Development of a national 7-day ensemble streamflow forecasting service for Australia

Reliable streamflow forecasts with associated uncertainty estimates are essential to manage and make better use of Australia's scarce surface water resources. Here we present the development of an operational 7 d ensemble streamflow forecasting service for Australia to meet the growing needs of users, primarily water and river managers, for probabilistic forecasts to support their decision making. We test the modelling methodology for 100 catchments to learn the characteristics of different rainfall forecasts from Numerical Weather Prediction (NWP) models, the effect of statistical processing on streamflow forecasts, the optimal ensemble size, and parameters of a bootstrapping technique for calculating forecast skill. A conceptual rainfall-runoff model, GR4H (hourly), and lag and route channel routing model that are in-built in the Short-term Water Information Forecasting Tools (SWIFT) hydrologic modelling package are used to simulate streamflow from input rainfall and potential evaporation. The statistical catchment hydrologic pre-processor (CHyPP) is used for calibrating rainfall forecasts, and the error reduction and representation in stages (ERRIS) model is used to reduce hydrological errors and quantify hydrological uncertainty. Calibrating raw forecast rainfall with CHyPP is an efficient method to significantly reduce bias and improve reliability for up to 7 lead days. We demonstrate that ERRIS significantly improves forecast skill up to 7 lead days. Forecast skills are highest in temperate perennially flowing rivers, while it is lowest in intermittently flowing rivers. A sensitivity analysis for optimising the number of streamflow ensemble members for the operational service shows that more than 200 members are needed to represent the forecast uncertainty. We show that the bootstrapping block size is sensitive to the forecast skill calculation. A bootstrapping block size of 1 month is recommended to capture maximum possible uncertainty. We present benchmark criteria for accepting forecast locations for the public service. Based on the criteria, 209 forecast locations out of a possible 283 are selected in different hydro-climatic regions across Australia for the public service. The service, which has been operational since 2019, provides daily updates of graphical and tabular products of ensemble streamflow forecasts along with performance information, for up to 7 lead days.

Automated summary

Researchers have developed an operational 7-day ensemble streamflow forecasting service for Australia, providing probabilistic forecasts to support decision making for water and river management. Through testing and analysis, they have identified effective methods for improving forecast accuracy and reliability, such as statistical processing and calibration techniques. They have also determined optimal ensemble and bootstrapping parameters to quantify forecast uncertainty.