Modelling spatial patterns of erosion in the West Usambara Mountains of Tanzania

Prompt location of sources and sinks of sediment within a catchment would allow more effective Soil and Water Conservation (SWC) planning. Distributed erosion models are valuable tools for watershed planning, but the quality of spatially distributed model predictions is seriously hampered by the natural complexity and spatial heterogeneity of the landscape system, coupled with limited spatio-temporal datasets of sufficient accuracy. This study aimed at developing a semi-empirical, spatially distributed erosion model to locate sources of sediment within a catchment in data scarce environments. In the experimental catchment of Kwalei, in the West Usambara Mountains of Tanzania, the spatial distribution of erosion and erosion factors was observed during two rainy seasons. In the catchment, overland flow was of dynamic Hortonian type: it was triggered by short and intense showers, but as it moved downward, it quickly reinfiltrated. These observations and measurements at the catchment outlet were used to build a hydrologic model to predict event-based overland flow depth that accounted for rainfall characteristics, land use, field topology, and reinfiltration length, i.e. the average travel distance of overland flow. The hydrologic model was coupled with the sediment phase of the Morgan, Morgan and Finney model to estimate field erosion rates. The best model simulations predicted correctly around 75 % of erosion pattern, but the uncertainty of model prediction due to sediment transport parameterisation was high: 10 % of fields were either classified as subject to severe or slight erosion depending on the sediment transport parameters. Analysis of the spatial patterns of erosion and erosion factors showed that in the Kwalei catchment the location of severely eroded areas was correlated to crust and vegetation cover, but the spatial extent of erosion depended upon the overland flow travel distance. Moreover, the spatial scale of the distribution of some farmers¿ indicators of erosion, i.e. signs that farmers use to assess erosion in their fields, was very close to that of eroded areas and overland flow distribution. Farmers¿ indicators of erosion were used to build a classification tree to predict the distribution of erosion. The resulting Farmers¿ Indicator Tree was the best among several erosion models tested in the area in predicting the spatial pattern of erosion. These findings open up possibilities to integrate more effectively farmers' knowledge into distributed modelling of hydrology and erosion