• Abrahart, R. J., , and See L. , 2002: Multi-model data fusion for river flow forecasting: An evaluation of six alternative methods based on two contrasting catchments. Hydrol. Earth Syst. Sci., 6 , 655670.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ajami, N. K., , Gupta H. , , Wagener T. , , and Sorooshian S. , 2004: Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system. J. Hydrol., 298 , 112135.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bates, J. M., , and Granger C. W. J. , 1969: The combination of forecasts. Oper. Res. Quart., 20 , 451468.

  • Beven, K. J., , and Freer J. , 2001: Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. J. Hydrol., 249 , 1129.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Butts, M. B., , Payne J. T. , , Kristensen M. , , and Madsen H. , 2004a: An evaluation of the impact of model structure on hydrological modelling uncertainty for streamflow prediction. J. Hydrol., 298 , 242266.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Butts, M. B., , Payne J. T. , , and Overgaard J. , 2004b: Improving streamflow simulations and flood forecasts with multi-model ensembles. Proceedings of the 6th International Conference on Hydroinformatics, S. Y. Liong, K. K. Phoon, and V. Babovic, Eds., World Scientific, 1189–1196.

    • Search Google Scholar
    • Export Citation
  • Clemen, R. T., 1989: Combining forecasts: A review and annotated bibliography. Int. J. Forecasting, 5 , 559583.

  • Dickinson, J. P., 1973: Some statistical results in the combination of forecast. Oper. Res. Quart., 24 , 253260.

  • Dickinson, J. P., 1975: Some comments on the combination of forecasts. Oper. Res. Quart., 26 , 205210.

  • Fraedrich, K., , and Smith N. R. , 1989: Combining predictive schemes in long-range forecasting. J. Climate, 2 , 291294.

  • Georgakakos, K. P., , Seo D. J. , , Gupta H. , , Schake J. , , and Butts M. B. , 2004: Characterizing streamflow simulation uncertainty through multimodel ensembles. J. Hydrol., 298 , 222241.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoeting, J. A., , Madigan D. , , Raftery A. E. , , and Volinsky C. T. , 1999: Bayesian model averaging: A tutorial. Stat. Sci., 14 , 382417.

  • Hogue, T. S., , Sorooshian S. , , Gupta V. K. , , Holz A. , , and Braatz D. , 2000: A multistep automatic calibration scheme for river forecasting models. J. Hydrometeor., 1 , 524542.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kharin, V. V., , and Zwiers F. W. , 2002: Climate predictions with multimodel ensembles. J. Climate, 15 , 793799.

  • Krishnamurti, T. N., , Kishtawal C. M. , , LaRow T. , , Bachiochi D. , , Zhang Z. , , Williford C. E. , , Gadgil S. , , and Surendran S. , 1999: Improved skill of weather and seasonal climate forecasts from multimodel superensemble. Science, 285 , 15481550.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., , Kishtawal C. M. , , Shin D. W. , , and Williford C. E. , 2000a: Improving tropical precipitation forecasts from a multianalysis superensemble. J. Climate, 13 , 42174227.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., , Kishtawal C. M. , , Zhang Z. , , LaRow T. , , Bachiochi D. , , and Williford C. E. , 2000b: Multimodel ensemble forecasts for weather and seasonal climate. J. Climate, 13 , 41964216.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., and Coauthors, 2001: Real-time multianalysis-multimodel superensemble forecasts of precipitation using TRMM and SMM/I products. Mon. Wea. Rev., 129 , 28612883.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., and Coauthors, 2002: Superensemble forecasts for weather and climate. Proc. ECMWF Seminar on Predictability of Weather and Climate, Reading, United Kingdom, ECMWF.

  • Maurer, E. P., , O’Donnell G. M. , , Lettenmaier D. P. , , and Roads J. O. , 2001: Evaluation of the land surface water budget in NCEP/NCAR and NCEP/DOE reanalyses using an off-line hydrologic model. J. Geophys. Res., 106 , D16. 1784117862.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mayers, M., , Krishnamurti T. N. , , Depradine C. , , and Moseley L. , 2001: Numerical weather prediction over the eastern Caribbean using Florida State University (FSU) global and regional spectral models and multi-model/multi-analysis super-ensemble. Meteor. Atmos. Phys., 78 , 7588.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Newbold, P., , and Granger C. W. J. , 1974: Experience with forecasting univariate time series and the combination of forecasts. J. Roy. Stat. Soc., 137A , 131146.

    • Search Google Scholar
    • Export Citation
  • Reed, S., , Koren V. , , Smith M. , , Zhang Z. , , Moreda F. , , and Seo D. J. , and DMIP Participants, 2004: Overall distributed model intercomparison project results. J. Hydrol., 298 , 2760.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Russo, R., , Peano A. , , Becchi I. , , and Bemporad G. A. , 1994: Advances in Distributed Hydrology. Water Resources Publications, 416 pp.

  • Shamseldin, A. Y., , and O’Connor K. M. , 1999: A real-time combination method for the outputs of different rainfall–runoff models. Hydrol. Sci. J., 44 , 895912.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shamseldin, A. Y., , O’Connor K. M. , , and Liang G. C. , 1997: Methods for combining the outputs of different rainfall–runoff models. J. Hydrol., 197 , 203229.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Singh, V. P., 1995: Computer Models of Watershed Hydrology. Water Resources Publications, 1144 pp.

  • Singh, V. P., , and Frevert D. K. , 2002a: Mathematical Models of Large Watershed Hydrology. Water Resources Publications, 914 pp.

  • Singh, V. P., , and Frevert D. K. , 2002b: Mathematical Modeling of Small Watershed Hydrology and Applications. Water Resources Publications, 972 pp.

    • Search Google Scholar
    • Export Citation
  • Smith, M. B., , Seo D-J. , , Koren V. I. , , Reed S. , , Zhang Z. , , Duan Q. , , Moreda F. , , and Cong S. , 2004: The distributed model intercomparison project (DMIP): Motivation and experiment design. J. Hydrol., 298 , 426.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, P. D., 1976: How to improve accuracy by combining independent forecasts. Mon. Wea. Rev., 105 , 228229.

  • Vieux, B. E., 2001: Distributed Hydrologic Modeling Using GIS. Kluwer Academic, 294 pp.

  • Winkler, R. L., 1989: Combining forecasts: A philosophical basis and some current issues. Int. J. Forecasting, 5 , 605609.

  • Xiong, L. H., , Shamseldin A. Y. , , and O’Connor K. M. , 2001: A non-linear combination of the forecasts of rainfall–runoff models by the first-order Takagi–Sugeno fuzzy system. J. Hydrol., 245 , 196217.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yun, W. T., , Stefanova L. , , and Krishnamurti T. N. , 2003: Improvement of the multimodel superensemble technique for seasonal forecasts. J. Climate, 16 , 38343840.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Multimodel Combination Techniques for Analysis of Hydrological Simulations: Application to Distributed Model Intercomparison Project Results

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  • 1 University of California, Irvine, Irvine, California
  • | 2 Lawrence Livermore National Laboratory, Livermore, California
  • | 3 University of California, Irvine, Irvine, California
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Abstract

This paper examines several multimodel combination techniques that are used for streamflow forecasting: the simple model average (SMA), the multimodel superensemble (MMSE), modified multimodel superensemble (M3SE), and the weighted average method (WAM). These model combination techniques were evaluated using the results from the Distributed Model Intercomparison Project (DMIP), an international project sponsored by the National Weather Service (NWS) Office of Hydrologic Development (OHD). All of the multimodel combination results were obtained using uncalibrated DMIP model simulations and were compared against the best-uncalibrated as well as the best-calibrated individual model results. The purpose of this study is to understand how different combination techniques affect the accuracy levels of the multimodel simulations. This study revealed that the multimodel simulations obtained from uncalibrated single-model simulations are generally better than any single-member model simulations, even the best-calibrated single-model simulations. Furthermore, more sophisticated multimodel combination techniques that incorporated bias correction step work better than simple multimodel average simulations or multimodel simulations without bias correction.

Corresponding author address: Newsha K. Ajami, Dept. of Civil and Environmental Engineering, University of California, Irvine, E/4130 Engineering Gateway, Irvine, CA 82697-2175. Email: nkhodata@uci.edu

Abstract

This paper examines several multimodel combination techniques that are used for streamflow forecasting: the simple model average (SMA), the multimodel superensemble (MMSE), modified multimodel superensemble (M3SE), and the weighted average method (WAM). These model combination techniques were evaluated using the results from the Distributed Model Intercomparison Project (DMIP), an international project sponsored by the National Weather Service (NWS) Office of Hydrologic Development (OHD). All of the multimodel combination results were obtained using uncalibrated DMIP model simulations and were compared against the best-uncalibrated as well as the best-calibrated individual model results. The purpose of this study is to understand how different combination techniques affect the accuracy levels of the multimodel simulations. This study revealed that the multimodel simulations obtained from uncalibrated single-model simulations are generally better than any single-member model simulations, even the best-calibrated single-model simulations. Furthermore, more sophisticated multimodel combination techniques that incorporated bias correction step work better than simple multimodel average simulations or multimodel simulations without bias correction.

Corresponding author address: Newsha K. Ajami, Dept. of Civil and Environmental Engineering, University of California, Irvine, E/4130 Engineering Gateway, Irvine, CA 82697-2175. Email: nkhodata@uci.edu

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