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Water 2017, 9(8), 609;

Approximate Explicit Solution to the Green-Ampt Infiltration Model for Estimating Wetting Front Depth

State Key Laboratory Base of Eco-hydraulic Engineering in Arid Area, Xi’an University of Technology, Xi’an 710048, China
College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling 712100, China
Author to whom correspondence should be addressed.
Received: 10 July 2017 / Revised: 4 August 2017 / Accepted: 9 August 2017 / Published: 22 August 2017
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Obtaining reliable information on the wetting front depth of soil is beneficial to the understanding of one-dimensional (1D) vertical infiltration under border irrigation. This paper presents an approximate explicit solution to the Green-Ampt (GA) infiltration model for estimating the wetting front depth. Moreover, the model proposed in this study, the GA model, the Ali model, and the Stone model were validated and evaluated using laboratory experimental data and HYDRUS-1D simulation. Statistical comparisons (root mean square error-RMSE, mean absolute percent relative error-MAPRE, and percent bias-PB) of the estimated data with the measured and simulated data were conducted. The models were ranked on the basis of their overall performance index (OPI). The results demonstrated that all four models can be used to estimate the wetting front depth of 1D vertical infiltration for a wide range of soil textures; the proposed model provided the most accurate result. According to comparisons of the estimated values with the measured and simulated values, the maximum RMSE, MAPRE, and PB were 1.74 cm, 6.92%, and −6.74%, respectively, for the proposed model. On the basis of the OPI, the optimal model was the proposed model, followed by the Ali, GA, and Stone models. View Full-Text
Keywords: Green-Ampt; explicit equation; wetting front; vertical infiltration Green-Ampt; explicit equation; wetting front; vertical infiltration

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Nie, W.-B.; Li, Y.-B.; Fei, L.-J.; Ma, X.-Y. Approximate Explicit Solution to the Green-Ampt Infiltration Model for Estimating Wetting Front Depth. Water 2017, 9, 609.

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