Journal of Irrigation and Drainage Engineering

In this study, a systematic framework is introduced to prioritize several practical canal operation alternatives applying to modernization and rehabilitation projects. Several operation techniques, ranging from improved manual canal operation to centralized automatic operation, are considered. Ten different operation methods are selected as alternatives for the modernization and rehabilitation project. Mathematical models of the proposed alternatives are designed and potential applications of them are examined. The designed models are tested on an accurate simulation model of a large canal system in Iran. In regards to multicriteria inherent in selecting an appropriate operation method, multiple criteria are considered including 13 technical, social, operational, and economic criteria. Two well-known multicriteria decision-making (MCDM) methods of technique for order preference by similarity to ideal solution (TOPSIS) and simple additive weighting (SAW) have been applied to prioritize alternatives. The relative weights of each criterion are determined based on aggregation of entropy method and decision-maker’s preferences during the decision making procedure. The obtained results show that Alternative 9, representing a centralized control method, has been the first-ranked alternative with the score values of 0.845 and 0.854, respectively, based on TPOSIS and SAW methods. Based on demands and expectation of the local water authorities, each of the proposed alternatives can be selected with different levels of satisfaction. According to the results, in the cases that automatic control application is restricted, the improved manual methods would be reasonable operational alternatives.

Grass-reference (potential) evapotranspiration ($E{T}_o$) is often used in climate change studies to evaluate potential impacts of climate change on hydrologic balances and vegetation response. In this process, empirical temperature or radiation-based empirical equations are often used because of a lack of long-term climate data to solve the combination-based energy balance equations, but this may result in false determinations of trends and magnitudes of water use. To quantify potential differences associated with using such empirical models with respect to the FAO56 Penman-Monteith method (PM), various $E{T}_o$ models were evaluated in five locations that have significantly different climatic characteristics [subhumid, semiarid, arid, humid (subtropical), and Mediterranean-type] in Nebraska (Clay Center and Scottsbluff), Florida (Gainesville), Arizona (Phoenix), and California (Davis). In general, the performance of most methods not only varied with climatic conditions, but also with the time step used (i.e., daily, monthly, and long-term cumulative basis). Combination methods provided lower root mean squared difference (RMSD) in all locations due to accounting for aerodynamic and energy terms of the surface energy balance. On a daily time step, the FAO24 Penman equation provided the lowest RMSD values (0.35, 0.42, 0.23, 0.37, and $0.37\mathrm{mm}/\mathrm{d}$ for Davis, Gainesville, Phoenix Clay Center, and Scottsbluff, respectively). In the most arid location (Phoenix) all combination methods overestimated $E{T}_o$ relative to the FAO56-PM, whereas the FAO24 Penman method performed best in Phoenix as well as in humid (Gainesville) and windy (Clay Center) locations. However, at wind speed values higher than $4\mathrm{m}/\mathrm{s}$, FAO24 Penman estimates were up to 40% higher than the FAO56-PM estimates. In general, 1948 Penman provided higher overestimations when compared to FAO56-PM for days with solar radiation values below $10\mathrm{MJ}/{\mathrm{m}}^2/\mathrm{d}$. Among temperature methods, Hargreaves method positioned first among the temperature methods in three of five locations and its RMSD of estimates in driest and most humid climates (Phoenix and Gainesville) were smallest. Most of the radiation methods underestimated in all the locations; with the exception of Gainesville, where the only method that presented this behavior was Makkink. Substantial differences were observed when comparing methods’ cumulative annual $E{T}_o$ to the FAO56-PM values. The performance of some of the methods differed substantially with location. For example, the FAO24 Radiation method ranked first in Davis, Clay Center, and Scottsbluff. In the extremely dry and windy location of Scottsbluff it also ranked first overall in the cumulative analysis, and second at Clay Center. This method performed poorly in Phoenix, where it ranked last on daily analysis. The results of this study can provide a reference in terms of potential errors associated with using various combination, temperature, and radiation-based empirical models in estimating $E{T}_o$ with respect to the FAO56-PM in various climatic conditions for different time steps.

While evapotranspiration ($ET$) has been a subject of research for decades, there is insufficient data, analyses and information on frequency (probability) values and distribution of this important variable for various climatic regions. Frequency distributions of grass-reference and alfalfa-reference (potential) evapotranspiration ($E{T}_o$ and $E{T}_r$) were quantified and analyzed using long-term measured climate datasets for five locations in the United States that have significantly different climatic characteristics [Mediterranean-type climate, Davis, CA ($E{T}_o$); arid, Phoenix, AZ ($E{T}_o$); humid/subtropical, Gainesville, FL ($E{T}_o$); a transition zone between subhumid and semiarid climate, Clay Center, NE ($E{T}_r$); and a semiarid climate, Scottsbluff, NE ($E{T}_r$)]. Two Nebraska locations presented higher variability of $E{T}_{\mathrm{ref}}$ among all locations because of their highly turbulent climatic characteristics with abrupt changes in wind speed, humidity and air temperature. At Davis, the peak $E{T}_o$ month is July with an average of $7.01\pm 1.33\mathrm{mm}/\mathrm{d}$. During this time, the probability of $E{T}_o$ was between 6 and $8\mathrm{mm}/\mathrm{d}$ with 78% of probability of occurrence. The peak $E{T}_o$ month in Phoenix is June with a long-term average of $7.37\pm 1.26\mathrm{mm}/\mathrm{d}$. During this time, there is a 51% probability of $E{T}_o$ being between 7 and $8\mathrm{mm}/\mathrm{d}$, with a 94% of probability if the range is expanded to 6 and $9\mathrm{mm}/\mathrm{d}$. For the months with higher precipitation, $E{T}_{\mathrm{ref}}$ exhibited more variability atributbale to the erratic rain intensity and frequency (July and August $E{T}_o$ estimates present a standard deviation of 1.45 and $1.58\mathrm{mm}/\mathrm{d}$, respectively). This rainy season produces high $E{T}_o$ variation. $E{T}_o$ frequency curve in Gainesville had more gradual increase and decrease with much smother fluctuations between the months than any other location. Unlike other locations, there is not a clear peak $E{T}_o$ month in Gainesville; May has the highest long-term average $E{T}_o$ ($4.6\pm 1.18\mathrm{mm}/\mathrm{d}$), but June and July averages present less than $0.4\mathrm{mm}/\mathrm{d}$ of difference than May. For May, the probability of $E{T}_o$ being between 4 and $5\mathrm{mm}/\mathrm{d}$ is 44%. For April and June, the probability of this occurrence is 47 and 34%, respectively. The peak $E{T}_r$ month at Clay Center is June with a long-term average of $7.20\pm 1.89\mathrm{mm}/\mathrm{d}$. The standard deviation of $E{T}_r$ at Clay Center throughout most of the year is higher than those in other locations with the highest values from April to June. $E{T}_r$ estimations in the peak month (June) are between 7 and $8\mathrm{mm}/\mathrm{d}$ with a probability of occurrence of 18.7%. The peak $E{T}_r$ month in Scottsbluff is July ($7.78\pm 1.41\mathrm{mm}/\mathrm{d}$). During this month, the most likely occurrence in $E{T}_r$ is between 7 and $8\mathrm{mm}/\mathrm{d}$ with a probability of 25%; if the $E{T}_r$ range is expanded from 6 to $8\mathrm{mm}/\mathrm{d}$, the probability of this $E{T}_r$ range increases to 49%. If another 1 mm is added to the frequency interval, the probability would increase to 63%, showing the substantial variability of $E{T}_r$ in this location as a function of abrupt climatic patterns. The $E{T}_o$ and $E{T}_r$ frequency distribution data and information presented in this study are among the first datasets for various climatic conditions and can be invaluable for water resources and planning and allocation, irrigation management as well as designing irrigation systems and other water resources-related infrastructures.

Liberalization of agricultural markets is changing the irrigation focus from maximizing yields to maximizing profits. This study was conducted to develop a self-programmable irrigation scheduling strategy for solid sets aimed at improving profits. It is based on simulation models and water and crop prices. The economic optimization of solid-set irrigation is constrained by the fact that the efficiency is not previously known because of the unpredictability of seasonal weather and the accumulated effect of nonuniform irrigations in the soil water depletion (SWD) uniformity. Consequently, seasonal revenue and cost functions cannot be determined. The strategy addresses this constraint by (1) refilling SWD under weather conditions that minimize water losses when crop is not water stressed and SWD is high and uniform enough to prevent percolation; and (2) estimating revenue and cost functions for each irrigation event in order to determine the economically optimal water depth when SWD uniformity is so low that percolation and/or crop water stress are unavoidable. The strategy is presented in this paper and assessed under different conditions in the companion paper.

The companion paper presented a self-programmable profit-oriented irrigation scheduling strategy based on simulation models. This study is an assessment of the strategy and aims to contribute to the analysis of its viability. The assessment is based on the simulation of corn irrigated with a typical solid-set system under 81 scenarios that combine different conditions of weather, soil, and prices over a period of nine years. Two simpler self-programmable yield-oriented strategies serve as benchmarks for the assessment. The profit-oriented strategy achieves up to $168\mathrm{e}\mathrm{uros}/\mathrm{ha}$ higher profit, depending on the conditions of the scenario. Lower percolation and better harnessing of initial soil water are the main drivers of the profit gap, in which the influence of prices is low because of the high efficiency achieved. This water conservation (up to 128 mm) could also deliver additional benefits that future researches should analyze. The models and a multidisciplinary approach provide a better understanding of the technical and economic processes that enable the development of advanced self-programmable strategies that lead to wiser, more situation-specific irrigation decisions.

This study considers the clogging progression prediction on the permeable pavement by using artificial neural networks (ANNs). Clogging, which is caused primarily by sediment deposition, may result in performance failure of permeable pavement. Measuring the volumetric water content (VWC) by time domain reflectometers (TDRs) is an automated method to track the speed of clogging. Monitoring peak VWC during rain events has been used as an indication of clogging progression over the permeable pavement. New nonlinear solutions are developed to estimate the peak VWC using a multilayer perceptron (MLP) structure. The rain event variables and the maintenance treatment were formulated as the basic site characteristic parameters that affect the clogging progression. Five ANN models are constructed from the recorded VWC to compute the peak VWC from the rainfall parameters and maintenance treatment. A comprehensive set of data, including various rain event characteristics obtained from the rain gauge and the conducted maintenance on the permeable pavement, are used for training and testing the neural network models. The performances of the ANN models are assessed and the results demonstrate the satisfactory accuracy of the models as compared with the measured values. A parametric study is completed to determine the relative importance of peak VWC resulting from the variation of the study parameters. The results indicate that the models are effectively capable of estimating the peak VWC by the permeable pavements for different locations along the permeable pavement. The MLP models consider all known contribution factors and provide more precise prediction value than the linear model. Peak 5-min intensity, the previous rainfall depth, and the cumulative rainfall depth from the installation are the most effective parameters on the hydrologic performance of the permeable pavement. Designing permeable pavement based on the important parameters can lead to more efficient future design.

Types C and D inlet grates have a large surface area to drain storm runoff collected along highway medians. As always, highway debris presents a clogging problem to these area grates. Under the assumption that debris would be accumulated on the water surface, an inclined angle was applied to an area grate. It is hoped that the submerged portion of the area grate will remain open to drain stormwater. The selection of inclined angle should be related to the hydraulic efficiency and the amount of floating debris in stormwater. However, there is not any quantifiable guidance as to how to choose the inclined angle for an area grate. In this study, a series of inclined angles, ranging from 0 to function as a horizontal grate to 90° to operate like a side grate, is investigated for flow interception capacity. A new set of orifice and weir formulas with an inclined angle is derived from energy principles and then tested in a $1/3$-scaled model in laboratory. The method of least-square error was used to identify the best fitted values for discharge coefficients as a function of inclined angle with or without an inlet depression. The set of new equations derived and calibrated in this study can significantly improve the current design procedures for Types C and D grates used for highway median drains.

In mountainous watersheds, rainfall generates runoff quickly because of the decreased depression storage, high downslope flow velocity, and smaller chance for rainwater infiltration. In order to obtain precise event-based runoff estimations in mountainous watersheds, a slope-adjusted curve number (${\mathrm{CN}}_{\mathrm{II}\alpha }$) with a smaller initial abstraction ratio ($\lambda$) is indispensable in the standard natural resources conservation service (NRCS) curve number (CN) model. Using measured rainfall-runoff data from 39 mountainous watersheds in South Korea, this study investigated two existing ${\mathrm{CN}}_{\mathrm{II}\alpha }$ approaches and suggested a new approach that was accompanied by a lower $\lambda$ value. The new ${\mathrm{CN}}_{\mathrm{II}\alpha }$ equation was calibrated with 1,402 measured rainfall-runoff events from 31 watersheds and validated with 377 rainfall-runoff events from the remaining eight watersheds. Most of the runoff events, used for both calibration and validation, were underestimated using a CN without a slope-adjusting factor. By considering the combined effect of the proposed ${\mathrm{CN}}_{\mathrm{II}\alpha }$ and setting $\lambda$ equal to 0.01, the performance measures based on the root mean squares error (in mm), Nash-Sutcliffe efficiency, and coefficient of determination were significantly improved from averages of 27.10, 0.64, and 0.75 to 18.69, 0.82, and 0.87, respectively, as compared to the standard NRCS model. The proposed modification exhibited superior results compared to the two existing ${\mathrm{CN}}_{\mathrm{II}\alpha }$ approaches. Findings from this study support the adjustment of both the CN and $\lambda$ in the NRCS model to increase its runoff prediction capabilities.

The combined effect of limited water resources and ever-growing water demand necessitates the use of more-accurate approaches for reservoir water-balance analyses. Therefore, this study aims to develop a new operational scheme by considering flood control and variations in irrigation efficiency for simulating reservoir water-balance components. This study is conducted on three reservoirs in Korea, which were monitored for 4 years. Based on the findings of the sensitivity analyses, irrigation efficiency variation appears to be most sensitive to the outflow variables and was thus calibrated on a yearly basis. Compared with the two other schemes, i.e., with respective exclusions of flood control and irrigation efficiency variations, the new scheme performs better in simulating reservoir water levels. Additionally, it is found that without the consideration of the newly developed scheme, significant errors could occur in the simulation of water-balance components, which could lead to incorrect decision-making. It is concluded that the new scheme could increase the simulation accuracy of the water-balance components and help local community workers in water planning.

For designing a steady-state drainage system, a drain flow formula coupled with the Dupuit-Forcheimer form of the differential equation of groundwater flow is used. First, the most-applied drain flow formulas in steady-state conditions are reviewed and compared using as a dependent variable the ratio between the maximum water table height and the distance between two lines of parallel drains. These equation are also tested using experimental field data measured in three plots drained by surface pipe drains having different values of drain spacing. Then, applying the dimensional analysis and the self-similarity theory, a new drain spacing formula is theoretically deduced and compared with the solutions available in the literature. Finally, the analysis shows that the most-applied drain flow formulas have the general mathematical shape deduced by dimensional analysis and self-similarity condition.

Management of surface and ground water resources in drought-stricken regions with increasing water consumption poses a major challenge to water resource managers and decision makers. The situation is even graver in areas naturally classified as semiarid regions. Conjunctive management of ground and surface water is an appropriate strategy in such regions and in areas involving many stakeholders with various views and different water demands. It is, indeed, an effective approach that can enhance the reliability of water supplies. In the present study, the artificial neural networks (ANNs) and genetic algorithm (GA) are exploited to develop a simulation multiobjective optimization model. Conflict resolution models are also used to solve the problems and to seek a reliably stable solution for the study area, one of the subbasins of the Zayandehrood River Basin in central Iran, where recent water shortages and increased demands have led to conflicts between the Regional Water Company and the Agriculture Organization. To address the issue, the Pareto frontier or trade-off curve is generated and a unique solution is obtained by applying two conflict resolution methods. The conjunctive management model is then applied to establish stable and satisfactory conditions governing the relations between stakeholders that aim at supplying at least 65% of their demands and limiting groundwater level drawdown to 3 m per year.