| Abstract: | Abstract Hydrological modelling has faced the problem of ungauged basins for many years: how does one estimate hydrological characteristics for a river for which there are no data? Whatever the kind of model, it needs at least hydroclimatic input data and discharge data for calibration. However, the Yates model does not need any discharge data for calibration: it is a pre-calibrated model from a vegetation—climate classification map. In the specific context of West and Central Africa, where data are often of poor quality and very scarce, it is interesting to compare the performance of such a model with those of calibrated models, and with observed data. For this study, a platform including different semi-global rainfall—runoff models which allow the estimation of monthly runoff at a spatial resolution of 0.5° × 0.5° was used. The performance of the Yates model is very close to those of calibrated models, so that one can say that this simple model, based simply on a vegetation—climate classification, can be a very useful prediction tool in regions of scarce and unreliable data, such as those of interest to the International Association of Hydrological Sciences (IAHS) initiative on prediction in ungauged basins (PUB). Therefore, this model was applied to a period covering the last 30 years, and to a data set covering the first decades of the 21st century, from a climatic scenario of doubling the CO2 concentration in the atmosphere. The results show that, in West Africa, where drought conditions have now prevailed for 35 years, water resources should still be decreasing in the future, following the general decreasing trend of rainfall projected by the climatic scenarios. |
