• Bedient, P. B., W. C. Huber, and B. E. Vieux, 2013: Hydrology and Floodplain Analysis. 5th ed. Pearson Education, 801 pp.

  • Bivand, R. S., E. J. Pebesma, and V. Gomez-Rubo, 2008: Applied Spatial Data Analysis with R. Springer, 378 pp.

  • Buchanan, B. P., Z. M. Easton, R. L. Schneider, and M. T. Walter, 2013: Modeling the hydrologic effects of roadside ditch networks on receiving waters. J. Hydrol., 486, 293305, doi:10.1016/j.jhydrol.2013.01.040.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Dunne, T., and R. D. Black, 1970: Partial area contributions to storm runoff in a small New England watershed. Water Resour. Res., 6, 12961311, doi:10.1029/WR006i005p01296.

    • Search Google Scholar
    • Export Citation
  • Georgakakos, K. P., 2006: Analytical results for operational flash flood guidance. J. Hydrol., 317, 81103, doi:10.1016/j.jhydrol.2005.05.009.

    • Search Google Scholar
    • Export Citation
  • Gesch, D. B., 2007: The National Elevation Dataset. Digital Elevation Model Technologies and Applications: The DEM Users Manual, 2nd ed. D. F. Maune, Ed., American Society for Photogrammetry and Remote Sensing, 99–118.

  • Gesch, D. B., M. Oimoen, S. Greenlee, C. Nelson, M. Steuck, and D. Tyler, 2002: The National Elevation Dataset. Photogramm. Eng. Remote Sens., 68, 511.

    • Search Google Scholar
    • Export Citation
  • Hollis, G. E., 1975: Effect of urbanization on floods of different recurrence interval. Water Resour. Res., 11, 431435, doi:10.1029/WR011i003p00431.

    • Search Google Scholar
    • Export Citation
  • Homa, E. S., C. Brown, K. McGarigal, B. W. Compton, and S. D. Jackson, 2013: Estimating hydrologic alteration from basin characteristics in Massachusetts. J. Hydrol., 503, 196208, doi:10.1016/j.jhydrol.2013.09.008.

    • Search Google Scholar
    • Export Citation
  • Horton, R. E., 1940: An approach toward a physical interpretation of infiltration-capacity. Soil Sci. Soc. Amer. Proc., 5, 399417, doi:10.2136/sssaj1941.036159950005000C0075x.

    • Search Google Scholar
    • Export Citation
  • Javelle, P., C. Fouchier, P. Arnaud, and J. Lavabre, 2010: Flash flood warning at ungauged locations using radar rainfall and antecedent soil moisture estimations. J. Hydrol., 394, 267274, doi:10.1016/j.jhydrol.2010.03.032.

    • Search Google Scholar
    • Export Citation
  • Jessup, S. M., and A. T. DeGaetano, 2008: A statistical comparison of the properties of flash flooding and nonflooding precipitation events in portions of New York and Pennsylvania. Wea. Forecasting, 23, 114130, doi:10.1175/2007WAF2006066.1.

    • Search Google Scholar
    • Export Citation
  • Jessup, S. M., and S. J. Colucci, 2012: Organization of flash-flood-producing precipitation in the northeast United States. Wea. Forecasting, 27, 345361, doi:10.1175/WAF-D-11-00026.1.

    • Search Google Scholar
    • Export Citation
  • Lee, D., 2013: CARBayes: An R package for Bayesian spatial modeling with conditional autoregressive priors. J. Stat. Software, 55, 124, doi:10.18637/jss.v055.i13.

    • Search Google Scholar
    • Export Citation
  • Leopold, L. B., 1991: Lag times for small drainage basins. Catena, 18, 157171, doi:10.1016/0341-8162(91)90014-O.

  • Leroux, B. G., X. Lei, and N. Breslow, 2000: Estimation of disease rates in small areas: A new mixed model for spatial dependence. Statistical Models in Epidemiology, the Environment, and Clinical Trials, Springer, 179–191.

  • Maddox, R. A., C. F. Chappell, and L. R. Hoxit, 1979: Synoptic and meso-α scale aspects of flash flood events. Bull. Amer. Meteor. Soc., 60, 115123, doi:10.1175/1520-0477-60.2.115.

    • Search Google Scholar
    • Export Citation
  • NCEI, 2015: Storm Events Database. NOAA, accessed 7 January 2016. [Available online at http://www.ncdc.noaa.gov/stormevents.]

  • NCEI, 2016: Storm Data FAQ page. NOAA, accessed 3 May 2016. [Available online at https://www.ncdc.noaa.gov/stormevents/faq.jsp.]

  • Nikolopoulos, E. I., E. N. Anagnostou, M. Borga, E. R. Vivoni, and A. Papadopoulos, 2011: Sensitivity of a mountain basin flash flood to initial wetness condition and rainfall variability. J. Hydrol., 402, 165178, doi:10.1016/j.jhydrol.2010.12.020.

    • Search Google Scholar
    • Export Citation
  • NRCC, 2013: Extreme precipitation in New York and New England. Cornell University, accessed 10 June 2011. [Available online at http://precip.eas.cornell.edu.]

  • NRCS, 2010: U.S. General Soil Map (STATSGO2). U.S. Department of Agriculture, accessed 9 March 2010. [Available online at http://sdmdataaccess.nrcs.usda.gov.]

  • Ntelekos, A. A., K. P. Georgakakos, and W. F. Krajewski, 2006: On the uncertainties of flash flood guidance: Toward probabilistic forecasting of flash floods. J. Hydrometeor., 7, 896915, doi:10.1175/JHM529.1.

    • Search Google Scholar
    • Export Citation
  • NWS, 2012: National Weather Service manual 10-950. Definitions and general terminology. NOAA, 5 pp. [Available online at http://www.nws.noaa.gov/directives/sym/pd01009050curr.pdf.]

  • NWS, 2014: Hydrologic Information Center—Flood loss data. NOAA, accessed 25 September 2014. [Available online at http://www.nws.noaa.gov/hic/index.shtml.]

  • NWS, 2015: Performance Management. NOAA, accessed 7 January 2016. [Available online at https://verification.nws.noaa.gov.]

  • NWS, 2016: Central NY/northeast PA flash flood climatology. NOAA, accessed 12 May 2016. [Available online at http://www.weather.gov/bgm/hydrologyFlashFloodClimo.]

  • R Core Team, 2013: R: A language and environment for statistical computing. R Foundation for Statistical Computing. [Available online at http://www.R-project.org/.]

  • Rose, S., and N. E. Peters, 2001: Effects of urbanization on streamflow in the Atlanta area (Georgia, USA): A comparative hydrological approach. Hydrol. Processes, 15, 14411457, doi:10.1002/hyp.218.

    • Search Google Scholar
    • Export Citation
  • Schmittner, K. E., and P. Giresse, 1996: Modelling and application of the geomorphic and environmental controls on flash flood flow. Geomorphology, 16, 337347, doi:10.1016/0169-555X(96)00002-5.

    • Search Google Scholar
    • Export Citation
  • Smith, G., 2003: Flash flood potential: Determining the hydrologic response of FFMP basins to heavy rain by analyzing their physiographic characteristics. Accessed 2 February 2016. [Available online at http://www.cbrfc.noaa.gov/papers/ffp_wpap.pdf.]

  • Spiegelhalter, D. J., N. G. Best, B. R. Carlin, and A. van der Linde, 2002: Bayesian measures of model complexity and fit. J. Roy. Stat. Soc., 64B, 583616, doi:10.1111/1467-9868.00353.

    • Search Google Scholar
    • Export Citation
  • U.S. Census Bureau, 1994: County subdivisions. Geographic Areas Reference Manual, accessed 30 October 2014. [Available online at http://www2.census.gov/geo/pdfs/reference/GARM/Ch8GARM.pdf.]

  • U.S. Census Bureau, 2012: TIGER/Line Shapefiles joined with demographic data. Accessed 7 March 2014. [Available online at http://www.census.gov/geo/maps-data/data/tiger-data.html.]

  • U.S. Department of Commerce, 1970: National Oceanic and Atmospheric Administration. Department Organization Order 25-2B.

  • USGS, 2011: GAGES-II: Geospatial Attributes of Gages for Evaluating Streamflow, version II vector digital data. Accessed 12 August 2014. [Available online at http://water.usgs.gov/GIS/metadata/usgswrd/XML/gagesII_Sept2011.xml.]

  • U.S. Secretary of Commerce, 1938: Flood Control Act of 1938. 33 U.S.C. §706. [Available online at http://uscode.house.gov/view.xhtml?req=granuleid:USC-prelim-title33-section706&num=0&edition=prelim.]

  • Walter, M. T., V. K. Mehta, A. M. Marrone, J. Boll, P. Gérard-Merchant, T. S. Steenhuis, and M. F. Walter, 2003: A simple estimation of the prevalence of Hortonian flow in New York City’s watersheds. J. Hydrol. Eng., 8, 214218, doi:10.1061/(ASCE)1084-0699(2003)8:4(214).

    • Search Google Scholar
    • Export Citation
  • Xian, G., C. Homer, J. Demitz, J. Fry, N. Hossain, and J. Wickham, 2011: Change of impervious surface area between 2001 and 2006 in the conterminous United States. Photogramm. Eng. Remote Sens., 77, 758762.

    • Search Google Scholar
    • Export Citation
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Does Population Affect the Location of Flash Flood Reports?

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  • 1 Soil and Crop Sciences Section, School of Integrative Plant Science, Cornell University, Ithaca, New York
  • | 2 Department of Biological and Environmental Engineering, Cornell University, Ithaca, New York
  • | 3 Department of Natural Resources, Cornell University, Ithaca, New York
  • | 4 Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, New York
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Abstract

Flash floods cause more fatalities than any other weather-related natural hazard and cause significant damage to property and infrastructure. It is important to understand the underlying processes that lead to these infrequent but high-consequence events. Accurately determining the locations of flash flood events can be difficult, which impedes comprehensive research of the phenomena. While some flash floods can be detected by automated means (e.g., streamflow gauges), flash floods (and other severe weather events) are generally based on human observations and may not reflect the actual distribution of event locations. The Storm Data–Storm Events Database, which is produced from National Weather Service reports, was used to locate reported flash floods within the forecast area of the Binghamton, New York, Weather Forecast Office between 2007 and 2013. The distribution of those reports was analyzed as a function of environmental variables associated with flood generation including slope, impervious area, soil saturated hydraulic conductivity ksat, representative rainfall intensity, and representative rainfall depth, as well as human population. A spatial conditional autoregressive model was used to test the hypothesis that flash flood reports are made more frequently in areas with higher populations, even when other flood-generating processes are considered. Slope, soil saturated hydraulic conductivity, and impervious area are significant predictors of flash flood reports. When population is added as a predictor, the model is similarly robust, but impervious area and ksat are no longer significant predictors. These results may challenge the assumption that flash flood reports are strongly biased by population.

Corresponding author address: Rebecca Marjerison, 1006 Bradfield Hall, Soil and Crop Sciences Section, School of Integrative Plant Sciences, Cornell University, Ithaca, NY 14850. E-mail: rdm95@cornell.edu

Abstract

Flash floods cause more fatalities than any other weather-related natural hazard and cause significant damage to property and infrastructure. It is important to understand the underlying processes that lead to these infrequent but high-consequence events. Accurately determining the locations of flash flood events can be difficult, which impedes comprehensive research of the phenomena. While some flash floods can be detected by automated means (e.g., streamflow gauges), flash floods (and other severe weather events) are generally based on human observations and may not reflect the actual distribution of event locations. The Storm Data–Storm Events Database, which is produced from National Weather Service reports, was used to locate reported flash floods within the forecast area of the Binghamton, New York, Weather Forecast Office between 2007 and 2013. The distribution of those reports was analyzed as a function of environmental variables associated with flood generation including slope, impervious area, soil saturated hydraulic conductivity ksat, representative rainfall intensity, and representative rainfall depth, as well as human population. A spatial conditional autoregressive model was used to test the hypothesis that flash flood reports are made more frequently in areas with higher populations, even when other flood-generating processes are considered. Slope, soil saturated hydraulic conductivity, and impervious area are significant predictors of flash flood reports. When population is added as a predictor, the model is similarly robust, but impervious area and ksat are no longer significant predictors. These results may challenge the assumption that flash flood reports are strongly biased by population.

Corresponding author address: Rebecca Marjerison, 1006 Bradfield Hall, Soil and Crop Sciences Section, School of Integrative Plant Sciences, Cornell University, Ithaca, NY 14850. E-mail: rdm95@cornell.edu
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