Abstract
The present paper applies a simplified Green-Ampt (GA) infiltration model for the numerical modelling of seepage processes within the body of the Maga earth dam, located in the Far-North Cameroon, under rainfall conditions. The parametrization approach makes possible the predictions of infiltrations parameters for various textures that constitute the body of earth dam and its vicinities. The effects of soil texture, permeability coefficient, rain intensity and initial moisture on infiltration rate and cumulative depth are investigated using numerical simulations carried out by running an Excel VBA code. A proposed optimization procedure of the literature is then used to improve the GA model parameters and accuracy of predictions. A case sensitivity analysis of the infiltration model parameters has also been made. Initial and saturated water contents, rain intensity and permeability coefficient at saturation have been identified as the most influents parameters for the model predictions, with obvious interactions between inputs. The contribution of main effects and interactions of above inputs ranges between 68.8 % and 90.03 % of the total variance. Comparisons of numerical results with the experimental ones obtained from the literature on real cases of simulated rainfall on main sub-Saharan soils have been done. The results show that numerical simulations underestimated 60 % and 56.25 % of the hydrodynamics parameters respectively for one and four simulated rain-sequence with relative errors (RE) ranging between -76.22 % and 300.25 %. The most representative results were obtained for 53.13 % of the predicted hydrodynamics parameters with RE value ranging between -23.90 % and 12.75 %. The obtained results proved that, the simplified GA model exhibited an acceptable accuracy in predicting seepage processes for ferruginous and degraded vertic soils. Therefore, the present study contributes to understand the role of numerical simulations tools in modelling and predicting subsurface phenomenon as seepage, for areas with high floods risks in a context of significant climate changes.