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 post-processing approaches based on three
transformations – logarithmic (Log), log-sinh (Log-Sinh), and Box–Cox with
λ=0.2 (BC0.2) – and identifies the best-performing scheme for
post-processing monthly and seasonal (3-months-ahead) 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 of forecasts substantially
improves reliability, with more than 90 % of forecasts classified as
reliable. In terms of sharpness, the BC0.2 scheme substantially 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 schemes. The improvements in forecast reliability and
sharpness achieved using the BC0.2 post-processing scheme will help water
managers and users of the forecasting service make better-informed decisions
in planning and management of water resources. Highlights. Uncorrected and
post-processed streamflow forecasts (using three transformations, namely Log,
Log-Sinh, and BC0.2) are evaluated over 300 diverse Australian catchments.
Post-processing enhances streamflow forecast reliability, increasing the
percentage of catchments with reliable predictions from 50 % to over
90 %. The BC0.2 transformation achieves substantially better forecast
sharpness than the Log-Sinh and Log transformations, particularly in dry
catchments.