Seamless streamflow forecasting at daily to monthly scales: MuTHRE lets you have your cake and eat it too

Abstract

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 and sharp over a range of lead times (1–30 d) and aggregation timescales (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 timescale 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 timescale of the non-seamless forecast. This study compares forecasts from two probabilistic streamflow post-processing (QPP) models, namely 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 Australian Community Climate and Earth System Simulator – Seasonal (ACCESS-S) numerical weather prediction model. Evaluating monthly forecasts with key performance metrics (reliability, sharpness, bias, and continuous ranked probability score 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 also a preferred choice for future research development and practical adoption in streamflow forecasting.