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Verification of Medium- to Long-Range Hydrological Forecasts

  • Luc PerreaultEmail author
  • Jocelyn GaudetEmail author
  • Louis DelormeEmail author
  • Simon ChatelainEmail author
Reference work entry

Abstract

Hydrological forecasting is crucial for hydropower production and risk management related to extreme events. Since uncertainty cannot be eliminated from such a process, forecasts should be probabilistic in nature, taking the form of probability distributions over future events. However, verification tools adapted to probabilistic hydrological forecasting have only been recently considered. How can such forecasts be verified accurately? In this chapter a simple theoretical framework proposed by Gneiting et al. (2007) is employed to provide a formal guidance to verify probabilistic forecasts. Some strategies and scoring rules used to measure the performance of hydrological forecasting systems, namely, Hydro-Québec, are presented. Monte Carlo simulation experiments and applications to a real archive of operational medium-range forecasts are also presented. An experiment is finally performed to evaluate long-range hydrological forecasts in a decisional perspective, by employing hydrological forecasts in a stochastic midterm planning model designed for optimizing electricity production. Future research perspectives and operational challenges on diagnostic approaches for hydrological probabilistic forecasts are given.

Keywords

Probabilistic forecasting Hydrological forecasts Proper scoring rules Skill scores Estimation Multivariate verification Energy score Economic value of forecasts 

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)CrossRefGoogle Scholar
  2. J.E. Bickel, Some comparisons among quadratic, spherical, and logarithmic scoring rules. Decis. Anal. 4, 49–65 (2007)CrossRefGoogle Scholar
  3. R.B. Birge, F. Louveaux, Introduction to Stochastic Programming. Springer Series in Operations Research (Springer, New York, 1997)Google Scholar
  4. J. Bröcker, L.A. Smith, Scoring probabilistic forecasts: the importance of being proper. Weather Forecast. 22, 382–388 (2007)CrossRefGoogle Scholar
  5. J.D. Brown, M. He, S. Regonda, L. Wu, H. Lee, D.J. Seo, Verification of temperature, precipitation, and streamflow forecasts from the NOAA/NWS hydrologic ensemble forecast service (HEFS): 2 streamflow verification. J. Hydrol. 519, 2847–2868 (2014)CrossRefGoogle Scholar
  6. H.L. Cloke, F. Pappenberger, Ensemble flood forecasting: a review. J. Hydrol. 375, 613–626 (2009)CrossRefGoogle Scholar
  7. 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
  8. G. Day, Extended streamflow forecasting using NWSRFS. J. Water Resour. Plan. Manag. 111, 157–170 (1985)CrossRefGoogle Scholar
  9. P. Friederichs, T. Thorarinsdottir, Forecast verification scores for extreme value distributions with an application to peak wind prediction. Environ. Sci. Technol. 23, 579–594 (2012)Google Scholar
  10. C. Genest, A.-C. Favre, Everything you always wanted to know about copula modeling but were afraid to ask. J. Hydrol. Eng. 12, 347–368 (2007)CrossRefGoogle Scholar
  11. T. Gneiting, A.E. Raftery, Strictly proper scoring rules, prediction, and estimation. J. Am. Stat. Assoc. 102, 359–378 (2007)CrossRefGoogle Scholar
  12. T. Gneiting, F. Balabdaoui, A.E. Raftery, Probabilistic forecasts, calibration and sharpness. Journal of the Royal Statistical Society Series B: Statistical Methodology 69, 243–268 (2007)CrossRefGoogle Scholar
  13. T. Gneiting, L.I. Stanberry, E.P. Grimit, L. Held, N.A. Johnson, Assessing probabilistic forecasts of multivariate quantities, with applications to ensemble predictions of surface winds. Test 17, 211–235 (2008)CrossRefGoogle Scholar
  14. T.Gneiting, R. Ranjan, Comparing density forecasts using threshold- and quantile-weighted scoring rules. Jounal of Business & Economic Statistics 29, 411–422 (2011)CrossRefGoogle Scholar
  15. P.J. Huber, Robust Statistics (Wiley, New York, 1981)CrossRefGoogle Scholar
  16. V.R. Jose, A characterization for the spherical scoring rule. Theor. Decis. 66, 263–281 (2009)CrossRefGoogle Scholar
  17. R. Krzysztofowicz, The case for probabilistic forecasting in hydrology. J. Hydrol. 249, 2–9 (2001)CrossRefGoogle Scholar
  18. P. Naveau, R. de Fondeville, D. Cooley, H. Benveniste et al., Scores (CRPS), inference and extremes. Séminaire Statistique des Sommets de Rochebrune, 30 Mar–4 Apr 2014Google Scholar
  19. L. Perreault, Vérification de prévisions hydrologiques probabilistes – Version 2. Technical Report IREQ-2013-0149, Institut de recherche d’Hydro-Québec (2013)Google Scholar
  20. L. Perreault, R. Garçon, J. Gaudet, Modelling hydrologic time series using regime switching models and measures of atmospheric circulation. La Houille Blanche 6, 111–123 (2007)CrossRefGoogle Scholar
  21. P. Pinson, J. Tastu, Discrimination Ability of the Energy Score. Technical Report, Technical University of Denmark (2013)Google Scholar
  22. C. Robert, G. Casella, Monte Carlo Statistical Methods (Springer, New York, 2000)Google Scholar
  23. M. Scheuerer, T.M. Hamill, Variogram-based proper scoring rules for probabilistic forecasts of multivariate quantities. Mon. Weather Rev. 143, 1321–1334 (2015)CrossRefGoogle Scholar
  24. R. Selten, Axiomatic characterization of the quadratic scoring rule. Exp. Econ. 1, 43–62 (1998)CrossRefGoogle Scholar
  25. O.G.B. Sveinsson, U. Lall, V. Fortin, L. Perreault, J. Gaudet, S. Zebiak, Y. Kushnir, Forecasting spring reservoir inflows in Churchill Falls basin in Quebec Canada. J. Hydrol. Eng. 13, 426–437 (2008)CrossRefGoogle Scholar
  26. M. Taillardat, O. Mestre, M. Zamo, P. Naveau, Calibrated ensemble forecasts using quantile regression forests and ensemble model output statistics. Mon Weather Rev, 144, 2375–2393 (2016)CrossRefGoogle Scholar
  27. M. Taillardat, Méthodes Non-Paramétriques de Post-Traitement des Prévisions d’Ensemble. PhD Thesis (2017)Google Scholar
  28. F. Weber, L. Perreault, V. et Fortin, Measuring the performance of hydrological forecasts for hydropower production at BC Hydro and Hydro-Québec, in Proceeding of the 18th Conference on Climate Variability and Change, AMS, Atlanta, 30 Jan–2 Feb 2006Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.IREQ Hydro-Québec Research InstituteVarennesCanada
  2. 2.McGill UniversityMontrealCanada

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