@article{fdi:010065394,
  title = {{S}tatistical seasonal streamflow forecasting using probabilistic approach over {W}est {A}frican {S}ahel},
  author = {{D}jibo, {A}. {G}. and {K}arambiri, {H}. and {S}eidou, {O}. and {S}ittichok, {K}. and {P}aturel, {J}ean-{E}mmanuel and {S}aley, {H}. {M}.},
  editor = {},
  language = {{ENG}},
  abstract = {{R}unoff changes are tightly connected to precipitation in {W}est {A}frican {S}ahel in such a way that any impact on precipitation would result in potential changes in runoff. {U}nfortunately, climate change and variability impacts induced changes in streamflow which directly disturb water availability for socioeconomic activities particularly agricultural sector which constitutes the main survival issue of {W}est {A}frican population. {T}hus, available streamflow information a few months in advance prior to a rainy season with an acceptable forecasting skill will immensely benefit for water users to make an operational planning for water management decision. {S}treamflow is usually either forecasted directly by linking streamflow to predictors through a multiple linear regression or indirectly using a rainfall-runoff model to transform predicted rainfall into streamflow. {S}easonal annual mean streamflow and maximum monthly streamflow were forecasted in this study by using two statistical methods based on change point detection using {N}ormalized {B}ayes {F}actors. {E}ach method uses one of the following predictors: {S}ea level pressure, air temperature and relative humidity ({RHUM}). {M}odels {M}1 and {M}2 respectively allow for change in model parameters according to rainfall amplitude ({M}1), or along time ({M}2). {T}hey were compared to forecasting models where precipitation is obtained using the classical linear model with constant parameters ({M}3) and the climatology ({M}4). {T}he obtained results revealed that model {M}3 using {RHUM} as predictor at a lag time of 8 months was the best method for seasonal annual streamflow forecast. {W}hereas, model {M}1 using air temperature as predictor at a lag time of 4 months is the best model to predict maximum monthly streamflow in the {S}irba watershed.},
  keywords = {{S}treamflow forecast ; {B}ayes factor ; {SWAT} ; {P}osterior probability ; {S}irba ; watershed ; {S}ahel ; {AFRIQUE} {DE} {L}'{OUEST} ; {SAHEL} ; {BURKINA} {FASO} ; {NIGER}},
  booktitle = {},
  journal = {{N}atural {H}azards},
  volume = {79},
  numero = {2},
  pages = {699--722},
  ISSN = {0921-030{X}},
  year = {2015},
  DOI = {10.1007/s11069-015-1866-8},
  URL = {https://www.documentation.ird.fr/hor/fdi:010065394},
}