Abstract
The National Seasonal Streamflow Forecasting Service operated by the Bureau of Meteorology since 2010 delivers monthly updates of 3 month ensemble forecasts at 147 locations across 75 river basins using the statistical Bayesian joint probability (BJP). Seasonal forecasts are communicated to the public using statistical concepts such as “chances,” “ensembles,” “lower/higher than median,” etc. However, these concepts require advanced competencies in statistics, and they cannot be conveyed to a general audience easily. This chapter focuses on the challenge of communicating forecast skill to a wide range of users more effectively. A simple forecast performance measure called the “Aggregated Forecast Performance Index (AFPI)” was introduced which captures key attributes such as forecast reliability and accuracy and combines them into a single easy-to-understand and well-informed aggregated measure. Based on this index, it was demonstrated that bureau’s seasonal streamflow forecasts are reliable. They also offer improved accuracy by narrowing down the forecast uncertainty (up to 25 %) with respect to reference climatology and hence offer a value proposition for water managers to improve their decision-making.
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L. Alfieri, F. Pappenberger, F. Wetterhall, T. Haiden, D. Richardson, P. Salamon, Evaluation of ensemble streamflow predictions in Europe. J. Hydrol. 517, 913–922 (2014). doi:10.1016/j.jhydrol.2014.06.035
F. Chiew, J. Vaze, K.J. Hennessy, Climate Data for Hydrologic Scenario Modelling Across the Murray-Darling Basin: A Report to the Australian Government from the CSIRO Murray-Darling Basin Sustainable Yields Project (CSIRO, Canberra, 2008)
A.P. Dawid, Present position and potential developments: some personal views: statistical theory: the prequential approach. J. R. Stat. Soc. Ser. A 147, 278–292 (1984)
J.G. De Gooijer, D. Zerom, Kernel-based multistep-ahead predictions of the US short-term interest rate. J. Forecast. 19, 335–353 (2000)
C.A.T. Ferro, D.S. Richardson, A.P. Weigel, On the effect of ensemble size on the discrete and continuous ranked probability scores. Meteorol. Appl. 15, 19–24 (2008). doi:10.1002/met.45
T. Gneiting, F. Balabdaoui, A.E. Raftery, Probabilistic forecasts, calibration and sharpness. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 69, 243–268 (2007)
F. Laio, S. Tamea, Verification tools for probabilistic forecasts of continuous hydrological variables, Hydrology and Earth System Sciences. Hydrol. Earth Syst. Sci. 11, 1267–1277 (2007). www.hydrol-earth-syst-sci.net/11/1267/2007/
T.A. McMahon, B.L. Finlayson, A. Haines, R. Srikanthan, Runoff variability: a global perspective. IASH-AISH 168, 3–11 (1987)
R.E. Morss, J.L. Demuth, J.K. Lazo, Communicating uncertainty in weather forecasts: a survey of the US public. Weather Forecast. 23(5), 974–991 (2008)
A.H. Murphy, What is a good forecast? An essay on the nature of goodness in weather forecasting. Weather Forecast. 8(2), 281–293 (1993)
D.E. Robertson, Q.J. Wang, A Bayesian approach to predictor selection for seasonal streamflow forecasting. J. Hydrometeorol. 13, 155–171 (2012). doi:10.1175/JHM-D-10-05009.1
D. Shin, A. Schepen, T. Peatey, S. Zhou, A. MacDonald, T. Chia, J. Perkins, N. Plummer, WAFARi: a new modelling system for seasonal streamflow forecasting service of the Bureau of Meteorology, Australia. MODSIM2011. Perth (2011).
M. Thyer, B. Renard, D. Kavetski, G. Kuczera, S.W. Franks, S. Srikanthan, Critical evaluation of parameter consistency and predictive uncertainty in hydrological modeling: a case study using Bayesian total error analysis. Water Resour. Res. 45, 22 (2009)
N.K. Tuteja, D. Shin, R. Laugesen, U. Khan, Q. Shao, E. Wang, M. Li, H. Zheng, G. Kuczera, D. Kavetski, G. Evin, M. Thyer, A. MacDonald, T. Chia, B. Le, Experimental evaluation of the dynamic seasonal streamflow forecasting approach, Technical Report, Bureau of Meteorology, Melbourne (2011). http://www.bom.gov.au/water/about/publications/document/dynamic_seasonal_streamflow_forecasting.pdf
R.A. Vertessy, Water information services for Australians. Aust. J. Water Resour 16(2), 91–106 (2013). doi:10.7158/W13-MO01.2013.16.2
Q.J. Wang, D.E. Robertson, Multisite probabilistic forecasting of seasonal flows for streams with zero value occurrences. Water Resour. Res. 47, W02546 (2011)
Q.J. Wang, D.E. Robertson, F.H.S. Chiew, A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites. Water Resour. Res. 45, W05407 (2009)
D.S. Wilks, Statistical Methods in the Atmospheric Sciences – An Introduction (Academic, San Diego, 1995)
T. Wilson, P. Feikema, J. Ridout, 2013 User feedback on the seasonal streamflow forecasts service, Bureau of Meteorology (2014)
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Tuteja, N.K., Zhou, S., Lerat, J., Wang, Q.J., Shin, D., Robertson, D.E. (2016). Overview of Communication Strategies for Uncertainty in Hydrological Forecasting in Australia. In: Duan, Q., Pappenberger, F., Thielen, J., Wood, A., Cloke, H., Schaake, J. (eds) Handbook of Hydrometeorological Ensemble Forecasting. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40457-3_73-1
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DOI: https://doi.org/10.1007/978-3-642-40457-3_73-1
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