%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Djibo, A. G.
%A Karambiri, H.
%A Seidou, O.
%A Sittichok, K.
%A Philippon, N.
%A Paturel, Jean-Emmanuel
%A Saley, H. M.
%T Linear and Non-Linear approaches for statistical seasonal rainfall forecast in the Sirba watershed region (SAHEL)
%D 2015
%L fdi:010068846
%G ENG
%J Climate
%@ 2225-1154
%K rainfall forecasting ; neural network ; non-linear principal component ; analysis ; Sirba basin ; West African monsoon ; air temperature
%K NIGER ; SAHEL
%M ISI:000363970200014
%N 3
%P 727-752
%R 10.3390/cli3030727
%U https://www.documentation.ird.fr/hor/fdi:010068846
%> https://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers17-05/010068846.pdf
%V 3
%W Horizon (IRD)
%X Since the 90s, several studies were conducted to evaluate the predictability of the Sahelian rainy season and propose seasonal rainfall forecasts to help stakeholders to take the adequate decisions to adapt with the predicted situation. Unfortunately, two decades later, the forecasting skills remains low and forecasts have a limited value for decision making while the population is still suffering from rainfall interannual variability: this shows the limit of commonly used predictors and forecast approaches for this region. Thus, this paper developed and tested new predictors and new approaches to predict the upcoming seasonal rainfall amount over the Sirba watershed. Predictors selected through a linear correlation analysis were further processed using combined linear methods to identify those having high predictive power. Seasonal rainfall was forecasted using a set of linear and non-linear models. An average lag time up to eight months was obtained for all models. It is found that the combined linear methods performed better than non-linear, possibly because non-linear models require larger and better datasets for calibration. The R-2, Nash and Hit rate score are respectively 0.53, 0.52, and 68% for the combined linear approach; and 0.46, 0.45, 61% for non-linear principal component analysis.
%$ 062