Estimating Input Parameters for Four Infiltration Models from Basic Soil, Vegetation, and Rainfall Properties

Abstract

Quantifying infiltration is a challenging and time‐consuming process because the parameters are very difficult to measure directly. Estimating them from dependent and readily measurable parameters is a more efficient alternative. Many existing studies have focused on deriving the parameters in the Green–Ampt model from basic soil properties, with few related to other infiltration models or factors. In this study, infiltration experiments were conducted on 28 small plots using a field rainfall simulator, with the purposes to: (i) investigate the effect of various factors on the parameters in four popular infiltration models; and (ii) develop predictive equations for each parameter from its identified major controlling factors. To achieve the best outcome, four factor sets including different numbers of factors were applied in a regression analysis. Results showed that each infiltration parameter was controlled by not only soil factors but also vegetation and rainfall. Soil properties alone were not sufficient to predict these infiltration parameters, while the regression equations developed from the full set of factors showed much higher accuracy. In addition, a simpler factor set including only the four most readily obtainable factors was evaluated, but the expected success was not achieved. However, the inclusion of the saturated hydraulic conductivity greatly improved the results to an acceptable level. The results of this study demonstrate the contribution of the effects of vegetation and rainfall on various infiltration parameters. The simplified predictive equations are also expected to improve the outcome with regard to effort, time, and cost in determining these parameters.