Abstract
Study region The Sirba watershed, Niger and Burkina Faso countries, West Africa. Study focus Water resources management in the Sahel region, West Africa, is extremely difficult because of high inter-annual rainfall variability. Unexpected floods and droughts often lead to severe humanitarian crises. Seasonal rainfall forecasting is one possible way to increase resilience to climate variability by providing information in advance about the amount of rainfall expected in each upcoming rainy season. Rainfall forecasting models often arbitrarily assume that rainfall is linked to predictors by a multiple linear regression with parameters that are independent of time and of predictor magnitude. Two probabilistic methods based on change point detection that allow the relationship to change according to time or rainfall magnitude were developed in this paper using normalized Bayes factors. Each method uses one of the following predictors: sea level pressure, air temperature and relative humidity. Method M1 allows for change in model parameters according to annual rainfall magnitude, while M2 allows for changes in model parameters with time. M1 and M2 were compared to the classical linear model with constant parameters (M3) and to the climatology (M4). New hydrological insights for the region The model that allows a change in the predictor\textendashpredictand relationship according to rainfall amplitude (M1) and uses air temperature as predictor is the best model for seasonal rainfall forecasting in the study area.