Evaluating residual error approaches for post-processing monthly and seasonal streamflow forecasts

Abstract. Streamflow forecasting is prone to substantial uncertainty due to errors in meteorological forecasts, hydrological model structure and parameterization, as well as in the observed rainfall and streamflow data used to calibrate the models. Statistical streamflow post-processing is an important technique available to improve the probabilistic properties of the forecasts. This study evaluates three residual error models based on the logarithmic (Log), log-sinh (Log-Sinh) and Box-Cox with λ = 0.2 (BC0.2) transformation schemes and identifies the best performing scheme for post-processing monthly and seasonal (3-months) streamflow forecasts, such as those produced by the Australian Bureau of Meteorology. Using the Bureau’s operational dynamic streamflow forecasting system, we carry out comprehensive analysis of the three post-processing schemes across 300 Australian catchments with a wide range of hydro-climatic conditions. Forecast verification is assessed using reliability and sharpness metrics, as well as the Continuous Ranked Probability Skill Score (CRPSS). Results show that the uncorrected forecasts (i.e. without post-processing) are unreliable at half of the catchments. Post-processing using the three residual error models substantially improves reliability, with more than 90 % of forecasts classified as reliable. In terms of sharpness, the BC0.2 scheme significantly outperforms the Log and Log-Sinh schemes. Overall, the BC0.2 scheme achieves reliable and sharper-than-climatology forecasts at a larger number of catchments than the Log and Log-Sinh error models. This study is significant because the reliable and sharper forecasts obtained using the BC0.2 post-processing scheme will help water managers and users of the forecasting service to make better-informed decisions in planning and management of water resources.