Assessing the Skill of Medium-Range Ensemble Precipitation and Streamflow Forecasts from the Hydrologic Ensemble Forecast Service (HEFS) for the Upper Trinity River Basin in North Texas

Sunghee Kim Department of Civil Engineering, The University of Texas at Arlington, Arlington, Texas

Search for other papers by Sunghee Kim in
Current site
Google Scholar
PubMed
Close
,
Hossein Sadeghi Department of Civil Engineering, The University of Texas at Arlington, Arlington, Texas

Search for other papers by Hossein Sadeghi in
Current site
Google Scholar
PubMed
Close
,
Reza Ahmad Limon Department of Civil Engineering, The University of Texas at Arlington, Arlington, Texas

Search for other papers by Reza Ahmad Limon in
Current site
Google Scholar
PubMed
Close
,
Manabendra Saharia Department of Civil Engineering, The University of Texas at Arlington, Arlington, Texas

Search for other papers by Manabendra Saharia in
Current site
Google Scholar
PubMed
Close
,
Dong-Jun Seo Department of Civil Engineering, The University of Texas at Arlington, Arlington, Texas

Search for other papers by Dong-Jun Seo in
Current site
Google Scholar
PubMed
Close
,
Andrew Philpott West Gulf River Forecast Center, NOAA/NWS, Fort Worth, Texas

Search for other papers by Andrew Philpott in
Current site
Google Scholar
PubMed
Close
,
Frank Bell West Gulf River Forecast Center, NOAA/NWS, Fort Worth, Texas

Search for other papers by Frank Bell in
Current site
Google Scholar
PubMed
Close
,
James Brown Hydrologic Solutions Limited, Southampton, United Kingdom

Search for other papers by James Brown in
Current site
Google Scholar
PubMed
Close
, and
Minxue He Hydrology Branch, California Department of Water Resources, Sacramento, California

Search for other papers by Minxue He in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

To issue early warnings for the public to act, for emergency managers to take preventive actions, and for water managers to operate their systems cost-effectively, it is necessary to maximize the time horizon over which streamflow forecasts are skillful. In this work, we assess the value of medium-range ensemble precipitation forecasts generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) in increasing the lead time and skill of streamflow forecasts for five headwater basins in the upper Trinity River basin in north-central Texas. The HEFS uses ensemble mean precipitation forecasts from the Global Ensemble Forecast System (GEFS) of the National Centers for Environment Prediction (NCEP). For comparative evaluation, we verify ensemble streamflow forecasts generated with the HEFS forced by the GEFS forecast with those forced by the short-range quantitative precipitation forecasts (QPFs) from the NWS West Gulf River Forecast Center (WGRFC) based on guidance from the NCEP’s Weather Prediction Center. We also assess the benefits of postprocessing the raw ensemble streamflow forecasts and evaluate the impact of selected parameters within the HEFS on forecast quality. The results show that the use of medium-range precipitation forecasts from the GEFS with the HEFS extends the time horizon for skillful forecasting of mean daily streamflow by 1–3 days for significant events when compared with using only the 72-h River Forecast Center (RFC) QPF with the HEFS. The HEFS forced by the GEFS also improves the skill of two-week-ahead biweekly streamflow forecast by about 20% over climatological forecast for the largest 1% of the observed biweekly flow.

Current affiliation: Bannister Engineering, LLC, Mansfield, Texas.

Current affiliation: National Center for Atmospheric Research, Boulder, Colorado.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Reza Ahmad Limon, rezaahmad.limon@mavs.uta.edu

Abstract

To issue early warnings for the public to act, for emergency managers to take preventive actions, and for water managers to operate their systems cost-effectively, it is necessary to maximize the time horizon over which streamflow forecasts are skillful. In this work, we assess the value of medium-range ensemble precipitation forecasts generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) in increasing the lead time and skill of streamflow forecasts for five headwater basins in the upper Trinity River basin in north-central Texas. The HEFS uses ensemble mean precipitation forecasts from the Global Ensemble Forecast System (GEFS) of the National Centers for Environment Prediction (NCEP). For comparative evaluation, we verify ensemble streamflow forecasts generated with the HEFS forced by the GEFS forecast with those forced by the short-range quantitative precipitation forecasts (QPFs) from the NWS West Gulf River Forecast Center (WGRFC) based on guidance from the NCEP’s Weather Prediction Center. We also assess the benefits of postprocessing the raw ensemble streamflow forecasts and evaluate the impact of selected parameters within the HEFS on forecast quality. The results show that the use of medium-range precipitation forecasts from the GEFS with the HEFS extends the time horizon for skillful forecasting of mean daily streamflow by 1–3 days for significant events when compared with using only the 72-h River Forecast Center (RFC) QPF with the HEFS. The HEFS forced by the GEFS also improves the skill of two-week-ahead biweekly streamflow forecast by about 20% over climatological forecast for the largest 1% of the observed biweekly flow.

Current affiliation: Bannister Engineering, LLC, Mansfield, Texas.

Current affiliation: National Center for Atmospheric Research, Boulder, Colorado.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Reza Ahmad Limon, rezaahmad.limon@mavs.uta.edu
Save
  • Adams, T., and J. Ostrowski, 2010: Short lead-time hydrologic ensemble forecasts from numerical weather prediction model ensembles. World Environmental and Water Resources Congress 2010, Providence, RI, American Society of Civil Engineers, 2294–2304, https://doi.org/10.1061/41114(371)237.

    • Crossref
    • Export Citation
  • Brown, J. D., 2015a: Ensemble Verification System (EVS) user’s manual. Hydrologic Solutions Limited, 130 pp., https://vlab.ncep.noaa.gov/documents/207461/1893026/EVS_MANUAL.pdf .

  • Brown, J. D., 2015b: An evaluation of the minimum requirements for meteorological reforecasts from the Global Ensemble Forecast System (GEFS) of the U.S. National Weather Service (NWS) in support of the calibration and validation of the NWS Hydrologic Ensemble Forecast Service (HEFS). Tech. Rep. prepared by Hydrologic Solutions Limited for the Office of Hydrologic Development, 120 pp., http://www.nws.noaa.gov/oh/hrl/hsmb/docs/hep/publications_presentations/HSL_LYNT_DG133W-13-CQ-0042_SubK_2013_1003_Task_3_Deliverable_04_report_FINAL.pdf.

  • Brown, J. D., J. Demargne, D.-J. Seo, and Y. Liu, 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, https://doi.org/10.1016/j.envsoft.2010.01.009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, J. D., L. Wu, M. He, S. Regonda, H. Lee, and D.-J. Seo, 2014a: Verification of temperature, precipitation, and streamflow forecasts from the NOAA/NWS Hydrologic Ensemble Forecast Service (HEFS): 1. Experimental design and forcing verification. J. Hydrol., 519, 28692889, https://doi.org/10.1016/j.jhydrol.2014.05.028.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, J. D., M. He, S. Regonda, L. Wu, H. Lee, and D.-J. Seo, 2014b: Verification of temperature, precipitation, and streamflow forecasts from the NOAA/NWS Hydrologic Ensemble Forecast Service (HEFS): 2. Streamflow verification. J. Hydrol., 519, 28472868, https://doi.org/10.1016/j.jhydrol.2014.05.030.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Burnash, R. J. C., 1995: The NWS River Forecast System–Catchment Modeling. Computer Models of Watershed Hydrology, V. P. Singh, Ed., Water Resources Publications, 311–366.

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

  • Clark, M., S. Gangopadhyay, L. Hay, B. Rajagopalan, and R. Wilby, 2004: The Schaake shuffle: A method for reconstructing space–time variability in forecasted precipitation and temperature fields. J. Hydrometeor., 5, 243262, https://doi.org/10.1175/1525-7541(2004)005<0243:TSSAMF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collischonn, W., C. E. M. Tucci, R. T. Clarke, S. C. Chou, L. G. Guilhon, M. Cataldi, and D. Allasia, 2007: Medium-range reservoir inflow predictions based on quantitative precipitation forecasts. J. Hydrol., 344, 112122, https://doi.org/10.1016/j.jhydrol.2007.06.025.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cosgrove, B., D. Gochis, T. Graziano, and E. Clark, 2017: From continental scale to neighborhood scale: Operational hydrologic modeling with the National Water Model. Accessed 2 May 2017, http://www.awrancrs.org/images/Presentations/Feb23-2017/Cosgrove.AWRA.NWM.Overview.2017.pdf.

  • Demargne, J., J. D. Brown, Y. Liu, D.-J. Seo, L. Wu, Z. Toth, and Y. Zhu, 2010: Diagnostic verification of hydrometeorological and hydrologic ensembles. Atmos. Sci. Lett., 11, 114122, https://doi.org/10.1002/asl.261.

    • Crossref
    • 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, 7998, https://doi.org/10.1175/BAMS-D-12-00081.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Di Liberto, T., 2015: Flood disaster in Texas and Oklahoma. Climate.gov, accessed 4 April 2016, https://www.climate.gov/news-features/event-tracker/flood-disaster-texas-and-oklahoma.

  • Georgakakos, K. P., D.-J. Seo, H. Gupta, J. Schaake, and M. B. Butts, 2004: Towards the characterization of streamflow simulation uncertainty through multimodel ensembles. J. Hydrol., 298, 222241, https://doi.org/10.1016/j.jhydrol.2004.03.037.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Georgakakos, K. P., and Coauthors, 2006: Integrated forecast and reservoir management (INFORM) for Northern California: System development and initial demonstration. HRC Tech. Rep. 5, 446 pp., http://www.hrc-lab.org/projects/projectpdfs/INFORM_REPORTS/FINAL_PHASE_I/HRC%20Technical%20Report%20No.%205.pdf.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Graziano, T., E. Clark, B. Cosgrove, and D. Gochis, 2017: Transforming National Oceanic and Atmospheric Administration (NOAA) water resources prediction. 31st Conf. on Hydrology, Seattle, WA, Amer. Meteor. Soc., 2A.2, https://ams.confex.com/ams/97Annual/webprogram/Paper314016.html.

  • Green, D. M., and J. A. Swets, 1966: Signal Detection Theory and Psychophysics. Wiley, 455 pp.

  • Hamill, T. M., R. Hagedorn, and J. S. Whitaker, 2008: Probabilistic forecast calibration using ECMWF and GFS ensemble reforecasts. Part II: Precipitation. Mon. Wea. Rev., 136, 26202632, https://doi.org/10.1175/2007MWR2411.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., G. T. Bates, J. S. Whitaker, D. R. Murray, M. Fiorino, T. J. Galarneau Jr., Y. Zhu, and W. Lapenta, 2013: NOAA’s second-generation global medium-range ensemble reforecast dataset. Bull. Amer. Meteor. Soc., 94, 15531565, https://doi.org/10.1175/BAMS-D-12-00014.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hartman, R., D.-J. Seo, B. Lawrence, S. Shumate, J. Ostrowski, J. Halquist, C. Dietz, and M. Mullusky, 2007: The Experimental Ensemble Forecast System (XEFS) Design and Gap Analysis. Rep. of the XEFS Design and Gap Analysis Team, 50 pp., http://www.nws.noaa.gov/oh/rfcdev/docs/XEFS_design_gap_analysis_report_final.pdf.

  • Hersbach, H., 2000: Decomposition of the continuous ranked probability score for ensemble prediction systems. Wea. Forecasting, 15, 559570, https://doi.org/10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jolliffe, I. T., and D. B. Stephenson, 2003: Forecast Verification. A Practicioner’s Guide in Atmospheric Science. Wiley, 240 pp.

  • Liu, Y., and Coauthors, 2012: Advancing data assimilation in operational hydrologic forecasting: Progresses, challenges, and emerging opportunities. Hydrol. Earth Syst. Sci., 16, 38633887, https://doi.org/10.5194/hess-16-3863-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mason, S. J., and N. E. Graham, 2002: Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation. Quart. J. Roy. Meteor. Soc., 128, 21452166, https://doi.org/10.1256/003590002320603584.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nazari, B., D.-J. Seo, and R. Muttiah, 2016: Assessing the impact of variations in hydrologic, hydraulic and hydrometeorological controls on inundation in urban areas. J. Water Manage. Model., https://doi.org/10.14796/JWMM.C408.

    • Crossref
    • Export Citation
  • Nelson, B., O. Prat, D.-J. Seo, and E. Habib, 2016: Assessment and implications of Stage IV quantitative precipitation estimates for product intercomparisons. Wea. Forecasting, 31, 371394, https://doi.org/10.1175/WAF-D-14-00112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Norouzi, A., 2016: Improving hydrologic prediction for large urban areas through stochastic analysis of scale-dependent runoff response, advanced sensing and high-resolution modeling. Ph.D. dissertation, Dept. of Civil Engineering, The University of Texas at Arlington, 280 pp., https://rc.library.uta.edu/uta-ir/handle/10106/27137.

  • NWS, 2017a: Meteorological Ensemble Forecast Processor (MEFP) user’s manual. Office of Hydrologic Development, National Weather Service, 168 pp., https://vlab.ncep.noaa.gov/documents/207461/1893026/MEFP_Users_Manual.pdf.

  • NWS, 2017b: Ensemble Postprocessor (EnsPost) user’s manual. Office of Hydrologic Development, National Weather Service, 69 pp., https://vlab.ncep.noaa.gov/documents/207461/1893026/EnsPost_Users_Manual.pdf.

  • Regonda, S. K., D.-J. Seo, B. Lawrence, J. D. Brown, and J. Demargne, 2013: Short-term ensemble streamflow forecasting using operationally-produced single-valued streamflow forecasts–A Hydrologic Model Output Statistics (HMOS) approach. J. Hydrol., 497, 8096, https://doi.org/10.1016/j.jhydrol.2013.05.028.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roe, J., and Coauthors, 2010: NOAA’s Community Hydrologic Prediction System. Second Joint Federal Interagency Conf., Las Vegas, NV, Advisory Committee on Water Information, 12 pp., https://training.weather.gov/nwstc/CHPS/roe.pdf.

  • Roundy, J. K., X. Yuan, J. Schaake, and E. F. Wood, 2015: A framework for diagnosing seasonal prediction through canonical event analysis. Mon. Wea. Rev., 143, 24042418, https://doi.org/10.1175/MWR-D-14-00190.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saharia, M., 2013: Ensemble streamflow forecasting for the upper Trinity River basin in Texas. M.S. thesis, Dept. of Civil Engineering, The University of Texas at Arlington, 76 pp., http://hdl.handle.net/10106/23926.

  • Schaake, J., and Coauthors, 2007: Precipitation and temperature ensemble forecasts from single-value forecasts. Hydrol. Earth Syst. Sci. Discuss., 4, 655717, https://doi.org/10.5194/hessd-4-655-2007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scheuerer, M., and T. M. Hamill, 2015: Statistical postprocessing of ensemble precipitation forecasts by fitting censored, shifted gamma distributions. Mon. Wea. Rev., 143, 45784596, https://doi.org/10.1175/MWR-D-15-0061.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seo, D.-J., L. Cajina, R. Corby, and T. Howieson, 2009: Automatic state updating for operational streamflow forecasting via variational data assimilation. J. Hydrol., 367, 255275, https://doi.org/10.1016/j.jhydrol.2009.01.019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seo, D.-J., J. Demargne, L. Wu, Y. Liu, J. D. Brown, S. Regonda, and H. Lee, 2010: Hydrologic ensemble prediction for risk-based water resources management and hazard mitigation. Second Joint Federal Interagency Conf., Las Vegas, NV, Advisory Committee on Water Information, 27 pp., http://www.nws.noaa.gov/oh/hrl/hsmb/docs/hep/publications_presentations/Seo_et_al_JFIC_June2010.pdf.

  • Seo, D.-J., Y. Liu, H. Moradkhani, and A. Weerts, 2014: Ensemble prediction and data assimilation for operational hydrology. J. Hydrol., 519, 26612662, https://doi.org/10.1016/j.jhydrol.2014.11.035.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • TWDB, 2015: Water Data for Texas. Texas Water Development Board, http://waterdatafortexas.org.

  • WGRFC, 2015: WGRFC QPF page. West Gulf River Forecast Center, https://www.weather.gov/wgrfc/wgrfcqpfpage.

  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. Academic Press, 648 pp.

  • Wu, L., D.-J. Seo, J. Demargne, and J. D. Brown, 2008: Generation of ensemble precipitation forecasts from single-value QPF via mixed-type meta-Gaussian model. 2008 Fall Meeting, San Francisco, CA, Amer. Geophys. Union, Abstract H14D-07.

  • Wu, L., J. Schaake, J. D. Brown, J. Demargne, and R. Hartman, 2010: Generation of medium-range precipitation ensemble forecasts from the GFS ensemble mean at the basin scale. 2010 Fall Meeting, San Francisco, CA, Amer. Geophys. Union, Abstract H23A-1165.

  • Wu, L., D.-J. Seo, J. Demargne, J. D. Brown, S. Cong, and J. Schaake, 2011: Generation of ensemble precipitation forecast from single-valued quantitative precipitation forecast for hydrologic ensemble prediction. J. Hydrol., 399, 281298, https://doi.org/10.1016/j.jhydrol.2011.01.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yuan, X., E. F. Wood, and M. Liang, 2014: Integrating weather and climate prediction: Toward seamless hydrologic forecasting. Geophys. Res. Lett., 41, 58915896, https://doi.org/10.1002/2014GL061076.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, X., Y. Zhu, D. Hou, Y. Luo, J. Peng, and R. Wobus, 2017: Performance of the New NCEP Global Ensemble Forecast System in a parallel experiment. Wea. Forecasting, 32, 19892004, https://doi.org/10.1175/WAF-D-17-0023.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 865 245 26
PDF Downloads 590 217 18