Seamless subseasonal probabilistic streamflow forecasting: MuTHRE lets you have your cake and eat it too

Abstract

Subseasonal streamflow forecasts inform a multitude of water management decisions, from early flood warning to reservoir operation. ‘Seamless’ probabilistic forecasts, i.e., forecasts that are reliable and sharp over a range of lead times (1-30 days) and aggregation time scales (e.g. daily to monthly) are of clear practical interest. However, existing forecast products are often ‘non-seamless’, i.e., developed and applied 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 forecasts from two probabilistic streamflow post-processing (QPP) models: the recently developed seamless daily Multi-Temporal Hydrological Residual Error (MuTHRE) model and the more traditional (non-seamless) monthly QPP model used in the Australian Bureau of Meteorology’s Dynamic Forecasting System. Streamflow forecasts from both post-processing models are generated for 11 Australian catchments, using the GR4J hydrological model and pre-processed rainfall forecasts from the ACCESS-S numerical weather prediction model. Evaluating monthly forecasts with key performance metrics (reliability, sharpness, bias and CRPS skill score), we find that the seamless MuTHRE model achieves essentially the same performance as the non-seamless monthly QPP model for the vast majority of metrics and temporal stratifications (months and years). As such, MuTHRE provides the capability of ‘seamless’ daily streamflow forecasts with no loss of performance at the monthly scale – the modeller can proverbially ‘have their cake and eat it too’. This finding demonstrates that seamless forecasting technologies, such as the MuTHRE post-processing model, are not only viable, but a preferred choice for future research development and practical adoption in streamflow forecasting.