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Errors in predicting furrow irrigation performance using single measures of infiltration
Authors:Philip K Langat  Steven R Raine  Rod J Smith
Institution:(1) Faculty of Engineering and Surveying, University of Southern Queensland, Toowoomba, QLD, 4350, Australia;(2) Cooperative Research Centre for Irrigation Futures and National Centre for Engineering in Agriculture, Faculty of Engineering and Surveying, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
Abstract:Commercial performance evaluations of surface irrigation are commonly conducted using infiltration functions obtained at a single inflow rate. However, evaluations of alternative irrigation management (e.g. flow rate, cut-off strategy) and design (e.g. field length) options using simulation models often rely on this single measured infiltration function, raising concerns over the accuracy of the predicted performance improvements. Measured field data obtained from 12 combinations of inflow rate and slope over two irrigations were used to investigate the accuracy of simulated surface irrigation performance due to changes in the infiltration. Substantial errors in performance prediction were identified due to (a) infiltration differences at various inflow rates and slopes and (b) the method of specifying the irrigation cut-off. Where the irrigation cut-off at various inflow rates was specified as a fixed time identified from simulations using the infiltration measured at a single inflow rate, then the predicted application efficiency was generally well correlated with the application efficiency measured under field conditions at the various inflow rates. However, the predictions of distribution uniformity (DU) were poor. Conversely, specifying the irrigation cut-off as a function of water advance distance resulted in adequate predictions of DU but poor predictions of application efficiency. Adjusting the infiltration function for the change in wetted perimeter at different inflow rates improved the accuracy of the performance predictions and substantially reduced the error in performance prediction associated with the cut-off recommendation strategy.
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