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Assessment of an ensemble seasonal streamflow forecasting system for Australia

Citation

Bennett, JC and Wang, QJ and Robertson, DE and Schepen, A and Li, M and Michael, K, Assessment of an ensemble seasonal streamflow forecasting system for Australia, Hydrology and Earth System Sciences, 21, (12) pp. 6007-6030. ISSN 1027-5606 (2017) [Refereed Article]


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Copyright Statement

© 2017 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

DOI: doi:10.5194/hess-21-6007-2017

Abstract

Despite an increasing availability of skilful long-range streamflow forecasts, many water agencies still rely on simple resampled historical inflow sequences (stochastic scenarios) to plan operations over the coming year. We assess a recently developed forecasting system called "forecast guided stochastic scenarios" (FoGSS) as a skilful alternative to standard stochastic scenarios for the Australian continent. FoGSS uses climate forecasts from a coupled ocean–land–atmosphere prediction system, post-processed with the method of calibration, bridging and merging. Ensemble rainfall forecasts force a monthly rainfall–runoff model, while a staged hydrological error model quantifies and propagates hydrological forecast uncertainty through forecast lead times. FoGSS is able to generate ensemble streamflow forecasts in the form of monthly time series to a 12-month forecast horizon.

FoGSS is tested on 63 Australian catchments that cover a wide range of climates, including 21 ephemeral rivers. In all perennial and many ephemeral catchments, FoGSS provides an effective alternative to resampled historical inflow sequences. FoGSS generally produces skilful forecasts at shorter lead times ( <  4 months), and transits to climatology-like forecasts at longer lead times. Forecasts are generally reliable and unbiased. However, FoGSS does not perform well in very dry catchments (catchments that experience zero flows more than half the time in some months), sometimes producing strongly negative forecast skill and poor reliability. We attempt to improve forecasts through the use of (i) ESP rainfall forcings, (ii) different rainfall–runoff models, and (iii) a Bayesian prior to encourage the error model to return climatology forecasts in months when the rainfall–runoff model performs poorly. Of these, the use of the prior offers the clearest benefit in very dry catchments, where it moderates strongly negative forecast skill and reduces bias in some instances. However, the prior does not remedy poor reliability in very dry catchments.

Overall, FoGSS is an attractive alternative to historical inflow sequences in all but the driest catchments. We discuss ways in which forecast reliability in very dry catchments could be improved in future work.

Item Details

Item Type:Refereed Article
Keywords:forecast guided stochastic scenarios, streamflow forecasting, climate forecasting, inflow sequence
Research Division:Earth Sciences
Research Group:Physical Geography and Environmental Geoscience
Research Field:Surfacewater Hydrology
Objective Division:Environment
Objective Group:Land and Water Management
Objective Field:Forest and Woodlands Water Management
UTAS Author:Bennett, JC (Mr James Bennett)
UTAS Author:Michael, K (Dr Kelvin Michael)
ID Code:123318
Year Published:2017
Web of Science® Times Cited:6
Deposited By:Oceans and Cryosphere
Deposited On:2018-01-03
Last Modified:2018-05-28
Downloads:26 View Download Statistics

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