| Abstract: | Under increasing population pressure, soil erosion has become a threat in the East African Highlands, and erosion modelling can be useful to quantify this threat. To test its applicability for this region, the LISEM soil erosion model was applied to two small catchments, one in the Usumbara Mountains, Tanzania, and the other on the slopes of Mount Kenya. Input data for the model were collected in both catchments, as were data on runoff and erosion that were used for calibration and validation of the model. LISEM was first calibrated on catchment outlet data, and afterwards simulated spatial patterns of erosion were compared to available erosion data. The results showed that LISEM can, after calibration, give good discharge predictions for some events, but not for all. However, LISEM generally overpredicted soil loss from the catchments. Comparison with observed erosion patterns did not show overprediction, but according to the model, erosion was more widespread than was observed. There are several reasons for these discrepancies. First, it is difficult to obtain enough accurate data to run the model, such as accurate maps, rainfall data and soil and plant characteristics. Second, it is also difficult to obtain accurate data to evaluate the performance of the model, either for the catchment outlet or spatially, therefore observed erosion rates are also uncertain. Third, the model could not deal correctly with complex events, i.e. those having double rainfall peaks, and might also have difficulties with catchment characteristics such as soil type and the complexity of land use. Finally, LISEM could not deal with events in which throughflow or baseflow played a role, which was to be expected since those processes are not simulated by LISEM. Nevertheless, LISEM could be calibrated to give good discharge predictions for some events, and also gave reasonable results when compared to data obtained from erosion plots. Furthermore, only complex, distributed, storm‐based models such as LISEM can give spatial predictions for single storms. Therefore, it is concluded that if the aim is spatial prediction on an event basis, there is no alternative to complex erosion models such as LISEM, but if the aim is to predict average annual erosion, the data‐demanding, physically based LISEM erosion model may not be the most appropriate model. Copyright © 2005 John Wiley & Sons, Ltd. |
