Journal of Irrigation and Drainage Engineering

Two-dimensional flow from a point or line source is simulated in an irrigated basin with a nonlevel soil surface; the goal is to predict the distribution uniformity of infiltrated depths. The zero-inertia approximation to the equations of motion allows computation in both wet and dry areas. A fully implicit, nonlinear finite-difference scheme is developed for the solution, but practical numerical considerations suggest local linearization instead. Both isotropic and anisotropic resistance to flow are considered. Results in basins with irregular bottom configurations and small inflows show stream advance confined to the lowest elevations in the basin.

A two-dimensional (2D) model of unsteady shallow-water flow in surface irrigation was developed to evaluate the influence of field-grading precision on surface irrigation performance. This paper presents field data for verification of this 2D model. No attempt was made here to evaluate irrigation performance. Verification of such models relies on independent estimates of parameters for infiltration and roughness. To accomplish this, water surface elevations were measured at 26 points within a 3 ha level basin. A double-bubbler system was used to obtain relative water depths. Field surveys were used to convert these to water surface elevations and field water depths, from which surface water volumes over time were computed. The infiltration function was determined by matching inflow minus surface volume over time with computed subsurface volume. A value of Manning n (0.05) was found for which advance and water depth hydrographs were both well predicted with the 2D model. Differences in advance for a plane versus undulating field surface were minor, except near the end of advance.

Due to spatially varying conditions the improvement of furrow irrigation efficiency should be sought not just for a limited number of furrows or for one specific irrigation event. A simplified predictive modeling approach of the averaged advance-infiltration process is proposed in this paper. Horton’s equation, derived from the asymptotic form of the Talsma-Parlange infiltration equation, allows us to use a predictive approach for the advance infiltration process by means of the exact solution of the Lewis and Milne water balance equation. The references to the works of White and Sully, for a surface point source, result in the use of parameters which characterize the hydraulic properties of the soil: Δθ (saturated water content minus initial water content); Ks (saturated conductivity); and $\lambda c$ (macroscopic capillary length). The physical meaning of parameters involved in the proposed modeling is attested using field experiments carried out in a loamy soil plot context. Assuming a same Δθ measured value before irrigation for the whole of a 30 furrow sample, the averaged values of $\lambda c$ and Ks obtained from calibration on the advance trajectory are comparable to those derived from local infiltration tests (disk permeameter and double ring methods). The applicability of the model is then extended to heavy clay soil where the parameters $\lambda c$ and Ks still agree with the values proposed in the literature. This paper can be considered as a contribution to the development of a tool for evaluating the impact of irrigation practices on the efficiency at the plot and cropping season scale.

One of the main problems in the management of large water supply and distribution systems is the forecasting of daily demand in order to schedule pumping effort and minimize costs. This paper examines methodologies for consumer demand modeling and prediction in a real-time environment for an on-demand irrigation water distribution system. Approaches based on linear multiple regression, univariate time series models (exponential smoothing and ARIMA models), and computational neural networks (CNNs) are developed to predict the total daily volume demand. A set of templates is then applied to the daily demand to produce the diurnal demand profile. The models are established using actual data from an irrigation water distribution system in southern Spain. The input variables used in various CNN and multiple regression models are (1) water demands from previous days; (2) climatic data from previous days (maximum temperature, minimum temperature, average temperature, precipitation, relative humidity, wind speed, and sunshine duration); (3) crop data (surfaces and crop coefficients); and (4) water demands and climatic and crop data. In CNN models, the training method used is a standard back-propagation variation known as extended-delta-bar-delta. Different neural architectures are compared whose learning is carried out by controlling several threshold determination coefficients. The nonlinear CNN model approach is shown to provide a better prediction of daily water demand than linear multiple regression and univariate time series analysis. The best results were obtained when water demand and maximum temperature variables from the two previous days were used as input data.

Efficient water management is one of the key elements in successful operation of irrigation schemes in arid and semiarid regions. An integrated water management model was developed by combining an unsaturated flow model and a groundwater simulation model. These combined models serve as a tool for decision making in irrigation water management to maintain the water tables at a safe depth. The integrated model was applied on a regional scale in Sirsa Irrigation Circle, covering about a 0.42 million ha area in the northwestern part of Haryana, India, which is faced with serious waterlogging and salinity problems in areas underlain with saline ground irrigated by the canal network. The model was calibrated using the agrohydrologic data for the period 1977–1981 and validated for the period 1982–1990 by keeping the calibrating parameters unchanged. The model was used to study the long-term impact of two water management interventions related to the canal irrigation system—change in pricing system of irrigation water, and water supply according to demand—on the extent of waterlogging risk. Both of these strategies, if implemented, would considerably reduce aquifer recharge and consequently waterlogging risk, compared to the existing practice. The water supply according to demand strategy was slightly more effective in reducing aquifer recharge than the water pricing intervention. The implementation of the proposed water pricing policy would pose no problem in fitting into the existing irrigation system, and thus it would be easier to implement, compared to the water supply according to demand strategy, when taking technical, financial, and social considerations into account.

Comparison among commonly used reference evapotranspiration (ET) equations in the United States and the recently recommended ASCE standardized reference ET equation was made as part of the ASCE standardization effort. Analyses used hourly and daily weather data from 49 geographically diverse sites in the United States. Calculations were performed for both grass and alfalfa reference crops in a consistent manner, using weather data that passed integrity and quality assessment checks. Comparisons were made between reference ET computed by the various methods and the ASCE Penman-Monteith (PM) equation used for a daily calculation time step. In addition, calculations using hourly time steps and summed daily were compared with daily calculations for the same method as well as against the ASCE-PM method. Results showed that the ASCE standardized equation agreed best with the full form of ASCE-PM. The results provide a basis for objectively assessing the relative performance of reference ET equations in a variety of climates and support adoption of a standardized equation as recommended by the ASCE Task Committee.

Expressions for the rate and volume of flow depletion of semipervious streams due to pumping are presented in computationally simple forms. Analytical expressions have been proposed to take into account both partial penetration and semipervious bed and banks of the stream. Graphs suitable for engineering applications are presented for siting wells, and the effect of an intermittent pumping cycle on the rate and volume of stream flow depletion has also been discussed. The exclusive volume of flow depletion during a cycle is shown to vary with the selection of the end of the cycle.

In recent years, artificial neural networks (ANNs) have been applied to forecasting in many areas of engineering. In this note, a sequentially adaptive radial basis function network is applied to the forecasting of reference evapotranspiration $\left({\mathrm{ET}}_o\right)\text{.}$ The sequential adaptation of parameters and structure is achieved using an extended Kalman filter. The criterion for network growing is obtained from the Kalman filter’s consistency test, while the criteria for neuron/connection pruning are based on the statistical parameter significance test. The weather parameter data (air temperature, relative humidity, wind speed, and sunshine) were available at Nis, Serbia and Montenegro, from January 1977 to December 1996. The monthly reference evapotranspiration data were obtained by the Penman-Monteith method, which is proposed as the sole standard method for the computation of reference evapotranspiration. The network learned to forecast ${\mathrm{ET}}_{o\text{,}t+1}$ based on ${\mathrm{ET}}_{o\text{,}t-11}$ and ${\mathrm{ET}}_{o\text{,}t-23}\text{.}$ The results show that ANNs can be used for forecasting reference evapotranspiration with high reliability.

The hydraulic characteristics of a side sluice gate were studied experimentally. It was found that the specific energy remains constant along the side sluice gate. The coefficient of discharge for the side sluice gate is related to the main channel Froude number and the ratio of upstream depth of flow to sluice gate opening for free flow. It also depends on an additional parameter: the ratio of tailwater depth to the gate opening for submerged flow. Suitable equations for discharge coefficient are obtained.

Expressions for the factor K to relate the total frictional head loss, average outlet operating pressure head, and the inlet pressure head of a multioutlet pipeline are developed. In the developed expressions, the factor K is a function of the number of outlets on different pipe diameters, combination of diameters, and position of the first outlet from the inlet. Values of the factor K obtained from the developed expressions are compared with constant values being taken as per existing practice. The comparison suggests using the developed expressions for accurate computation of the factor K for multioutlet pipelines especially comprising of two or more diameters. An example is presented to compute the inlet pressure head of a multioutlet pipeline using the factor K.