Advertisement

Overview of Communication Strategies for Uncertainty in Hydrological Forecasting in Australia

With a Focus on “Assessing Forecast Quality of the National Seasonal Streamflow Forecast Service”
  • Narendra Kumar TutejaEmail author
  • Senlin Zhou
  • Julien Lerat
  • Q. J. Wang
  • Daehyok Shin
  • David E. Robertson
Reference work entry

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.

Keywords

Seasonal streamflow forecasting Ensemble forecasting Uncertainty estimation Forecast verification Aggregated forecast performance Forecast accuracy Forecast precision Forecast reliability Continuous Rank Probability Score (CRPS) Root mean squared error (RMSE) Root mean squared error in probability space (RMSEP) Hit rates Bayesian joint probability model (BJP) 

References

  1. 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).  https://doi.org/10.1016/j.jhydrol.2014.06.035CrossRefGoogle Scholar
  2. 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)Google Scholar
  3. 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)CrossRefGoogle Scholar
  4. J.G. De Gooijer, D. Zerom, Kernel-based multistep-ahead predictions of the US short-term interest rate. J. Forecast. 19, 335–353 (2000)CrossRefGoogle Scholar
  5. 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).  https://doi.org/10.1002/met.45CrossRefGoogle Scholar
  6. T. Gneiting, F. Balabdaoui, A.E. Raftery, Probabilistic forecasts, calibration and sharpness. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 69, 243–268 (2007)CrossRefGoogle Scholar
  7. 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/CrossRefGoogle Scholar
  8. T.A. McMahon, B.L. Finlayson, A. Haines, R. Srikanthan, Runoff variability: a global perspective. IASH-AISH 168, 3–11 (1987)Google Scholar
  9. 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)CrossRefGoogle Scholar
  10. 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)CrossRefGoogle Scholar
  11. D.E. Robertson, Q.J. Wang, A Bayesian approach to predictor selection for seasonal streamflow forecasting. J. Hydrometeorol. 13, 155–171 (2012).  https://doi.org/10.1175/JHM-D-10-05009.1CrossRefGoogle Scholar
  12. 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).Google Scholar
  13. 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)CrossRefGoogle Scholar
  14. 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
  15. R.A. Vertessy, Water information services for Australians. Aust. J. Water Resour 16(2), 91–106 (2013).  https://doi.org/10.7158/W13-MO01.2013.16.2CrossRefGoogle Scholar
  16. Q.J. Wang, D.E. Robertson, Multisite probabilistic forecasting of seasonal flows for streams with zero value occurrences. Water Resour. Res. 47, W02546 (2011)Google Scholar
  17. 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)Google Scholar
  18. D.S. Wilks, Statistical Methods in the Atmospheric Sciences – An Introduction (Academic, San Diego, 1995)Google Scholar
  19. T. Wilson, P. Feikema, J. Ridout, 2013 User feedback on the seasonal streamflow forecasts service, Bureau of Meteorology (2014)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Narendra Kumar Tuteja
    • 1
    Email author
  • Senlin Zhou
    • 2
  • Julien Lerat
    • 1
  • Q. J. Wang
    • 3
  • Daehyok Shin
    • 2
  • David E. Robertson
    • 3
  1. 1.Bureau of MeteorologyCanberraAustralia
  2. 2.Bureau of MeteorologyDocklandsAustralia
  3. 3.CSIRO Land and WaterClaytonAustralia

Personalised recommendations