The Bani basin was classified into 4 clusters of similar catchments (Figures 2-4),  The topographic variables (Elev, ElevMin, ElevMax, Slo1), precipitation and the geographical position of the sub-catchment (Lat) were demonstrated to be the most important causes of similarity between catchments belonging to Cluster 2 and Cluster 4 (Table 2),  This study permitted to propose the two nomenclature: Group of northerly flat and semi-arid catchments, and group of southerly hilly and humid catchments.

Results showed that the model performance can be judged as very good (Moriasi et al., 2007) especially considering limited data condition and high climate, land use and soil type variabilities in the studied basin (Figure 1).  Prediction uncertainty is acceptable: most of the observed data (around 80& ) are bracketed by the 95PPU within an acceptable width (R-factor < 1). However, model is characterized by more prediction uncertainties during high flows (Figure 2).  The most sensitive parameters are mostly related to surface runoff reflecting the dominance of this process on the streamflow generation (Table 1).

The objective of this study was to assess the performance and predictive uncertainty of the Soil and Water Assessment Tool (SWAT) model on the Bani River Basin, at catchment and subcatchment levels. The SWAT model was calibrated using the Generalized Likelihood Uncertainty Estimation (GLUE) approach. Potential Evapotranspiration (PET) and biomass were considered in theverificationofmodeloutputsaccuracy. GlobalSensitivityAnalysis(GSA)wasusedforidentifying important model parameters. Results indicated a good performance of the global model at daily as well as monthly time steps with adequate predictive uncertainty. PET was found to be overestimated but biomass was better predicted in agricultural land and forest. Surface runoff represents the dominant process on streamflow generation in that region. Individual calibration at subcatchment scale yielded better performance than when the global parameter sets were applied. These results are very useful and provide a support to further studies on regionalization to make prediction in ungauged basins.