Hydrol. Earth Syst. Sci., 21, 6007–6030, 2017 https://doi.org/10.5194/hess-21-6007-2017 © Author(s) 2017. This work is distributed under the Creative Commons Attribution 4.0 License. Assessment of an ensemble seasonal streamflow forecasting system for Australia James C. Bennett1,2, Quan J. Wang3, David E. Robertson1, Andrew Schepen4, Ming Li5, and Kelvin Michael2 1CSIRO Land & Water, Clayton, Victoria, Australia 2Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Australia 3Department of Infrastructure Engineering, University of Melbourne, Parkville, Victoria, Australia 4CSIRO Land & Water, Dutton Park, Queensland, Australia 5CSIRO Data61, Floreat, Western Australia, Australia Correspondence to: James C. Bennett (
[email protected]) Received: 5 July 2017 – Discussion started: 7 July 2017 Revised: 8 October 2017 – Accepted: 29 October 2017 – Published: 30 November 2017 Abstract. Despite an increasing availability of skilful long- els, and (iii) a Bayesian prior to encourage the error model range streamflow forecasts, many water agencies still rely to return climatology forecasts in months when the rainfall– on simple resampled historical inflow sequences (stochas- runoff model performs poorly. Of these, the use of the prior tic scenarios) to plan operations over the coming year. We offers the clearest benefit in very dry catchments, where it assess a recently developed forecasting system called “fore- moderates strongly negative forecast skill and reduces bias cast guided stochastic scenarios” (FoGSS) as a skilful alter- in some instances. However, the prior does not remedy poor native to standard stochastic scenarios for the Australian con- reliability in very dry catchments. tinent. FoGSS uses climate forecasts from a coupled ocean– Overall, FoGSS is an attractive alternative to historical in- land–atmosphere prediction system, post-processed with the flow sequences in all but the driest catchments.