Linear and Non-Linear Approaches for Statistical Seasonal Rainfall Forecast in the Sirba Watershed Region (SAHEL)

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 remain...

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Bibliographic Details
Published in:Climate (Basel) 2015-09, Vol.3 (3), p.727-752
Main Authors: Djibo, Abdouramane Gado, Karambiri, Harouna, Seidou, Ousmane, Sittichok, Ketvara, Philippon, Nathalie, Paturel, Jean Emmanuel, Saley, Hadiza Moussa
Format: Article
Language:English
Subjects:
air temperature
Climatology
Correlation analysis
Drought
Earth Sciences
Forecasting
neural network
non-linear principal component analysis
Precipitation
Principal components analysis
Rain
Rainfall
rainfall forecasting
Rainy season
Sciences of the Universe
Seasons
Sirba basin
Stream flow
Watersheds
West African monsoon
Wind
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Summary: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 R2, 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.
ISSN:2225-1154
2225-1154
DOI:10.3390/cli3030727