comparable level of performance with those derived from the existing statistical model (BJP) currently used by the Bureau to issue seasonal forecasts. These results demonstrate a major achievement considering the number of modelling components involved, their respective complexity, the number of forecast sites covered, the range of climate conditions encountered and the constraints of running such system in an operational setting Regardless of completion of this important milestone, the forecast skill could be improved under certain conditions, particularly in dry catchments and several forecast sites in Queensland and Tasmania.