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Physically Based Model for Simulating Flow in Furrow Irrigation. II: Model Evaluation
Authors:D. R. Mailapalli  N. S. Raghuwanshi  R. Singh
Affiliation:1Postdoctoral Scholar, Dept. of Land, Air, and Water Resources, Univ. of California, Davis, CA 95616. E-mail: dmailapalli@ucdavis.edu
2Professor, Dept. of Agricultural and Food Engineering, Indian Institute of Technology, Kharagpur, West Bengal 721302, India (corresponding author). E-mail: nsr@agfe.iitkgp.ernet.in
3Professor, Dept. of Agricultural and Food Engineering, Indian Institute of Technology, Kharagpur, West Bengal 721302, India. E-mail: rsingh@agfe.iitkgp.ernet.in
Abstract:In this study, the physically based furrow irrigation model presented in Part 1 was evaluated using the experimental data collected from a field plot consisting of three 40-m-long free-drained furrows of parabolic shape and having a top width of 0.30 m, a depth of 0.15 m, and a slope of 0.5%. The irrigation experiments were carried out with a constant inflow of 0.2–0.5ls?1 and 0.3–0.7ls?1 on bare and cropped fields, respectively. The field data pertaining to furrow cross section, advance and recession times, water depth and velocity, and runoff rate and volume were collected from the irrigation experiments. The model performance was studied for simulating advance, recession, infiltration, and runoff using two–dimensional (2D) Fok, one–dimensional (1D) Green-Ampt, and KL infiltration functions (Part 1) by estimating the root mean square error and index of agreement (Ia). For all the irrigations performed under bare and cropped furrow conditions, the model slightly overpredicted the advance time and runoff rate and slightly underpredicted the recession time and infiltration using the 2D Fok, 1D Green-Ampt, and KL infiltration functions. Furthermore, simulated results were closer to their observed counterparts for 2D Fok infiltration function than for 1D Green-Ampt and KL infiltration functions. Sensitivity analysis was carried out to study the effect of ±5 and ±10% change in 2D or 1D infiltration parameters (i.e., Ks and Sav) and KL infiltration parameters (i.e., Ak and Bk) on outputs. Ks is the most sensitive parameter for predicting advance time, infiltration, and runoff followed by Sav. In the KL infiltration parameter, Bk is the most sensitive parameter for estimating advance and recession times, infiltration, and runoff. The test results of the model suggested that the model can be used as a tool for designing and managing furrow irrigation.
Keywords:Furrow irrigation  Hydraulic models  Sensitivity analysis  
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