Errors in predicting furrow irrigation performance using single measures of infiltration |
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Authors: | Philip K Langat Steven R Raine Rod J Smith |
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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 |
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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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