Calibration of On-Demand Irrigation Network Models

Abstract

In this study, a new procedure for calibrating on-demand irrigation network models was developed. This procedure used a new objective function called maximum data with a reasonable error (MDRE) for calibrating the network. It was compared with the two more commonly used objective functions in calibration procedures that are the simple least squares (SLS) and the maximum likelihood estimator for the heteroscedastic error case (HMLE). In order to carry out the calibration, a quasi-Newton optimization method was used having as variable the Hazen-Williams head losses coefficient (C). This procedure was applied to an on-demand irrigation network located in Tarazona de La Mancha (Albacete, Spain) where flow and pressure at hydrant level was measured. The calibration procedure using the MDRE objective function was applied considering all the pressure control points simultaneously and the obtained results were compared with the results of considering the pressure control points independently. Therefore, the effect of the location of the pressure control point was studied. Results showed that, when the proposed objective function was used, the root mean squared error (RMSE) comparing the measured and simulated data after calibration was lower than when the SLS or HMLE objective functions were used. The location of the pressure control points throughout the irrigation network could affect the results; therefore, it was more accurate to use all the control points simultaneously than independently in the calibration process.