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I. Zalachori; M.-H. Ramos; R. Garçon; T. Mathevet; J. Gailhard (2012)
Publisher: Copernicus Publications
Journal: Advances in Science and Research
Languages: English
Types: Article

Classified by OpenAIRE into

arxiv: Physics::Atmospheric and Oceanic Physics, Physics::Geophysics
The aim of this paper is to investigate the use of statistical correction techniques in hydrological ensemble prediction. Ensemble weather forecasts (precipitation and temperature) are used as forcing variables to a hydrologic forecasting model for the production of ensemble streamflow forecasts. The impact of different bias correction strategies on the quality of the forecasts is examined. The performance of the system is evaluated when statistical processing is applied: to precipitation and temperature forecasts only (pre-processing from the hydrological model point of view), to flow forecasts (post-processing) and to both. The pre-processing technique combines precipitation ensemble predictions with an analog forecasting approach, while the post-processing is based on past errors of the hydrological model when simulating streamflows. Forecasts from 11 catchments in France are evaluated. Results illustrate the importance of taking into account hydrological uncertainties to improve the quality of operational streamflow forecasts.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Bartholmes, J. C., Thielen, J., Ramos, M. H., and Gentilini, S.: The european flood alert system EFAS - Part 2: Statistical skill assessment of probabilistic and deterministic operational forecasts, Hydrol. Earth Syst. Sci., 13, 141-153, doi:10.5194/hess-13-141- 2009, 2009.
    • Boucher M.-A., Tremblay, D., Delorme, L., Perreault, L., and Anctil, F.: Hydro-economic assessment of hydrological forecasting systems, J. Hydrol., 416-417, 133-144, 2012.
    • Brown, J. D. and Seo, D.-J.: A nonparametric post-processor for bias-correction of hydrometeorological and hydrologic ensemble forecasts, J. Hydrometeorol., 11, 642-665, 2010.
    • Casati, B., Wilson, L. J., Stephenson, D. B., Nurmi, P., Ghelli, A., Pocernich, M., Damrath, U., Ebert, E. E., Brown, B. G., and Mason, S.: Forecast verification: current status and future directions, Meteorol. Appl., 15, 3-18, 2008.
    • Cloke, H. and Pappenberger, F.: Ensemble Flood Forecasting: A Review, J. Hydrol., 375, 613-626, 2009.
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    • Garc¸on, R.: Mode`le global Pluie-De´bit pour la pre´vision et la pre´de´termination des crues (Lumped rainfall-runoff model for flood forecasting and design flood estimation), La Houille Blanche, 7/8, 88-95, 1999 (in French).
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    • Hashino, T., Bradley, A. A., and Schwartz, S. S.: Evaluation of biascorrection methods for ensemble streamflow volume forecasts, Hydrol. Earth Syst. Sci., 11, 939-950, doi:10.5194/hess-11-939- 2007, 2007.
    • Jaun, S. and Ahrens, B.: Evaluation of a probabilistic hydrometeorological forecast system, Hydrol. Earth Syst. Sci., 13, 1031- 1043, doi:10.5194/hess-13-1031-2009, 2009.
    • Krzysztofowicz, R.: Bayesian theory of probabilistic forecasting via deterministic hydrologic model, Water Resour. Res., 35, 2739- 2750, 1999.
    • Laio, F. and Tamea, S.: Verification tools for probabilistic forecasts of continuous hydrological variables, Hydrol. Earth Syst. Sci., 11, 1267-1277, doi:10.5194/hess-11-1267-2007, 2007.
    • Lugiez, F. and Guillot, P.: Dix anne´es de pre´visions d'apports a` Electricite´ de France, IAHS-AISH P., 51, 558-566, 1960.
    • Mathevet, T.: Erreur empirique de mode`le, Note technique internet EDF-DTG D4165/NT/2010-00395-A, Internal Technical Report, p. 11, 2010.
    • Obled, C., Bontron, G., and Garc¸on, R.: Quantitative precipitation forecasts: a statistical adaptation of model outputs though an analogues sorting approach, Atmos. Res., 63, 303-324, 2002.
    • Olsson, J. and Lindstro¨m, G.: Evaluation and calibration of operational hydrological ensemble forecasts in Sweden, J. Hydrol., 350, 14-24, 2008.
    • Raftery, A. E., Gneiting, T., Balabdaoui, F., and Polakowski, M.: Using Bayesian model averaging to calibrate forecast ensembles, Mon. Weather Rev., 133, 1155-1174, 2005.
    • Ramos, M. H., Mathevet, Th., Thielen, J., and Pappenberger, F.: Communicating uncertainty in hydro-meteorological forecasts: mission impossible?, Meteorol. Appl., 17, 223-265, 2010.
    • Randrianasolo, A., Ramos, M.-H., Thirel, G., Andre´assian, V., and Martin, E.: Comparing the scores of hydrological ensemble forecasts issued by two different hydrological models, Atmos. Sci. Lett., 11, 100-107, 2010.
    • Schaake, J., Pailleux, J., Thielen, J., Arritt, R., Hamill, T., Luo, L., Martin, E., McCollor, D., and Pappenberger, F.: Summary of recommendations of the first workshop on Postprocessing and Downscaling Atmospheric Forecasts for Hydrologic Applications held at Me´te´o-France, Toulouse, France, 15-18 June 2009, Atmos. Sci. Lett., 11, 59-63, 2010.
    • Thirel, G., Rousset-Regimbeau, F., Martin, E., and Habets, F.: On the impact of short-range meteorological forecasts for ensemble stream flow predictions, J. Hydrometeorol., 9, 1301-1317, 2008.
    • Velazquez, J. A., Anctil, F., Ramos, M. H., and Perrin, C.: Can a multi-model approach improve hydrological ensemble forecasting? A study on 29 French catchments using 16 hydrological model structures, Adv. Geosci., 29, 33-42, 2011, http://www.adv-geosci.net/29/33/2011/.
    • Verkade, J. S. and Werner, M. G. F.: Estimating the benefits of single value and probability forecasting for flood warning, Hydrol. Earth Syst. Sci., 15, 3751-3765, doi:10.5194/hess-15-3751- 2011, 2011.
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    • Zhao, L., Duan, Q., Schaake, J., Ye, A., and Xia, J.: A hydrologic post-processor for ensemble streamflow predictions, Adv. Geosci., 29, 51-59, 2011, http://www.adv-geosci.net/29/51/2011/.
    • Zorita, E. and von Storch, H.: The analog method as a simple statistical downscaling technique: Comparison with more complicated methods, J. Climate, 12, 2474-2489, 1999.
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