Journal of Irrigation and Drainage Engineering

Actual evapotranspiration (ET) is commonly estimated at daily time intervals as the product of a crop coefficient and a reference-crop evapotranspiration (${\mathrm{ET}}_0$) that is calculated by using a daily time step. When subdaily time steps are used, crop coefficients must be multiplied by adjustment factors to account for the discrepancy between ${\mathrm{ET}}_0$ calculated by using daily and subdaily time steps. These adjustment factors depend on the method used to calculate ${\mathrm{ET}}_0$. By using the ASCE and FAO-56 Penman-Monteith methods with data from several meteorological stations in Florida, the ASCE equation is shown to be preferable for all locations and seasons because it requires the least adjustment to the crop coefficient when 15-min and 1-h time steps are used. The required adjustment factors depend on location and season, are greatest in the summer, and are approximately the same for 15-min and 1-h time steps. A comparative evaluation between daily ${\mathrm{ET}}_0$ and values of potential evapotranspiration (PET) provided by three public databases shows that PET estimates should generally not be used as substitutes for ${\mathrm{ET}}_0$, because the relationship between PET and ${\mathrm{ET}}_0$ varies significantly with location and season. For all locations and seasons considered in this study, daily ${\mathrm{ET}}_0$ agrees most closely with the PET given by the Florida Automated Weather Network.

Estimation of evaporation, a major component of the hydrologic cycle, is required for a variety of purposes in water resources development and management. This paper investigates the abilities of genetic programming (GP) to improve the accuracy of daily evaporation estimation. In the first part of the study, different GP models, comprising various combinations of daily climatic variables, namely, air temperature, sunshine hours, wind speed, and relative humidity, were developed to evaluate the degree of the effect of each variable on daily pan evaporation. A dynamic modeling of evaporation was also performed, with the current climatic variables and one of the previous variables, to evaluate the effect of their time series on evaporation. In the second part of the study, the estimated solar radiation data were used as input vectors instead of recorded sunshine values. Statistics such as correlation coefficient ($R$), root mean square error (RMSE), coefficient of residual mass (CRM) and scatter index (SI) were used to measure the performance of models. Tthe dynamic model approach was shown to give the best results with relatively fewer errors and higher correlations. To assess the ability of GP relative to the neuro-fuzzy (NF) and artificial neural networks (ANN), several NF and ANN models were developed by using the same data set. The obtained results showed the superiority of GP to the NF and ANN approaches. The Stephen-Stewart and Christiansen methods were also considered for comparison. The results indicated that the proposed GP model performed quite well in modeling evaporation processes from the available climatic data. The results also showed that the estimated solar radiation data can be applied successfully instead of the recorded sunshine data.

The aim of this research was to determine the amount of irrigation water, irrigation interval, and water consumption that gave the greatest yield, to determine the effect of irrigation on fruit quality characteristics, and to investigate variations in soil moisture in Redhaven peaches irrigated by drip irrigation in the Aegean region of Turkey. The study was performed in 2003 and 2004 on split plots in randomized blocks with three replications. Main treatments were 3 and 6 days between irrigations, and subtreatments comprised four different pan coefficients ($K_{p1.25}$, $K_{p1.00}$, $K_{p0.75}$, and $K_{p0.50}$). According to the 2-year averages of peach yields, the effect on yield of the amount of irrigation water was found to be significant ($p<0.01$), but the effect of the irrigation interval was found not to be significant. Total yield varied between 5,966 and $16,340\mathrm{kg}{\mathrm{ha}}^{-1}$, and marketable yield between 5,349 and $14,164\mathrm{kg}{\mathrm{ha}}^{-1}$, according to irrigation treatments. A maximum average yield of $14,101\mathrm{kg}{\mathrm{ha}}^{-1}$ was obtained from treatment $K_{p1.00}$. Average irrigation water amount for this treatment was 482 mm, average water consumption was 705 mm, and the $K_{pc}$ value was 0.785. Maximum water-use efficiency (WUE) of $2.02\mathrm{kg}{\mathrm{m}}^{-3}$ was obtained from $K_{p1.00}$. The yield response factor ($ky$) was found to be 1.2. Weight of individual fruit varied between 203 and 253 g, height varied from 6.3–6.6 cm, diameter from 7.2–7.7 cm, soluble dry matter from 10.8–14.5%, and juice pH from 4.14–4.37. In the years of the study, the declining trend of soil moisture was greater in the treatments that received little irrigation water than in those that received more. After irrigation was ended, soil moisture decreased rapidly and eventually reached the wilting point. To conclude, when setting up a drip-irrigation program for fully grown peach trees in the Aegean region, the irrigation interval may be 3 or 6 days. The amount of water to be applied at each irrigation can be determined by correcting the total evaporation from a Class-A evaporation pan over the chosen irrigation interval by a coefficient of 0.785.

In the short-term planning (7–14 days) and operation of complex irrigation systems, an estimate of irrigation water demand (IWD) is of a fundamental concern. To predict the IWD, a reliable estimate of the expected rainfall during any irrigation period is of fundamental importance. Rainfall is generally predicted with a certain probability of exceedance. However, the standard flood flow-frequency distributions cannot be used for prediction of rainfall of such short durations because these rainfall series in general consist of zero values. Two methods, the total probability theorem (TPT) with three normalizing transformations (i.e., power, log, and square root), and the leaky law (LL) were used to predict the rainfall of short durations (7–14 days, depending upon the number of irrigations per season) in the Goulburn irrigation area (GIA) of Victoria, Australia. Investigations indicated that the TPT using the power transformation (TPTP) was more effective in modeling the short-term data series than the log (TPTL) and square-root transformations (TPTS). Although the overall fitting of the short-term rainfall data series by the LL method was significantly (99%) better than the TPT method, some series could not be fitted by the LL method. This revealed that the LL method could not model all short-term rainfall data series. Results showed that although both the TPT and LL were quite satisfactory in predicting short-term seasonal rainfall of short durations in the study area, none of them was individually able to model all the short-term rainfall series. Hence, the joint use of TPT and LL methods was recommended for short-term rainfall prediction of the study area.

This article describes the hydraulic behavior of a mixed water level control hydromechanical gate present in several irrigation canals. The automatic gate is termed “mixed” because it can hold either the upstream water level or the downstream water level constant according to the flow conditions. Such a complex behavior is obtained through a series of side tanks linked by orifices and weirs. No energy supply is needed in this regulation process. The mixed flow gate is analyzed and a mathematical model for its function is proposed, assuming the system is at equilibrium. The goal of the modeling was to better understand the mixed gate function and to help adjust their characteristics in the field or in a design process. The proposed model is analyzed and evaluated using real data collected on a canal in the south of France. The results show the ability of the model to reproduce the function of this complex hydromechanical system. The mathematical model is also implemented in software dedicated to hydraulic modeling of irrigation canals, which can be used to design and evaluate management strategies.

Mass angle of repose (that is, the angle at which a mass of sliding particles will come to rest) is a fundamental material property that is needed to design drainage channel linings composed of open-graded riprap stone, also called dumped rock riprap, which protect earthen slopes from erosion by flowing water. Multiple regression analysis of measurements at 74 stockpiles of dumped natural and crushed rock shows the angle to depend primarily on rock particle angularity and, to lesser extents, on the gradation and the median particle diameter of the stone mixture, the angle increasing with angularity, mixture nonuniformity, and particle size. Uncomplicated expressions are developed from the data to calculate the expected mass angle of repose of open-graded rock riprap along with prediction intervals.