Jump to Main Content
Evaluation of runoff, peak flow and sediment yield for events simulated by the AnnAGNPS model in a belgian agricultural watershed
- Zema, D.A., Bingner, R.L., Denisi, P., Govers, G., Licciardello, F., Zimbone, S.M.
- Land degradation & development 2012 v.23 no.3 pp. 205
- agricultural runoff, agricultural watersheds, databases, hydrologic models, land degradation, model validation, pollution load, prediction, sediment yield, simulation models, soil erosion models, water erosion, Belgium
- The AnnAGNPS model, widely utilized as a practical tool for addressing erosion problems and land use planning, was implemented in a small agricultural watershed located in central Belgium, to assess its prediction capacity of runoff, peak flow and sediment yield in humid temperate conditions. Model performance was evaluated at the event scale by using a database reporting hydrological, geomorphologic and land use data collected during a 2‐year period. Seventeen events were modelled and compared with the corresponding observations at the watershed outlet. The model performed well in predicting the largest runoff volumes, as shown by the high values achieved for the coefficients of efficiency (E = 0·89) and determination (r2 = 0·92). However, some events resulted in zero runoff simulation. The prediction capability for peak flow and sediment yield was poor (E = 0·35 and 0·16, respectively). This inaccuracy can have several causes: the internal model deficiencies may be due to the incomplete representation of watershed complex processes, while external problems may be related to the conditions within the modelled watershed and the quality of recorded data. On the whole the AnnAGNPS model may be considered as being suitable to simulate the significant runoff events in the experimental watershed. However, the model may be seen as better suited for comparative assessments of alternative management and policy scenarios and for gross estimation of nutrient loads rather than the precise prediction of a single event, consequently helping in the prediction of land degradation problems in the experimented conditions.