• Ahmad, A. U., , and Mirza M. M. Q. , 2000: Review of causes and dimensions of floods with particular reference to Flood ‘98: National perspectives. Perspectives on Flood 1998, Q. K. Ahmad et al., Eds., University Press Limited, 67–84.

    • Search Google Scholar
    • Export Citation
  • Akaike, H., 1974: A new look at the statistical model identification. IEEE Trans. Autom. Control, 19 , 716723.

  • Beven, K. J., 2000: Rainfall-Runoff Modelling: The Primer. John Wiley and Sons, Ltd., 360 pp.

  • Box, G. E. P., , and Jenkins G. M. , 1970: Time Series Analysis: Forecasting and Control. Holden-Day, Inc., 553 pp.

  • Buizza, R., , Bidlot J-R. , , Wedi N. , , Fuentes M. , , Hamrud M. , , Holt G. , , and Vitart F. , 2007: The new ECMWF VAREPS (Variable Resolution Ensemble Prediction System). Quart. J. Roy. Meteor. Soc., 133 , 681695.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Calheiros, R. V., , and Zawadzki I. I. , 1987: Reflectivity–rain rate relationships for radar hydrology in Brazil. J. Climate Appl. Meteor., 26 , 118132.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • CEGIS, 2006: Sustainable end-to-end climate/flood forecast application through pilot projects showing measurable improvements. CEGIS Base Line Rep., 78 pp.

    • Search Google Scholar
    • Export Citation
  • Central Water Commission, 1988: Water resources of India. CWC Publication 30/88.

  • Chatfield, C., 1996: The Analysis of Time Series: An Introduction. Chapman & Hall, 283 pp.

  • Chow, V. T., , Maidment D. R. , , and Mays L. W. , 1988: Applied Hydrology. McGraw-Hill, 572 pp.

  • Davis, J. C., 1986: Statistics and Data Analysis in Geology. 2nd ed. John Wiley, 646 pp.

  • Georgakakos, K. P., , Seo D-J. , , Gupta H. , , Schaake J. , , and Butts M. B. , 2004: Towards the characterization of streamflow simulation uncertainty through multimodel ensembles. J. Hydrol., 298 , 222241.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Granger, C. W. J., , and Newbold P. , 1986: Forecasting Economic Time Series. 2nd ed. Academic Press, 338 pp.

  • Haltiner, J. P., , and Salas J. D. , 1988: Short-term forecasting of snowmelt runoff using ARMAX models. Water Resour. Bull., 24 , 10831089.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Helsel, D. R., , and Hirsch R. M. , 1992: Statistical methods in water resources. Statistical Methods in Water Resources, Studies in Environmental Science Series, Vol. 49, Elsevier, 522 pp.

    • Search Google Scholar
    • Export Citation
  • Hopson, T. M., 2005: Operational flood-forecasting for Bangladesh. Ph.D. thesis, University of Colorado, 225 pp.

  • Huber, P. J., 1981: Robust Statistics. Wiley, 308 pp.

  • Huffman, G. J., , Adler R. F. , , Curtis S. , , Bolvin D. T. , , and Nelkin E. J. , 2005: Global rainfall analyses at monthly and 3-hr time scales. Measuring Precipitation from Space: EURAINSAT and the Future, V. Levizzani, P. Bauer, and J. F. Turk, Eds., Springer, 722 pp.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and Coauthors, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8 , 3855.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jian, J., , Webster P. J. , , and Hoyos C. D. , 2009: Large-scale controls on Ganges and Brahmaputra river discharge on intraseasonal and seasonal time-scales. Quart. J. Roy. Meteor. Soc., 135 (639) 353370.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joyce, R. J., , Janowiak J. E. , , Arkin P. A. , , and Xie P. , 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5 , 487503.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Katz, R. W., 1981: On some criteria for estimating the order of a Markov chain. Technometrics, 23 , 243249.

  • Krishnamurti, T. N., , Kishtawal C. M. , , LaRow T. E. , , Bachiochi D. R. , , Zhang Z. , , Williford C. E. , , Gadgil S. , , and Surendran S. , 1999: Improved Weather and Seasonal Climate Forecasts from Multimodel Superensemble. Science, 285 , 15481550.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lees, M., , Young P. , , Ferguson S. , , Beven K. , , and Burns J. , 1994: An adaptive flood warning scheme for the River Nith at Dumfries. Second International Conference on River Flood Hydraulics, W. R. White and J. Watts, Eds., Wiley and Sons, 65–75.

    • Search Google Scholar
    • Export Citation
  • Marco, J. B., , Harboe R. , , and Salas J. D. , Eds. 1993: Stochastic Hydrology and Its Use in Water Resources Systems Simulation and Optimization. NATO Advanced Study Institute Series, Vol. 237, Kluwer Academic Publishers, 483 pp.

    • Search Google Scholar
    • Export Citation
  • Mirza, M. M. Q., 2003: The Choice of Stage-Discharge Relationship for the Ganges and Brahmaputra Rivers in Bangladesh. Nord. Hydrol., 34 , 321342.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mirza, M. M. Q., 2004: The Ganges Water Diversion: Environmental Effects and Implications. Water Science and Technology Library Series, Vol. 49, Kluwer Academic Publishers, 364 pp.

    • Search Google Scholar
    • Export Citation
  • Molteni, F., , Buizza R. , , Palmer T. N. , , and Petroliagis T. , 1996: The ECMWF Ensemble Prediction System: Methodology and validation. Quart. J. Roy. Meteor. Soc., 122 , 73119.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nash, J. E., 1958: The form of the instantaneous unit hydrograph. Surface Water, Prevision, Evaporation, IASH Publication 45, Vol. 3–4, IAHS, 114–121.

    • Search Google Scholar
    • Export Citation
  • Nelder, J. A., , and Mead R. , 1965: A simplex method for function minimization. Comput. J., 7 , 308313.

  • Press, W. H., , Teukolsky S. A. , , Vetterling W. T. , , and Flannery B. P. , 1992: Numerical Recipes in FORTRAN: The Art of Scientific Computing. Cambridge University Press, 963 pp.

    • Search Google Scholar
    • Export Citation
  • Riverside Technology Inc. and EGIS, 2000: Main report. Vol. 1, Information for flood management in Bangladesh, Report for CARE-Bangladesh, 131 pp.

    • Search Google Scholar
    • Export Citation
  • Schwarz, G., 1978: Estimating the dimension of a model. Ann. Stat., 6 , 461464.

  • Sherman, L. K., 1932: Streamflow from rainfall by unit-graph method. Eng. News-Rec., 108 , 501505.

  • Singh, V. P., , Sharma N. , , and Ojha C. S. P. , 2004: Brahmaputra Basin Water Resources. Water Science and Technology Library Series, Vol. 47, Kluwer Academic Publishers, 632 pp.

    • Search Google Scholar
    • Export Citation
  • Taylor, J. R., 1982: An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements. University Science Books, 270 pp.

    • Search Google Scholar
    • Export Citation
  • Thornthwaite, C. W., , and Holzman B. , 1939: The determination of evaporation from land and water surfaces. Mon. Wea. Rev., 67 , 411.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Webster, P. J., , Hopson T. , , Hoyos C. , , Subbiah A. , , Chang H-R. , , and Grossman R. , 2006: A three-tier overlapping prediction scheme: Tools for strategic and tactical decisions in the developing world. Predictability of Weather and Climate, T. N. Palmer and R. Hagedorn, Eds., Cambridge University Press, 645–673.

    • Search Google Scholar
    • Export Citation
  • Webster, P. J., and Coauthors, 2010: Extended-range probabilistic forecasts of Ganges and Brahmaputra floods in Bangladesh. Bull. Amer. Meteor. Soc., in press.

    • Search Google Scholar
    • Export Citation
  • Xie, P. P., , Rudolf B. , , Schneider U. , , and Arkin P. A. , 1996: Gauge-based monthly analysis of global land precipitation from 1971 to 1994. J. Geophys. Res., 101 , (D14). 1902319034.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yates, D., , Gangopadhyay S. , , Rajagopalan B. , , and Strzepek K. , 2003: A technique for generating regional climate scenarios using a nearest-neighbor algorithm. Water Resour. Res., 39 , 1199. doi:10.1029/2002WR001769.

    • Search Google Scholar
    • Export Citation
  • Young, P. C., 2002: Advances in real-time flood forecasting. Philos. Trans. Roy. Soc. London, A360 , 14331450.

  • Young, P. C., , and Beven K. J. , 1994: Data-based mechanistic modelling and the rainfall-flow non-linearity. Environmetrics, 5 , 335363.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 91 91 22
PDF Downloads 80 80 17

A 1–10-Day Ensemble Forecasting Scheme for the Major River Basins of Bangladesh: Forecasting Severe Floods of 2003–07

View More View Less
  • 1 Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, Colorado
  • | 2 Schools of Earth and Atmospheric Sciences and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia
© Get Permissions
Restricted access

Abstract

This paper describes a fully automated scheme that has provided calibrated 1–10-day ensemble river discharge forecasts and predictions of severe flooding of the Brahmaputra and Ganges Rivers as they flow into Bangladesh; it has been operational since 2003. The Bangladesh forecasting problem poses unique challenges because of the frequent life-threatening flooding of the country and because of the absence of upstream flow data from India means that the Ganges and Brahmaputra basins must be treated as if they are ungauged. The meteorological–hydrological forecast model is a hydrologic multimodel initialized by NASA and NOAA precipitation products, whose states and fluxes are forecasted forward using calibrated European Centre for Medium-Range Weather Forecasts ensemble prediction system products, and conditionally postprocessed to produce calibrated probabilistic forecasts of river discharge at the entrance points of the Ganges and Brahmaputra into Bangladesh. Forecasts with 1–10-day horizons are presented for the summers of 2003–07. Objective verification shows that the forecast system significantly outperforms both a climatological and persistence forecast at all lead times. All severe flooding events were operationally forecast with significant probability at the 10-day horizon, including the extensive flooding of the Brahmaputra in 2004 and 2007, with the latter providing advanced lead-time warnings for the evacuation of vulnerable residents.

+ Current affiliation: Advanced Study Program and Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado

Corresponding author address: Thomas M. Hopson, NCAR, P.O. Box 3000, Boulder, CO 80307-3000. Email: hopson@ucar.edu

Abstract

This paper describes a fully automated scheme that has provided calibrated 1–10-day ensemble river discharge forecasts and predictions of severe flooding of the Brahmaputra and Ganges Rivers as they flow into Bangladesh; it has been operational since 2003. The Bangladesh forecasting problem poses unique challenges because of the frequent life-threatening flooding of the country and because of the absence of upstream flow data from India means that the Ganges and Brahmaputra basins must be treated as if they are ungauged. The meteorological–hydrological forecast model is a hydrologic multimodel initialized by NASA and NOAA precipitation products, whose states and fluxes are forecasted forward using calibrated European Centre for Medium-Range Weather Forecasts ensemble prediction system products, and conditionally postprocessed to produce calibrated probabilistic forecasts of river discharge at the entrance points of the Ganges and Brahmaputra into Bangladesh. Forecasts with 1–10-day horizons are presented for the summers of 2003–07. Objective verification shows that the forecast system significantly outperforms both a climatological and persistence forecast at all lead times. All severe flooding events were operationally forecast with significant probability at the 10-day horizon, including the extensive flooding of the Brahmaputra in 2004 and 2007, with the latter providing advanced lead-time warnings for the evacuation of vulnerable residents.

+ Current affiliation: Advanced Study Program and Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado

Corresponding author address: Thomas M. Hopson, NCAR, P.O. Box 3000, Boulder, CO 80307-3000. Email: hopson@ucar.edu

Supplementary Materials

    • Supplemental Materials (DOC 3.64 MB)
Save