@article{fdi:010094416,
  title = {{C}limatic a priori information for the {GEV} distribution's shape parameter of annual maximum flow series},
  author = {{E}l {A}dlouni, {S}. and {K}abbaj, {G}. and {K}im, {H}. and {V}illarini, {G}. and {W}asko, {C}. and {T}ramblay, {Y}ves},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he {G}eneralized {E}xtreme {V}alue ({GEV}) distribution encompasses various models with unique characteristics, such as upper or lower bounds, complicating the application of the maximum likelihood algorithm in hydrological frequency analysis. {W}hen proposed, the {G}eneralized {M}aximum {L}ikelihood ({GML}) approach addressed some computational challenges in maximum likelihood estimation but remains sensitive to constraints on the shape parameter. {T}hese constraints on the support of the shape parameter do not consider the variability on the tail behavior of annual maximum flow series in various hydroclimatic regions. {T}o mitigate this, we introduce the {E}xtended {GML} ({EGML}), which incorporates a priori information on the shape parameter to reduce model specification bias in annual maximum flows, particularly when working with short data records. {B}ased on the statistical characteristics of the monthly flows for the training set of the data series and a classification by {F}uzzy {C}-{M}eans ({FCM}) we developed four classes representing similar hydrological behaviors. {T}his classification analysis was then combined with the {K}oppen climate regions to propose the a priori distributions for the {GEV} shape parameter across the four classes to better characterize the tail behaviour of annual maximum flow series distribution. {A} comparison of the 100-year return period quantile estimated with the {EGML} and {GML} methods reveals significant differences, particularly for the arid climate class.},
  keywords = {{G}eneralized {M}aximum {L}ikelihood ({GML}) ; {G}eneralized {E}xtreme {V}alue ({GEV}) ; distribution ; {F}lood frequency analysis ; {M}odel specification bias ; {K}oppen ; climate regions ; {M}aximum flow distribution tail},
  booktitle = {},
  journal = {{J}ournal of {H}ydrology},
  volume = {661},
  numero = {{C}},
  pages = {133789 [11 p.]},
  ISSN = {0022-1694},
  year = {2025},
  DOI = {10.1016/j.jhydrol.2025.133789},
  URL = {https://www.documentation.ird.fr/hor/fdi:010094416},
}