Journal of Irrigation and Drainage Engineering

Efficient management of irrigation/drainage systems relies to a large extent on the ability to predict a priori the response of the system to a proposed management plan. However, such numerical prediction of system response (e.g., root-zone hydrosalinity conditions and crop yield) are dependent on several parameters that are spatially and/or temporally variable and are related to soil properties, atmospheric conditions, crop type, and the amount and salinity of water application. This paper presents enhancements to the Colorado State University Irrigation And Drainage (CSUID) model that expand its capacity to fully simulate the dynamics of variably saturated flow and transport in a three-dimensional (3D) variably saturated porous media and to account for spatial and temporal heterogeneity in input parameters. The CSUID model has the ability to simulate irrigation/drainage activities and their impact on the soil and underlying groundwater, as well as on crop yield, taking into account the spatial and temporal variability in available soil moisture, including matric stress, waterlogging, and osmotic stress due to salinity. Having a model with these capabilities is an important step towards understanding the influence of heterogeneous inputs and parameter uncertainty on the design and management of field-scale irrigation/drainage systems. Four benchmark problems are used in this paper to test the CSUID model and the model performs well. In addition, an example is presented to demonstrate model capabilities in representing a 3D time-varying irrigated field with and without subsurface drainage.

Spatial heterogeneity and measurement error in parameters representing flow and salt transport properties in irrigated and drained fields make numerical prediction of soil hydrosalinity conditions, crop yield response, and other variables prone to uncertainty. This paper presents a method to account for uncertainty of correlated regionalized parameters in simulating key performance variables using a three-dimensional (3D) flow and transport model, the Colorado State University Irrigation and Drainage (CSUID) model, coupled with multivariate Monte Carlo simulation. Sequential indicator simulation is used to generate 3D correlated realizations for hydraulic conductivity, porosity, residual water content, van Genuchten parameters, and dispersivity. Other semiempirical parameters that control crop water uptake, irrigation efficiency, and subsurface drainage conductance also are randomized. The generated ensembles for each of the considered parameters are processed with the CSUID model to obtain the spatial statistical moments of root-zone hydrosalinity conditions and relative crop yield for an irrigated and drained alfalfa field. In addition, the statistical properties of hydrographs of drainage effluent and salt concentration are explored. Results show that parameter uncertainty significantly affects the predicted hydrosalinity responses, consequently affecting uncertainty in the estimate of relative crop yield throughout the field. Spatial distributions of the coefficient of variation of the ensemble of predicted values of water table depth and soil salinity ranged from 1–21% and 12–31%, respectively, across the field at the end of the 60-day simulated period. Corresponding spatial distributions of the coefficient of variation of the predicted relative yield of alfalfa values ranged from 16–36%. With respect to the simulated period, the temporal distribution of the coefficient of variation of the drainage effluent rate and salt concentration ranged from 23–42% and 1.5–0.8%, respectively.

Improved irrigation management depends on accurate irrigation scheduling that should be based on estimates of daily crop water requirements. Traditional methods for estimating daily crop water requirements use a generic crop coefficient ($K_c$) that is based on point measurement and is developed for crops growing under optimal conditions that do not reflect actual growing conditions in regular fields. Therefore, daily water requirements based on these coefficients are not always accurate, require using stress coefficients, and obtaining crop coefficients under actual growing condition using traditional point measurement approaches, such as lysimeters or soil moisture monitoring, is impractical. Surface energy balance models can be used to directly estimate actual evapotranspiration (ET) in many fields, and these estimates can be used to develop regional crop coefficients that reflect the actual growing conditions. The objective of this study is to develop regional remote sensing–based corn $K_c$s for the South Platte River basin in Colorado. The ReSET-Raster surface energy balance model is used to estimate the actual ET for irrigated grain corn in the South Platte River basin of Colorado. The study covered five growing seasons (2001, 2004, 2005, 2006, and 2007) and a total of 104 Landsat 5 and 7 images were used. Normalized difference vegetation index (NDVI) computed from Landsat reflectance bands was used to separate the fields into two sets based on the crop planting date: (1) on-time planting, and (2) late planting. For each of these two sets of fields, a $K_c$ curve was developed using remote sensing data, and a third $K_c$ curve was developed using all fields. Growers can select the $K_c$ curve to use based on their knowledge of their planting date. The three sets of crop coefficients were compared to traditional $K_c$ curves. The differences between the comparison curves and the remote sensing–based $K_c$ highlight the importance of developing regional and planting date–based crop $K_c$ curves using crops under actual growing conditions.

Much research has been done to determine levels of contamination in runoff from urban and rural catchment areas. Some authors compare their results with other authors or look for relationships between the variations of pollutant concentration and the catchment characteristics, but they seldom focus on determining the statistical significance of their results. This paper presents a review of 37 papers selected from a total of 169 papers consulted about runoff-water quality throughout the world (America, Asia, and Europe). The results presented in these 37 papers have been reviewed and statistically analyzed to evaluate the influence of catchment-area characteristics (location, size, average daily traffic, and type of land use) on the storm water runoff pollution. For the cases studied, most of the results about the polluting agents studied are comparable and no important element of the catchment characteristics had significant influence on the pollutants’ concentration.

This paper presents the application of a distributed model predictive controller (DMPC) to the control of an accurate model of an actual irrigation canal in Spain. The canal is modeled using the Saint-Venant equations and implemented using the well-known Simulation of Irrigation Canals (SIC) modeling software for irrigation canals. The DMPC algorithm has been implemented in Matlab and interfaced to SIC. In the distributed-control algorithms, the local controllers exchange information so that their control policies are optimal in the sense of getting the best value of a performance index. The results show that the proposed distributed-control algorithm obtains better control performance than a more-conventional decentralized control scheme without information exchange. This better performance translates directly into money and resource savings.

This paper addresses the problem of the development of a distributed linear quadratic Gaussian (LQG) controller for a water delivery canal. The control structure proposed relies on a set of LQG control agents interconnected through a communication network. Each of these local control agents controls a canal reach made of a pool and the corresponding downstream gate and receives information (output signal and control moves) only from the corresponding canal reach and the ones that are adjacent to it. An algorithm is proposed to achieve coordinated action of the different local control agents. This distributed control structure is compared with centralized multivariable LQG control. Several aspects with incidence on performance are addressed, including the modification of the quadratic cost to ensure a constraint on closed-loop poles, the use of a nonlinear filter to limit noise effects, and the impact of a quantization commonly forced in gate position. Experimental results obtained in a pilot canal are presented.

The aim of this paper is to improve and complete the results presented in a previous work published on this journal about an experimental campaign focused on the hydraulic efficiency of continuous transverse grates for draining paved areas. This type of surface drainage structure is commonly used in urban or impervious areas (public squares, airport pavements, parks, and pedestrian areas), where conventional inlets are not recommended because they are rendered ineffective by the lack of a well-defined channelized flow path and gradients capable of directing flows toward the collecting hydraulic structures. In this paper, a methodology is presented to estimate the hydraulic efficiency of nontested continuous transverse grates. Empirical expressions are proposed to relate grate hydraulic performance to flow parameters and grate geometry without the need for laboratory tests. Moreover, the spectrum of the grates was extended in terms of dimensions and hydraulic design with respect to the first study, to enlarge the range of validity of the proposed empirical expressions.

Surface water and groundwater are two important resources that can be used simultaneously to meet demand. To sustain these resources in a planning horizon, different conditions of supply and demand can be balanced by operational rules. Those rules can be extracted from prior experience while operating a conjunctive water system and then applied in future periods. Genetic algorithm (GA) is an evolutionary algorithm that has been extensively used to determine optimal operation rules. Commonly, the operation rule pattern is fixed by a linear equation, and GA calculates optimal coefficients in a defined pattern. In this paper, to compute a more effective operation rule that yields a better objective function value compared to GA for the same conditions, a fixed-length gene genetic programming (FLGGP) rule is developed and extracted based on genetic programming (GP). The FLGGP rules are employed in an aquifer-dam system with two subsystems, in which each subsystem can supply parts of the demand. Results show that the obtained objective function value using the FLGGP rules is 26 percent better compared to the common linear rule by GA. The developed operational rule uses more functions and mathematical operators than numerical variables. Thus, FLGGP is more flexible and effective in determining optimal rule curves for a conjunctive aquifer-dam system.

Air-water flow experiments were conducted on some flat and pooled stepped spillways with slopes of 8.9° and 26.6° in transition and skimming flows. The study comprised the observations of the flow patterns, characteristic air-water flow properties, and energy dissipation performances. The air-water flow properties showed some differences in terms of interfacial velocity, bubble count rate, and turbulence intensity between the stepped chutes for the two channel slopes. These differences were also reflected in the residual energy data, highlighting a better energy dissipation rate for the pooled stepped spillway with slope of 8.9°. However, the aerated flows on the pooled stepped spillways exhibited some hydrodynamic instabilities, and a safe operation must be tested in physical models. The flat stepped spillway appeared to be the preferable design in terms of energy dissipation and flow stability.