• Atger, F., 2001: Verification of intense precipitation forecasts from single models and ensemble prediction systems. Nonlinear Processes Geophys., 8, 401417.

    • Search Google Scholar
    • Export Citation
  • Bales, J., , Wagner C. R. , , Tighe K. C. , , and Terziotti S. , 2007: LiDAR-derived flood-inundation maps for real-time flood-mapping applications, Tar River Basin, North Carolina. North Carolina Floodplain Mapping Program, USGS, Reston, VA, 52 pp. [Available online at http://pubs.usgs.gov/sir/2007/5032/pdf/SIR2007-5032.pdf.]

  • Behrangi, A., , Khakbaz B. , , Jaw T. C. , , AghaKouchak A. , , Hsu K. , , and Sorooshian S. , 2011: Hydrologic evaluation of satellite precipitation products over a mid-size basin. J. Hydrol., 397, 225237.

    • Search Google Scholar
    • Export Citation
  • Bitew, M. M., , and Gebremichael M. , 2011: Assessment of satellite rainfall products for streamflow simulation in medium watersheds of the Ethiopian highlands. Hydrol. Earth Syst. Sci., 15, 11471155.

    • Search Google Scholar
    • Export Citation
  • Burnash, R. J. C., , Ferral R. L. , , and McGuire R. A. , 1973: A generalized streamflow simulation system—Conceptual modeling for digital computers. Tech. Rep. to the Joint Federal and State River Forecast Center, U.S. National Weather Service and California Department of Water Resources, Sacramento, 204 pp.

  • Cloke, H. L., , and Pappenberger F. , 2009: Ensemble flood forecasting: A review. J. Hydrol., 375, 613626.

  • Duan, Q., , Gupta V. K. , , and Sorooshian S. , 1993: Shuffled complex evolution approach for effective and efficient global minimization. J. Optim. Theory Appl., 76, 501521.

    • Search Google Scholar
    • Export Citation
  • Gourley, J. J., , Hong Y. , , Flamig Z. L. , , Wang J. , , Vergara H. , , and Anagnostou E. N. , 2011: Hydrologic evaluation of rainfall estimates from radar, satellite, gauge, and combinations on Ft. Cobb basin, Oklahoma. J. Hydrometeor., 12, 973988.

    • Search Google Scholar
    • Export Citation
  • Gourley, J. J., , Erlingis J. M. , , Hong Y. , , and Wells E. B. , 2012: Evaluation of tools used for monitoring and forecasting flash floods in the United States. Wea. Forecasting, 27, 158173.

    • Search Google Scholar
    • Export Citation
  • Gouweleeuw, B. T., , Thielen J. , , Franchello G. , , de Roo A. P. J. , , 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
  • Guetter, A. K., , Georgakakos K. P. , , and Tsonis A. A. , 1996: Hydrologic applications of satellite data: 2. Flow simulation and soil water estimates. J. Geophys. Res., 101 (D21), 26 52726 538.

    • Search Google Scholar
    • Export Citation
  • Hong, Y., , Hsu K. L. , , Sorooshian S. , , and Gao X. , 2004: Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. J. Appl. Meteor., 43, 18341852.

    • Search Google Scholar
    • Export Citation
  • Hossain, F., , and Anagnostou E. N. , 2006a: A two-dimensional satellite rainfall error model. IEEE Trans. Geosci. Remote Sens., 44, 15111522.

    • Search Google Scholar
    • Export Citation
  • Hossain, F., , and Anagnostou E. N. , 2006b: Using a multi-dimensional satellite rainfall error model to characterize uncertainty in soil moisture fields simulated by an offline land surface model. Geophys. Res. Lett., 32, L15402, doi:10.1029/2005GL023122.

    • Search Google Scholar
    • Export Citation
  • Hossain, F., , Anagnostou E. N. , , and Dinku T. , 2004: Sensitivity analyses of satellite rainfall retrieval and sampling error on flood prediction uncertainty. IEEE Trans. Geosci. Remote Sens., 42, 130139, doi:10.1109/TGRS.2003.818341.

    • 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.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Khan, S. I., and Coauthors, 2011: 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
  • Kidd, C. K., , Kniveton D. R. , , Todd M. C. , , and Bellerby T. J. , 2003: Satellite rainfall estimation using combined passive microwave and infrared algorithms. J. Hydrometeor., 4, 10881104.

    • Search Google Scholar
    • Export Citation
  • Koren, V., , Smith M. , , Wang D. , , and Zhang Z. , 2000: Use of soil property data in the derivation of conceptual rainfall-runoff model parameters. Preprints, 15th Conf. on Hydrology, Long Beach, CA, Amer. Meteor. Soc., 2.16. [Available online at https://ams.confex.com/ams/annual2000/webprogram/Paper6074.html.]

  • Koren, V., , Smith M. , , Duan Q. , , Duan Q. , , Gupta H. , , Sorooshian S. , , Rousseau A. , , and Turcotte R. , 2003: Use of a priori parameter estimates in the derivation of 9 spatially consistent parameter sets of rainfall-runoff models. Calibration of Watershed Models, Q. Duan et al., Eds., Water Science and Application, Vol. 6, Amer. Geophys. Union, 239–254.

  • Koren, V., , Reed S. , , Smith M. , , Zhang Z. , , and Seo D. J. , 2004: Hydrology laboratory research modeling system (HL-RMS) of the US national weather service. J. Hydrol., 291, 297318.

    • Search Google Scholar
    • Export Citation
  • Kummerow, C., , Barnes W. , , Kozu T. , , Shiue J. , , and Simpson J. , 1998: The Tropical Rainfall Measuring Mission (TRMM) sensor package. J. Atmos. Oceanic Technol., 15, 809817.

    • Search Google Scholar
    • Export Citation
  • Li, L., and Coauthors, 2009: Evaluation of the real-time TRMM-based multi-satellite precipitation analysis for an operational flood prediction system in Nzoia Basin, Lake Victoria, Africa. Nat. Hazards, 50, 109123, doi:10.1007/s11069-008-9324-5.

    • Search Google Scholar
    • Export Citation
  • Maggioni, V., , Reichle R. H. , , and Anagnostou E. N. , 2011: The effect of satellite-rainfall error modeling on soil moisture prediction uncertainty. J. Hydrometeor., 12, 413428.

    • Search Google Scholar
    • Export Citation
  • Nijssen, B., , and Lettenmaier D. P. , 2004: Effect of precipitation sampling error on simulated hydrological Fluxes and states: Anticipating the Global Precipitation Measurement satellites. J. Geophys. Res., 109, D02103, doi:10.1029/2003JD003497.

    • Search Google Scholar
    • Export Citation
  • Nikolopoulos, E. I., , Anagnostou E. N. , , Hossain F. , , Gebremichael M. , , and Borga M. , 2010: Understanding the scale relationships of uncertainty propagation of satellite rainfall through a distributed hydrologic model. J. Hydrometeor., 11, 520532.

    • Search Google Scholar
    • Export Citation
  • Nikolopoulos, E. I., , Anagnostou E. N. , , and Borga M. , 2012: Using high-resolution satellite rainfall products to simulate a major flash flood event in northern Italy. J. Hydrometeor., 14, 171185.

    • Search Google Scholar
    • Export Citation
  • NWS, OHD, HL, and HSMB, 2009: Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) User's Manual V. 2.4.3. CBRFC Internal Doc., 108 pp. [Available online at http://amazon.nws.noaa.gov/articles/HRL_Pubs_PDF_May12_2009/zc/RDHM_User_Manual.pdf.]

  • Pappenberger, F., , Beven K. J. , , Hunter N. M. , , Bates P. D. , , Gouweleeuw B. T. , , Thielen J. , , and de Roo A. P. J. , 2005: Cascading model uncertainty from medium range weather forecasts (10 days) through a rainfall-runoff model to flood inundation predictions within the European Flood Forecasting System (EFFS). Hydrol. Earth Syst. Sci., 9, 381393.

    • Search Google Scholar
    • Export Citation
  • Pappenberger, F., , Bartholmes J. , , Thielen J. , , Cloke H. L. , , Buizza R. , , and de Roo A. , 2008: New dimensions in early flood warning across the globe using grand-ensemble weather predictions. Geophys. Res. Lett., 35, L10404, doi:10.1029/2008GL033837.

    • Search Google Scholar
    • Export Citation
  • Pokhrel, P., , Gupta H. V. , , and Wagener T. , 2008: A spatial regularization approach to parameter estimation for a distributed watershed model. Water Resour. Res., 44, W12419, doi:10.1029/2007WR006615.

    • Search Google Scholar
    • Export Citation
  • Seo, D. J., , and Breidenbach J. P. , 2002: Real-time correction of spatially nonuniform bias in radar rainfall data using rain gauge measurements. J. Hydrometeor., 3, 93111.

    • Search Google Scholar
    • Export Citation
  • Sorooshian, S., , Hsu K. , , Gao X. , , Gupta H. V. , , Imam B. , , and Braithwaite D. , 2000: Evaluation of PERSIANN system satellite- based estimates of tropical rainfall. Bull. Amer. Meteor. Soc., 81, 20352046.

    • Search Google Scholar
    • Export Citation
  • Su, F., , Hong Y. , , and Lettenmeaier D. P. , 2008: Evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) and its utility in hydrologic prediction in the La Plata Basin. J. Hydrometeor., 9, 622640.

    • Search Google Scholar
    • Export Citation
  • Thielen, J., , Bartholmes J. , , Ramos M.-H. , , and de Roo A. , 2009: The European flood alert system—Part 1: Concept and development. Hydrol. Earth Syst. Sci., 13, 125140.

    • Search Google Scholar
    • Export Citation
  • USGS, 2007: Lowest streamflows in more than 110 years for some North Carolina rivers as drought worsens. USGS News Release, 31 August. [Available online at http://www.usgs.gov/newsroom/article.asp?ID=1767.]

  • USGS, cited 2011: Instantaneous Data Archive. [Available online at http://nwis.waterdata.usgs.gov/nwis.]

  • Van Cooten, S., and Coauthors, 2011: The CI-FLOW Project: A system for total water level prediction from the summit to the sea. Bull. Amer. Meteor. Soc., 92, 14271442.

    • Search Google Scholar
    • Export Citation
  • Verbunt, M., , Walser A. , , Gurtz J. , , Montani A. , , and Schar C. , 2007: Probabilistic flood forecasting with a limited-area ensemble prediction system: Selected case studies. J. Hydrometeor., 8, 897909.

    • Search Google Scholar
    • Export Citation
  • Vrugt, J. A., , ter Braak C. J. F. , , Gupta H. V. , , and Robinson B. A. , 2008: Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling? Stochastic Environ. Res. Risk Assess., 23, 10111026, doi:10.1007/s00477-008-0274-y.

    • Search Google Scholar
    • Export Citation
  • Vrugt, J. A., , ter Braak C. J. F. , , Diks C. G. H. , , Robinson B. A. , , Hyman J. M. , , and Higdon D. , 2009: Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling. Int. J. Nonlinear Sci. Numer. Simul., 10, 273290.

    • Search Google Scholar
    • Export Citation
  • Yilmaz, K. K., , Gupta H. V. , , and Wagener T. , 2008: A process-based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model. Water Resour. Res., 44, W09417, doi:10.1029/2007WR006716.

    • 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.

    • Search Google Scholar
    • Export Citation
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Investigating the Applicability of Error Correction Ensembles of Satellite Rainfall Products in River Flow Simulations

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  • 1 * Department of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut
  • | 2 Hydrometeorology and Remote-Sensing Laboratory, Civil Engineering and Environmental Science, University of Oklahoma, and National Severe Storms Laboratory, NOAA, Norman, Oklahoma
  • | 3 National Severe Storms Laboratory, NOAA, Norman, Oklahoma
  • | 4 Hydrometeorology and Remote-Sensing Laboratory, Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma
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Abstract

This study uses a stochastic ensemble-based representation of satellite rainfall error to predict the propagation in flood simulation of three quasi-global-scale satellite rainfall products across a range of basin scales. The study is conducted on the Tar-Pamlico River basin in the southeastern United States based on 2 years of data (2004 and 2006). The NWS Multisensor Precipitation Estimator (MPE) dataset is used as the reference for evaluating three satellite rainfall products: the Tropical Rainfall Measuring Mission (TRMM) real-time 3B42 product (3B42RT), the Climate Prediction Center morphing technique (CMORPH), and the Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS). Both ground-measured runoff and streamflow simulations, derived from the NWS Research Distributed Hydrologic Model forced with the MPE dataset, are used as benchmarks to evaluate ensemble streamflow simulations obtained by forcing the model with satellite rainfall corrected using stochastic error simulations from a two-dimensional satellite rainfall error model (SREM2D). The ability of the SREM2D ensemble error corrections to improve satellite rainfall-driven runoff simulations and to characterize the error variability of those simulations is evaluated. It is shown that by applying the SREM2D error ensemble to satellite rainfall, the simulated runoff ensemble is able to envelope both the reference runoff simulation and observed streamflow. The best (uncorrected) product is 3B42RT, but after applying SREM2D, CMORPH becomes the most accurate of the three products in the prediction of runoff variability. The impact of spatial resolution on the rainfall-to-runoff error propagation is also evaluated for a cascade of basin scales (500–5000 km2). Results show a doubling in the bias from rainfall to runoff at all basin scales. Significant dependency to catchment area is exhibited for the random error propagation component.

Corresponding author address: Emmanouil N. Anagnostou, Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269. E-mail: manos@engr.uconn.edu

Abstract

This study uses a stochastic ensemble-based representation of satellite rainfall error to predict the propagation in flood simulation of three quasi-global-scale satellite rainfall products across a range of basin scales. The study is conducted on the Tar-Pamlico River basin in the southeastern United States based on 2 years of data (2004 and 2006). The NWS Multisensor Precipitation Estimator (MPE) dataset is used as the reference for evaluating three satellite rainfall products: the Tropical Rainfall Measuring Mission (TRMM) real-time 3B42 product (3B42RT), the Climate Prediction Center morphing technique (CMORPH), and the Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS). Both ground-measured runoff and streamflow simulations, derived from the NWS Research Distributed Hydrologic Model forced with the MPE dataset, are used as benchmarks to evaluate ensemble streamflow simulations obtained by forcing the model with satellite rainfall corrected using stochastic error simulations from a two-dimensional satellite rainfall error model (SREM2D). The ability of the SREM2D ensemble error corrections to improve satellite rainfall-driven runoff simulations and to characterize the error variability of those simulations is evaluated. It is shown that by applying the SREM2D error ensemble to satellite rainfall, the simulated runoff ensemble is able to envelope both the reference runoff simulation and observed streamflow. The best (uncorrected) product is 3B42RT, but after applying SREM2D, CMORPH becomes the most accurate of the three products in the prediction of runoff variability. The impact of spatial resolution on the rainfall-to-runoff error propagation is also evaluated for a cascade of basin scales (500–5000 km2). Results show a doubling in the bias from rainfall to runoff at all basin scales. Significant dependency to catchment area is exhibited for the random error propagation component.

Corresponding author address: Emmanouil N. Anagnostou, Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269. E-mail: manos@engr.uconn.edu
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