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

Climate

Abdouramane Gado Djibo⁴  Harouna Karambiri⁴  Ousmane Seidou³  Ketvara Sittichok³  Nathalie Philippon²  Jean Emmanuel Paturel¹ 

[1] Institut de Recherche pour le Développement (IRD), Abidjan 08 BP 3800, Côte d’Ivoire; E-Mail:;Centre de Recherches de Climatologie, UMR6282 Biogéosciences CNRS, Université de Bourgogne, Dijon 21000, France; E-Mail:;Department of Civil Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada; E-Mails:;International Institute for Water and Environmental Engineering (2iE), 01 BP 594, Ouagadougou 01, Burkina Faso; E-Mail:

关键词: rainfall forecasting;    neural network;    non-linear principal component analysis;    Sirba basin;    West African monsoon;    air temperature;   

DOI  :  10.3390/cli3030727

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.