Statistical seasonal streamflow forecasting using probabilistic approach over West African Sahel

Runoff changes are tightly connected to precipitation in West African Sahel in such a way that any impact on precipitation would result in potential changes in runoff. Unfortunately, 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 West African population. Thus, 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. Streamflow 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. Seasonal annual mean streamflow and maximum monthly streamflow were forecasted in this study by using two statistical methods based on change point detection using Normalized Bayes Factors. Each method uses one of the following predictors: Sea level pressure, air temperature and relative humidity (RHUM). Models M1 and M2 respectively allow for change in model parameters according to rainfall amplitude (M1), or along time (M2). They were compared to forecasting models where precipitation is obtained using the classical linear model with constant parameters (M3) and the climatology (M4). The obtained results revealed that model M3 using RHUM as predictor at a lag time of 8 months was the best method for seasonal annual streamflow forecast. Whereas, model M1 using air temperature as predictor at a lag time of 4 months is the best model to predict maximum monthly streamflow in the Sirba watershed. Copyright Springer Science+Business Media Dordrecht 2015