Evaluation of infiltration models with different numbers of fitting parameters in different soil texture classes

Abstract

In this study, the ability of eight different infiltration models (i.e. Green and Ampt, Philip, SCS (US-Soil Conservation Service), Kostiakov, Horton, Swartzendruber, Modified Kostiakov (MK) and Revised Modified Kostiakov (RMK) models) were evaluated by least-squares fitting to measured infiltration data. Six comparison criteria including coefficient of determination (R²), mean root mean square error (MRMSE), root mean square error (RMSE), the F-statistic (F), C_p statistic of Mallows (C_p) and Akaike information criterion (AIC) were used to determine the best performing model with the least number of fitting parameters. Results indicated that R² and MRMSE were not suitable for model selection. A more valid comparison was achieved by F, C_p, AIC and RMSE statistics. The RMK model including four parameters had the best performance with the majority of soils studied. RMK was better than the MK model in approximately 51.6, 57, 68.5 and 70.6% of soils, when using F, C_p, AIC and RMSE statistics, respectively, and for the other models, a higher per cent of soils was obtained. The RMK model was the best for loam, clay loam and silty clay loam soils, but the MK model was the best for silty loam soils.

Keywords:

• infiltration models
• C_p statistic
• Akaike criterion
• F-statistic