Journal of Irrigation and Drainage Engineering

Results are presented from an experimental study of the stage discharge relationship for a porous pipe buried under loose laid aggregate. A series of steady-state experiments were performed in which the depth of flow over the pipe was measured for a given pipe discharge. The stage-discharge relationship for the entire system is well described by an orifice equation when the water surface in the aggregate trench was horizontal and the pipe was running full at the outlet. An energy equation analysis shows that the dominant energy losses are due to flow through the small side wall orifices into the pipe and from friction losses along the pipe. Friction losses in the aggregate are shown to be negligible, which again supports the orifice equation model. Discharge coefficients are reported for three commonly available porous pipes. The results of this research can be used in the sizing and placement of subsurface drains in aggregate filled storm water infiltration practices such as infiltration trenches and porous pavements.

Severe droughts were experienced by many southerly states of the Indian subcontinent during the past decade. Farmer suicides were reported from the state of Andhra Pradesh year after year, especially when the monsoons failed. The government of India conducted expensive rain enhancement experiments through artificial cloud seeding; this did happen, but at enormous infrastructural costs and organizational endeavors. In this paper, the attempt is to find alternative shower generation mechanisms at the scale of an individual cropland. The application of a new rain simulator is explored. The crux of the problem lies in generating showers with drops that not only fall with approximately the same speeds as the natural rain showers but also have matching drop size distributions. Previous simulators have not looked at this aspect; none have attempted to recreate a drop size spectrum that is indistinguishable from natural rain showers. This is the paper’s mainstay; new calculations are shown for a uniquely fabricated rain simulator that allows tridirectional maneuverability with regard to the spraying geometry. Only when the nozzle placed at 2.5 m from the ground is tilted at an upward angle of 80° with respect to the boom’s horizontal axis, a resultant spectrum is obtained that closely matches the northeast monsoon shower spectra at optimal crosswind speeds. In contrast, when the spray nozzles point vertically downwards, as in sprinklers, the resultant droplet spectra do not match real rain shower spectra; the number concentrations of both large and small droplets are overpredicted with fall velocities grossly higher than their terminal velocities. The droplet energy flux obtained from a vertically downward tilted nozzle reach the highest value of $24\mathrm{W}·{\mathrm{m}}^{-2}$. On the contrary, the maximum energy flux value from a skyward projected shower slides down to $2.0\mathrm{W}·{\mathrm{m}}^{-2}$ matching fluxes for natural rainfall.

Although olive trees are well known to be resistant to drought, the economic advantages of irrigation have been clearly demonstrated. Today, the olive growing areas in the Mediterranean Basin suffer from water shortages and increased competition from other sectors for the available water resources. This study was conducted in an olive tree plantation in western Turkey in 2008–2010. In the study, the effects of different irrigation levels on yield and the physiological and morphological development of olive trees (Olea europaea L. cv. Memecik) were investigated, and the optimal irrigation schedule was determined according to the findings of the study. Six different irrigation treatments were created using a drip irrigation system. The treatments were: a control ($S_C$) in which irrigation amounts were scheduled based on the soil moisture content of the top 90 cm of root depth by the gravimetric method and five treatments that received an amount of water equivalent to 25% ($S_{0.25}$), 50% ($S_{0.50}$), 75% ($S_{0.75}$), 100% ($S_{1.00}$), and 125% ($S_{1.25}$) of the five-day cumulative evaporation from a class A Pan. The total amount of irrigation water applied to the treatments during the irrigation seasons ranged from 175 to 874 mm; however, actual evapotranspiration ranged from 253 to 902 mm. In both cases, the lowest and highest values were, respectively, in $S_{0.25}$ and $S_{1.25}$. Compared with the amount of irrigation water applied to $S_C$, approximately 72% less water was applied to $S_{0.25}$ and 40% more water was applied to $S_{1.25}$. Yields between treatments ranged from 24.88 to $37.35{\mathrm{kg}\text{tree}}^{-1}$ and from 0.47 to $0.63\mathrm{kg}{\mathrm{m}}^{-3}$, but no significant differences were detected in yield between treatments ($p<0.05$). Significant differences between treatments were detected in some morphological features such as shoot length, shoot diameter, canopy volume, and fruit set ratio. In contrast, no significant differences between treatments were detected in other morphological features such as number of buds on a shoot, number of flowers per bud, and number of flowers per shoot. Taking the average of the years of the experiment, the lowest and highest values for predawn, midmorning and midday measurements of leaf water potential were obtained, respectively, from $S_{0.25}$ and $S_{1.25}$. It was concluded from the findings of this study that irrigation of Memecik olive trees should be scheduled based on the amount of water equivalent to 25% ($S_{0.25}$) of the five-day cumulative evaporation from a class A pan. Irrigation scheduling using deficit irrigation allowed considerable water savings in olives, with minimal effects on yield.

DRAINMOD-DSSAT is an integrated model recently developed to simulate the hydrology, water quality, and crop growth for artificially drained croplands. DRAINMOD-DSSAT is an advanced research tool that contributes to increasing productivity, reducing cost, and enhancing sustainability of crop production on high water table soils with artificial drainage. In this study, the performance of the model was evaluated using a six-year data set (1985–1990) collected from a subsurface drained agricultural research site in Indiana in the United States. Subsurface drains were installed at three different spacings (5, 10, and 20 m). During the simulation period, all treatments were planted to corn receiving high preplant N-fertilization rates. Rainfall patterns varied significantly among the years. DRAINMOD-DSSAT predictions of monthly and annual drainage flow, and nitrate losses, were in good agreement with measured values. Similarly, variations in corn yield patterns were well captured by the model across different treatments. Other crop growth-related variables and soil-N process rates were reasonably predicted compared with values reported in the literature. These results demonstrated the potential of DRAINMOD-DSSAT to simulate different components of an agricultural system involving different management practices and subjected to variable climatic conditions.

The availability of irrigation water and its equitable allocation in a large-scale rice irrigation scheme of Malaysia have been modeled. The model reliably estimates available water for irrigation at the intake of the main canal and simulates the recommended irrigation deliveries for 120 tertiary canals. Different water allocation and management scenarios were evaluated based on the sensitivity of the growth stages of rice to water, varying field-water demand, and perceived water shortages. The model provides a quantitative assessment not only of water allocation for irrigation but also of day-to-day or periodic irrigation delivery performances for a large-scale rice irrigation system. It provides 86% adequacy and 87% equity of irrigation delivery in the main season (August–December). The corresponding performance indicators provided by the model are 74 and 89% in the off-season (February–June). The dependability of water supply is higher in the off-season than in the main season, while the relative water supply (RWS) is the converse. RWS often becomes $>1.0$ in the main season, while such a RWS is rarely obtained in the off-season. The model augments the water delivery performance of the scheme and hence would serve as a useful tool for irrigation managers in decision making.

This paper studies how to improve the efficiency of a new system for catchment, pretreatment, and treatment of runoff water (SCPT). This system is integrated into an urban sustainable gravity settler that can decrease diffusive pollution. This study provides important advantages for the ecosystem by improving new sustainable drainage to clean runoff water. In this paper, an investigation methodology known as hybrid engineering (HE) was used. HE combines experimental tests and numerical simulations, both of them conducted on a 1:4-scale prototype. In this study, numerical simulations by the finite-volume method (FVM) and experimental tests by particle image velocimetry (PIV) were compared. A strong correlation between the numerical and experimental analysis was found. Next, the efficiency of the SCPT was optimized by design of experiments (DOE). Analysis of experimental and numerical results and their comparison are presented in this paper.

With the development of surface energy balance analyses, remote sensing has become a spatially explicit and quantitative methodology for understanding evapotranspiration (ET), a critical requirement for water resources planning and management. Limited temporal resolution of satellite images and cloudy skies present major limitations that impede continuous estimates of ET. This study introduces a practical approach that overcomes (in part) the previous limitations by implementing machine learning techniques that are accurate and robust. The analysis was applied to the Canal B service area of the Delta Canal Company in central Utah using data from the 2009–2011 growing seasons. Actual ET was calculated by an algorithm using data from satellite images. A relevance vector machine (RVM), which is a sparse Bayesian regression, was used to build a spatial model for ET. The RVM was trained with a set of inputs consisting of vegetation indexes, crops, and weather data. ET estimated via the algorithm was used as an output. The developed RVM model provided an accurate estimation of spatial ET based on a Nash-Sutcliffe coefficient ($E$) of 0.84 and a root-mean-squared error (RMSE) of $0.5\mathrm{mm}{\mathrm{day}}^{-1}$. This methodology lays the groundwork for estimating ET at a spatial scale for the days when a satellite image is not available. It could also be used to forecast daily spatial ET if the vegetation indexes model inputs are extrapolated in time and the reference ET is forecasted accurately.

Practical plans for agricultural water use within multiobjective frameworks require feasible solutions that meet the objectives of competing interests and this is the root of common decision problems that plague water resources systems. To find the best solutions among the set of feasible solutions, decision-makers can analyze their worth functions, which is the goal of the surrogate worth trade-off (SWT) method, which enables assessment of the worth functions after solutions that are optimal to separate groups are analyzed. The SWT method uses Lagrange multipliers to determine the set of Pareto optimal solutions and requires the exact equation of each objective function and its derivative or gradient. This is normally impractical in watershed scale problems because each objective function comprises a set of interactive physical and hydrological equations, but the problem can be partially overcome by incorporating a genetic algorithm. This approach was applied to a case study of California’s San Joaquin River watershed to approximate optimum rates of reduction in agricultural water allocations for environmental purposes. In the case study, decision-makers were aided in assessing their worth functions on the basis of the optimal solutions presented to them. The genetic algorithm optimization tool was linked to a watershed simulation model using the soil and water assessment tool (SWAT) to simulate streamflow and salinity. Model results showed that SWAT provides satisfactory predictions for salinity, which can be used in the trade-off analysis. The compromised rates of agricultural water allocations resulted in a significant increase in the system’s reliability and decreased its vulnerability to salinity.