• Addor, N., S. Jaun, F. Fundel, and M. Zappa, 2011: An operational hydrological ensemble prediction system for the city of Zurich (Switzerland): Skill, case studies and scenarios. Hydrol. Earth Syst. Sci., 15, 23272347, doi:10.5194/hess-15-2327-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Akima, H., 1978: A method of bivariate interpolation and smooth surface fitting for irregularly distributed data points. ACM Trans. Math. Software, 4, 148164, doi:10.1145/355780.355786.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Akima, H., 1996: Algorithm 761: Scattered-data surface fitting that has the accuracy of a cubic polynomial. ACM Trans. Math. Software, 22, 362371, doi:10.1145/232826.232856.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Amengual, A., R. Romero, M. Gómez, A. Martín, and S. Alonso, 2007: A hydrometeorological modeling study of a flash flood event over Catalonia, Spain. J. Hydrometeor., 8, 282303, doi:10.1175/JHM577.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Amengual, A., R. Romero, and S. Alonso, 2008: Hydrometeorological ensemble simulations of flood events over a small basin of Majorca Island, Spain. Quart. J. Roy. Meteor. Soc., 134, 12211242, doi:10.1002/qj.291.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Amengual, A., R. Romero, M. Vich, and S. Alonso, 2009: Inclusion of potential vorticity uncertainties into a hydrometeorological forecasting chain: Application to a medium size basin of Mediterranean Spain. Hydrol. Earth Syst. Sci., 13, 793811, doi:10.5194/hess-13-793-2009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Amengual, A., V. Homar, and O. Jaume, 2015: Potential of a probabilistic hydrometeorological forecasting approach for the 28 September 2012 extreme flash flood in Murcia, Spain. Atmos. Res., 166, 1023, doi:10.1016/j.atmosres.2015.06.012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Avellano, 2009: The Data Assimilation Research Testbed: A community facility. Bull. Amer. Meteor. Soc., 90, 12831296, doi:10.1175/2009BAMS2618.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Angevine, W. M., H. Jiang, and T. Mauritsen, 2010: Performance of an eddy diffusivity–mass flux scheme for shallow cumulus boundary layers. Mon. Wea. Rev., 138, 28952912, doi:10.1175/2010MWR3142.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Borga, M., P. Boscolo, F. Zanon, and M. Sangati, 2007: Hydrometeorological analysis of the 29 August 2003 flash flood in the eastern Italian Alps. J. Hydrometeor., 8, 10491067, doi:10.1175/JHM593.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bossard, M., J. Feranec, and J. Otahel, 2000: CORINE land cover technical guide—Addendum 2000. Tech. Rep. 40, European Environment Agency, 105 pp. [Available online at http://image2000.jrc.ec.europa.eu/reports/corine_tech_guide_add.pdf.]

  • Bryan, G. H., J. C. Wyngaard, and J. M. Fritsch, 2003: Resolution requirements for the simulation of deep moist convection. Mon. Wea. Rev., 131, 23942416, doi:10.1175/1520-0493(2003)131<2394:RRFTSO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buizza, R., 2003: Weather prediction: Ensemble prediction. Encyclopedia of Atmospheric Sciences, Academic Press, 2546–2557, doi:10.1016/B0-12-227090-8/00461-9.

    • Crossref
    • Export Citation
  • Buizza, R., and T. N. Palmer, 1995: The singular-vector structure of the atmospheric general circulation. J. Atmos. Sci., 52, 14341456, doi:10.1175/1520-0469(1995)052<1434:TSVSOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camarasa Belmonte, A. M., and F. Segura Beltrán, 2001: Flood events in Mediterranean ephemeral streams (ramblas) in Valencia region, Spain. Catena, 45, 229249, doi:10.1016/S0341-8162(01)00146-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carrió, D. S., and V. Homar, 2016: Potential of sequential EnKF for the short-range prediction of a maritime severe weather event. Atmos. Res., 178–179, 426444, doi:10.1016/j.atmosres.2016.04.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clarke, A. D., T. Uehara, and J. N. Porter, 1997: Atmospheric nuclei and related aerosol fields over the Atlantic: Clean subsiding air and continental pollution during ASTEX. J. Geophys. Res., 102, 25 28125 292, doi:10.1029/97JD01555.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coniglio, M. C., J. Correia Jr., P. T. Marsh, and F. Kong, 2013: Verification of convection-allowing WRF Model forecasts of the planetary boundary layer using sounding observations. Wea. Forecasting, 28, 842862, doi:10.1175/WAF-D-12-00103.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Delgado, J., J. A. Peláez, R. Tomás, A. Estévez, C. López Casado, C. Doménech, and A. Cuenca, 2006: Evaluación de la susceptibilidad de las laderas a sufrir inestabilidades inducidas por terremotos: Aplicación a la cuenca de drenaje del río Serpis (provincia de Alicante). Rev. Soc. Geol. Esp., 19, 197218.

    • Search Google Scholar
    • Export Citation
  • Doswell, C. A., H. E. Brooks, and R. A. Maddox, 1996: Flash flood forecasting: An ingredients-based methodology. Wea. Forecasting, 11, 560581, doi:10.1175/1520-0434(1996)011<0560:FFFAIB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dowell, D. C., F. Zhang, L. J. Wicker, C. Snyder, and N. A. Crook, 2004: Wind and temperature retrievals in the 17 May 1981 Arcadia, Oklahoma, supercell: Ensemble Kalman filter experiments. Mon. Wea. Rev., 132, 19822005, doi:10.1175/1520-0493(2004)132<1982:WATRIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Drobinski, P., and Coauthors, 2014: HyMeX: A 10-year multidisciplinary program on the Mediterranean water cycle. Bull. Amer. Meteor. Soc., 95, 10631082, doi:10.1175/BAMS-D-12-00242.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Du, J., S. L. Mullen, and F. Sanders, 1997: Short-range ensemble forecasting of quantitative precipitation. Mon. Wea. Rev., 125, 24272459, doi:10.1175/1520-0493(1997)125<2427:SREFOQ>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 1989: Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 30773107, doi:10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evans, J. P., M. Ekström, and F. Ji, 2012: Evaluating the performance of a WRF physics ensemble over south-east Australia. Climate Dyn., 39, 12411258, doi:10.1007/s00382-011-1244-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evensen, G., 2003: The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn., 53, 343367, doi:10.1007/s10236-003-0036-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grimit, E. P., and C. F. Mass, 2007: Measuring the ensemble spread–error relationship with a probabilistic approach: Stochastic ensemble results. Mon. Wea. Rev., 135, 203221, doi:10.1175/MWR3262.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guillijns, S., O. B. Mendoza, J. Chandrasekar, B. L. R. De Moor, D. S. Bernstein, and A. Ridley, 2006: What is the ensemble Kalman filter and how well does it work? 2006 American Control Conf., Minneapolis, MN, IEEE, 6 pp., doi:10.1109/ACC.2006.1657419.

    • Crossref
    • Export Citation
  • Hong, S.-Y., and J.-O. J. Lim, 2006: The WRF single-moment 6-class microphysics scheme (WSM6). J. Korean Meteor. Soc., 42 (2), 129151.

    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., N. Yign, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 23182341, doi:10.1175/MWR3199.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houtekamer, P. L., and J. Derome, 1995: Methods for ensemble prediction. Mon. Wea. Rev., 123, 21812196, doi:10.1175/1520-0493(1995)123<2181:MFEP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houtekamer, P. L., and H. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796811, doi:10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, X.-M., J. W. Nielsen-Gammon, and F. Zhang, 2010: Evaluation of three planetary boundary layer schemes in the WRF Model. J. Appl. Meteor. Climatol., 49, 18311843, doi:10.1175/2010JAMC2432.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hudson, J. G., 1993: Cloud condensation nuclei. J. Appl. Meteor., 32, 596607, doi:10.1175/1520-0450(1993)032<0596:CCN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janjić, Z. I., 1994: The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer and turbulence closure schemes. Mon. Wea. Rev., 122, 927945, doi:10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jankov, I., W. A. Gallus Jr., M. Segal, B. Shaw, and S. E. Koch, 2005: The impact of different WRF Model physical parameterizations and their interactions on warm season MCS rainfall. Wea. Forecasting, 20, 10481060, doi:10.1175/WAF888.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kistler, R., and Coauthors, 2001: The NCEP–NCAR 50-Year Reanalysis: Monthly means CD-ROM and documentation. Bull. Amer. Meteor. Soc., 82, 247267, doi:10.1175/1520-0477(2001)082<0247:TNNYRM>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kolios, S., and H. Feidas, 2010: Warm season climatology of mesoscale convective systems in the Mediterranean Basin using satellite data. Theor. Appl. Climatol., 102, 2942, doi:10.1007/s00704-009-0241-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • MAGRAMA , 2011: Mapa de caudales máximos. Tech. Memo., Ministerio de Agricultura, Alimentacion y Medio Ambiente, Madrid, Spain, 67 pp. [Available online at http://www.mapama.gob.es/es/agua/temas/gestion-de-los-riesgos-de-inundacion/memoria_tecnica_v2_junio2011_tcm7-162773.pdf.]

  • Mansell, E. R., C. L. Ziegler, and E. C. Bruning, 2010: Simulated electrification of a small thunderstorm with two-moment bulk microphysics. J. Atmos. Sci., 67, 171194, doi:10.1175/2009JAS2965.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marquis, J., Y. Richardson, P. Markowski, D. Dowell, J. Wurman, K. Kosiba, P. Robinson, and G. Romine, 2014: An investigation of the Goshen County, Wyoming, tornadic supercell of 5 June 2009 using EnKF assimilation of mobile mesonet and radar observations collected during VORTEX2. Part I: Experiment design and verification of the EnKF analyses. Mon. Wea. Rev., 142, 530554, doi:10.1175/MWR-D-13-00007.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marsigli, C., 2009: COSMO Priority Project “Short Range Ensemble Prediction System” (SREPS): Final report. COSMO Tech. Rep. 13, Consortium for Small Scale Modelling, 33 pp. [Available online at http://www2.cosmo-model.org/content/model/documentation/techReports/docs/techReport13.pdf.]

  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16 66316 682, doi:10.1029/97JD00237.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Molteni, F., R. Buizza, T. N. Palmer, and T. Petroliagis, 1996: The ECMWF ensemble prediction system: Methodology and validation. Quart. J. Roy. Meteor. Soc., 122, 73119, doi:10.1002/qj.49712252905.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Montaldo, N., V. Toninelli, J. D. Albertson, M. Mancini, and P. A. Troch, 2003: The effect of background hydrometeorological conditions on the sensitivity of evapotranspiration to model parameters: Analysis with measurements from an Italian alpine catchment. Hydrol. Earth Syst. Sci., 7, 848861, doi:10.5194/hess-7-848-2003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Montaldo, N., G. Ravazzani, and M. Mancini, 2007: On the prediction of the Toce alpine basin floods with distributed hydrologic models. Hydrol. Processes, 21, 608621, doi:10.1002/hyp.6260.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mullen, S. L., and D. P. Baumhefner, 1988: Sensitivity to numerical simulations of explosive oceanic cyclogenesis to changes in physical parameterizations. Mon. Wea. Rev., 116, 22892329, doi:10.1175/1520-0493(1988)116<2289:SONSOE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakanishi, M., and H. Niino, 2006: An improved Mellor–Yamada level 3 model: Its numerical stability and application to a regional prediction of advecting fog. Bound.-Layer Meteor., 119, 397407, doi:10.1007/s10546-005-9030-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nash, J. E., and J. V. Sutcliffe, 1970: River flow forecasting through conceptual models. Part I: A discussion of principles. J. Hydrol., 10, 282290, doi:10.1016/0022-1694(70)90255-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pastor, F., I. Gómez, and M. J. Estrela, 2010: Numerical study of the October 2007 flash flood in the Valencia region (eastern Spain): The role of orography. Nat. Hazards Earth Syst. Sci., 10, 13311345, doi:10.5194/nhess-10-1331-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pielke, R., and Y. Mahrer, 1975: Representation of the heated planetary boundary layer in mesoscale models with coarse vertical resolution. J. Atmos. Sci., 32, 22882308, doi:10.1175/1520-0469(1975)032<2288:ROTHPB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ponce, V. M., and R. H. Hawkins, 1996: Runoff curve number: Has it reached maturity? J. Hydrol. Eng., 1, 1119, doi:10.1061/(ASCE)1084-0699(1996)1:1(11).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Puigdefabregas, J., G. del Barrio, M. M. Boer, L. Gutiérrez, and A. Solé, 1998: Differential responses of hillslope and channel elements to rainfall events in a semi-arid area. Geomorphology, 23, 337351, doi:10.1016/S0169-555X(98)00014-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rabuffetti, D., G. Ravazzani, C. Corbari, and M. Mancini, 2008: Verification of operational quantitative discharge forecast (QDF) for a regional warning system—The AMPHORE case studies in the upper Po River. Nat. Hazards Earth Syst. Sci., 8, 161173, doi:10.5194/nhess-8-161-2008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ravazzani, G., M. Mancini, I. Giudici, and P. Amadio, 2007: Effects of soil moisture parameterization on a real-time flood forecasting system based on rainfall thresholds. Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management, E. Boegh et al., Eds., IAHS Publ. 313, 407–416.

    • Search Google Scholar
    • Export Citation
  • Ravazzani, G., C. Corbari, S. Morella, P. Gianoli, and M. Mancini, 2012: Modified Hargreaves–Samani equation for the assessment of reference evapotranspiration in Alpine river basins. J. Irrig. Drain. Eng., 138, 592599, doi:10.1061/(ASCE)IR.1943-4774.0000453.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ravazzani, G., P. Gianoli, S. Meucci, and M. Mancini, 2014: Assessing downstream impacts of detention basins in urbanized river basins using a distributed hydrological model. Water Resour. Manage., 28, 10331044, doi:10.1007/s11269-014-0532-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ravazzani, G., A. Amengual, A. Ceppi, V. Homar, R. Romero, G. Lombardia, and M. Mancini, 2016: Potentialities of ensemble strategies for flood forecasting over the Milano urban area. J. Hydrol., 539, 237253, doi:10.1016/j.jhydrol.2016.05.023.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roberts, N. M., and H. W. Lean, 2008: Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Wea. Rev., 136, 7897, doi:10.1175/2007MWR2123.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Romero, R., J. A. Guijarro, C. Ramis, and S. Alonso, 1998: A 30-year (1964–1993) daily rainfall data base for the Spanish Mediterranean regions: First exploratory study. Int. J. Climatol., 18, 541560, doi:10.1002/(SICI)1097-0088(199804)18:5<541::AID-JOC270>3.0.CO;2-N.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scherrer, S. C., C. Appenzeller, P. Eckert, and D. Cattani, 2004: Analysis of the spread–skill relations using the ECMWF ensemble prediction system over Europe. Wea. Forecasting, 19, 552565, doi:10.1175/1520-0434(2004)019<0552:AOTSRU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., and Coauthors, 2010: Toward improved convection-allowing ensembles: Model physics sensitivities and optimizing probabilistic guidance with small ensemble membership. Wea. Forecasting, 25, 263280, doi:10.1175/2009WAF2222267.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sippel, J. A., S. A. Braun, F. Zhang, and Y. Weng, 2013: Ensemble Kalman filter assimilation of simulated HIWRAP Doppler velocity data in a hurricane. Mon. Wea. Rev., 141, 26832704, doi:10.1175/MWR-D-12-00157.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., doi:10.5065/D68S4MVH.

    • Crossref
    • Export Citation
  • Snyder, C., and F. Zhang, 2003: Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 131, 16631677, doi:10.1175//2555.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stanski, H. R., L. J. Wilson, and W. R. Burrows, 1989: Survey of common verification methods in meteorology. 2nd ed., Research Rep. MSRB 89-5, WWW Tech. Rep. 8, WMO/TD 358, World Meteorological Organization. [Available online at http://www.cawcr.gov.au/projects/verification/Stanski_et_al/Stanski_et_al.html.]

  • Stensrud, D. J., J.-W. Bao, and T. T. Warner, 2000: Using initial and model physics perturbations in short-range ensemble simulations of mesoscale convective events. Mon. Wea. Rev., 128, 20772107, doi:10.1175/1520-0493(2000)128<2077:UICAMP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tanamachi, R. L., L. J. Wicker, D. C. Dowell, H. B. Bluestein, D. T. Dawson, and M. Xue, 2013: EnKF assimilation of high-resolution, mobile Doppler radar data of the 4 May 2007 Greensburg, Kansas, supercell into a numerical cloud model. Mon. Wea. Rev., 141, 625648, doi:10.1175/MWR-D-12-00099.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tapiador, F. J., W. K. Tao, J. J. Shi, C. F. Angelis, M. A. Martinez, C. Marcos, A. Rodríguez, and A. Hou, 2012: A comparison of perturbed initial conditions and multiphysics ensembles in a severe weather episode in Spain. J. Appl. Meteor. Climatol., 51, 489504, doi:10.1175/JAMC-D-11-041.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106, 71837192, doi:10.1029/2000JD900719.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tewari, M., and Coauthors, 2004: Implementation and verification of the unified NOAH land surface model in the WRF Model. 20th Conf. on Weather Analysis and Forecasting/16th Conf. on Numerical Weather Prediction, Seattle, WA, Amer. Meteor. Soc., 14.2a. [Available online at https://ams.confex.com/ams/84Annual/techprogram/paper_69061.htm.]

  • Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 50955115, doi:10.1175/2008MWR2387.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Toth, Z., and E. Kalnay, 1993: Ensemble forecasting at NMC: The generation of perturbations. Bull. Amer. Meteor. Soc., 74, 23172330, doi:10.1175/1520-0477(1993)074<2317:EFANTG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Uppala, S. M., and Coauthors, 2005: The ERA-40 re-analysis. Quart. J. Roy. Meteor. Soc., 131, 29613012, doi:10.1256/qj.04.176.

  • USDA, 1986: Urban hydrology for small watersheds. USDA TR-55, 164 pp. [Available online at https://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/stelprdb1044171.pdf.]

  • Verbunt, M., A. Walser, J. Gurtz, A. Montani, and C. Schär, 2007: Probabilistic flood forecasting with a limited-area ensemble prediction system: Selected case studies. J. Hydrometeor., 8, 897909, doi:10.1175/JHM594.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vincendon, B., V. Ducrocq, O. Nuissier, and B. Vié, 2011: Perturbation of convection-permitting NWP forecasts for flash-flood ensemble forecasting. Nat. Hazards Earth Syst. Sci., 11, 15291544, doi:10.5194/nhess-11-1529-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., W. C. Skamarock, and J. B. Klemp, 1997: The resolution dependence of explicitly modeled convective systems. Mon. Wea. Rev., 125, 527548, doi:10.1175/1520-0493(1997)125<0527:TRDOEM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Academic Press, 648 pp.

  • Zhang, F., C. Snyder, and J. Sun, 2004: Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble Kalman filter. Mon. Wea. Rev., 132, 12381253, doi:10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zheng, Y., K. Alapaty, J. A. Herwehe, A. D. Del Genio, and D. Niyogi, 2016: Improving high-resolution weather forecasts using the Weather Research and Forecasting (WRF) Model with an updated Kain–Fritsch scheme. Mon. Wea. Rev., 144, 833860, doi:10.1175/MWR-D-15-0005.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 6 6 6
PDF Downloads 6 6 6

A Comparison of Ensemble Strategies for Flash Flood Forecasting: The 12 October 2007 Case Study in Valencia, Spain

View More View Less
  • 1 Grup de Meteorologia, Departament de Física, Universitat de les Illes Balears, Palma, Mallorca, Spain
  • | 2 Gruppo di Idrologia Fisica, Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Milan, Italy
  • | 3 Grup de Meteorologia, Departament de Física, Universitat de les Illes Balears, Palma, Mallorca, Spain
Restricted access

Abstract

On 12 October 2007, several flash floods affected the Valencia region, eastern Spain, with devastating impacts in terms of human, social, and economic losses. An enhanced modeling and forecasting of these extremes, which can provide a tangible basis for flood early warning procedures and mitigation measures over the Mediterranean, is one of the fundamental motivations of the international Hydrological Cycle in the Mediterranean Experiment (HyMeX) program. The predictability bounds set by multiple sources of hydrological and meteorological uncertainty require their explicit representation in hydrometeorological forecasting systems. By including local convective precipitation systems, short-range ensemble prediction systems (SREPSs) provide a state-of-the-art framework to generate quantitative discharge forecasts and to cope with different sources of external-scale (i.e., external to the hydrological system) uncertainties. The performance of three distinct hydrological ensemble prediction systems (HEPSs) for the small-sized Serpis River basin is examined as a support tool for early warning and mitigation strategies. To this end, the Flash-Flood Event–Based Spatially Distributed Rainfall–Runoff Transformation–Water Balance (FEST-WB) model is driven by ground stations to examine the hydrological response of this semiarid and karstic catchment to heavy rains. The use of a multisite and novel calibration approach for the FEST-WB parameters is necessary to cope with the high nonlinearities emerging from the rainfall–runoff transformation and heterogeneities in the basin response. After calibration, FEST-WB reproduces with remarkable accuracy the hydrological response to intense precipitation and, in particular, the 12 October 2007 flash flood. Next, the flood predictability challenge is focused on quantitative precipitation forecasts (QPFs). In this regard, three SREPS generation strategies using the WRF Model are analyzed. On the one side, two SREPSs accounting for 1) uncertainties in the initial conditions (ICs) and lateral boundary conditions (LBCs) and 2) physical parameterizations are evaluated. An ensemble Kalman filter (EnKF) is also designed to test the ability of ensemble data assimilation methods to represent key mesoscale uncertainties from both IC and subscale processes. Results indicate that accounting for diversity in the physical parameterization schemes provides the best probabilistic high-resolution QPFs for this particular flash flood event. For low to moderate precipitation rates, EnKF and pure multiple physics approaches render undistinguishable accuracy for the test situation at larger scales. However, only the multiple physics QPFs properly drive the HEPS to render the most accurate flood warning signals. That is, extreme precipitation values produced by these convective-scale precipitation systems anchored by complex orography are better forecast when accounting just for uncertainties in the physical parameterizations. These findings contribute to the identification of ensemble strategies better targeted to the most relevant sources of uncertainty before flash flood situations over small catchments.

© 2017 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 e-mail: Arnau Amengual, arnau.amengual@uib.es

Abstract

On 12 October 2007, several flash floods affected the Valencia region, eastern Spain, with devastating impacts in terms of human, social, and economic losses. An enhanced modeling and forecasting of these extremes, which can provide a tangible basis for flood early warning procedures and mitigation measures over the Mediterranean, is one of the fundamental motivations of the international Hydrological Cycle in the Mediterranean Experiment (HyMeX) program. The predictability bounds set by multiple sources of hydrological and meteorological uncertainty require their explicit representation in hydrometeorological forecasting systems. By including local convective precipitation systems, short-range ensemble prediction systems (SREPSs) provide a state-of-the-art framework to generate quantitative discharge forecasts and to cope with different sources of external-scale (i.e., external to the hydrological system) uncertainties. The performance of three distinct hydrological ensemble prediction systems (HEPSs) for the small-sized Serpis River basin is examined as a support tool for early warning and mitigation strategies. To this end, the Flash-Flood Event–Based Spatially Distributed Rainfall–Runoff Transformation–Water Balance (FEST-WB) model is driven by ground stations to examine the hydrological response of this semiarid and karstic catchment to heavy rains. The use of a multisite and novel calibration approach for the FEST-WB parameters is necessary to cope with the high nonlinearities emerging from the rainfall–runoff transformation and heterogeneities in the basin response. After calibration, FEST-WB reproduces with remarkable accuracy the hydrological response to intense precipitation and, in particular, the 12 October 2007 flash flood. Next, the flood predictability challenge is focused on quantitative precipitation forecasts (QPFs). In this regard, three SREPS generation strategies using the WRF Model are analyzed. On the one side, two SREPSs accounting for 1) uncertainties in the initial conditions (ICs) and lateral boundary conditions (LBCs) and 2) physical parameterizations are evaluated. An ensemble Kalman filter (EnKF) is also designed to test the ability of ensemble data assimilation methods to represent key mesoscale uncertainties from both IC and subscale processes. Results indicate that accounting for diversity in the physical parameterization schemes provides the best probabilistic high-resolution QPFs for this particular flash flood event. For low to moderate precipitation rates, EnKF and pure multiple physics approaches render undistinguishable accuracy for the test situation at larger scales. However, only the multiple physics QPFs properly drive the HEPS to render the most accurate flood warning signals. That is, extreme precipitation values produced by these convective-scale precipitation systems anchored by complex orography are better forecast when accounting just for uncertainties in the physical parameterizations. These findings contribute to the identification of ensemble strategies better targeted to the most relevant sources of uncertainty before flash flood situations over small catchments.

© 2017 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 e-mail: Arnau Amengual, arnau.amengual@uib.es
Save