@article{fdi:010062854,
  title = {{F}lood risk under future climate in data sparse regions : linking extreme value models and flood generating processes},
  author = {{T}ramblay, {Y}ves and {A}moussou, {E}. and {D}origo, {W}. and {M}ah{\'e}, {G}il},
  editor = {},
  language = {{ENG}},
  abstract = {{F}or many areas in the world, there is a need for future projections of flood risk in order to improve the possible mitigation actions. {H}owever, such an exercise is often made difficult in data-sparse regions, where the limited access to hydrometric data does not allow calibrating hydrological models in a robust way under non-stationary conditions. {I}n this study we present an approach to estimate possible changes in flood risks, which incorporates flood generating processes into statistical models for extreme values. {T}his approach is illustrated for a {W}est {A}frican catchment, the {M}ono {R}iver (12,900 km2), with discharge, precipitation and temperature data available between 1988 and 2010 and where the dominant flood generating process is soil saturation. {A} soil moisture accounting ({SMA}) model, calibrated against a merged surface soil moisture microwave satellite dataset, is used to estimate the annual maximum soil saturation level that is related to the location parameter of a generalized extreme value model of annual maximum discharge. {W}ith such a model, it is possible to estimate the changes in flood quantiles from the changes in the annual maximum soil saturation level. {A}n ensemble of regional climate models from the {ENSEMBLES}–{AMMA} project are then considered to estimate the potential future changes in soil saturation and subsequently the changes in flood risks for the period 2028–2050. {A} sensitivity analysis of the non-stationary flood quantiles has shown that with the projected changes on precipitation (−2%) and temperature (+1.22°) under the scenario {A}1{B}, the projected flood quantiles would stay in the range of the observed variability during 1988–2010. {T}he proposed approach, relying on low data requirements, could be useful to estimate the projected changes in flood risks for other data-sparse catchments where the dominant flood-generating process is soil saturation.},
  keywords = {{INONDATION} ; {ESTIMATION} ; {PREVISION} ; {CRUE} ; {VARIABILITE} ; {MODELISATION} ; {PRECIPITATION} ; {TEMPERATURE} ; {DONNEES} {SATELLITE} ; {RELATION} {SOL} {EAU} ; {HUMIDITE} {DU} {SOL} ; {SATURATION} ; {SIMULATION} ; {METHODOLOGIE} ; {COURS} {D}'{EAU} ; {BASSIN} {VERSANT} ; {CHANGEMENT} {CLIMATIQUE} ; 1988 2010 ; 2028 2050 ; {RISQUE} {CLIMATIQUE} ; {VALEURS} {EXTREMES} ; {DONNEES} {IMPARFAITES} ; {TOGO} ; {BENIN} ; {AFRIQUE} {DE} {L}'{OUEST} ; {MONO} {BASSIN} {VERSANT}},
  booktitle = {},
  journal = {{J}ournal of {H}ydrology},
  volume = {519},
  numero = {part {A}},
  pages = {549--558},
  ISSN = {0022-1694},
  year = {2014},
  DOI = {10.1016/j.jhydrol.2014.07.052},
  URL = {https://www.documentation.ird.fr/hor/fdi:010062854},
}