Reply on RC1

Abstract

Subseasonal streamflow forecasts inform a multitude of water management decisions, from early flood warning to reservoir operation. ‘Seamless’ forecasts, i.e., forecasts that are reliable over a range of lead times (1–30 days) and when aggregated to multiples time scales (e.g. daily and monthly) are of clear practical interest. However, existing forecasting products are often ‘non-seamless’, i.e., designed for a single time scale and lead time (e.g. 1 month ahead). If seamless forecasts are to be a viable replacement for existing ‘non-seamless’ forecasts, it is important that they offer (at least) similar predictive performance at the time scale of the non-seamless forecast. This study compares the recently developed seamless daily Multi-Temporal Hydrological Residual Error (MuTHRE) model to the (non-seamless) monthly streamflow post-processing (QPP) model that was used in the Australian Bureau of Meteorology’s Dynamic Forecasting System. Streamflow forecasts from both models are generated for 11 Australian catchments, using the GR4J hydrological model and post-processed rainfall forecasts from the ACCESS-S climate model. Evaluating monthly forecasts with key performance metrics (reliability, sharpness, bias and CRPS skill score), we find that the seamless MuTHRE model provides essentially the same performance as the non-seamless monthly QPP model for the vast majority of metrics and temporal stratifications (months and years). When this outcome is combined with the numerous practical benefits of seamless forecasts it is clear that seamless forecasting technologies, such as the MuTHRE model, are not only viable, but a preferred choice for future research development and practical adoption of streamflow forecasting.