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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 14, issue 2 | Copyright
Nat. Hazards Earth Syst. Sci., 14, 219-233, 2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 13 Feb 2014

Research article | 13 Feb 2014

The challenge of forecasting high streamflows 1–3 months in advance with lagged climate indices in southeast Australia

J. C. Bennett1, Q. J. Wang1, P. Pokhrel*,1, and D. E. Robertson1 J. C. Bennett et al.
  • 1CSIRO Land and Water, Graham Road, Highett, Victoria 3190, Australia
  • *now at: Entura, 89 Cambridge Park Drive, Cambridge, Tasmania 7170, Australia

Abstract. Skilful forecasts of high streamflows a month or more in advance are likely to be of considerable benefit to emergency services and the broader community. This is particularly true for mesoscale catchments (< 2000 km2) with little or no seasonal snowmelt, where real-time warning systems are only able to give short notice of impending floods. In this study, we generate forecasts of high streamflows for the coming 1-month and coming 3-month periods using large-scale ocean–atmosphere climate indices and catchment wetness as predictors. Forecasts are generated with a combination of Bayesian joint probability modelling and Bayesian model averaging. High streamflows are defined as maximum single-day streamflows and maximum 5-day streamflows that occur during each 1-month or 3-month forecast period. Skill is clearly evident in the 1-month forecasts of high streamflows. Surprisingly, in several catchments positive skill is also evident in forecasts of large threshold events (exceedance probabilities of 25%) over the next month. Little skill is evident in forecasts of high streamflows for the 3-month period. We show that including lagged climate indices as predictors adds little skill to the forecasts, and thus catchment wetness is by far the most important predictor. Accordingly, we recommend that forecasts may be improved by using accurate estimates of catchment wetness.

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