Hydrometeorological Analysis and Remote Sensing of Extremes: Was the July 2012 Beijing Flood Event Detectable and Predictable by Global Satellite Observing and Global Weather Modeling Systems?

Yu Zhang * School of Civil Engineering and Environmental Science, and Advanced Radar Research Center, and Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

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Yang Hong * School of Civil Engineering and Environmental Science, and Advanced Radar Research Center, and Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

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Xuguang Wang School of Meteorology, and Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

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Jonathan J. Gourley NOAA/National Severe Storms Laboratory, National Weather Center, Norman, Oklahoma

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Xianwu Xue School of Civil Engineering and Environmental Science, and Advanced Radar Research Center, University of Oklahoma, Norman, Oklahoma

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Manabendra Saharia School of Civil Engineering and Environmental Science, and Advanced Radar Research Center, University of Oklahoma, Norman, Oklahoma

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Guangheng Ni Department of Hydraulic Engineering, Tsinghua University, Beijing, China

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Gaili Wang ** State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Science, Beijing, China

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Yong Huang Anhui Meteorological Bureau, Hefei, China

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Sheng Chen School of Civil Engineering and Environmental Science, and Advanced Radar Research Center, University of Oklahoma, Norman, Oklahoma

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Guoqiang Tang Department of Hydraulic Engineering, Tsinghua University, Beijing, China

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Abstract

Prediction, and thus preparedness, in advance of flood events is crucial for proactively reducing their impacts. In the summer of 2012, Beijing, China, experienced extreme rainfall and flooding that caused 79 fatalities and economic losses of $1.6 billion. Using rain gauge networks as a benchmark, this study investigated the detectability and predictability of the 2012 Beijing event via the Global Hydrological Prediction System (GHPS), forced by the NASA Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis at near–real time and by the deterministic and ensemble precipitation forecast products from the NOAA Global Forecast System (GFS) at several lead times. The results indicate that the disastrous flooding event was detectable by the satellite-based global precipitation observing system and predictable by the GHPS forced by the GFS 4 days in advance. However, the GFS demonstrated inconsistencies from run to run, limiting the confidence in predicting the extreme event. The GFS ensemble precipitation forecast products from NOAA for streamflow forecasts provided additional information useful for estimating the probability of the extreme event. Given the global availability of satellite-based precipitation in near–real time and GFS precipitation forecast products at varying lead times, this study demonstrates the opportunities and challenges that exist for an integrated application of GHPS. This system is particularly useful for the vast ungauged regions of the globe.

Corresponding author address: Dr. Yang Hong, National Weather Center, ARRC Suite 4610, 120 David L. Boren Blvd., Norman, OK 73072. E-mail: yanghong@ou.edu

Abstract

Prediction, and thus preparedness, in advance of flood events is crucial for proactively reducing their impacts. In the summer of 2012, Beijing, China, experienced extreme rainfall and flooding that caused 79 fatalities and economic losses of $1.6 billion. Using rain gauge networks as a benchmark, this study investigated the detectability and predictability of the 2012 Beijing event via the Global Hydrological Prediction System (GHPS), forced by the NASA Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis at near–real time and by the deterministic and ensemble precipitation forecast products from the NOAA Global Forecast System (GFS) at several lead times. The results indicate that the disastrous flooding event was detectable by the satellite-based global precipitation observing system and predictable by the GHPS forced by the GFS 4 days in advance. However, the GFS demonstrated inconsistencies from run to run, limiting the confidence in predicting the extreme event. The GFS ensemble precipitation forecast products from NOAA for streamflow forecasts provided additional information useful for estimating the probability of the extreme event. Given the global availability of satellite-based precipitation in near–real time and GFS precipitation forecast products at varying lead times, this study demonstrates the opportunities and challenges that exist for an integrated application of GHPS. This system is particularly useful for the vast ungauged regions of the globe.

Corresponding author address: Dr. Yang Hong, National Weather Center, ARRC Suite 4610, 120 David L. Boren Blvd., Norman, OK 73072. E-mail: yanghong@ou.edu
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  • Adhikari, P., Hong Y. , Douglas K. , Kirschbaum D. , Gourley J. , Adler R. , and R. Brakenridge G. , 2010: A digitized global flood inventory (1998–2008): Compilation and preliminary results. Nat. Hazards, 55, 405422, doi:10.1007/s11069-010-9537-2.

    • Search Google Scholar
    • Export Citation
  • Bartholmes, J., and Todini E. , 2005: Coupling meteorological and hydrological models for flood forecasting. Hydrol. Earth Syst. Sci., 9, 333346, doi:10.5194/hess-9-333-2005.

    • Search Google Scholar
    • Export Citation
  • Bradley, A. A., Hashino T. , and Schwartz S. S. , 2003: Distributions-oriented verification of probability forecasts for small data samples. Wea. Forecasting, 18, 903917, doi:10.1175/1520-0434(2003)018<0903:DVOPFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bradley, A. A., Schwartz S. S. , and Hashino T. , 2004: Distributions-oriented verification of ensemble streamflow predictions. J. Hydrometeor., 5, 532545, doi:10.1175/1525-7541(2004)005<0532:DVOESP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Brakenridge, G. R., Nghiem S. V. , Anderson E. , and Mic R. , 2007: Orbital microwave measurement of river discharge and ice status. Water Resour. Res., 43, W04405, doi:10.1029/2006WR005238.

    • Search Google Scholar
    • Export Citation
  • Brown, J. D., Demargne J. , Seo D.-J. , and Liu Y. , 2010: The Ensemble Verification System (EVS): A software tool for verifying ensemble forecasts of hydrometeorological and hydrologic variables at discrete locations. Environ. Modell. Software, 25, 854872, doi:10.1016/j.envsoft.2010.01.009.

    • Search Google Scholar
    • Export Citation
  • Brown, J. D., Seo D.-J. , and Du J. , 2012: Verification of precipitation forecasts from NCEP’s Short-Range Ensemble Forecast (SREF) system with reference to ensemble streamflow prediction using lumped hydrologic models. J. Hydrometeor., 13, 808836, doi:10.1175/JHM-D-11-036.1.

    • Search Google Scholar
    • Export Citation
  • Buizza, R., 2008: The value of probabilistic prediction. Atmos. Sci. Lett., 9, 3642, doi:10.1002/asl.170.

  • Cloke, H., and Pappenberger F. , 2009: Ensemble flood forecasting: A review. J. Hydrol., 375, 613626, doi:10.1016/j.jhydrol.2009.06.005.

    • Search Google Scholar
    • Export Citation
  • Demargne, J., and Coauthors, 2009: Application of forecast verification science to operational river forecasting in the U.S. National Weather Service. Bull. Amer. Meteor. Soc., 90, 779784, doi:10.1175/2008BAMS2619.1.

    • Search Google Scholar
    • Export Citation
  • Demargne, J., and Coauthors, 2014: The Science of NOAA’s operational Hydrologic Ensemble Forecast Service. Bull. Amer. Meteor. Soc., 95, 79–98, doi:10.1175/BAMS-D-12-00081.1.

    • Search Google Scholar
    • Export Citation
  • Gneiting, T., Balabdaoui F. , and Raftery A. E. , 2007: Probabilistic forecasts, calibration and sharpness. J. Roy. Stat. Soc., 69B, 243268, doi:10.1111/j.1467-9868.2007.00587.x.

    • Search Google Scholar
    • Export Citation
  • Gouweleeuw, B., Thielen J. , Franchello G. , Roo A. D. , and Buizza R. , 2005: Flood forecasting using medium-range probabilistic weather prediction. Hydrol. Earth Syst. Sci., 9, 365380, doi:10.5194/hess-9-365-2005.

    • Search Google Scholar
    • Export Citation
  • Han, J., and Pan H. L. , 2011: Revision of convection and vertical diffusion schemes in the NCEP global forecast system. Wea. Forecasting, 26, 520533, doi:10.1175/WAF-D-10-05038.1.

    • Search Google Scholar
    • Export Citation
  • Hlavcova, K., Szolgay J. , Kubes R. , Kohnova S. , and Zvolenský M. , 2006: Routing of numerical weather predictions through a rainfall–runoff model. Transboundary Floods: Reducing Risks through Flood Management, J. Marsalek, G. Stancalie, and G. Balint, Eds., Springer, 79–90, doi:10.1007/1-4020-4902-1_8.

  • Hong, Y., Adler R. F. , Hossain F. , Curtis S. , and Huffman G. J. , 2007: A first approach to global runoff simulation using satellite rainfall estimation. Water Resour. Res., 43, W08502, doi:10.1029/2006WR005739.

    • Search Google Scholar
    • Export Citation
  • Hopson, T. M., and Webster P. J. , 2010: A 1–10-day ensemble forecasting scheme for the major river basins of Bangladesh: Forecasting severe floods of 2003–07. J. Hydrometeor., 11, 618641, doi:10.1175/2009JHM1006.1.

    • 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, doi:10.1175/JHM560.1.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., Adler R. F. , Bolvin D. T. , and Nelkin E. J. , 2010: The TRMM multi-satellite precipitation analysis (TMPA). Satellite Rainfall Applications for Surface Hydrology, M. Gebremichael and F. Hossain, Eds., Springer, 3–22, doi:10.1007/978-90-481-2915-7_1.

  • Jolliffe, I. T., and Stephenson D. B. , Eds., 2011: Forecast Verification: A Practitioner’s Guide in Atmospheric Science. 2nd ed. John Wiley & Sons, 292 pp.

  • Kanamitsu, M., and Coauthors, 1991: Recent changes implemented into the global forecast system at NMC. Wea. Forecasting, 6, 425435, doi:10.1175/1520-0434(1991)006<0425:RCIITG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Khan, S. I., and Coauthors, 2011a: Hydroclimatology of Lake Victoria region using hydrologic model and satellite remote sensing data. Hydrol. Earth Syst. Sci., 15, 107117, doi:10.5194/hess-15-107-2011.

    • Search Google Scholar
    • Export Citation
  • Khan, S. I., and Coauthors, 2011b: Satellite remote sensing and hydrologic modeling for flood inundation mapping in Lake Victoria basin: Implications for hydrologic prediction in ungauged basins. IEEE Trans. Geosci. Remote Sens.,49, 8595, doi:10.1109/TGRS.2010.2057513.

    • Search Google Scholar
    • Export Citation
  • Liang, X., Lettenmaier D. P. , Wood E. F. , and Burges S. J. , 1994: A simple hydrologically based model of land-surface water and energy fluxes for general-circulation models. J. Geophys. Res., 99, 14 41514 428, doi:10.1029/94JD00483.

    • Search Google Scholar
    • Export Citation
  • Nijssen, B., Lettenmaier D. P. , Liang X. , Wetzel S. W. , and Wood E. F. , 1997: Streamflow simulation for continental-scale river basins. Water Resour. Res., 33, 711724, doi:10.1029/96WR03517.

    • Search Google Scholar
    • Export Citation
  • Pappenberger, F., Scipal K. , and Buizza R. , 2008: Hydrological aspects of meteorological verification. Atmos. Sci. Lett., 9, 4352, doi:10.1002/asl.171.

    • Search Google Scholar
    • Export Citation
  • Schaake, J. C., Hamill T. M. , Buizza R. , and Clark M. , 2007: HEPEX: The Hydrological Ensemble Prediction Experiment. Bull. Amer. Meteor. Soc., 88, 15411547, doi:10.1175/BAMS-88-10-1541.

    • Search Google Scholar
    • Export Citation
  • Seo, D. J., Herr H. D. , and Schaake J. C. , 2006: A statistical post-processor for accounting of hydrologic uncertainty in short-range ensemble streamflow prediction. Hydrol. Earth Syst. Sci. Discuss., 3, 19872035, doi:10.5194/hessd-3-1987-2006.

    • Search Google Scholar
    • Export Citation
  • Smith, K., and Ward R. , 1998: Floods: Physical Processes and Human Impacts. Wiley, 394 pp.

  • Theis, S., Hense A. , and Damrath U. , 2005: Probabilistic precipitation forecasts from a deterministic model: A pragmatic approach. Meteor. Appl., 12, 257268, doi:10.1017/S1350482705001763.

    • Search Google Scholar
    • Export Citation
  • Wang, J., and Coauthors, 2011: The coupled routing and excess storage (CREST) distributed hydrological model. Hydrol. Sci. J., 56, 8498, doi:10.1080/02626667.2010.543087.

    • Search Google Scholar
    • Export Citation
  • Wang, X., 2010: Incorporating ensemble covariance in the gridpoint statistical interpolation variational minimization: A mathematical framework. Mon. Wea. Rev., 138, 29902995, doi:10.1175/2010MWR3245.1.

    • Search Google Scholar
    • Export Citation
  • Wang, X., Parrish D. , Kleist D. , and Whitaker J. , 2013: GSI 3DVar-based ensemble–variational hybrid data assimilation for NCEP Global Forecast System: Single-resolution experiments. Mon. Wea. Rev., 141, 40984117, doi:10.1175/MWR-D-12-00141.1.

    • Search Google Scholar
    • Export Citation
  • Westerhoff, R. S., Kleuskens M. P. H. , Winsemius H. C. , Huizinga H. J. , Brakenridge G. R. , and Bishop C. , 2013: Automated global water mapping based on wide-swath orbital synthetic-aperture radar. Hydrol. Earth Syst. Sci., 17, 651663, doi:10.5194/hess-17-651-2013.

    • Search Google Scholar
    • Export Citation
  • Wu, H., Adler R. F. , Hong Y. , Tian Y. , and Policelli F. , 2012: Evaluation of global flood detection using satellite-based rainfall and a hydrologic model. J. Hydrometeor., 13, 12681284, doi:10.1175/JHM-D-11-087.1.

    • Search Google Scholar
    • Export Citation
  • Yang, F., Pan H. L. , Krueger S. K. , Moorthi S. , and Lord S. J. , 2006: Evaluation of the NCEP Global Forecast System at the ARM SGP site. Mon. Wea. Rev., 134, 36683690, doi:10.1175/MWR3264.1.

    • Search Google Scholar
    • Export Citation
  • Yilmaz, K. K., Adler R. F. , Tian Y. , Hong Y. , and Pierce H. F. , 2010: Evaluation of a satellite-based global flood monitoring system. Int. J. Remote Sens., 31, 37633782, doi:10.1080/01431161.2010.483489.

    • Search Google Scholar
    • Export Citation
  • Zappa, M., Fundel F. , and Jaun S. , 2013: A ‘Peak-Box’ approach for supporting interpretation and verification of operational ensemble peak-flow forecasts. Hydrol. Processes, 27, 117131, doi:10.1002/hyp.9521.

    • Search Google Scholar
    • Export Citation
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