• AP, 2015: Texas floods: Damage tops $45m in Houston as state starts to rebuild. Guardian, 31 May, https://www.theguardian.com/us-news/2015/may/31/texas-floods-damage-missing-deaths.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ashley, S. T., and W. S. Ashley, 2008: Flood fatalities in the United States. J. Appl. Meteor. Climatol., 47, 805818, https://doi.org/10.1175/2007JAMC1611.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ashley, W. S., T. L. Mote, P. G. Dixon, S. L. Trotter, E. J. Powell, J. D. Durkee, and A. J. Grundstein, 2003: Distribution of mesoscale convective complex rainfall in the United States. Mon. Wea. Rev., 131, 30033017, https://doi.org/10.1175/1520-0493(2003)131<3003:DOMCCR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bartels, D. L., and R. A. Maddox, 1991: Midlevel cyclonic vortices generated by mesoscale convective systems. Mon. Wea. Rev., 119, 104118, https://doi.org/10.1175/1520-0493(1991)119<0104:MCVGBM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barthold, F. E., T. E. Workoff, B. A. Cosgrove, J. J. Gourley, D. R. Novak, and K. M. Mahoney, 2015: Improving flash flood forecasts: The HMT-WPC flash flood and intense rainfall experiment. Bull. Amer. Meteor. Soc., 96, 18591866, https://doi.org/10.1175/BAMS-D-14-00201.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berghuijs, W. R., R. A. Woods, C. J. Hutton, and M. Sivapalan, 2016: Dominant flood generating mechanisms across the United States. Geophys. Res. Lett., 43, 43824390, https://doi.org/10.1002/2016GL068070.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Best, M. J., and Coauthors, 2015: The plumbing of land surface models: Benchmarking model performance. J. Hydrometeor., 16, 14251442, https://doi.org/10.1175/JHM-D-14-0158.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boone, A., and Coauthors, 2009: The AMMA Land Surface Model Intercomparison Project (ALMIP). Bull. Amer. Meteor. Soc., 90, 18651880, https://doi.org/10.1175/2009BAMS2786.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bosart, L. F., and F. Sanders, 1981: The Johnstown flood of July 1977: A long-lived convective system. J. Atmos. Sci., 38, 16161642, https://doi.org/10.1175/1520-0469(1981)038<1616:TJFOJA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bowman, K. P., and C. R. Homeyer, 2017: GridRad—Three-dimensional gridded NEXRAD WSR-88D radar data. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, accessed 13 October 2022, https://doi.org/10.5065/D6NK3CR7.

    • Crossref
    • Export Citation
  • Cui, W., X. Dong, B. Xi, and Z. Feng, 2021: Climatology of linear mesoscale convective system morphology in the United States based on random forests method. J. Climate, 34, 72577276, https://doi.org/10.1175/JCLI-D-20-0862.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dougherty, E., and K. L. Rasmussen, 2019: Climatology of flood-producing storms and their associated rainfall characteristics in the United States. Mon. Wea. Rev., 147, 38613877, https://doi.org/10.1175/MWR-D-19-0020.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fan, Y., and Coauthors, 2019: Hillslope hydrology in global change research and Earth system modeling. Water Resour. Res., 55, 17371772, https://doi.org/10.1029/2018WR023903.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, Z., 2019: Mesoscale convective system (MCS) database over United States. USDOE Office of Science, accessed 13 October 2022, https://doi.org/10.5439/1571643.

    • Crossref
    • Export Citation
  • Feng, Z., S. Hagos, A. K. Rowe, C. D. Burleyson, M. N. Martini, and S. P. de Szoeke, 2015: Mechanisms of convective cloud organization by cold pools over tropical warm ocean during the AMIE/DYNAMO field campaign. J. Adv. Model. Earth Syst., 7, 357381, https://doi.org/10.1002/2014MS000384.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, Z., L. R. Leung, R. A. Houze Jr, S. Hagos, J. Hardin, Q. Yang, B. Han, and J. Fan, 2018: Structure and evolution of mesoscale convective systems: Sensitivity to cloud microphysics in convection‐permitting simulations over the United States. J. Adv. Model. Earth Syst., 10, 14701494, https://doi.org/10.1029/2018MS001305.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, Z., R. A. Houze Jr, L. R. Leung, F. Song, J. C. Hardin, J. Wang, W. I. Gustafson Jr, and C. R. Homeyer, 2019: Spatiotemporal characteristics and large-scale environments of mesoscale convective systems east of the Rocky Mountains. J. Climate, 32, 73037328, https://doi.org/10.1175/JCLI-D-19-0137.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fritsch, J. M., R. J. Kane, and C. R. Chelius, 1986: The contribution of mesoscale convective weather systems to the warm-season precipitation in the United States. J. Appl. Meteor. Climatol., 25, 1333–1345, https://doi.org/10.1175/1520-0450(1986)025<1333:TCOMCW>2.0.CO;2.

    • Crossref
    • Export Citation
  • Fritsch, J. M., J. D. Murphy, and J. S. Kain, 1994: Warm core vortex amplification over land. J. Atmos. Sci., 51, 17801807, https://doi.org/10.1175/1520-0469(1994)051<1780:WCVAOL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Geerts, B., 1998: Mesoscale convective systems in the southeast United States during 1994–95: A survey. Wea. Forecasting, 13, 860869, https://doi.org/10.1175/1520-0434(1998)013<0860:MCSITS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Getirana, A. C. V., A. Boone, and C. Peugeot, 2014: Evaluating LSM-based water budgets over a West African basin assisted with a river routing scheme. J. Hydrometeor., 15, 23312346, https://doi.org/10.1175/JHM-D-14-0012.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gochis, D. J., and Coauthors, 2018: The WRF-Hydro modeling system technical description (version 5.0). NCAR Tech. Note, 107 pp., https://ral.ucar.edu/sites/default/files/public/WRF-HydroV5TechnicalDescription_update512019_0.pdf.

    • Crossref
    • Export Citation
  • Gourley, J. J., and Coauthors, 2013: A unified flash flood database across the United States. Bull. Amer. Meteor. Soc., 94, 799805, https://doi.org/10.1175/BAMS-D-12-00198.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haberlie, A. M., and W. S. Ashley, 2019: A radar-based climatology of mesoscale convective systems in the United States. J. Climate, 32, 15911606, https://doi.org/10.1175/JCLI-D-18-0559.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A., Jr, 2004: Mesoscale convective systems. Rev. Geophys., 42, RG4003, https://doi.org/10.1029/2004RG000150.

  • Houze, R. A., Jr, B. F. Smull, and P. Dodge, 1990: Mesoscale organization of springtime rainstorms in Oklahoma. Mon. Wea. Rev., 118 613654, https://doi.org/10.1175/1520-0493(1990)118<0613:MOOSRI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, H., L. R. Leung, and Z. Feng, 2020a: Observed warm‐season characteristics of MCS and non‐MCS rainfall and their recent changes in the central United States. Geophys. Res. Lett., 47, e2019GL086783, https://doi.org/10.1029/2019GL086783.

    • Search Google Scholar
    • Export Citation
  • Hu, H., L. R. Leung, and Z. Feng, 2020b: Understanding the distinct impacts of MCS and non-MCS rainfall on the surface water balance in the central US using a numerical water-tagging technique. J. Hydrometeor., 21, 2343–2357, https://doi.org/10.1175/JHM-D-20-0081.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, H., Z. Feng, and L.-Y. R. Leung, 2021a: Linking flood frequency with mesoscale convective systems in the US. Geophys. Res. Lett., 48, e2021GL092546, https://doi.org/10.1029/2021GL092546.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, H., L. R. Leung, and Z. Feng, 2021b: Early warm-season mesoscale convective systems dominate soil moisture–precipitation feedback for summer rainfall in central United States. Proc. Natl. Acad. Sci. USA, 118, e2105260118, https://doi.org/10.1073/pnas.2105260118.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janowiak, J. E., R. J. Joyce, and Y. Yarosh, 2001: A real-time global half-hourly pixel-resolution infrared dataset and its applications. Bull. Amer. Meteor. Soc., 82, 205218, https://doi.org/10.1175/1520-0477(2001)082<0205:ARTGHH>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kundzewicz, Z. W., and Coauthors, 2014: Flood risk and climate change: Global and regional perspectives. Hydrol. Sci. J., 59, 128, https://doi.org/10.1080/02626667.2013.857411.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kunkel, K. E., D. R. Easterling, D. A. R. Kristovich, B. Gleason, L. Stoecker, and R. Smith, 2012: Meteorological causes of the secular variations in observed extreme precipitation events for the conterminous United States. J. Hydrometeor., 13, 11311141, https://doi.org/10.1175/JHM-D-11-0108.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Laing, A. G., and J. M. Fritsch, 1997: The global population of mesoscale convective complexes. Quart. J. Roy. Meteor. Soc., 123, 389405, https://doi.org/10.1002/qj.49712353807.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, Y., 2011: GCIP/EOP surface: Precipitation NCEP/EMC 4KM Gridded Data (GRIB) Stage IV Data, version 1.0. UCAR/NCAR-Earth Observing Laboratory, accessed 13 October 2022, https://data.eol.ucar.edu/dataset/21.006.

    • Crossref
    • Export Citation
  • Maddox, R. A., L. R. Hoxit, C. F. Chappell, and F. Caracena, 1978: Comparison of meteorological aspects of the Big Thompson and Rapid City flash floods. Mon. Wea. Rev., 106, 375389, https://doi.org/10.1175/1520-0493(1978)106<0375:COMAOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martinez, M., and B. Brunfeld, 2015: Texas floods: Enough rain fell in May to cover the entire state 8 inches deep. CNN, 31 May 2015, https://www.cnn.com/2015/05/31/us/severe-weather/index.html.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Merz, B., and Coauthors, 2014: Floods and climate: Emerging perspectives for flood risk assessment and management. Nat. Hazards Earth Syst. Sci., 14, 19211942, https://doi.org/10.5194/nhess-14-1921-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Niu, G.-Y., and Coauthors, 2011: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NWS, 2018: National Weather Service Instruction 10-1065. National Oceanic and Atmospheric Administration, http://www.nws.noaa.gov/directives/.

    • Crossref
    • Export Citation
  • Parker, M. D., and R. H. Johnson, 2000: Organizational modes of midlatitude mesoscale convective systems. Mon. Wea. Rev., 128, 34133436, https://doi.org/10.1175/1520-0493(2001)129<3413:OMOMMC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pinto, J. O., J. A. Grim, and M. Steiner, 2015: Assessment of the High-Resolution Rapid Refresh Model’s Ability to predict mesoscale convective systems using object-based evaluation. Wea. Forecasting, 30, 892913, https://doi.org/10.1175/WAF-D-14-00118.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prein, A. F., C. Liu, K. Ikeda, R. Bullock, R. M. Rasmussen, G. J. Holland, and M. Clark, 2017: Simulating North American mesoscale convective systems with a convection-permitting climate model. Climate Dyn., 55, 95–110, https://doi.org/10.1007/s00382-017-3993-2.

    • Crossref
    • Export Citation
  • Ralph, F. M., P. J. Neiman, G. N. Kiladis, K. Weickmann, and D. W. Reynolds, 2011: A multiscale observational case study of a Pacific atmospheric river exhibiting tropical–extratropical connections and a mesoscale frontal wave. Mon. Wea. Rev., 139, 11691189, https://doi.org/10.1175/2010MWR3596.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saharia, M., P.-E. Kirstetter, H. Vergara, J. J. Gourley, and Y. Hong, 2017: Characterization of floods in the United States. J. Hydrol., 548, 524535, https://doi.org/10.1016/j.jhydrol.2017.03.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schumacher, R. S., and R. H. Johnson, 2005: Organization and environmental properties of extreme-rain-producing mesoscale convective systems. Mon. Wea. Rev., 133, 961976, https://doi.org/10.1175/MWR2899.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schumacher, R. S., and R. H. Johnson, 2008: Mesoscale processes contributing to extreme rainfall in a midlatitude warm-season flash flood. Mon. Wea. Rev., 136, 39643986, https://doi.org/10.1175/2008MWR2471.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sharma, A., C. Wasko, and D. P. Lettenmaier, 2018: If precipitation extremes are increasing, why aren’t floods? Water Resour. Res., 54, 85458551, https://doi.org/10.1029/2018WR023749.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Song, F., Z. Feng, L. R. Leung, R. A. Houze Jr., J. Wang, J. Hardin, and C. R. Homeyer, 2019: Contrasting spring and summer large-scale environments associated with mesoscale convective systems over the U.S. Great Plains. J. Climate, 32, 67496767, https://doi.org/10.1175/JCLI-D-18-0839.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stevenson, S. N., and R. S. Schumacher, 2014: A 10-year survey of extreme rainfall events in the central and eastern United States using gridded multisensor precipitation analyses. Mon. Wea. Rev., 142, 31473162, https://doi.org/10.1175/MWR-D-13-00345.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tang, Y., J. Winkler, S. Zhong, X. Bian, D. Doubler, L. Yu, and C. Walters, 2017: Future changes in the climatology of the Great Plains low-level jet derived from fine resolution multi-model simulations. Sci. Rep., 7, 5029, https://doi.org/10.1038/s41598-017-05135-0.

    • Search Google Scholar
    • Export Citation
  • Torres-Alavez, J. A., and Coauthors, 2021: Future projections in the climatology of global low-level jets from CORDEX-CORE simulations. Climate Dyn., 57, 15511569, https://doi.org/10.1007/s00382-021-05671-6.

    • Search Google Scholar
    • Export Citation
  • Trier, S. B., and C. A. Davis, 2002: Influence of balanced motions on heavy precipitation within a long-lived convectively generated vortex. Mon. Wea. Rev., 130, 877899, https://doi.org/10.1175/1520-0493(2002)130<0877:IOBMOH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Trier, S. B., C. A. Davis, and W. C. Skamarock, 2000: Long-lived mesoconvective vortices and their environment. Part II: Induced thermodynamic destabilization in idealized simulations. Mon. Wea. Rev., 128, 33963412, https://doi.org/10.1175/1520-0493(2000)128<3396:LLMVAT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Trier, S. B., C. A. Davis, D. A. Ahijevych, M. L. Weisman, and G. H. Bryan, 2006: Mechanisms supporting long-lived episodes of propagating nocturnal convection within a 7-day WRF Model simulation. J. Atmos. Sci., 63, 24372461, https://doi.org/10.1175/JAS3768.1.

    • Search Google Scholar
    • Export Citation
  • Trier, S. B., F. Chen, K. W. Manning, M. A. LeMone, and C. A. Davis, 2008: Sensitivity of the PBL and precipitation in 12-day simulations of warm-season convection using different land surface models and soil wetness conditions. Mon. Wea. Rev., 136, 23212343, https://doi.org/10.1175/2007mwr2289.1.

    • Search Google Scholar
    • Export Citation
  • Trier, S. B., F. Chen, C. A. Davis, and R. E. Carbone, 2014: Mechanisms governing the persistence and diurnal cycle of a heavy rainfall corridor. J. Atmos. Sci., 71, 41024126, https://doi.org/10.1175/JAS-D-14-0134.1.

    • Search Google Scholar
    • Export Citation
  • Tuttle, J. D., and C. A. Davis, 2006: Corridors of warm season precipitation in the central United States. Mon. Wea. Rev., 134, 22972317, https://doi.org/10.1175/MWR3188.1.

    • Search Google Scholar
    • Export Citation
  • Villarini, G., 2016: On the seasonality of flooding across the continental United States. Adv. Water Resour., 87, 8091, https://doi.org/10.1016/j.advwatres.2015.11.009.

    • Search Google Scholar
    • Export Citation
  • Vischel, T., T. Lebel, S. Massuel, and B. Cappelaere, 2009: Conditional simulation schemes of rain fields and their application to rainfall–runoff modeling studies in the Sahel. J. Hydrol., 375, 273286, https://doi.org/10.1016/j.jhydrol.2009.02.028.

    • Search Google Scholar
    • Export Citation
  • Wang, S.-Y. S., W.-R. Huang, H.-H. Hsu, and R. R. Gillies, 2015: Role of the strengthened El Niño teleconnection in the May 2015 floods over the southern Great Plains. Geophys. Res. Lett., 42, 81408146, https://doi.org/10.1002/2015GL065211.

    • Search Google Scholar
    • Export Citation
  • Wright, D. B., G. Yu, and J. F. England, 2020: Six decades of rainfall and flood frequency analysis using stochastic storm transposition: Review, progress, and prospects. J. Hydrol., 585, 124816, https://doi.org/10.1016/j.jhydrol.2020.124816.

    • Search Google Scholar
    • Export Citation
  • Ye, S., H.-Y. Li, L. R. Leung, J. Guo, Q. Ran, Y. Demissie, and M. Sivapalan, 2017: Understanding flood seasonality and its temporal shifts within the contiguous United States. J. Hydrometeor., 18, 19972009, https://doi.org/10.1175/JHM-D-16-0207.1.

    • Search Google Scholar
    • Export Citation
  • Zhu, Z., D. B. Wright, and G. Yu, 2018: The impact of rainfall space‐time structure in flood frequency analysis. Water Resour. Res., 54, 89838998, https://doi.org/10.1029/2018WR023550.

    • Search Google Scholar
    • Export Citation
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Quantifying Flood Frequency Associated with Clustered Mesoscale Convective Systems in the United States

Huancui HuaAtmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington

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Zhe FengaAtmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington

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L. Ruby LeungaAtmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington

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Abstract

Mesoscale convective systems (MCSs) that are clustered in time and space can have a broader impact on flooding because they have larger area coverage than that of individual MCSs. The goal of this study is to understand the flood likelihood associated with MCS clusters. To achieve this, floods in the Storm Events Database in April–August of 2007–17 are matched with clustered MCSs identified from a high-resolution MCS dataset and terrestrial conditions in a land surface dataset over the central-eastern United States. Our analysis indicates that clustered MCSs preferentially occurring in April–June are more effective at producing floods, which also last longer due to the greater rainfall per area and wetter initial soil conditions and, hence, produce greater runoff per area than nonclustered MCSs. Similar increases of flood occurrence with cluster-total rainfall size and wetter soils are also observed for each MCS cluster, especially for the overlapping rainfall areas within each cluster. These areas receive rainfall from multiple MCSs that progressively wet the soils and are therefore associated with higher flood likelihood. This study underscores the importance to understand clustered MCSs to better understand flood risks and their future changes.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding authors: Huancui Hu, huancui.hu@pnnl.gov; L. Ruby Leung, ruby.leung@pnnl.gov

Abstract

Mesoscale convective systems (MCSs) that are clustered in time and space can have a broader impact on flooding because they have larger area coverage than that of individual MCSs. The goal of this study is to understand the flood likelihood associated with MCS clusters. To achieve this, floods in the Storm Events Database in April–August of 2007–17 are matched with clustered MCSs identified from a high-resolution MCS dataset and terrestrial conditions in a land surface dataset over the central-eastern United States. Our analysis indicates that clustered MCSs preferentially occurring in April–June are more effective at producing floods, which also last longer due to the greater rainfall per area and wetter initial soil conditions and, hence, produce greater runoff per area than nonclustered MCSs. Similar increases of flood occurrence with cluster-total rainfall size and wetter soils are also observed for each MCS cluster, especially for the overlapping rainfall areas within each cluster. These areas receive rainfall from multiple MCSs that progressively wet the soils and are therefore associated with higher flood likelihood. This study underscores the importance to understand clustered MCSs to better understand flood risks and their future changes.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding authors: Huancui Hu, huancui.hu@pnnl.gov; L. Ruby Leung, ruby.leung@pnnl.gov

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