Journal of Irrigation and Drainage Engineering

Computing accurately the response time of an open channel is of extreme importance for management operations on canal networks, such as feed-forward control problems. The methods proposed in the literature to approximate the response time do not always account for the influence of a cross structure at the downstream end of a canal pool, which may have a significant impact on the response time. This paper proposes a new approach to compute the response time, accounting explicitly for the backwater and the feedback effects due to the downstream cross structure. The method provides a distributed analytical expression of the response time as a function of the characteristics of the canal (geometry, roughness) and of the downstream cross structure. A test canal with a weir or a gate at the downstream end is used to compare the new method with some of the others. Results show that the proposed expression accurately reproduces the response time for various backwater and downstream boundary conditions.

A downstream controller is designed for an irrigation canal reach using a design technique called quantitative feedback control theory (QFT). The performance of this controller is compared to a proportional, integral, derivative (PID) controller and a linear quadratic regulator (LQR) controller. In this study, the QFT controller is designed for a single canal reach because it best demonstrates how a controller is designed. Previous research for this canal model provided data for comparison. For the operating conditions that are defined in this paper, the QFT controller is shown to have slightly better performance than the PID controller and better performance than the LQR controller. When the canal hydraulic roughness is increased, the QFT controller still performed better than the PID controller.

Coupling the unsteady open-channel flow equations of surface irrigation with the equation of variably saturated porous media flow is a computationally complex problem because of the dependence of infiltration on surface-flow depths. Several models of this coupled process have been developed, all of which solve the surface and subsurface flow iteratively at each time step of the surface-flow solution. This study presents an alternative strategy, in which stand-alone surface and subsurface-flow models are used to conduct the calculations sequentially and iteratively at the time level of the irrigation event. At each iteration, the subsurface-flow results are computed using the surface-flow depths generated by the surface-flow model at the current iteration. Infiltration results computed at selected computational nodes are fitted to an empirical infiltration function, and then fed back to the surface-flow model. The proposed strategy, labeled external iterative coupling, was prototyped for border and basin irrigation systems using the WinSRFR and HYDRUS-1D models. The proposed procedure produced irrigation performance results comparable to those generated with an internally coupled model, even when using a single representative location to calibrate the empirical infiltration equation used by the surface-flow model. In comparison with models that iterate at each time step, the proposed coupling strategy reduces the computational effort and improves convergence. The approach provides a practical alternative for coupling existing and future surface and subsurface flow models.

A typical irrigation scheduling problem is one of preparing a schedule to service a group of outlets that may be serviced simultaneously. This problem has an analogy with the classical multimachine earliness/tardiness scheduling problem in operations research (OR). In previously published work, integer programming was used to solve irrigation scheduling problems; however, such scheduling problems belong to a class of combinatorial optimization problems known to be computationally demanding. This is widely reported in OR literature. Hence integer programs (IPs) can be used only to solve relatively small problems typically in a research environment where considerable computational resources and time can be allocated to solve a single schedule. For practical applications, metaheuristics such as genetic algorithms, simulated annealing, or tabu search methods need to be used. However, these need to be formulated carefully and tested thoroughly. The current research explores the potential of genetic algorithms to solve the simultaneous irrigation scheduling problem. For this purpose, two models are presented: the stream tube model and the time block model. These are formulated as genetic algorithms, which are then tested extensively, and the solution quality is compared with solutions from an IP. The suitability of these models for the simultaneous irrigation scheduling problem is reported.

The study investigates the accuracy of linear genetic programming (LGP), which is an extension to genetic programming (GP) technique, in daily reference evapotranspiration $\left({\text{ET}}_0\right)$ modeling. The daily climatic data, solar radiation, air temperature, relative humidity, and wind speed from three stations, Windsor, Oakville, and Santa Rosa, in central California, are used as inputs to the LGP to estimate ${\text{ET}}_0$ obtained using the FAO-56 Penman-Monteith equation. The accuracy of the LGP is compared with those of the support vector regression (SVR), artificial neural network (ANN), and those of the following empirical models: the California irrigation management system Penman, Hargreaves, Ritchie, and Turc methods. The root-mean-square errors, mean-absolute errors, and determination coefficient $\left(R^2\right)$ statistics are used for evaluating the accuracy of the models. Based on the comparison results, the LGP is found to be superior alternative to the SVR and ANN techniques.

Evapotranspiration is critical to many applications including water resource management, irrigation scheduling, and environmental studies. Many models based on meteorological data have already been developed to estimate reference evapotranspiration $\left({\text{ET}}_0\right)$ in various climatic and geographical conditions. The main purpose of this study was to evaluate the performances of the Makkink, Priestley-Taylor, and Hargreaves models versus the Penman-Monteith FAO-56 (PMF-56) method in arid and semiarid regions of Iran during 1993–2005 and to identify the alternative ${\text{ET}}_0$ model that presents results closest to the PMF-56 method. Additionally, a regional estimation of monthly ${\text{ET}}_0$ with the best-performed model is presented by using the spatially distributed physical parameters and geographical information system. The results indicated that the Hargreaves model was the best model to estimate ${\text{ET}}_0$ in eastern arid and semiarid regions of Iran. The spatial distribution maps of ${\text{ET}}_0$ showed that ${\text{ET}}_0$ values increased from north to south as the aridity increased in the study area. The estimated total monthly ${\text{ET}}_0$ revealed a significant variation during the growing seasons (April–September) so that the study region experienced the highest and lowest ${\text{ET}}_0$ values of 250 and 80 mm in July and April, respectively.

The present work aims at approaching the study of the performance and uncertainty associated with an irrigation scheduling method based on a soil-water balance. On a daily time step, a water-balance-based irrigation scheduling model has been developed. A Monte Carlo simulation of the irrigation scheduling model is developed using a series of actual daily weather data of evapotranspiration and precipitation and bootstrapping stochastic technique to resampling them. Performance evaluation measurements and their uncertainty are studied by means of several parameters: reliability, resiliency, vulnerability, total irrigation water allocation, total water loosed by deep percolation, and actual evapotranspiration/potential evapotranspiration rate along the growing season. The behaviors of 12 different types of soils (between coarse-textured soils and fine-textured soils) are compared using pedotransfer functions. Total available water (TAW) is the most important hydraulic property of the soil as far as irrigation scheduling performance is concerned. The statistical relationship between evaluation performance measures and TAW has been calculated. Soils with high values of TAW perform better. Rooting depth $\left(Z_r\right)$ and fraction of TAW that can be depleted from the root zone before moisture stress $\left(p\right)$ are two variables that directly affect the TAW. It has been studied how evaluation performance measurements change when $Z_r$ and $p$ change too. High values of $Z_r$ and $p$ perform better too.