Journal of Irrigation and Drainage Engineering

In this paper it was developed and tested a hydrologically based, spatially distributed crop yield model for the purpose of modeling pasture dynamics within the Mediterranean environment of Marghine Goceano, in Sardinia region, Italy. The model uses daily information of weather drivers given by temperature, precipitation, and solar radiation, and provides daily spatially distributed estimates of soil water content, runoff, and pasture growth, including leaf area index (LAI) and biomass. Pasture grass growth for 6 years (2001–2006) was simulated, and yearly average biomass yield ($\mathrm{t}{\mathrm{ha}}^{-1}$) was compared against ground-based and satellite-based estimates from previous studies, finding acceptable model performance. Intraseasonal biomass dynamics was evaluated during year 2004 and compared against pointwise ground measures from former studies, with acceptable results. The spatially distributed leaf area index maps were compared against their satellite-derived counterparts (9.3-km pixel size, 2005–2006), available on a biweekly basis. The pasture model provides acceptable performance in depicting daily, spatially distributed dynamics of pasture grass, and may be useful when tackling a number of issues, including, e.g., (1) assessment of pasture (and fodder) productivity under current climate and management, (2) short-term to medium-term forecast of pasture yield for seasonal herding, and (3) modified pasture conditions under prospective climate change using climate forcing from general circulation models and other climate models. Future developments may regard inclusion of nutrient dynamics and study of (over)grazing effect upon pasture dynamics.

Water resources, in time, place, and quantity, are often poorly matched to the needs of humanity. This disparity tends to be more accentuated in arid and semiarid regions, predominant in Iran. Reservoir systems are a common means to control and manage water resources. Due to limited resources and the increasing demands for water, these systems must be optimally operated to maximize the efficiency of water use. Evolutionary optimization algorithms provide reliable and simple methods for solving complex optimization problems. One of those methods, the water cycle algorithm (WCA), is used in this paper to find optimal operation strategies for the Karon-4 reservoir and a four-reservoir system in Iran. The results demonstrate the high efficiency and reliability of the WCA in solving reservoir operation problems.

Irrigation schedules are used in many irrigation schemes that operate on a rotational or arranged-demand basis. Determining these schedules is a complex problem, especially when done by hand. Operations research tools such as single machine scheduling are already used to schedule irrigation turns in systems where only one single user can irrigate simultaneously. This paper shows how multimachine scheduling can be used to determine arranged-demand schedules for systems where two or more users can irrigate at the same time. Two models are presented, as follows: (1) the simple multimachine scheduling model that establishes a schedule for systems where all outlets/user discharges are identical, and (2) the complex multimachine scheduling model that determines the schedule when flows to individual outlet/users are not necessarily identical.

A new, simple, parametric instantaneous unit hydrograph (IUH) with conceptual and physical justification is proposed. The new IUH has only two parameters, one of which is the time base of the unit hydrograph, an obvious physically conceivable parameter. The new mathematical form of the IUH has the properties of a statistical frequency distribution, which qualifies it for a new statistical distribution, although this distribution is not addressed in statistical books. The use of the new IUH simplifies the computations involved in obtaining the runoff from a complex rainfall event, and the calculations can even be performed on a handheld calculator or an Excel worksheet. The applicability of the proposed IUH is illustrated by using several sets of published multistorm data, wherein an optimization method is used for estimating the representative parameters of the new IUH from a single representative rainfall-runoff event obtained by processing all multistorm events. The performance criteria used in this paper give equal weight to small and large peak storm events. The new IUH with the proposed method performs better than the more computationally complex IUHs or UHs. It outperforms the least squares and linear programming approaches by prior investigators for identifying the UH based on the normalized standard error of estimate. The new IUH and proposed method are useful to field engineers and practitioners with enhanced practical utility. The new bounded statistical distribution, developed as a by-product, may find applications in other branches of applied science, economics, and engineering.

To address water shortage and improve water delivery operations, decision support systems (DSSs) have been developed and utilized throughout the United States and the world. One critical aspect that is often neglected during the development and implementation of DSSs is validation, which can result in flawed water distribution and rejection of the DSS by water users and managers. This paper presents the results of a significant validation effort for a DSS in the Middle Rio Grande Conservancy District (MRGCD). The validation resulted in a refined application efficiency of 45%, a refined readily available water for farmers to irrigate to a value of 20%, and a Nash-Sutcliffe modeling efficiency of 0.86 for soil moisture depletion patterns. Overall, the validation and refinement of input parameters resulted in a DSS model that accurately predicts evaportranspiration and can be used to schedule water delivery. The refinement of the DSS input parameters resulted in an increased 15,600 acre-ft diversion suggested by the DSS, indicating that the original DSS input parameters would have adversely affected farmers in the MRGCD. The paper showed that validation of a DSS is crucial if such a program is to be successfully utilized to deliver irrigation water.

Quantification of crop response to the amount of water applied, available, or used is important for decision making to ensure effective, profitable, conservative agricultural production. However, these variables and responses may have interannual attributes and long-term research has rarely quantified interannual variations of irrigation-yield production functions (IYPF), evapotranspiration-yield production functions (ETYPF), and yield response factors (Ky). This long-term research measured grain yield, actual crop evapotranspiration (ETa), increase in ETa attributable to various irrigation levels, basal evapotranspiration (ETb; ET required to establish grain yield), IYPF, ETYPF, and seasonal Ky for maize (Zea mays L.) from 2005 to 2010 growing seasons. Four full and limited irrigation levels [fully irrigated (FIT), 75% FIT, 60% FIT, and 50% FIT] and rainfed treatment were imposed. Seasonal ETa increased linearly with increasing irrigation and the slopes of the ETa versus seasonal irrigation relationships exhibited substantial interannual variation. All-treatment average ETa values were 561, 583, 592, 660, 591, and 628 mm from 2005 to 2010, respectively. Irrigation amounts significantly impacted grain yield in all years with considerable variation among seasons. All-treatment average yield increases attributable to irrigation were 7.7, 4.6, 1.6, 5.8, and 2.7 ton/ha, relative to rainfed treatment, from 2005 through 2010, respectively; six-year average yield increase was $4.5\mathrm{t}/\mathrm{ha}$. Grain yield had a curvilinear relationship with seasonal irrigation amounts ($R^2=0.79$) and yield increased with irrigation up to approximately 180 mm of irrigation water ($15.5\mathrm{t}/\mathrm{ha}$ grain yield); thereafter, irrigation became excessive and diminishing returns occurred. Interannual variation of grain yield produced per unit of irrigation was observed owing to differences in rainfed yield response to precipitation. Totals of 0.92, 1.72, 0.09, 1.06, 1.90, and $0.08\mathrm{t}/\mathrm{ha}$ grain yield were produced per 25.4 mm of irrigation applied (beyond the intercept) from 2005 through 2010, respectively. Based on the pooled (average of all years) curvilinear IYPF equation, 25.4 mm of irrigation produced approximately $1.45\mathrm{t}/\mathrm{ha}$ grain yield [beyond intercept ($9.29\mathrm{t}/\mathrm{ha}$)]. Grain yield had a very strong, linear increase with seasonal ETa ($R^2>0.92$). The ETYPFs exhibited less interannual variation than the IYPFs and the slope of the ETYPFs ranged from 0.0336 in 2008 to 0.0662 in 2010, with the wettest year (2008) having the lowest slope. Six-year average ETYPF had a high $R^2$ (0.93) with an average slope of 0.0409. ETb of individual years also exhibited substantial interannual variation: 263, 319, 314, 209, 319, and 418 mm for the 2005, 2006, 2007, 2008, 2009, and 2010 growing seasons, respectively. Six-year average pooled data indicated that 279 mm of ETb is needed to establish grain yield. Based on the ETYPF data from individual years, 25.4 mm of ETa resulted in 0.96, 1.20, 1.14, 0.85, 1.22, and $1.68\mathrm{t}/\mathrm{ha}$ maize yield (beyond the intercept) from 2005 through 2010. When individual treatments were considered, 25.4 mm of ETa resulted in 0.98, 0.40, 0.62, 1.01, and $0.48\mathrm{t}/\mathrm{ha}$ grain yield (beyond intercept) for rainfed, 50% FIT, 60% FIT, 75% FIT, and FIT, respectively. The climatic conditions in August were the most influential in determining the slope of the ETYPFs. Seasonal average Ky also exhibited interannual variation, which was 1.89, 1.89, 1.39, 1.80, and 2.64 in 2006, 2007, 2008, 2009, and 2010, respectively. This research provides important data, information, and analyses in terms of interannual variation of various parameters on maize response to water.

Quantification of various water use efficiency (crop water productivity) indices for different irrigation regimes can aid in making effective in-season water management decisions and developing crop water productivity models. However, these critical variables can be affected by interannual variation in climatic conditions, and long-term research that provides such data and information is extremely rare. Maize (Zea mays L.) irrigation water use efficiency (IWUE), crop water use efficiency (CWUE), evapotranspiration water use efficiency (ETWUE), annual precipitation use efficiency (ANNPUE), growing season precipitation use efficiency (GRSPUE), and the newly developed growing season precipitation use efficiency with respect to rainfed yield (${\mathrm{GRSPUE}}_{\mathrm{rainfed}}$) were measured under full and limited irrigation and rainfed conditions from 2005 to 2010 in south-central Nebraska. An additional new irrigation efficiency term [irrigation-evapotranspiration use efficiency (IRRETUE)] was developed and tested to evaluate the efficiency of irrigation management with respect to actual crop evapotranspiration (ETa). Four irrigation treatments were imposed [fully irrigated treatment (FIT), 75% FIT, 60% FIT, and 50% FIT] and a control rainfed treatment. CWUE varied from $1.21\mathrm{kg}/{\mathrm{m}}^3$ for rainfed treatment in the dry year of 2005 to $2.51\mathrm{kg}/{\mathrm{m}}^3$ for 60% FIT in the below average year of 2009. CWUE exhibited substantial interannual variation. The lowest CWUE was always obtained under rainfed production, whereas the highest value was usually observed under FIT, except in 2009. The FIT and 75% FIT CWUE values were very similar. Six-year average CWUE values were 2.34, 2.32, 2.29, 2.24, and $1.73\mathrm{kg}/{\mathrm{m}}^3$ for FIT, 75% FIT, 60% FIT, 50% FIT, and rainfed, respectively, with SDs of 0.06, 0.09, 0.14, 0.12, and $0.38\mathrm{kg}/{\mathrm{m}}^3$, respectively. IWUE varied from $1.75\mathrm{kg}/{\mathrm{m}}^3$ for 50% FIT in the wettest year (2008) to $5.9\mathrm{kg}/{\mathrm{m}}^3$ for the same treatment in 2009. The six-year average IWUE values were 4.01, 4.45, 4.48, and $4.13\mathrm{kg}/{\mathrm{m}}^3$ for FIT, 75% FIT, 60% FIT, and 50% FIT, respectively, with SD values of 0.82, 0.84, 0.83, and $1.88\mathrm{kg}/{\mathrm{m}}^3$ for the same treatments. There was considerable interannual variation in IWUE for the same treatment; this variation between treatments in a given year was greater than the interannual variation for the same treatment. The IWUE was maximized with limited irrigation treatments (mostly 50% FIT, followed by 75% FIT and 60% FIT). ETWUE values varied from 1.18 for 50% FIT in 2008 to $7.16\mathrm{kg}/{\mathrm{m}}^3$ for 60% FIT in 2010; ETWUE decreased linearly with seasonal ETa ($R^2>0.80$) in 2006, 2007, and 2009 and decreased with seasonal ETa, displaying a low $R^2$ of 0.26 in 2010. In the wettest year (2008), ETWUE increased with ETa ($R^2=0.31$). The new efficiency index, IRRETUE, ranged from 37.8% (62.2% underirrigation) for 50% FIT in 2010 to as high as 149.6% (49.6% overirrigation) for 50% FIT in 2008. On a long-term average, IRRETUE values were 87, 93.2, 88, and 89.2% for FIT, 75% FIT, 60% FIT, and 50% FIT, respectively. The new IRRETUE can be a very effective tool to evaluate the effectiveness of irrigation management practices with respect to meeting crop ETa for maximum yield. When the annual precipitation is considered, FIT resulted in greater ANNPUE, followed closely by 75% FIT and other limited irrigation treatments. The ${\mathrm{GRSPUE}}_{\mathrm{rainfed}}$ values on all-treatment average basis were 32, 46, 66, 90, 61, and 82% (for FIT, 75% FIT, 60% FIT, and 50% FIT) lower than the GRSPUE values from 2005 through 2010. May precipitation consistently had the greatest influence on GRSPUE in all years, with high $R^2$ of 0.73, 0.72, 0.71, and 0.76 for FIT, 75% FIT, 60% FIT, and 50% FIT, respectively. The rainfed treatment, however, was most influenced by April precipitation ($R^2=0.62$). May precipitation was also the most influential on the ${\mathrm{GRSPUE}}_{\mathrm{rainfed}}$, with high $R^2$ values of 0.67, 0.77, 0.87, and 0.82 for FIT, 75% FIT, 60% FIT, and 50% FIT, respectively. These research results are some of the first long-term research results reported in the literature for full and limited irrigated and rainfed maize productivity indices and can provide invaluable data and information for developing effective in-season water management decisions and determining crop productivity responses to different irrigation/water management conditions.