Next Article in Journal
Application of an Online-Coupled Regional Climate Model, WRF-CAM5, over East Asia for Examination of Ice Nucleation Schemes: Part II. Sensitivity to Heterogeneous Ice Nucleation Parameterizations and Dust Emissions
Previous Article in Journal
Public Perception of Climate Change in a Period of Economic Crisis
Article Menu

Export Article

Open AccessArticle
Climate 2015, 3(3), 727-752; doi:10.3390/cli3030727

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

1
International Institute for Water and Environmental Engineering (2iE), 01 BP 594, Ouagadougou 01, Burkina Faso
2
Department of Civil Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
3
Centre de Recherches de Climatologie, UMR6282 Biogéosciences CNRS, Université de Bourgogne, Dijon 21000, France
4
Institut de Recherche pour le Développement (IRD), Abidjan 08 BP 3800, Côte d'Ivoire
5
Centre Africain d'Études Supérieures en Gestion (CESAG), Dakar BP 3802, Sénégal
*
Author to whom correspondence should be addressed.
Received: 19 June 2015 / Revised: 27 August 2015 / Accepted: 1 September 2015 / Published: 14 September 2015
View Full-Text   |   Download PDF [1169 KB, uploaded 14 September 2015]   |  

Abstract

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. View Full-Text
Keywords: rainfall forecasting; neural network; non-linear principal component analysis; Sirba basin; West African monsoon; air temperature rainfall forecasting; neural network; non-linear principal component analysis; Sirba basin; West African monsoon; air temperature
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Djibo, A.G.; Karambiri, H.; Seidou, O.; Sittichok, K.; Philippon, N.; Paturel, J.E.; Saley, H.M. Linear and Non-Linear Approaches for Statistical Seasonal Rainfall Forecast in the Sirba Watershed Region (SAHEL). Climate 2015, 3, 727-752.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Climate EISSN 2225-1154 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top