Comparison of infiltration models to simulate flood events at the field scale

Most runoff simulation and infiltration models have been developed at the global-catchment scale or the local-soil column scale. Few models have been specifically developed at the scale of agricultural fields and there are no guidelines to help modellers choose an adequate model to simulate overland flow and hence analyse the impact of different soil management practices on flood generation. A comparison is undertaken to select and calibrate models that simulate Hortonian overland flow at the field or small plot scale. The proposed methodology couples a runoff production model to a unit hydrograph transfer function. Four different models were tested: Philip, Morel-Seytoux, Horton and SCS. These models differ by their mathematical structure and the parameters to be calibrated while input hydrologic data are the same site data: rainfall/runoff and initial water content. The models are calibrated on 14 events and validated on 14 others. The results of both the calibration and validation phases are compared on the basis of their performance with regards to six objective criteria, three global criteria and three relative criteria representing volume, peakflow, and the root mean square error. The first type of criteria gives more weight to strong events whereas the second considers all events to be of equal weight. The results show that the calibrated parameter values are dependent on the type of objective criteria used. Furthermore, when analysing the performance of the six objective criteria used, it can be seen that the global volume, global RMSE and relative peakflow criteria give the best compromise between bias and precision. Within the selected modelling framework, Morel-Seytoux's model performed better than the other three and the SCS gave the worst results. Horton's model showed to be more consistent in overall performance than Philip's model. Results also highlight problems related to the simulation of low flow events and intermittent rainfall events.