Journal of Irrigation and Drainage Engineering

Reference evapotranspiration (${\mathrm{ET}}_o$) estimates are often required for use in water resources planning and irrigation scheduling. Ten ${\mathrm{ET}}_o$ estimation methods ranging from simple temperature-based to data-extensive combination methods, including Hargreaves (HAR), improved Hargreaves (IHA), FAO-24 Radiation (RAD), Ritchi-type (RIT), FAO-24 Class-A Pan with pan coefficients of Doorenbos and Pruitt (PEV) and empirical regression coefficient (SEV), combination methods McIlroy (McI), FAO-Penman with wind functions of Watts and Hancock (W_H) and Meyer (M_PY), and the Penman-Monteith (P_M) were evaluated at three sites, namely, Aspendale, Griffith, and Tatura in the Goulburn-Murray Irrigation Area (GMIA) of southeastern Australia. At Aspendale, 4 out of 10 ${\mathrm{ET}}_o$ methods (McI, M_PY, SEV, and RAD) overestimated the ${\mathrm{ET}}_o$ estimates; at Griffith no method overestimated them, whereas at Tatura only the RAD method overestimated ${\mathrm{ET}}_o$. The overestimations were at Griffith, McI (1%), M_PY (10%), and SEV (4%); at Tatura, RAD (2%). At the Griffith and Tatura sites, almost all methods showed a strong tendency to underestimate daily ${\mathrm{ET}}_o$ estimates throughout the entire range of evaporative demand. Overall, the underestimation ranges observed were McI (12–27%), W_H (7–22%), RIT (6–25%), PEV (19–31%), HAR (18–31%), and IHA (8–11%). The underestimation of daily ${\mathrm{ET}}_o$ estimates by the P_M method ranged from 21 to 29%, raising caution about its use as a base method (without calibration against measured data under local conditions) to evaluate other ${\mathrm{ET}}_o$ methods, as has been advocated in recent literature. The use of the McI method as the top-ranked method at Aspendale and Tatura, and the W_H method at Griffith, indicated that no single daily ${\mathrm{ET}}_o$ estimation method using meteorological data was satisfactory for all three sites. Generally, the combination methods proved to be the most accurate ${\mathrm{ET}}_o$ estimates. At Tatura, the fact that the RAD method was ahead of the W_H and M_P combination methods indicates how a less data-intensive ${\mathrm{ET}}_o$ method, if calibrated, can perform even better than a physically based combination method. All ${\mathrm{ET}}_o$ estimation methods required local calibration against measured lysimeter ${\mathrm{ET}}_o$ data for better performance.

Estimation of evapotranspiration (ET) is necessary in water resources management, farm irrigation scheduling, and environmental assessment. Hence, in practical hydrology, it is often necessary to reliably and consistently estimate evapotranspiration. In this study, two artificial intelligence (AI) techniques, including artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), were used to compute garlic crop water requirements. Various architectures and input combinations of the models were compared for modeling garlic crop evapotranspiration. A case study in a semiarid region located in Hamedan Province in Iran was conducted with lysimeter measurements and weather daily data, including maximum temperature, minimum temperature, maximum relative humidity, minimum relative humidity, wind speed, and solar radiation during 2008–2009. Both ANN and ANFIS models produced reasonable results. The ANN, with 6-6-1 architecture, presented a superior ability to estimate garlic crop evapotranspiration. The estimates of the ANN and ANFIS models were compared with the garlic crop evapotranspiration ($\mathrm{E}{\mathrm{T}}_{\mathrm{c}}$) values measured by lysimeter and those of the crop coefficient approach. Based on these comparisons, it can be concluded that the ANN and ANFIS techniques are suitable for simulation of $\mathrm{E}{\mathrm{T}}_{\mathrm{c}}$.

One problem associated with subsurface drip irrigation (SDI) is the reduction in discharge resulting from soil-water back-pressure at the emitter outlets. An experimental setup was made to measure emitter discharge and pressure at the emitter outlet in different soils. Experiments were carried out with 2–$24\mathrm{L}/\mathrm{h}$ noncompensating and compensating emitters, operating at a constant lateral pressure of 10 m. Emitter discharge was reduced to a range of 2–10% for noncompensating models and to less than 1% for compensating models. Soil pressure ranged from 0.15–2.07 m. Laboratory conditions were simulated with HYDRUS-2D/3D. Experimental values of discharge and soil pressure showed good agreement with estimated values. Finally, maximum emitter discharge to limit the decrease of discharge was determined for an operating pressure of 10 m. For a 10% decrease, considering a constant radius of the spherical cavity in the soil, maximum emitter discharge was $2.35\mathrm{L}/\mathrm{h}$ for loamy soil and $12.44\mathrm{L}/\mathrm{h}$ for sandy soil for noncompensating emitters, and 10.73 and $54.51\mathrm{L}/\mathrm{h}$, respectively for compensating emitters. These values increased when considering a cavity radius variable with emitter discharge.

Unsaturated soil hydraulic parameters and functions used in numerical models to simulate water flow and solute transport in the unsaturated zone are generally considered invariant of soil water salinity levels. This study uses 5 years of field soil water salinity levels at three observation sites from the Land Retirement Demonstration Project (LRDP) (2006) located in western Fresno County, California, to test the hypothesis that field unsaturated soil hydraulic properties are also a function of soil water salinity level. The HYDRUS-1D software package for simulating one-dimensional (1D) movement of water, heat, and multiple solutes in variably saturated media, and Parameter Estimation (PEST), a model-independent parameter optimizer, is used to optimize the soil hydraulic parameters and downward bottom flux corresponding to three different average soil salinity levels at each site. The results show that at the same pressure head, soil water content is less with higher soil water salinity as compared with lower soil water salinity. It is thus concluded that the use of soil water salinity invariant soil water hydraulic parameters in numerical modeling can seriously compromise predictions, especially for a variable soil water salinity environment.

A model for the simulation of furrow irrigation advance was developed based on the Saint-Venant equations for the one-dimensional surface flow and the two-dimensional Richards equation for porous media flow. Solutions are computed numerically using finite differences for the surface flow and finite elements for the subsurface flow. Computations are internally coupled through an iterative procedure. Infiltration is computed with the Richards equation every five nodes used in the surface flow computations and by linear interpolation at the remaining nodes. In addition, the Richards equation is solved at the boundaries of the surface flow domain and in the vicinity of the wave front. The time step is calculated using the Courant–Friedrich–Lewy condition and a stability criterion that accounts for friction effects. This combined criterion prevents numerical instabilities and convergence problems, especially in cases of high friction coefficient, low discharge rates, and/or high infiltration rates resulting generally in low flow depth and slow irrigation advance. The model was evaluated against an approach involving high resolution correspondence used in both surface and subsurface flow, using different soil types, inflow discharge, and stability criteria.

Field tests were conducted to obtain irrigation evaluation and solute transport data. The data were used to calibrate and validate an advection-dispersion model for furrow irrigation. The empirical infiltration equation and roughness parameters were estimated from the evaluation data. The inflow rate was measured with a volumetric meter and a flume and resulted in different average inflow rates. Hydraulic simulation results proved nearly as accurate with infiltration function estimates derived from the meter or flume data despite the difference in measured flow rate. Hence, the calibrated infiltration functions provide limited clues about possible problems with the inflow data. The choice of the infiltration equation used to fit the data (Branch versus modified Kostiakov) produced greater differences in the hydraulic modeling results. The timing and spread of the solute concentration pulses were well predicted independently of the inflow data and infiltration equation used to fit the data. However, differences between the meter and flume inflow rate were clearly manifested in the predicted peak solute concentrations. Results highlight the importance of accurate inflow measurements for parameter estimation.

The main objective of this work is to allocate water with acceptable quality from a reservoir-river system to different water users and to determine the maximum acceptable pollution load that each user discharges to the river. To achieve this, a bargaining model is developed based on a system dynamics approach, and it is compared with an alternative model based on the Nash bargaining theory. The quality of water released from the reservoir is assessed considering selective withdrawal schemes, obtained from schemes based on operating rules derived from an artificial neural network (ANN) and a $K$-nearest neighborhood (NN) model. A river water-quality simulation model is also linked with the two bargaining models. These models are then applied to the Karkheh reservoir-river system and using a 50-year monthly time series of qualitative and quantitative data of this system. Finally, the performance criteria for the results of the models are determined and compared. This study shows the significant value of the system dynamics approach in resolving disputes over water allocation among stakeholders/water users of reservoir-river systems considering the water-quality aspects.