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This research proposes a novel generic method for irrigation scheduling in a canal network to optimize multiple objectives related to canal scheduling (e.g. maximizing water supply and minimizing imbalance of water distribution) within multiple hierarchical layers (e.g. the layers consisting of the main canal, distributaries) while utilizing traditional canal scheduling methods. It is based on modularizing the optimization process. The method is theoretically capable of optimizing an unlimited number of user-defined objectives within an unlimited number of hierarchical layers and only limited by resource availability (e.g. maximum canal capacity and water limitations) in the network. It allows flexible decision-making through quantification of the mutual effects of optimizing conflicting objectives and is adaptable to available multi-objective evolutionary algorithms. The method’s application is demonstrated using a hypothetical canal network example with six objectives and three hierarchical layers, and a real scenario with four objectives and two layers.

This research proposes A THEORETICAL FRAMEWORK based on model predictive control (MPC) for irrigation control to minimize both root zone soil moisture deficit (RZSMD) and irrigation amount under a limited water supply. We (i) investigate means to incorporate direct measurements to MPC (ii) introduce two Robust MPC techniques - Certainty Equivalence control (CE) and Disturbance Affine Feedback Control (DA) - to mitigate the uncertainty of weather forecasts, and (iii) provide conditions to obtain two important theoretical aspects of MPC - feasibility and stability - in the context of irrigation control. Our results show that system identification enables automation while incorporating direct measurements. Both DA and CE minimize RZSMD and irrigation amount under uncertain weather forecasts and always maintain soil moisture above wilting point subject to water availability. The theoretical results are compared against the model AQUACROP, weather data and forecasts from Shepparton, Australia. We also discuss the performance of Robust MPC under different water availability, soil, crop conditions. In general, MPC shows to be a promising tool for irrigation control. MPC is used to minimize both root zone soil moisture deficit and irrigation amount.System identification incorporates direct measurements to MPC enabling automation.Uncertainty in weather forecasts is mitigated using two modified Robust MPC approaches.Optimal operation can be guaranteed through the proposed method.Guaranteed operation above wilting point at all times subject to water availability.

In model-based irrigation control, the root zone soil moisture deficit (RZSMD) is maintained based on the water balance. To predict RZSMD in real-time, effective rainfall, irrigation and crop evapotranspiration need to be calculated online. Estimating the first two variables is more important yet tedious due to practical limitations of knowing the amount of water actually infiltrated into the soil. In order to solve this problem, we propose to apply system identification on water balance data to obtain a linear time series model. We further investigate how to carry out the modelling (i) under saturated conditions, (ii) when there is a rule-based irrigation control, and (iii) under measurement noise in the soil moisture readings. Using synthetic data we obtained a model fit above 80% in all cases. Additionally, we show the model optimality and applicability with an independent dataset, using residual tests. For two sets of field data, we observed model fits of 84% and 63%, and satisfaction in all residual tests. Simplicity in the model reduces calibration efforts whereas its linearity and adequacy recommend it for real-time irrigation control applications. In summary, the results indicate that a first order linear time series model based on system identification can successfully predict RZSMD in a real-time irrigation control system.