@article{PAR00009112,
  title = {{E}xtreme rainfall in {W}est {A}frica : a regional modeling},
  author = {{P}anthou, {G}. and {V}ischel, {T}. and {L}ebel, {T}hierry and {B}lanchet, {J}. and {Q}uantin, {G}. and {A}li, {A}.},
  editor = {},
  language = {{ENG}},
  abstract = {{I}n a world of increasing exposure of populations to natural hazards, the mapping of extreme rainfall remains a key subject of study. {S}uch maps are required for both flood risk management and civil engineering structure design, the challenge being to take into account the local information provided by point rainfall series as well as the necessity of some regional coherency. {T}wo approaches based on the extreme value theory are compared here, with an application to extreme rainfall mapping in {W}est {A}frica. {T}he first approach is a local fit and interpolation ({LFI}) consisting of a spatial interpolation of the generalized extreme value ({GEV}) distribution parameters estimated independently at each station. {T}he second approach is a spatial maximum likelihood estimation ({SMLE}); it directly estimates the {GEV} distribution over the entire region by a single maximum likelihood fit using jointly all measurements combined with spatial covariates. {F}ive {LFI} and three {SMLE} methods are considered, using the information provided by 126 daily rainfall series covering the period 1950-1990. {T}he methods are first evaluated in calibration. {T}hen the predictive skills and the robustness are assessed through a cross validation and an independent network validation process. {T}he {SMLE} approach, especially when using the mean annual rainfall as covariate, appears to perform better for most of the scores computed. {U}sing the {N}iamey 104 year time series, it is also shown that the {SMLE} approach has the capacity to deal more efficiently with the effect of local outliers by using the spatial information provided by nearby stations.},
  keywords = {{AFRIQUE} {DE} {L}'{OUEST}},
  booktitle = {},
  journal = {{W}ater {R}esources {R}esearch},
  volume = {48},
  numero = {},
  pages = {{W}08501},
  ISSN = {0043-1397},
  year = {2012},
  DOI = {10.1029/2012wr012052},
  URL = {https://www.documentation.ird.fr/hor/{PAR}00009112},
}