• Austin, P. M., and A. C. Bemis, 1950: A quantitative study of the “bright band” in radar precipitation echoes. J. Meteor., 7, 145151, https://doi.org/10.1175/1520-0469(1950)007<0145:AQSOTB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ayat, H., M. R. Kavianpour, S. Moazami, Y. Hong, and E. Ghaemi, 2018: Calibration of weather radar using Region Probability Matching Method (RPMM). Theor. Appl. Climatol., 134, 165176, https://doi.org/10.1007/s00704-017-2266-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aydin, K., V. N. Bringi, and L. Liu, 1995: Rain-rate estimation in the presence of hail using S-band specific differential phase and other radar parameters. J. Appl. Meteor., 34, 404410, https://doi.org/10.1175/1520-0450-34.2.404.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Balica, S. F., I. Popescu, L. Beevers, and N. G. Wright, 2013: Parametric and physically based modelling techniques for flood risk and vulnerability assessment: A comparison. Environ. Modell. Software, 41, 8492, https://doi.org/10.1016/j.envsoft.2012.11.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barge, B. L., R. G. Humphries, S. J. Mah, and W. K. Kuhnke, 1979: Rainfall measurements by weather radar: Applications to hydrology. Water Resour. Res., 15, 13801386, https://doi.org/10.1029/WR015i006p01380.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beneti, C., R. V. Calheiros, M. Sorribas, L. Calvetti, C. Oliveira, N. Rozin, and J. Ruviaro, 2019: Operational hydrological modelling of small watershed using QPE from dual-pol radar in Brazil. Preprints, 2019060026, https://doi.org/10.20944/preprints201906.0026.v1.

    • Crossref
    • Export Citation
  • Berkowitz, D. S., J. A. Schultz, S. Vasiloff, K. L. Elmore, C. D. Payne, and J. B. Boettcher, 2013: Status of dual pol QPE in the WSR-88D network. 27th Conf. on Hydrology, Austin, TX, Amer. Meteor. Soc., 2.2, https://ams.confex.com/ams/93Annual/webprogram/Paper221525.html.

  • Boluwade, A., K.-Y. Zhao, T. A. Stadnyk, and P. Rasmussen, 2018: Towards validation of the Canadian Precipitation Analysis (CaPA) for hydrologic modeling applications in the Canadian prairies. J. Hydrol., 556, 12441255, https://doi.org/10.1016/j.jhydrol.2017.05.059.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boodoo, S., D. Hudak, A. Ryzhkov, P. Zhang, N. Donaldson, D. Sills, and J. Reid, 2015: Quantitative precipitation estimation from a C-band dual-polarized radar for the 8 July 2013 flood in Toronto, Canada. J. Hydrometeor., 16, 20272044, https://doi.org/10.1175/JHM-D-15-0003.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Borga, M., F. Tonelli, R. J. Moore, and H. Andrieu, 2002: Long-term assessment of bias adjustment in radar rainfall estimation. Water Resour. Res., 38, 1266, https://doi.org/10.1029/2001WR000555.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bowering, E. A., A. M. Peck, and S. P. Simonovic, 2014: A flood risk assessment to municipal infrastructure due to changing climate part I: Methodology. Urban Water J., 11, 2030, https://doi.org/10.1080/1573062X.2012.758293.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brandes, E. A., A. V. Ryzhkov, and D. S. Zrnić, 2001: An evaluation of radar rainfall estimates from specific differential phase. J. Atmos. Oceanic Technol., 18, 363375, https://doi.org/10.1175/1520-0426(2001)018<0363:AEORRE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brandes, E. A., G. Zhang, and J. Vivekanandan, 2002: Experiments in rainfall estimation with a polarimetric radar in a subtropical environment. J. Appl. Meteor., 41, 674685, https://doi.org/10.1175/1520-0450(2002)041<0674:EIREWA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bringi, V. N., M. A. Rico-Ramirez, and M. Thurai, 2011: Rainfall estimation with an operational polarimetric C-band radar in the United Kingdom: Comparison with a gauge network and error analysis. J. Hydrometeor., 12, 935954, https://doi.org/10.1175/JHM-D-10-05013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chandrasekar, V., R. Keränen, S. Lim, and D. Moisseev, 2013: Recent advances in classification of observations from dual polarization weather radars. Atmos. Res., 119, 97111, https://doi.org/10.1016/j.atmosres.2011.08.014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, D., and A. Farrar, 2007: Evaluation of NARAD precipitation data for rainfall monitoring in eastern Ontario, Canada. Geomatics Solutions for Disaster Management, J. Li, S. Zlatanova, and A. G. Fabbri, Eds., Lecture Notes in Geoinformation and Cartography, Springer, 103–116, https://doi.org/10.1007/978-3-540-72108-6_8.

    • Crossref
    • Export Citation
  • Chiew, F. H. S., J. Teng, J. Vaze, D. A. Post, J. M. Perraud, D. G. C. Kirono, and N. R. Viney, 2009: Estimating climate change impact on runoff across southeast Australia: Method, results, and implications of the modeling method. Water Resour. Res., 45, W10414, https://doi.org/10.1029/2008WR007338.

    • 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
  • Crozier, C. L., P. I. Joe, J. W. Scott, H. N. Herscovitch, and T. R. Nichols, 1991: The king city operational Doppler radar: Development, all-season applications and forecasting. Atmos.–Ocean, 29, 479516, https://doi.org/10.1080/07055900.1991.9649414.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, Q., Q. Yang, J. Zhang, and S. Zhang, 2018: Impact of gauge representative error on a radar rainfall uncertainty model. J. Appl. Meteor. Climatol., 57, 27692787, https://doi.org/10.1175/JAMC-D-17-0272.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dalezios, N. R., 1988: Objective rainfall evaluation in radar hydrology. J. Water Resour. Plann. Manage., 114, 531546, https://doi.org/10.1061/(ASCE)0733-9496(1988)114:5(531).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Damant, C., G. L. Austin, A. Bellon, M. Osseyrane, and N. Nguyen, 1983: Radar rain forecasting for wastewater control. J. Hydraul. Eng., 109, 293297, https://doi.org/10.1061/(ASCE)0733-9429(1983)109:2(293).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dhiram, K., and Z. Wang, 2016: Evaluation on radar reflectivity-rainfall rate (Z-R) relationships for Guyana. Atmos. Climate Sci., 6, 489499, https://doi.org/10.4236/ACS.2016.64039.

    • Search Google Scholar
    • Export Citation
  • Diakakis, M., G. Deligiannakis, A. Pallikarakis, and M. Skordoulis, 2016: Factors controlling the spatial distribution of flash flooding in the complex environment of a metropolitan urban area. The case of Athens 2013 flash flood event. Int. J. Disaster Risk Reduct., 18, 171180, https://doi.org/10.1016/j.ijdrr.2016.06.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Douglas, R. H., 1990: The stormy weather group (Canada). Radar in Meteorology, D. Atlas, Ed., Springer, 61–68, https://doi.org/10.1007/978-1-935704-15-7_8.

    • Crossref
    • Export Citation
  • Duchon, C. E., and G. R. Essenberg, 2001: Comparative rainfall observations from pit and aboveground rain gauges with and without wind shields. Water Resour. Res., 37, 32533263, https://doi.org/10.1029/2001WR000541.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dufton, D. R. L., 2016: Quantifying uncertainty in radar rainfall estimates using an X-band dual polarisation weather radar. Ph.D. dissertation, University of Leeds, 229 pp.

  • Einfalt, T., K. Arnbjerg-Nielsen, C. Golz, N.-E. Jensen, M. Quirmbach, G. Vaes, and B. Vieux, 2004: Towards a roadmap for use of radar rainfall data in urban drainage. J. Hydrol., 299, 186202, https://doi.org/10.1016/S0022-1694(04)00365-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fabry, F., and I. Zawadzki, 1995: Long-term radar observations of the melting layer of precipitation and their interpretation. J. Atmos. Sci., 52, 838851, https://doi.org/10.1175/1520-0469(1995)052<0838:LTROOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fassnacht, S. R., 2003: Radar precipitation for winter hydrological. IAHS Publ., 282, 3542, https://iahs.info/uploads/dms/iahs_282_035.pdf.

    • Search Google Scholar
    • Export Citation
  • Fortin, V., G. Roy, N. Donaldson, and A. Mahidjiba, 2015: Assimilation of radar quantitative precipitation estimations in the Canadian Precipitation Analysis (CaPA). J. Hydrol., 531, 296307, https://doi.org/10.1016/j.jhydrol.2015.08.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fulton, R. A., J. P. Breidenbach, D.-J. Seo, D. A. Miller, and T. O’Bannon, 1998: The WSR-88D rainfall algorithm. Wea. Forecasting, 13, 377395, https://doi.org/10.1175/1520-0434(1998)013<0377:TWRA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Germann, U., 1999: Radome attenuation—A serious limiting factor for quantitative radar measurements? Meteor. Z., 8, 8590, https://doi.org/10.1127/metz/8/1999/85.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grayman, W. M., and P. S. Eagleson, 1971: Evaluation of radar and raingage systems for flood forecasting. Ralph M. Parsons Laboratory Tech. Rep. 138, Massachusetts Institute of Technology, 324 pp.

  • Guzman, J. A., D. N. Moriasi, M. L. Chu, P. J. Starks, J. L. Steiner, and P. H. Gowda, 2013: A tool for mapping and spatio-temporal analysis of hydrological data. Environ. Modell. Software, 48, 163170, https://doi.org/10.1016/j.envsoft.2013.06.014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hall, W., M. A. Rico-Ramirez, and S. Krämer, 2015: Classification and correction of the bright band using an operational C-band polarimetric radar. J. Hydrol., 531, 248258, https://doi.org/10.1016/j.jhydrol.2015.06.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, S., and P. Coulibaly, 2017: Bayesian flood forecasting methods: A review. J. Hydrol., 551, 340351, https://doi.org/10.1016/j.jhydrol.2017.06.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hapuarachchi, H. A. P., Q. J. Wang, and T. C. Pagano, 2011: A review of advances in flash flood forecasting. Hydrol. Processes, 25, 27712784, https://doi.org/10.1002/hyp.8040.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hubbert, J. C., M. Dixon, S. M. Ellis, and G. Meymaris, 2009: Weather radar ground clutter. Part I: Identification, modeling, and simulation. J. Atmos. Oceanic Technol., 26, 11651180, https://doi.org/10.1175/2009JTECHA1159.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jentsch, A., J. Kreyling, and C. Beierkuhnlein, 2007: A new generation of climate-change experiments: Events, not trends. Front. Ecol. Environ., 5, 365374, https://doi.org/10.1890/1540-9295(2007)5[365:ANGOCE]2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joe, P., and S. Lapczak, 2002: Evolution of the Canadian operational radar network. Proceedings of ERAD (2002), Copernicus, 370–382.

  • Kalinga, O. A., and T. Y. Gan, 2006: Semi-distributed modelling of basin hydrology with radar and gauged precipitation. Hydrol. Processes, 20, 37253746, https://doi.org/10.1002/hyp.6385.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khan, S. I., Z. Flamig, and Y. Hong, 2019: Flood monitoring system using distributed hydrologic modeling for Indus River Basin. Indus River Basin, Elsevier, 335355, https://doi.org/10.1016/b978-0-12-812782-7.00015-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krajewski, W. F., and et al. , 2010a: Towards better utilization of NEXRAD data in hydrology: An overview of Hydro-NEXRAD. J. Hydroinform., 13, 255266, https://doi.org/10.2166/hydro.2010.056.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krajewski, W. F., G. Villarini, and J. A. Smith, 2010b: Radar-rainfall uncertainties: Where are we after thirty years of effort? Bull. Amer. Meteor. Soc., 91, 8794, https://doi.org/10.1175/2009BAMS2747.1.

    • 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
  • Lack, S. A., and N. I. Fox, 2007: An examination of the effect of wind-drift on radar-derived surface rainfall estimations. Atmos. Res., 85, 217229, https://doi.org/10.1016/j.atmosres.2006.09.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madsen, H., G. Wilson, and H. C. Ammentorp, 2002: Comparison of different automated strategies for calibration of rainfall-runoff models. J. Hydrol., 261, 4859, https://doi.org/10.1016/S0022-1694(01)00619-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maki, M., S.-G. Park, and V. N. Bringi, 2005: Effect of natural variations in rain drop size distributions on rain rate estimators of 3 cm wavelength polarimetric radar. J. Meteor. Soc. Japan, 83, 871893, https://doi.org/10.2151/jmsj.83.871.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marshall, J. S., and W. M. K. Palmer, 1948: The distribution of raindrops with size. J. Meteor., 5, 165166, https://doi.org/10.1175/1520-0469(1948)005<0165:TDORWS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marx, A., H. Kunstmann, A. Bárdossy, and J. Seltmann, 2006: Radar rainfall estimates in an alpine environment using inverse hydrological modelling. Adv. Geosci., 9, 2529, https://doi.org/10.5194/adgeo-9-25-2006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mazzarella, V., I. Maiello, R. Ferretti, V. Capozzi, E. Picciotti, P. P. Alberoni, F. S. Marzano, and G. Budillon, 2020: Reflectivity and velocity radar data assimilation for two flash flood events in central Italy: A comparison between 3D and 4D variational methods. Quart. J. Roy. Meteor. Soc., 146, 348366, https://doi.org/10.1002/qj.3679.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McKee, J. L., and A. D. Binns, 2016: A review of gauge–radar merging methods for quantitative precipitation estimation in hydrology. Can. Water Resour. J., 41, 186203, https://doi.org/10.1080/07011784.2015.1064786.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meischner, P., 2005: Weather Radar: Principles and Advanced Applications. Springer, 315 pp.

  • Mekis, E., N. Donaldson, J. Reid, A. Zucconi, J. Hoover, Q. Li, R. Nitu, and S. Melo, 2018: An overview of surface-based precipitation observations at environment and climate change Canada. Atmos.–Ocean, 56, 7195, https://doi.org/10.1080/07055900.2018.1433627.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mekonnen, G. B., S. Matula, F. Doležal, and J. Fišák, 2015: Adjustment to rainfall measurement undercatch with a tipping-bucket rain gauge using ground-level manual gauges. Meteor. Atmos. Phys., 127, 241256, https://doi.org/10.1007/s00703-014-0355-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moore, R. J., A. E. Jones, D. A. Jones, K. B. Black, and V. A. Bell, 2004: Weather radar for flood forecasting: Some UK experiences. Sixth Int. Symp. on Hydrological Applications of Weather Radar, Melbourne, Australia, Bureau of Meteorology, 11 pp.

  • Moradkhani, H., and S. Sorooshian, 2008: General review of rainfall-runoff modeling: Model calibration, data assimilation, and uncertainty analysis, Hydrological Modelling and the Water Cycle, S. Sorooshian et al., Eds., Water Science and Technology Library, Vol 63, Springer, 1–24, https://doi.org/10.1007/978-3-540-77843-1_1.

    • Crossref
    • Export Citation
  • Natural Resources Canada, 2009: Canadian Land Cover, circa 2000 (Vector) - GeoBase Series - ARCHIVED. Accessed 26 November 2019, https://open.canada.ca/data/dataset/97126362-5a85-4fe0-9dc2-915464cfdbb7?activity_id=b4aec57f-c9b4-4543-84c0-7401c70fe52c.

  • Nerini, D., Z. Zulkafli, L. P. Wang, C. Onof, W. Buytaert, W. Lavado-Casimiro, and J. L. Guyot, 2015: A comparative analysis of TRMM–rain gauge data merging techniques at the daily time scale for distributed rainfall–runoff modeling applications. J. Hydrometeor., 16, 21532168, https://doi.org/10.1175/JHM-D-14-0197.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NOAA, 2018: NEXRAD Data Archive, Inventory and Access. Accessed 1 June 2019, https://www.ncdc.noaa.gov/nexradinv/.

  • Pachauri, R. K., and et al. , 2014: Climate Change 2014: Synthesis Report. IPCC, 168 pp.

  • Park, S. G., V. N. Bringi, V. Chandrasekar, M. Maki, and K. Iwanami, 2005: Correction of radar reflectivity and differential reflectivity for rain attenuation at X band. Part I: Theoretical and empirical basis. J. Atmos. Oceanic Technol., 22, 16211632, https://doi.org/10.1175/JTECH1803.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • P. C., S., M. Maki, S. Shimizu, T. Maesaka, D.-S. Kim, D.-I. Lee, and H. Iida, 2013: Correction of reflectivity in the presence of partial beam blockage over a mountainous region using X-band dual polarization radar. J. Hydrometeor., 14, 744764, https://doi.org/10.1175/JHM-D-12-077.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prat, O. P., and B. R. Nelson, 2015: Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge data sets at daily to annual scales (2002–2012). Hydrol. Earth Syst. Sci., 19, 20372056, https://doi.org/10.5194/hess-19-2037-2015https://doi.org/10.5194/HESSD-11-11489-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Price, K., S. T. Purucker, S. R. Kraemer, J. E. Babendreier, and C. D. Knightes, 2014: Comparison of radar and gauge precipitation data in watershed models across varying spatial and temporal scales. Hydrol. Processes, 28, 35053520, https://doi.org/10.1002/hyp.9890.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Public Safety Canada, 2019: The Canadian Disaster Database. Accessed 13 May 2020, https://www.publicsafety.gc.ca/cnt/rsrcs/cndn-dsstr-dtbs/index-en.aspx.

  • Rabiei, E., and U. Haberlandt, 2015: Applying bias correction for merging rain gauge and radar data. J. Hydrol., 522, 544557, https://doi.org/10.1016/j.jhydrol.2015.01.020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ran, Q., W. Fu, Y. Liu, T. Li, K. Shi, and B. Sivakumar, 2018: Evaluation of quantitative precipitation predictions by ECMWF, CMA, and UKMO for flood forecasting: Application to two basins in China. Nat. Hazards Rev., 19, 05018003, https://doi.org/10.1061/(ASCE)NH.1527-6996.0000282.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reggiani, P., and A. H. Weerts, 2008: A Bayesian approach to decision-making under uncertainty: An application to real-time forecasting in the river Rhine. J. Hydrol., 356, 5669, https://doi.org/10.1016/j.jhydrol.2008.03.027.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Richards, W. G., and C. L. Crozier, 1983: Precipitation measurement with a C-band weather radar in southern Ontario. Atmos.–Ocean, 21, 125137, https://doi.org/10.1080/07055900.1983.9649160.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ryzhkov, A. V., S. E. Giangrande, V. M. Melnikov, and T. J. Schuur, 2005: Calibration issues of dual-polarization radar measurements. J. Atmos. Oceanic Technol., 22, 11381155, https://doi.org/10.1175/JTECH1772.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ryzhkov, A. V., D. Hudak, and J. Scott, 2006: A new polarimetric scheme for attenuation correction at C band. Proc. Fourth European Conf. on Radar in Meteorology and Hydrology, Barcelona, Spain, GRAHI–UPC, 29–32, http://www.crahi.upc.edu/ERAD2006/proceedingsMask/00008.pdf.

  • Ryzhkov, A. V., P. Zhang, D. Hudak, J. Alford, M. Knight, and J. Conway, 2007: Validation of polarimetric methods for attenuation correction at C band. Proc. 33rd Conf. on Radar Meteorology, Cairns, Australia, Amer. Meteor. Soc., P11B.12, http://ams.confex.com/ams/pdfpapers/123122.pdf.

  • Ryzhkov, A. V., M. Diederich, P. Zhang, and C. Simmer, 2014: Potential utilization of specific attenuation for rainfall estimation, mitigation of partial beam blockage, and radar networking. J. Atmos. Oceanic Technol., 31, 599619, https://doi.org/10.1175/JTECH-D-13-00038.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sachidananda, M., and D. S. Zrnić, 1987: Rain rate estimates from differential polarization measurements. J. Atmos. Oceanic Technol., 4, 588598, https://doi.org/10.1175/1520-0426(1987)004<0588:RREFDP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schell, G. S., C. A. Madramootoo, G. L. Austin, and R. S. Broughton, 1992: Use of radar measured rainfall for hydrologic modelling. Can. Agric. Eng., 34, 4148.

    • Search Google Scholar
    • Export Citation
  • Şensoy, A., G. Uysal, and A. A. Şorman, 2016: Developing a decision support framework for real-time flood management using integrated models. J. Flood Risk Manag., 11, S866S883, https://doi.org/10.1111/jfr3.12280.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seo, D. J., E. Habib, H. Andrieu, and E. Morin, 2015: Hydrologic applications of weather radar. J. Hydrol., 531, 231233, https://doi.org/10.1016/j.jhydrol.2015.11.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sevruk, B., 1982: Methods of correction for systematic error in point precipitation measurement for operational use. Operational Hydrology Rep. 21, WMO Rep. 589, 91 pp., https://library.wmo.int/doc_num.php?explnum_id=1238.

  • Sills, D. M., and P. I. Joe, 2019: From pioneers to practitioners: A short history of severe thunderstorm research and forecasting in Canada. Atmos.–Ocean, 57, 249261, https://doi.org/10.1080/07055900.2019.1673145.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stellman, K. M., H. E. Fuelberg, R. Garza, and M. Mullusky, 2001: An examination of radar and rain gauge–derived mean areal precipitation over Georgia watersheds. Wea. Forecasting, 16, 133144, https://doi.org/10.1175/1520-0434(2001)016<0133:AEORAR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sugier, J., P. Tabary, J. Gourley, and K. Friedrich, 2006: Evaluation of dual-polarisation technology at C-band for operational weather radar network. EUMETNET Opera 2 Rep., 44 pp.

  • Tabios, G. Q., III, and J. D. Salas, 1985: A comparative analysis of techniques for spatial interpolation of precipitation 1. J. Amer. Water Resour. Assoc., 21, 365380, https://doi.org/10.1111/j.1752-1688.1985.tb00147.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thorndahl, S., T. Einfalt, P. Willems, J. E. Nielsen, M.-C. ten Veldhuis, K. Arnbjerg-Nielsen, M. R. Rasmussen, and P. Molnar, 2017: Weather radar rainfall data in urban hydrology. Hydrol. Earth Syst. Sci., 21, 13591380, https://doi.org/10.5194/hess-21-1359-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • TRCA, 2019a: Don River. Toronto and Region Conservation Authority, accessed 26 November 2019, https://trca.ca/conservation/watershed-management/don-river.

  • TRCA, 2019b: Watershed Features - Humber River. Toronto and Region Conservation Authority, accessed 26 November 2019, https://trca.ca/conservation/watershed-management/humber-river/watershed-features/.

  • Unduche, F., H. Tolossa, D. Senbeta, and E. Zhu, 2018: Evaluation of four hydrological models for operational flood forecasting in a Canadian Prairie watershed. Hydrol. Sci. J., 63, 11331149, https://doi.org/10.1080/02626667.2018.1474219.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Viney, N. R., J. Perraud, J. Vaze, F. H. S. Chiew, D. A. Post, and A. Yang, 2009: The usefulness of bias constraints in model calibration for regionalisation to ungauged catchments. 18th World IMACS Congress and MODSIM09 Int. Congress on Modelling and Simulation, Cairns, Australia, IMACS/MODSIM, 3421–3427.

  • Vivekanandan, J., D. N. Yates, and E. A. Brandes, 1999: The influence of terrain on rainfall estimates from radar reflectivity and specific propagation phase observations. J. Atmos. Oceanic Technol., 16, 837845, https://doi.org/10.1175/1520-0426(1999)016<0837:TIOTOR>2.0.CO;2.

    • 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
  • Wang, L.-P., S. Ochoa-Rodríguez, J. Van Assel, R. D. Pina, M. Pessemier, S. Kroll, P. Willems, and C. Onof, 2015: Enhancement of radar rainfall estimates for urban hydrology through optical flow temporal interpolation and Bayesian gauge-based adjustment. J. Hydrol., 531, 408426, https://doi.org/10.1016/j.jhydrol.2015.05.049.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weber, M. E., J. Y. Cho, J. S. Herd, J. M. Flavin, W. E. Benner, and G. S. Torok, 2007: The next generation multimission U.S. surveillance radar network. Bull. Amer. Meteor. Soc., 88, 17391752, https://doi.org/10.1175/BAMS-88-11-1739.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wijayarathne, D., P. Coulibaly, S. Boodoo, and D. Sills, 2020: Evaluation of radar-gauge merging techniques to be used in operational flood forecasting in urban watersheds. Water, 12, 1494, https://doi.org/10.3390/w12051494.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, H., X. Zhou, J. Hendrickx, E. Vivoni, H. Guan, Y. Tian, and E. Small, 2006: Evaluation of NEXRAD stage III precipitation data over a semiarid region. J. Amer. Water Resour. Assoc., 42, 237256, https://doi.org/10.1111/j.1752-1688.2006.tb03837.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, C. B., and N. A. Brunsell, 2008: Evaluating NEXRAD estimates for the Missouri River Basin: Analysis using daily raingauge data. J. Hydrol. Eng., 13, 549553, https://doi.org/10.1061/(ASCE)1084-0699(2008)13:7(549).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zahmatkesh, Z., S. Kumar Jha, P. Coulibaly, and T. Stadnyk, 2019: An overview of river flood forecasting procedures in Canadian watersheds. Can. Water Resour. J., 44, 213229, https://doi.org/10.1080/07011784.2019.1601598.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, J., Y. Qi, D. Kingsmill, and K. Howard, 2012: Radar-based quantitative precipitation estimation for the cool season in complex terrain: Case studies from the NOAA Hydrometeorology Testbed. J. Hydrometeor., 13, 18361854, https://doi.org/10.1175/JHM-D-11-0145.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Evaluation of Radar Quantitative Precipitation Estimates (QPEs) as an Input of Hydrological Models for Hydrometeorological Applications

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  • 1 School of Geography and Earth Sciences, McMaster University, Hamilton, Ontario, Canada
  • | 2 Cloud Physics and Severe Weather Research Section, Environment and Climate Change Canada, King City, Ontario, Canada
  • | 3 Department of Civil Engineering, and School of Geography and Earth Science, McMaster University, Hamilton, Ontario, Canada
  • | 4 Department of Civil and Environmental Engineering, University of Western Ontario, London, Ontario, Canada
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Abstract

Weather radar provides real-time, spatially distributed precipitation estimates, whereas traditional gauge data are restricted in space. The use of radar quantitative precipitation estimates (QPEs) as an input of hydrological models for hydrometeorological applications has increased with the development of weather radar worldwide. New dual-polarization technology and algorithms are showing improvements to radar QPEs. This study evaluates radar QPEs from C-band radar at King City, Canada (WKR), and NEXRAD S-band radar at Buffalo, New York (KBUF), to verify the reliability and accuracy for operational use in the Humber River (semiurban) and Don River (urban) watersheds in the Greater Toronto Area (GTA), Canada. Twenty rainfall events that occurred from 2011 to 2017 were determined from hourly gauge measurements and compared with nine radar QPEs. Rain rates were estimated with different algorithms using three dual-polarized reflectivity values: horizontal reflectivity Z, differential reflectivity ZDR, and specific differential phase KDP. The correlation coefficient, bias, detection, and root-mean-square error were calculated and averaged over all events for each gauge station to show the spatial distribution and in a similar pattern to represent the variation by the event. The quality of the results in terms of accuracy and reliability indicates that the radar QPEs from KBUF S-band and WKR C-band multiparameter rain rate estimators can be effectively used as precipitation forcing of hydrological models for hydrometeorological applications. The high spatiotemporal resolution, long-term data archive, and good percent detection of radar QPEs can facilitate hydrometeorological applications by providing a continuous time series for hydrological models.

Corresponding author: Dayal Wijayarathne, wijayard@mcmaster.ca

Abstract

Weather radar provides real-time, spatially distributed precipitation estimates, whereas traditional gauge data are restricted in space. The use of radar quantitative precipitation estimates (QPEs) as an input of hydrological models for hydrometeorological applications has increased with the development of weather radar worldwide. New dual-polarization technology and algorithms are showing improvements to radar QPEs. This study evaluates radar QPEs from C-band radar at King City, Canada (WKR), and NEXRAD S-band radar at Buffalo, New York (KBUF), to verify the reliability and accuracy for operational use in the Humber River (semiurban) and Don River (urban) watersheds in the Greater Toronto Area (GTA), Canada. Twenty rainfall events that occurred from 2011 to 2017 were determined from hourly gauge measurements and compared with nine radar QPEs. Rain rates were estimated with different algorithms using three dual-polarized reflectivity values: horizontal reflectivity Z, differential reflectivity ZDR, and specific differential phase KDP. The correlation coefficient, bias, detection, and root-mean-square error were calculated and averaged over all events for each gauge station to show the spatial distribution and in a similar pattern to represent the variation by the event. The quality of the results in terms of accuracy and reliability indicates that the radar QPEs from KBUF S-band and WKR C-band multiparameter rain rate estimators can be effectively used as precipitation forcing of hydrological models for hydrometeorological applications. The high spatiotemporal resolution, long-term data archive, and good percent detection of radar QPEs can facilitate hydrometeorological applications by providing a continuous time series for hydrological models.

Corresponding author: Dayal Wijayarathne, wijayard@mcmaster.ca
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