Quantifying the effect of climate variability on seasonal precipitation using Bayesian clustering approach in Kebir Rhumel Basin, Algeria

Abstract This paper presents a Bayesian clustering approach that allows quantifying the effect of climate variability on seasonal precipitation data in Kebir Rhumel Basin (KRB). We applied this approach to simultaneously identify clusters of stations with similar characteristics, the climate variability associated with each cluster and for the individual stations within each cluster. Both full pooling Bayesian clustering (FPBC) and partial pooling Bayesian clustering (PPBC) models with nonstationary generalized extreme value (GEV) distribution are applied to each season. In these models, the climate variable, namely the El Niño Southern Oscillation (ENSO), is included as a time-varying covariate with an appropriate basis function to represent the temporal variation of one or more of the parameters of the distribution. Results reveal that the partial pooling Bayesian clustering model provided the best fit for the seasonal precipitation data. The significant effect of ENSO differs from one season to another. During spring and autumn, ENSO significantly effects precipitation across large parts of KRB. Furthermore, the southern part and northern part of KRB are positively and negatively influenced by ENSO during winter and summer, respectively. Moreover, almost all stations during spring and autumn are negatively and positively influenced by ENSO, respectively. Finally, we demonstrated that the proposed model helps to reduce the uncertainty in the parameter estimation and provides more robust results.