Hydrologic State Influence on Riverine Flood Discharge for a Small Temperate Watershed (Fall Creek, United States): Negative Feedbacks on the Effects of Climate Change

James O. Knighton Department of Biological and Environmental Engineering, Cornell University, Ithaca, New York

Search for other papers by James O. Knighton in
Current site
Google Scholar
PubMed
Close
,
Arthur DeGaetano Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, New York

Search for other papers by Arthur DeGaetano in
Current site
Google Scholar
PubMed
Close
, and
M. Todd Walter Department of Biological and Environmental Engineering, Cornell University, Ithaca, New York

Search for other papers by M. Todd Walter in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Watershed flooding is a function of meteorological and hydrologic catchment conditions. Climate change is anticipated to affect air temperature and precipitation patterns such as altered total precipitation, increased intensity, and shorter event durations in the northeastern United States. While significant work has been done to estimate future meteorological conditions, much is currently unknown about future changes to distributions of hydrologic state variables. High-resolution hydrologic simulations of Fall Creek (Tompkins County, New York), a small temperate watershed (324 km2) with seasonal snowmelt, are performed to evaluate future climate change impacts on flood hydrology. The effects of hydrologic state and environmental variables on river flood stage are isolated and the importance of groundwater elevation, unsaturated soil moisture, snowpack, and air temperature is demonstrated. It is shown that the temporal persistence of these hydrologic state variables allows for an influence on watershed flood hydrology for up to 20 days. Finally, six hypothetical climate change forcing scenarios are simulated to estimate the influence of catchment conditions on the watershed runoff response. The possibility of drier summers and wetter springs with a reduced winter snowpack in the Northeast is also simulated. These hydrologic changes influence flood discharge in the opposite direction as climate effects because of a reduced snowpack accumulation and melt time. Strong hydrologic state influence on flood discharge may be most attributable to increased air temperature and decreased precipitation. Hydrologic state variables may change both the location and shape of seasonal flood discharge distributions despite expected consistency in the shape of precipitation statistic distributions.

© 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: James O. Knighton, james.knighton@gmail.com

Abstract

Watershed flooding is a function of meteorological and hydrologic catchment conditions. Climate change is anticipated to affect air temperature and precipitation patterns such as altered total precipitation, increased intensity, and shorter event durations in the northeastern United States. While significant work has been done to estimate future meteorological conditions, much is currently unknown about future changes to distributions of hydrologic state variables. High-resolution hydrologic simulations of Fall Creek (Tompkins County, New York), a small temperate watershed (324 km2) with seasonal snowmelt, are performed to evaluate future climate change impacts on flood hydrology. The effects of hydrologic state and environmental variables on river flood stage are isolated and the importance of groundwater elevation, unsaturated soil moisture, snowpack, and air temperature is demonstrated. It is shown that the temporal persistence of these hydrologic state variables allows for an influence on watershed flood hydrology for up to 20 days. Finally, six hypothetical climate change forcing scenarios are simulated to estimate the influence of catchment conditions on the watershed runoff response. The possibility of drier summers and wetter springs with a reduced winter snowpack in the Northeast is also simulated. These hydrologic changes influence flood discharge in the opposite direction as climate effects because of a reduced snowpack accumulation and melt time. Strong hydrologic state influence on flood discharge may be most attributable to increased air temperature and decreased precipitation. Hydrologic state variables may change both the location and shape of seasonal flood discharge distributions despite expected consistency in the shape of precipitation statistic distributions.

© 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: James O. Knighton, james.knighton@gmail.com
Save
  • Archibald, J. A., and M. T. Walter, 2014: Do energy‐based PET models require more input data than temperature‐based models?—An evaluation at four humid FluxNet sites. J. Amer. Water Resour. Assoc., 50, 497508, doi:10.1111/jawr.12137.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Balling, R. C., Jr., and G. B. Goodrich, 2011: Spatial analysis of variations in precipitation intensity in the USA. Theor. Appl. Climatol., 104, 415421, doi:10.1007/s00704-010-0353-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bell, V., A. Kay, H. Davies, and R. Jones, 2016: An assessment of the possible impacts of climate change on snow and peak river flows across Britain. Climatic Change, 136, 539553, doi:10.1007/s10584-016-1637-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blöschl, G., and Coauthors, 2007: At what scales do climate variability and land cover change impact on flooding and low flows? Hydrol. Processes, 21, 12411247, doi:10.1002/hyp.6669.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blöschl, G., and Coauthors, 2015: Increasing river floods: Fiction or reality? Wiley Interdiscip. Rev.: Water, 2, 329344, doi:10.1002/wat2.1079.

  • Bouwer, L. M., 2011: Have disaster losses increased due to anthropogenic climate change? Bull. Amer. Meteor. Soc., 92, 3946, doi:10.1175/2010BAMS3092.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brigode, P., and Coauthors, 2014: Sensitivity analysis of SCHADEX extreme flood estimations to observed hydrometeorological variability. Water Resour. Res., 50, 353370, doi:10.1002/2013WR013687.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Butler, D. R., 1989: The failure of beaver dams and resulting outburst flooding: A geomorphic hazard of the southeastern Piedmont. Geogr. Bull., 31, 2938.

    • Search Google Scholar
    • Export Citation
  • Ceppi, A., G. Ravazzani, A. Salandin, D. Rabuffetti, A. Montani, E. Borgonovo, and M. Mancini, 2013: Effects of temperature on flood forecasting: Analysis of an operative case study in Alpine basins. Nat. Hazards Earth Syst. Sci., 13, 10511062, doi:10.5194/nhess-13-1051-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Condon, L. E., S. Gangopadhyay, and T. Pruitt, 2015: Climate change and non-stationary flood risk for the upper Truckee River basin. Hydrol. Earth Syst. Sci., 19, 159175, doi:10.5194/hess-19-159-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dahlke, H. E., Z. M. Easton, D. R. Fuka, S. W. Lyon, and T. S. Steenhuis, 2009: Modelling variable source area dynamics in a CEAP watershed. Ecohydrology, 2, 337349, doi:10.1002/eco.58.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeGaetano, A. T., 2009: Time-dependent changes in extreme-precipitation return-period amounts in the continental United States. J. Appl. Meteor. Climatol., 48, 20862099, doi:10.1175/2009JAMC2179.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Diffenbaugh, N. S., J. S. Pal, R. J. Trapp, and F. Giorgi, 2005: Fine-scale processes regulate the response of extreme events to global climate change. Proc. Natl. Acad. Sci. USA, 102, 15 77415 778, doi:10.1073/pnas.0506042102.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Easton, Z. M., P. Gérard-Marchant, M. T. Walter, A. M. Petrovic, and T. S. Steenhuis, 2007: Hydrologic assessment of an urban variable source watershed in the northeast United States. Water Resour. Res., 43, W03413, doi:10.1029/2006WR005076.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elbialy, S., A. Mahmoud, B. Pradhan, and M. Buchroithner, 2014: Application of spaceborne synthetic aperture radar data for extraction of soil moisture and its use in hydrological modelling at Gottleuba catchment, Saxony, Germany. J. Flood Risk Manage., 7, 159175, doi:10.1111/jfr3.12037.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Freudiger, D., I. Kohn, K. Stahl, and M. Weiler, 2014: Large scale analysis of changing frequencies of rain-on-snow events and their impact on floods. Hydrol. Earth Syst. Sci., 18, 26952709, doi:10.5194/hess-18-2695-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Frumhoff, P. C., J. J. McCarthy, J. M. Melillo, S. C. Moser, and D. J. Wuebbles, 2007: Confronting climate change in the U.S. Northeast: Science, impacts, and solutions. Northeast Climate Impacts Assessment Rep., Union of Concerned Scientists, 146 pp. [Available online at http://www.ucsusa.org/sites/default/files/legacy/assets/documents/global_warming/pdf/confronting-climate-change-in-the-u-s-northeast.pdf.]

  • Fuka, D. R., M. T. Walter, J. A. Archibald, T. S. Steenhuis, and Z. M. Easton, 2013: EcoHydRology: A community modeling foundation for eco-hydrology, version 4.9. R package. [Available online at https://cran.r-project.org/web/packages/EcoHydRology/index.html.]

  • Genest, C., and A. C. Favre, 2007: Everything you always wanted to know about copula modeling but were afraid to ask. J. Hydrol. Eng., 12, 347368, doi:10.1061/(ASCE)1084-0699(2007)12:4(347).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gersonius, B., R. Ashley, A. Pathirana, and C. Zevenbergen, 2013: Climate change uncertainty: Building flexibility into water and flood risk infrastructure. Climatic Change, 116, 411423, doi:10.1007/s10584-012-0494-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gilroy, K. L., and R. H. McCuen, 2012: A nonstationary flood frequency analysis method to adjust for future climate change and urbanization. J. Hydrol., 414–415, 4048, doi:10.1016/j.jhydrol.2011.10.009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grusson, Y., X. Sun, S. Gascoin, S. Sauvage, S. Raghavan, F. Anctil, and J. M. Sáchez-Pérez, 2015: Assessing the capability of the SWAT model to simulate snow, snow melt and streamflow dynamics over an alpine watershed. J. Hydrol., 531, 574588, doi:10.1016/j.jhydrol.2015.10.070.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haberlandt, U., and I. Radtke, 2014: Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows. Hydrol. Earth Syst. Sci., 18, 353365, doi:10.5194/hess-18-353-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harrison, B., and R. Bales, 2015: Skill assessment of water supply outlooks in the Colorado River basin. Hydrology, 2, 112131, doi:10.3390/hydrology2030112.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hayhoe, K., and Coauthors, 2007: Past and future changes in climate and hydrological indicators in the US Northeast. Climate Dyn., 28, 381407, doi:10.1007/s00382-006-0187-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hayhoe, K., and Coauthors, 2008: Regional climate change projections for the Northeast USA. Mitigation Adapt. Strategies Global Change, 13, 425436, doi:10.1007/s11027-007-9133-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hirabayashi, Y., R. Mahendran, S. Koirala, L. Konoshima, D. Yamazaki, S. Watanabe, H. Kim, and S. Kanae, 2013: Global flood risk under climate change. Nat. Climate Change, 3, 816821, doi:10.1038/nclimate1911.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hirsch, R. M., and K. R. Ryberg, 2012: Has the magnitude of floods across the USA changed with global CO2 levels? Hydrol. Sci. J., 57, 19, doi:10.1080/02626667.2011.621895.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jabareen, Y., 2013: Planning the resilient city: Concepts and strategies for coping with climate change and environmental risk. Cities, 31, 220229, doi:10.1016/j.cities.2012.05.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jörg-Hess, S., N. Griessinger, and M. Zappa, 2015: Probabilistic forecasts of snow water equivalent and runoff in mountainous areas. J. Hydrometeor., 16, 21692186, doi:10.1175/JHM-D-14-0193.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knighton, J., and M. Walter, 2016: Critical rainfall statistics for predicting watershed flood responses: Rethinking the design storm concept. Hydrol. Processes, 30, 37883803, doi:10.1002/hyp.10888.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koplin, N., B. Schadler, D. Viviroli, and R. Weingartner, 2014: Seasonality and magnitude of floods in Switzerland under future climate change. Hydrol. Processes, 28, 25672578, doi:10.1002/hyp.9757.

    • 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, doi:10.1080/02626667.2013.857411.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kunkel, K. E., and Coauthors, 2013: Monitoring and understanding trends in extreme storms: State of knowledge. Bull. Amer. Meteor. Soc., 94, 499514, doi:10.1175/BAMS-D-11-00262.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lawrence, D., E. Paquet, J. Gailhard, and A. K. Fleig, 2014: Stochastic semi-continuous simulation for extreme flood estimation in catchments with combined rainfall–snowmelt flood regimes. Nat. Hazards Earth Syst. Sci., 14, 12831298, doi:10.5194/nhess-14-1283-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, J., P. Feng, and F. Chen, 2014: Effects of land use change on flood characteristics in mountainous area of Daqinghe watershed, China. Nat. Hazards, 70, 593607, doi:10.1007/s11069-013-0830-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lo, M.-H., and J. S. Famiglietti, 2010: Effect of water table dynamics on land surface hydrologic memory. J. Geophys. Res., 115, D22118, doi:10.1029/2010JD014191.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahanama, S., B. Livneh, R. Koster, D. Lettenmaier, and R. Reichle, 2012: Soil moisture, snow, and seasonal streamflow forecasts in the United States. J. Hydrometeor., 13, 189203, doi:10.1175/JHM-D-11-046.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mallakpour, I., and G. Villarini, 2015: The changing nature of flooding across the central United States. Nat. Climate Change, 5, 250254, doi:10.1038/nclimate2516.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Massari, C., L. Brocca, S. Barbetta, C. Papathanasiou, M. Mimikou, and T. Moramarco, 2014a: Using globally available soil moisture indicators for flood modelling in Mediterranean catchments. Hydrol. Earth Syst. Sci., 18, 839853, doi:10.5194/hess-18-839-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Massari, C., L. Brocca, T. Moramarco, Y. Tramblay, and J. F. D. Lescot, 2014b: Potential of soil moisture observations in flood modelling: Estimating initial conditions and correcting rainfall. Adv. Water Resour., 74, 4453, doi:10.1016/j.advwatres.2014.08.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Massari, C., A. Tarpanelli, L. Brocca, and T. Moramarco, 2015: Assimilating satellite soil moisture into rainfall–runoff modelling: Towards a systematic study. Geophysical Research Abstracts, Vol. 17, Abstract EGU2015-1780. [Available online at http://meetingorganizer.copernicus.org/EGU2015/EGU2015-1780.pdf.]

  • Moriasi, D. N., J. G. Arnold, M. W. Van Liew, R. L. Bingner, R. D. Harmel, and T. L. Veith, 2007: Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE, 50, 885900, doi:10.13031/2013.23153.

    • 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
  • Nied, M., Y. Hundecha, and B. Merz, 2013: Flood-initiating catchment conditions: A spatio-temporal analysis of large-scale soil moisture patterns in the Elbe River basin. Hydrol. Earth Syst. Sci., 17, 14011414, doi:10.5194/hess-17-1401-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NOAA/HDSC, 2016: Precipitation Frequency Data Server (PFDS). Accessed 28 December 2016. [Available online at http://hdsc.nws.noaa.gov/hdsc/pfds/index.html.]

  • NOAA/NCEI, 2015: Quality controlled local climatological data. Accessed 28 December 2016. [Available online at http://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets.]

  • Norbiato, D., M. Borga, S. Degli Esposti, E. Gaume, and S. Anquetin, 2008: Flash flood warning based on rainfall thresholds and soil moisture conditions: An assessment for gauged and ungauged basins. J. Hydrol., 362, 274290, doi:10.1016/j.jhydrol.2008.08.023.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NRCC, 2015: NRCC hourly precipitation database. Accessed 28 December 2016. [Available online at http://www.nrcc.cornell.edu/services/access/access.html.]

  • NRCS, 2015: Web Soil Survey. Accessed 11 November 2016. [Available online at http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm.]

  • Obeysekera, J., and J. D. Salas, 2014: Quantifying the uncertainty of design floods under nonstationary conditions. J. Hydrol. Eng., 19, 14381446, doi:10.1061/(ASCE)HE.1943-5584.0000931.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Palecki, M. A., J. R. Angel, and S. E. Hollinger, 2005: Storm precipitation in the United States. Part I: Meteorological characteristics. J. Appl. Meteor., 44, 933946, doi:10.1175/JAM2243.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Paquet, E., F. Garavaglia, R. Garçon, and J. Gailhard, 2013: The SCHADEX method: A semi-continuous rainfall–runoff simulation for extreme flood estimation. J. Hydrol., 495, 2337, doi:10.1016/j.jhydrol.2013.04.045.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Paschalis, A., S. Fatichi, P. Molnar, S. Rimkus, and P. Burlando, 2014: On the effects of small scale space–time variability of rainfall on basin flood response. J. Hydrol., 514, 313327, doi:10.1016/j.jhydrol.2014.04.014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Perju, E. R., D. Balin, S. Lane, and L. Zaharia, 2013: Changing flood magnitude and frequency in snow-melt dominated catchments: The case of the Bucegi Mountains, in the Romanian Carpathian region. Geophysical Research Abstracts, Vol. 15, Abstract EGU2013-10110. [Available online at http://meetingorganizer.copernicus.org/EGU2013/EGU2013-10110.pdf.]

  • 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. IAHS Publ., 313, 407–416.

  • Rossman, L. A., 2010: Storm water management model user’s manual, version 5.0. Rep. EPA/600/R-05/040, Environmental Protection Agency, 285 pp.

  • Salas, J. and Obeysekera, J. 2014: Revisiting the concepts of return period and risk for nonstationary hydrologic extreme events. J. Hydrol. Eng., 19, 554568, doi:10.1061/(ASCE)HE.1943-5584.0000820.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schiermeier, Q., 2011: Increased flood risk linked to global warming. Nature, 470, 316316, doi:10.1038/470316a.

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

  • Seidou, O., A. Ramsay, and I. Nistor, 2012: Climate change impacts on extreme floods I: Combining imperfect deterministic simulations and non-stationary frequency analysis. Nat. Hazards, 61, 647659, doi:10.1007/s11069-011-0052-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Serinaldi, F., 2015: Dismissing return periods! Stochastic Environ. Res. Risk Assess., 29, 11791189, doi:10.1007/s00477-014-0916-1.

  • Smith, B. K., J. A. Smith, M. L. Baeck, and A. J. Miller, 2015: Exploring storage and runoff generation processes for urban flooding through a physically based watershed model. Water Resour. Res., 51, 15521569, doi:10.1002/2014WR016085.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stedinger, J. R., and V. W. Griffis, 2011: Getting from here to where? Flood frequency analysis and climate. J. Amer. Water Resour. Assoc., 47, 506513, doi:10.1111/j.1752-1688.2011.00545.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Surfleet, C., and D. Tullos, 2013: Variability in effect of climate change on rain-on-snow peak flow events in a temperate climate. J. Hydrol., 479, 2434, doi:10.1016/j.jhydrol.2012.11.021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., R. Stouffer, and G. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, doi:10.1175/BAMS-D-11-00094.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tolson, B. A., and C. A. Shoemaker, 2007: Dynamically dimensioned search algorithm for computationally efficient watershed model calibration. Water Resour. Res., 43, W01413, doi:10.1029/2005WR004723.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tramblay, Y., L. Neppel, J. Carreau, and K. Najib, 2013: Non-stationary frequency analysis of heavy rainfall events in southern France. Hydrol. Sci. J., 58, 280294, doi:10.1080/02626667.2012.754988.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tramblay, Y., E. Amoussou, W. Dorigo, and G. Mahé, 2014: Flood risk under future climate in data sparse regions: Linking extreme value models and flood generating processes. J. Hydrol., 519, 549558, doi:10.1016/j.jhydrol.2014.07.052.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., 2011: Changes in precipitation with climate change. Climate Res., 47, 123138, doi:10.3354/cr00953.

  • USGS, 2015: USGS 04234000 Fall Creek near Ithaca, NY. Accessed 11 November 2016. [Available online at http://waterdata.usgs.gov/nwis/uv?04234000.]

  • USGS, 2016: National elevation dataset. Accessed 11 November 2016. [Available online at http://nationalmap.gov/elevation.html.]

  • Walter, M., P. Gérard-Marchant, T. Steenhuis, and M. Walter, 2005: Closure to “Simple estimation of prevalence of Hortonian flow in New York City watersheds” by M. Todd Walter, Vishal K. Mehta, Alexis M. Marrone, Jan Boll, Pierre Gérard-Marchant, Tammo S. Steenhuis, and Michael F. Walter. J. Hydrol. Eng., 10, 168169, doi:10.1061/(ASCE)1084-0699(2005)10:2(169).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wehner, M. F., 2013: Very extreme seasonal precipitation in the NARCCAP ensemble: Model performance and projections. Climate Dyn., 40, 5980, doi:10.1007/s00382-012-1393-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weijs, S. V., N. van de Giesen, and M. B. Parlange, 2013a: Data compression to define information content of hydrological time series. Hydrol. Earth Syst. Sci., 17, 31713187, doi:10.5194/hess-17-3171-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weijs, S. V., N. van de Giesen, and M. B. Parlange, 2013b: HydroZIP: How hydrological knowledge can be used to improve compression of hydrological data. Entropy, 15, 12891310, doi:10.3390/e15041289.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Westra, S., I. Varley, P. Jordan, R. Nathan, A. Ladson, A. Sharma, and P. Hill, 2010: Addressing climatic non-stationarity in the assessment of flood risk. Aust. J. Water Resour., 14, 116.

    • Search Google Scholar
    • Export Citation
  • Wood, A. W., and J. C. Schaake, 2008: Correcting errors in streamflow forecast ensemble mean and spread. J. Hydrometeor., 9, 132148, doi:10.1175/2007JHM862.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wood, A. W., T. Hopson, A. Newman, L. Brekke, J. Arnold, and M. Clark, 2016: Quantifying streamflow forecast skill elasticity to initial condition and climate prediction skill. J. Hydrometeor., 17, 651668, doi:10.1175/JHM-D-14-0213.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wyżga, B., J. Zawiejska, and A. Radecki-Pawlik, 2016: Impact of channel incision on the hydraulics of flood flows: Examples from Polish Carpathian rivers. Geomorphology, 272, 1020, doi:10.1016/j.geomorph.2015.05.017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ye, H., E. J. Fetzer, A. Behrangi, S. Wong, B. H. Lambrigtsen, C. Y. Wang, J. Cohen, and B. Gamelin, 2016: Increasing daily precipitation intensity associated with warmer air temperatures over Northern Eurasia. J. Climate, 29, 623636, doi:10.1175/JCLI-D-14-00771.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yossef, N. C., H. Winsemius, A. Weerts, R. Beek, and M. F. Bierkens, 2013: Skill of a global seasonal streamflow forecasting system, relative roles of initial conditions and meteorological forcing. Water Resour. Res., 49, 46874699, doi:10.1002/wrcr.20350.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1304 775 48
PDF Downloads 302 53 7