• Adams, T. E., 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
  • Adams, T. E., and T. C. Pagano, 2016: Flood Forecasting: A Global Perspective. Elsevier, 487 pp.

    • Crossref
    • Export Citation
  • Adams, T. E., and R. Dymond, 2018: Evaluation and benchmarking of operational short-range ensemble mean and median streamflow forecasts for the Ohio River basin. J. Hydrometeor., 19, 16891706, https://doi.org/10.1175/JHM-D-18-0102.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adams, T. E., and R. Dymond, 2019: The effect of QPF on real-time deterministic hydrologic forecast uncertainty. J. Hydrometeor., 20, 16871705, https://doi.org/10.1175/JHM-D-18-0202.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Alexander, C. R., S. S. Weygandt, T. G. Smirnova, S. Benjamin, P. Hofmann, E. P. James, and D. A. Koch, 2010: High Resolution Rapid Refresh (HRRR): Recent enhancements and evaluation during the 2010 convective season. 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., 9.2, https://ams.confex.com/ams/25SLS/techprogram/paper_175722.htm.

  • Ayalew, T. B., and W. F. Krajewski, 2017: Effect of river network geometry on flood frequency: A tale of two watersheds in Iowa. J. Hydrol. Eng., 22, 6017004, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001544.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamin, S. G., T. G. Smirnova, S. S. Weygandt, M. Hu, S. R. Sahm, B. D. Jamison, M. M. Wolfson, and J. O. Pinto, 2009: The HRRR 3-km storm-resolving, radar-initialized, hourly updated forecasts for air traffic management. Aviation, Range and Aerospace Meteorology Special Symp. on Weather-Air Traffic Management Integration, Phoenix, AZ, Amer. Meteor. Soc., P1.2, https://ams.confex.com/ams/89annual/techprogram/paper_150430.htm.

  • Blaylock, B., 2020: University of Utah HRRR Data Archive. Accessed 8 January 2020, http://home.chpc.utah.edu/~u0553130/Brian_Blaylock/cgi-bin/hrrr_download.cgi.

  • Budikova, D., J. S. M. Coleman, S. A. Strope, and A. Austin, 2010: Hydroclimatology of the 2008 Midwest floods. Water Resour. Res., 46, W12524, https://doi.org/10.1029/2010WR009206.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Calvetti, L., and A. J. Pereira Filho, 2014: Ensemble hydrometeorological forecasts using WRF hourly QPF and topmodel for a middle watershed. Adv. Meteor., 2014, 484120, https://doi.org/10.1155/2014/484120.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carpenter, T. M., and K. P. Georgakakos, 2006: Intercomparison of lumped versus distributed hydrologic model ensemble simulations on operational forecast scales. J. Hydrol., 329, 174185, https://doi.org/10.1016/j.jhydrol.2006.02.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • CAWCR, 2017: WWRP/WGNE Joint Working Group on forecast verification research. Accessed 1 August 2020, https://www.cawcr.gov.au/projects/verification/#Types_of_forecasts_and_verifications.

  • Ciach, G. J., W. F. Krajewski, and G. Villarini, 2007: Product-error-driven uncertainty model for probabilistic quantitative precipitation estimation with NEXRAD data. J. Hydrometeor., 8, 13251347, https://doi.org/10.1175/2007JHM814.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cloke, H. L., and F. Pappenberger, 2009: Ensemble flood forecasting: A review. J. Hydrol., 375, 613626, https://doi.org/10.1016/j.jhydrol.2009.06.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collischonn, W., R. Haas, I. Andreolli, and C. E. M. Tucci, 2005: Forecasting River Uruguay flow using rainfall forecasts from a regional weather-prediction model. J. Hydrol., 305, 8798, https://doi.org/10.1016/j.jhydrol.2004.08.028.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cuo, L., T. C. Pagano, and Q. J. Wang, 2011: A review of quantitative precipitation forecasts and their use in short- to medium-range streamflow forecasting. J. Hydrometeor., 12, 713728, https://doi.org/10.1175/2011JHM1347.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Demargne, J., M. Mullusky, K. Werner, T. Adams, S. Lindsey, N. Schwein, W. Marosi, and E. Welles, 2009: Application of forecast verification science to operational river forecasting in the U.S. National Weather Service. Bull. Amer. Meteor. Soc., 90, 779784, https://doi.org/10.1175/2008BAMS2619.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dolciné, L., H. Andrieu, D. Sempere-Torres, and D. Creutin, 2001: Flash flood forecasting with coupled precipitation model in mountainous Mediterranean basin. J. Hydrol. Eng., 6, 110, https://doi.org/10.1061/(ASCE)1084-0699(2001)6:1(1).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • ElSaadani, M., W. F. Krajewski, R. Goska, and M. B. Smith, 2018: An investigation of errors in distributed models’ stream discharge prediction due to channel routing. J. Amer. Water Resour. Assoc., 54, 742751, https://doi.org/10.1111/1752-1688.12627.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Georgakakos, K. P., 1986: A generalized stochastic hydrometeorological model for flood and flash-flood forecasting: 2. Case studies. Water Resour. Res., 22, 20962106, https://doi.org/10.1029/WR022i013p02096.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ghimire, G. R., and W. F. Krajewski, 2020: Hydrologic implications of wind farm effect on radar-rainfall observations. Geophys. Res. Lett., 47, e2020GL089188, https://doi.org/10.1029/2020GL089188.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ghimire, G. R., W. F. Krajewski, and R. Mantilla, 2018: A power law model for river flow velocity in Iowa basins. J. Amer. Water Resour. Assoc., 54, 10551067, https://doi.org/10.1111/1752-1688.12665.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ghimire, G. R., N. Jadidoleslam, W. F. Krajewski, and A. A. Tsonis, 2020: Insights on streamflow predictability across scales using horizontal visibility graph based networks. Front. Water, 2, 17, https://doi.org/10.3389/frwa.2020.00017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gupta, H. V., H. Kling, K. K. Yilmaz, and G. F. Martinez, 2009: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. J. Hydrol., 377, 8091, https://doi.org/10.1016/j.jhydrol.2009.08.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hardy, J., J. J. Gourley, P. E. Kirstetter, Y. Hong, F. Kong, and Z. L. Flamig, 2016: A method for probabilistic flash flood forecasting. J. Hydrol., 541, 480494, https://doi.org/10.1016/j.jhydrol.2016.04.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • IFC, 2020: IFC Archive. Accessed 1 August 2020, http://s-iihr51.iihr.uiowa.edu/precipitation/mrms_gc1h/.

  • Iowa Mesonet, 2020: Iowa environmental mesonet. Accessed 8 January 2020, https://mtarchive.geol.iastate.edu/.

  • Knoben, W. J. M., J. E. Freer, and R. A. Woods, 2019: Technical note: Inherent benchmark or not? Comparing Nash-Sutcliffe and Kling-Gupta efficiency scores. Hydrol. Earth Syst. Sci. Discuss., 23, 43234331, https://doi.org/10.5194/hess-23-4323-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krajewski, W. F., and et al. , 2017: Real-time flood forecasting and information system for the State of Iowa. Bull. Amer. Meteor. Soc., 98, 539554, https://doi.org/10.1175/BAMS-D-15-00243.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krajewski, W. F., G. R. Ghimire, and F. Quintero, 2020: Streamflow forecasting without models. J. Hydrometeor., 21, 16891704, https://doi.org/10.1175/JHM-D-19-0292.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kruger, A., W. F. Krajewski, J. J. Niemeier, D. L. Ceynar, and R. Goska, 2016: Bridge-mounted river stage sensors (BMRSS). IEEE Access, 4, 89488966, https://doi.org/10.1109/ACCESS.2016.2631172.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Larimer, O. J., 1957: Drainage Areas of Iowa Streams. Iowa Highway Research Board Bulletin 7, 404 pp.

  • Lin, C., S. Vasić, A. Kilambi, B. Turner, and I. Zawadzki, 2005: Precipitation forecast skill of numerical weather prediction models and radar nowcasts. Geophys. Res. Lett., 32, L14801, https://doi.org/10.1029/2005GL023451.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lobligeois, F., V. Andréassian, C. Perrin, P. Tabary, and C. Loumagne, 2014: When does higher spatial resolution rainfall information improve streamflow simulation? An evaluation using 3620 flood events. Hydrol. Earth Syst. Sci., 18, 575594, https://doi.org/10.5194/hess-18-575-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mantilla, R., 2007: Physical basis of statistical scaling in peak flows and stream flow hydrographs for topologic and spatially embedded random self-similar channel networks. Ph.D. thesis, University of Colorado Boulder, 144 pp.

  • Moser, B. A., W. A. Gallus, and R. Mantilla, 2015: An initial assessment of radar data assimilation on warm season rainfall forecasts for use in hydrologic models. Wea. Forecasting, 30, 14911520, https://doi.org/10.1175/WAF-D-14-00125.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NCEP, 2020: NCEP GFS 0.25 Degree Global Forecast Grids Historical Archive. Research Data Archive at the NCAR, accessed 1 August 2020, https://doi.org/10.5065/D65D8PWK .

    • Crossref
    • Export Citation
  • NOAA, 2020a: The High-Resolution Rapid Refresh (HRRR). Accessed 1 August 2020, https://rapidrefresh.noaa.gov/hrrr/.

  • NOAA, 2020b: The National Water Model (NWM). Office of Weather Prediction, accessed 1 August 2020, https://water.noaa.gov/about/nwm.

  • NOAA, 2020c: NCEP products inventory. Accessed 1 August 2020, https://www.nco.ncep.noaa.gov/pmb/products/gfs/.

  • Pagano, T., D. Garen, and S. Sorooshian, 2004: Evaluation of official western U.S. seasonal water supply outlooks, 1922–2002. J. Hydrometeor., 5, 896909, https://doi.org/10.1175/1525-7541(2004)005<0896:EOOWUS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pereira Fo, A. J., K. C. Crawford, and D. J. Stensrud, 1999: Mesoscale precipitation fields. Part II: Hydrometeorologic modeling. J. Appl. Meteor., 38, 102125, https://doi.org/10.1175/1520-0450(1999)038<0102:MPFPIH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Perez, G., R. Mantilla, and W. F. Krajewski, 2018: The influence of spatial variability of width functions on regional peak flow regressions. Water Resour. Res., 54, 76517669, https://doi.org/10.1029/2018WR023509.

    • Search Google Scholar
    • Export Citation
  • Prior, J. C., 1991: Landforms of Iowa. University of Iowa Press, 153 pp.

  • Qi, Y., S. Martinaitis, J. Zhang, and S. Cocks, 2016: A real-time automated quality control of hourly rain gauge data based on multiple sensors in MRMS system. J. Hydrometeor., 17, 16751691, https://doi.org/10.1175/JHM-D-15-0188.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Quintero, F., W. F. Krajewski, R. Mantilla, S. Small, and B.-C. Seo, 2016: A spatial–dynamical framework for evaluation of satellite rainfall products for flood prediction. J. Hydrometeor., 17, 21372154, https://doi.org/10.1175/JHM-D-15-0195.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Quintero, F., B.-C. Seo, and R. Mantilla, 2020: Improvement and evaluation of the Iowa Flood Center Hillslope Link Model (HLM) by calibration-free approach. J. Hydrol., 584, 124686, https://doi.org/10.1016/j.jhydrol.2020.124686.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rojas, M., F. Quintero, and W. F. Krajewski, 2020: Performance of the National Water Model in Iowa using independent observations. J. Amer. Water Resour. Assoc., 56, 568585, https://doi.org/10.1111/1752-1688.12820.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seo, B. C., F. Quintero, and W. F. Krajewski, 2018: High-resolution QPF uncertainty and its implications for flood prediction: A case study for the eastern Iowa flood of 2016. J. Hydrometeor., 19, 12891304, https://doi.org/10.1175/JHM-D-18-0046.1.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Sharma, S., and et al. , 2017: Eastern U.S. verification of ensemble precipitation forecasts. Wea. Forecasting, 32, 117139, https://doi.org/10.1175/WAF-D-16-0094.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sharma, S., R. Siddique, S. Reed, P. Ahnert, P. Mendoza, and A. Mejia, 2018: Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system. Hydrol. Earth Syst. Sci., 22, 18311849, https://doi.org/10.5194/hess-22-1831-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shrestha, D. L., D. E. Robertson, Q. J. Wang, T. C. Pagano, and H. A. P. Hapuarachchi, 2013: Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose. Hydrol. Earth Syst. Sci., 17, 19131931, https://doi.org/10.5194/hess-17-1913-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Silvestro, F., N. Rebora, and L. Ferraris, 2011: Quantitative flood forecasting on small- and medium-sized basins: A probabilistic approach for operational purposes. J. Hydrometeor., 12, 14321446, https://doi.org/10.1175/JHM-D-10-05022.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • USGS, 2020: USGS current water data for Iowa. Accessed 8 January 2020, https://waterdata.usgs.gov/ia/nwis/rt.

  • Villarini, G., and W. F. Krajewski, 2010: Review of the different sources of uncertainty in single polarization radar-based estimates of rainfall. Surv. Geophys., 31, 107129, https://doi.org/10.1007/s10712-009-9079-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vivoni, E. R., D. Entekhabi, R. L. Bras, V. Y. Ivanov, M. P. Van Horne, C. Grassotti, and R. N. Hoffman, 2006: Extending the predictability of hydrometeorological flood events using radar rainfall nowcasting. J. Hydrometeor., 7, 660677, https://doi.org/10.1175/JHM514.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Welles, E., S. Sorooshian, G. Carter, and B. Olsen, 2007: Hydrologic verification: A call for action and collaboration. Bull. Amer. Meteor. Soc., 88, 503512, https://doi.org/10.1175/BAMS-88-4-503.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilson, J. W., N. A. Crook, C. K. Mueller, J. Sun, and M. Dixon, 1998: Nowcasting thunderstorms: A status report. Bull. Amer. Meteor. Soc., 79, 20792099, https://doi.org/10.1175/1520-0477(1998)079<2079:NTASR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, W., R. Emerton, Q. Duan, A. W. Wood, F. Wetterhall, and D. E. Robertson, 2020: Ensemble flood forecasting: Current status and future opportunities. Wiley Interdiscip. Rev.: Water, 7, e1432, https://doi.org/10.1002/wat2.1432.

    • Search Google Scholar
    • Export Citation
  • Zalenski, G., W. F. Krajewski, F. Quintero, P. Restrepo, and S. Buan, 2017: Analysis of national weather service stage forecast errors. Wea. Forecasting, 32, 14411465, https://doi.org/10.1175/WAF-D-16-0219.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, J., and et al. , 2011: National Mosaic and Multi-Sensor QPE (NMQ) system description, results, and future plans. Bull. Amer. Meteor. Soc., 92, 13211338, https://doi.org/10.1175/2011BAMS-D-11-00047.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, J., and et al. , 2016: Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation: Initial operating capabilities. Bull. Amer. Meteor. Soc., 97, 621638, https://doi.org/10.1175/BAMS-D-14-00174.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, H., G. Tang, N. Li, F. Wang, Y. Wang, and D. Jian, 2011: Evaluation of precipitation forecasts from NOAA Global Forecast System in hydropower operation. J. Hydroinform., 13, 8195, https://doi.org/10.2166/hydro.2010.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Scale-Dependent Value of QPF for Real-Time Streamflow Forecasting

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  • 1 a Iowa Flood Center and IIHR-Hydroscience and Engineering, The University of Iowa, Iowa City, Iowa
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Abstract

Incorporating rainfall forecasts into a real-time streamflow forecasting system extends the forecast lead time. Since quantitative precipitation forecasts (QPFs) are subject to substantial uncertainties, questions arise on the trade-off between the time horizon of the QPF and the accuracy of the streamflow forecasts. This study explores the problem systematically, exploring the uncertainties associated with QPFs and their hydrologic predictability. The focus is on scale dependence of the trade-off between the QPF time horizon, basin-scale, space–time scale of the QPF, and streamflow forecasting accuracy. To address this question, the study first performs a comprehensive independent evaluation of the QPFs at 140 U.S. Geological Survey (USGS) monitored basins with a wide range of spatial scales (~10–40 000 km2) over the state of Iowa in the midwestern United States. The study uses High-Resolution Rapid Refresh (HRRR) and Global Forecasting System (GFS) QPFs for short and medium-range forecasts, respectively. Using Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimate (QPE) as a reference, the results show that the rainfall-to-rainfall QPF errors are scale dependent. The results from the hydrologic forecasting experiment show that both QPFs illustrate clear value for real-time streamflow forecasting at longer lead times in the short- to medium-range relative to the no-rain streamflow forecast. The value of QPFs for streamflow forecasting is particularly apparent for basin sizes below 1000 km2. The space–time scale, or reference time tr (ratio of forecast lead time to basin travel time), ~1 depicts the largest streamflow forecasting skill with a systematic decrease in forecasting accuracy for tr > 1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

G. R. Ghimire’s current affiliation: Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee.

Corresponding author: Ganesh R. Ghimire, ghimiregr@ornl.gov

Abstract

Incorporating rainfall forecasts into a real-time streamflow forecasting system extends the forecast lead time. Since quantitative precipitation forecasts (QPFs) are subject to substantial uncertainties, questions arise on the trade-off between the time horizon of the QPF and the accuracy of the streamflow forecasts. This study explores the problem systematically, exploring the uncertainties associated with QPFs and their hydrologic predictability. The focus is on scale dependence of the trade-off between the QPF time horizon, basin-scale, space–time scale of the QPF, and streamflow forecasting accuracy. To address this question, the study first performs a comprehensive independent evaluation of the QPFs at 140 U.S. Geological Survey (USGS) monitored basins with a wide range of spatial scales (~10–40 000 km2) over the state of Iowa in the midwestern United States. The study uses High-Resolution Rapid Refresh (HRRR) and Global Forecasting System (GFS) QPFs for short and medium-range forecasts, respectively. Using Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimate (QPE) as a reference, the results show that the rainfall-to-rainfall QPF errors are scale dependent. The results from the hydrologic forecasting experiment show that both QPFs illustrate clear value for real-time streamflow forecasting at longer lead times in the short- to medium-range relative to the no-rain streamflow forecast. The value of QPFs for streamflow forecasting is particularly apparent for basin sizes below 1000 km2. The space–time scale, or reference time tr (ratio of forecast lead time to basin travel time), ~1 depicts the largest streamflow forecasting skill with a systematic decrease in forecasting accuracy for tr > 1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

G. R. Ghimire’s current affiliation: Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee.

Corresponding author: Ganesh R. Ghimire, ghimiregr@ornl.gov

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