Journal of Irrigation and Drainage Engineering

In this study, an inexact minimax relative regret analysis (IMRA) method is proposed for planning the agricultural water management under uncertainty. The IMRA incorporates interval-parameter programming (IPP) and minimax relative regret analysis (MRA) within an integer programming framework. IMRA can deal with uncertainties expressed as both interval values and random variables; it is capable of seeking an optimal strategy through analyzing all possible scenarios without any assumption on their probabilities. The IMRA method is then applied to a real case of agricultural water management in the Zhangweinan River Basin, one of the main food and cotton producing regions in North China and facing severe water shortage. An interval relative regret matrix is obtained based on a matrix of interval-element system benefit, and it is helpful for decision makers to identify the optimal irrigation strategy with the maximum system benefit and the minimum worst relative regret.

The performance of ordinary kriging (OK) is impacted by different factors that characterize the data sets being interpolated. The following factors were investigated as part of this study to evaluate the performance of OK in mapping soil salinity: (1) sampling density (field scale, subbasin scale, and subbasin scale merged with field scale); (2) spatial point patterns (random, aggregated, and regular); (3) spatial and no spatial autocorrelations; (4) normal and skewed distributions; and (5) homogeneity and heterogeneity. The objective of this study is to evaluate the performance of the OK model against each of the aforementioned factors. To achieve this objective, 36 different data sets were selected from data collected from 1999 to 2008 in a study area in the Lower Arkansas River Valley in Colorado. These data sets were selected to represent the different factors used to evaluate the performance of OK, in which each factor is represented by three different data sets. Assessments of the OK model residuals and the cross-validation techniques were used to evaluate the performance of the model, both analytically and graphically. The results of this study show that the model performance is accurate when using the field-scale data sets and poor when using the subbasin-scale data sets. When the field-scale data sets are merged with the subbasin-scale data sets, the performance of the model improved significantly over the subbasin-scale data sets. The performance of the model is better when using random or aggregated data sets than when using regular data sets. The existence of spatial autocorrelation significantly improves the performance of the model as expected. When there is no spatial autocorrelation, the performance of the model is severely impacted. The model performs better using normally distributed rather than skewed data sets and also using homogeneous rather than heterogeneous data sets.

A field experiment was conducted during the 2009 and 2010 growing seasons to determine the effect of subsurface drainage (SSD) on evapotranspiration (ET) and crop coefficients ($K_c$) for a farm field in the Red River Valley of North Dakota. The total area of the field was 44 ha, half of which had subsurface drainage installed in the fall of 2002 at an approximate depth of 1.1 m and a spacing of 18.3 m. Corn (Zea mays) was planted in 2009 and soybean (Glycine max) in 2010. Evapotranspiration rates were measured in both the SSD and surface drained [or undrained (UD)] by using the eddy covariance (EC) method. The changes in water table and soil moisture content were monitored continuously in both fields. The $K_c$ for corn and soybean was developed by using the ET measured by the EC system, and the reference ET was estimated by using the American Society of Civil Engineers Environmental and Water Resources Institute alfalfa reference method. As expected, the use of SSD affected the ET in a seasonal pattern and the ET was crop dependent. Seasonally, higher ET was observed during spring and fall in the UD field attributable to shallower water table and higher soil moisture content. In the summer, a higher ET was found in the SSD field. The higher ET in the UD field in spring and fall, which was 109 and 191 mm in 2009 and 2010, compared with 105 and 176 mm in 2009 and 2010 in the SSD field, did not offset the higher ET in the SSD field in the summer, which was 310 and 351 mm in 2009 and 2010, compared with 249 and 324 mm in 2009 and 2010 in the UD field. For July and August, the ET in the SSD field was 31% greater in 2009 for corn and 14% greater in 2010 for soybean than that in the UD crop fields. For the entire growing season, the ET in the SSD field was 16% higher in 2009 and 7% higher in 2010 compared with the UD field. During the peak growing season (July), the $K_c$ was greater in the SSD field, with peak values of 0.70 for corn and 0.76 for soybean, but in the UD field, the peak $K_c$ values were only 0.54 for corn and 0.65 for soybean.

Basic experiments were conducted on a large-size broad-crested weir with a rounded corner. Detailed free-surface, velocity, and pressure measurements were performed for a range of flow conditions. The results showed the rapid flow distribution at the upstream end of the weir and next to the weir brink at large flow rates. The flow properties above the crest were analyzed taking into account the nonuniform velocity and nonhydrostatic pressure distributions. Introducing some velocity and pressure correction coefficients, it is shown that critical flow conditions were achieved above the weir crest for $0.1<x/{\mathrm{L}}_{\text{crest}}<1$. The velocity measurements highlighted a developing boundary layer. The data differed from the smooth turbulent boundary layer theory, although the present results were consistent with earlier studies. On average, the boundary stress was approximately ${\tau }_o/(\rho \times g\times H_1)$ 0.0015–0.0025.

Magnetized water has been studied by many researchers. However, to date, there is no study about the effects of magnetized water on soil chemical characteristics such as cations and anions in trickle irrigation. This study was performed at Gorgan Agricultural and Natural Resources Research Center, Gorgan Province, Iran, to investigate soil chemical characteristics under trickle irrigation using magnetized water. Two main treatments of magnetic and nonmagnetized water, and three subtreatments of irrigation water salts, including well water as control and addition of 1 or $2\mathrm{meq}/\mathrm{L}$ calcium carbonate (${\mathrm{CaCO}}_3$) to irrigation water, were used. The experiment was laid out with a completely randomized block design with three replications. The results showed that mean soil cations (such as calcium, sodium, and magnesium) and anions (such as bicarbonate, chloride, and sulfate) at soil depths of 0–20, 20–40, and 40–60 cm below the emitter for magnetized irrigation water treatment were lower than those with nonmagnetized irrigation water treatment, and the differences were significant ($p<0.05$). With experiments that continued to the end of the season, the effect of magnetized water was increased. Therefore, soil moisture for each irrigation in magnetized irrigation water was higher than nonmagnetized irrigation water and caused high leaching of soil salt so that this effect was significant. The use of magnetized water is recommended for trickle irrigation to reclaim soils with high cations and anions content such as calcium, sodium, and bicarbonate.

Aquifer parameter determination in practical hydrogeological studies exposes one of the most significant steps in groundwater resources assessment and management. Successful parameter estimation necessitates both a reliable methodology and field data. Different analytical solutions in the forms of type curves depend on well and aquifer configuration such as porous or fractured media; confined, unconfined, and leaky conditions; and small- and large-diameter wells. Most often Cooper-Jacob (CJ) straight line is used for aquifer parameter estimations from late time-drawdown data in cases of confined and unconfined aquifers. This straight-line method is attractive because of its simplicity and straightforward calculations without tedious and dubious type-curve matching procedure. The warranty of the CJ method application depends on the fact that the late time-drawdown measurements fall along the final portion of the Theis type curve. The main purpose of this paper is to present a general type straight-line model to determine aquifer properties of a confined leaky (semiconfined) aquifer by using the pumping test results. The CJ method is applicable in cases in which the straight-line slope on dimensionless semilogarithmic paper is equal to 2.3 only.

Continuous determination of suspended solids (SS) by turbidity measurements in drained peatland is expanding, increasing the demand for reliable calibration data for load calculations. Turbidity was used to estimate SS ($n=333$) and particulate nutrient loads ($n=157$) after peatland drainage for forestry in Central Finland for three study catchments. SS data from the drainage outlet correlated well with turbidity, demonstrating that turbidity can be used as an accurate measure of SS in drained peatland conditions. Measurement error was found to be approximately 8% ($p<0.01$). The proportion of organic and inorganic fractions in SS seemed to affect calibration. Total phosphorus, total nitrogen, and particulate phosphorus showed linear dependency on turbidity values because a fraction of these nutrients occurs in particulate form or adsorbed to organic matter. Regressions during snowmelt periods were markedly different from those in other periods that were related to changes in the dissolved/total nutrient ratio. A close correlation between nutrient content of SS and turbidity provided a novel method to calculate phosphorus load from drained peatland areas, which can be used to conduct nutrient load estimations during summer peak flows.