Evaluating post-processing approaches for monthly and seasonal streamflow forecasts

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dc.contributor.authorWoldemeskel, F.
dc.contributor.authorMcInerney, D.
dc.contributor.authorLerat, J.
dc.contributor.authorThyer, M.
dc.contributor.authorKavetski, D.
dc.contributor.authorShin, D.
dc.contributor.authorTuteja, N.
dc.contributor.authorKuczera, G.
dc.date.issued2018
dc.description.abstractStreamflow 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.
dc.description.statementofresponsibilityFitsum Woldemeskel, David McInerney, Julien Lerat, Mark Thyer, Dmitri Kavetski, Daehyok Shin, Narendra Tuteja and George Kuczera
dc.identifier.citationHydrology and Earth System Sciences, 2018; 22(12):6257-6278
dc.identifier.doi10.5194/hess-22-6257-2018
dc.identifier.issn1027-5606
dc.identifier.issn1607-7938
dc.identifier.orcidMcInerney, D. [0000-0003-4876-8281]
dc.identifier.orcidThyer, M. [0000-0002-2830-516X]
dc.identifier.orcidKavetski, D. [0000-0003-4966-9234]
dc.identifier.urihttp://hdl.handle.net/2440/117896
dc.language.isoen
dc.publisherCopernicus Gesellschaft
dc.relation.granthttp://purl.org/au-research/grants/arc/LP140100978
dc.rights© Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License.
dc.source.urihttps://doi.org/10.5194/hess-22-6257-2018
dc.titleEvaluating post-processing approaches for monthly and seasonal streamflow forecasts
dc.typeJournal article
pubs.publication-statusPublished

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