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by N'Guyen, C. C., Payrastre, O. and Gaume, E.
Abstract:
Flood frequency analyses are often based on recorded series at gauging stations. However, the length of the available data sets is usually too short to provide reliable estimates of extreme design floods. Hence, hydrologists have tried to make use of alternative sources of information to enrich the datasets used for the statistical inferences. Two main approaches were therefore proposed. The first consists in extending the information in time, making use of historical and paleoflood data. The second, spatial extension, consists in merging statistically homogeneous data to build large regional data samples. Recently, a combination of the two techniques aiming at including estimated extreme discharges at ungauged sites of a region in the regional flood frequency analyses has been proposed. This paper presents a consolidation of this approach and its comparison with the standard regional flood frequency approach proposed by Hosking & Wallis. A modification of the likelihood function is introduced to enable the simultaneous calibration of a regional index flood relation and of the parameters of the regional growth curve. Moreover, the efficiency of the proposed method is evaluated based on a large number of Monte Carlo simulated data sets. This work confirms that extreme peak discharges estimated at ungauged sites may be of great value for the evaluation of large return period (typically over 100 years) flood quantiles. They should therefore not be neglected despite the uncertainties associated to these estimates.
Reference:
N'Guyen, C. C., Payrastre, O. and Gaume, E., 2014: Regional flood frequency analyses involving extraordinary flood events at ungauged sites: further developments and validationsJournal of Hydrology, 508, 385-396.
Bibtex Entry:
@Article{NGuyen2014,
  Title                    = {Regional flood frequency analyses involving extraordinary flood events at ungauged sites: further developments and validations},
  Author                   = {N'Guyen, C. C. and Payrastre, O. and Gaume, E.},
  Journal                  = {Journal of Hydrology},
  Year                     = {2014},

  Month                    = {January},
  Pages                    = {385-396},
  Volume                   = {508},

  Abstract                 = {Flood frequency analyses are often based on recorded series at gauging stations. However, the length of the available data sets is usually too short to provide reliable estimates of extreme design floods. Hence, hydrologists have tried to make use of alternative sources of information to enrich the datasets used for the statistical inferences. Two main approaches were therefore proposed. The first consists in extending the information in time, making use of historical and paleoflood data. The second, spatial extension, consists in merging statistically homogeneous data to build large regional data samples. Recently, a combination of the two techniques aiming at including estimated extreme discharges at ungauged sites of a region in the regional flood frequency analyses has been proposed. This paper presents a consolidation of this approach and its comparison with the standard regional flood frequency approach proposed by Hosking & Wallis. A modification of the likelihood function is introduced to enable the simultaneous calibration of a regional index flood relation and of the parameters of the regional growth curve. Moreover, the efficiency of the proposed method is evaluated based on a large number of Monte Carlo simulated data sets. This work confirms that extreme peak discharges estimated at ungauged sites may be of great value for the evaluation of large return period (typically over 100 years) flood quantiles. They should therefore not be neglected despite the uncertainties associated to these estimates.},
  Copublication            = {3: 2 Fr, 1 Viet Nam},
  Doi                      = {10.1016/j.jhydrol.2013.09.058},
  Keywords                 = {Floods, Regional analysis, Statistics, Extremes, GEV},
  Owner                    = {hymexw},
  Timestamp                = {2014.03.27},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S0022169413008226}
}