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Efficiency and productivity of irrigation water based on water balance considering quality of return flows
Agricultural Water Management 231 (2020) 106025
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Efficiency and productivity of irrigation water based on water balance considering quality of return flows
T
Hasti Kazem Attar, Hamideh Noory*, Hamed Ebrahimian, Abdol-Majid Liaghat Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
A R T I C LE I N FO
A B S T R A C T
Keywords: Sefficiency Productivity Return flow Water quality Water usefulness
Efficiency is one of the most important assessment indicators in irrigation systems. Classical efficiency (CE) is not an exact index due to the lack of consideration of the return flows. Therefore, the neoclassical concepts of the efficiency are considered to take a part of losses of irrigation water as a return flow into account. Quality of the return flows may change in their path and it must be considered in evaluating the efficiency and productivity of irrigation water. This research was carried out to investigate this challenge. Sustainable efficiency (SE) was applied based on the water balance and quality of return flows. The methodology and detail for computing different parameters and their quality and beneficial coefficients in water balance equation were presented. Moghan irrigation and drainage network in the northwest of Iran was selected as the study area and CE and SE were calculated in meso and micro levels using the meteorological data, cropping pattern, irrigation water volume, natural and artificial drainages, infiltration and return flow quality. In addition, the irrigation water productivity was calculated by considering the volume of water based on the different concepts of efficiency. Quality coefficient related to return flow had different values in different months (0.85 in August and 1 in November and December). The results showed that about 87 % of inflow, 91 % of the rainfall, 89 % of the evapotranspiration, 13 % of the non-reusable water, and 91 % of the return flow were useful in the study area. The highest and the lowest efficiencies are occurred in September and November, respectively. The average of meso and micro Sefficiencies were 72 % and 47.5 %, respectively, and the CE was 37.9 %. The results showed that water productivity based on the SE is more than that of the CE. The water productivity at the meso level also showed a higher value than at the micro level.
1. Introduction As water resources systems become more complex, competition among different water users increases and need to food increases in the world, simple efficiency indicators have recognized insufficient in supporting an effective water resources systems design and evaluation. The efficiency is one of the most important evaluation indicators in the irrigation systems. Israelsen (1932) presented a concept that is nowadays regarded as the classical efficiency (CE). Many researchers (Willardson et al., 1994; Seckler, 1996; Perry, 1999; Jensen, 2007; Molden, et al., 2010; Huffaker, 2008; Ward and Pulido Velázquez, 2008) reported examples of misunderstandings in water management activities and water conservation programs due to the inadequacy of CE concept because of the lack of attention to the return flows. In irrigation, there are three sources of water losses including evaporation from the surfaces of land, water and plants that do not contribute to cultivated crop evapotranspiration, drainage losses (runoff
⁎
and deep percolation) and spillage losses (due to mismatches between water supply and demand). In CE, they are considered water losses to the system as a whole. CE at the farm can provide a meaningful concept of the efficiency, however, applying classical efficiency for water basins as a general principle, may guide us to wrong decisions (Keller and Keller, 1995). Various attempts have been made to solve these errors and mistakes, which has eventually led to the concept of neoclassical efficiency. Several scholars have proposed a distinction between the concept of classical and neoclassical irrigation efficiency (Keller et al., 1996; Seckler et al., 2003; Haie and Keller, 2008; Mateos, 2008). The concept of neoclassical efficiency has led to a shift in irrigation perspective from the water delivery systems to a wider perspective that considers irrigation planning and management along with all the water resources within a particular basin. In neoclassical efficiencies, the water losses of CE are not necessarily real water losses to the system as a whole and many of these losses are captured and recycled elsewhere in the system which called return flows (Seckler et al., 2003). Also in the
Corresponding author. E-mail address: [email protected] (H. Noory).
https://doi.org/10.1016/j.agwat.2020.106025 Received 21 July 2019; Received in revised form 8 January 2020; Accepted 8 January 2020 0378-3774/ © 2020 Elsevier B.V. All rights reserved.
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account the beneficial and quality dimensions. Due to the novelty of Sefficiency indicator based on water balance, the complexity of its calculations and the lack of its application in different study areas, the main purpose of this study was to provide a methodology and details for computing various water balance components and Sefficiency with considering the quality of return flows. Another purpose is evaluation of irrigation water productivity by taking into account the different concepts of efficiency. For this purpose, Moghan irrigation and drainage network in the northwest of Iran was selected as the study area because of its great importance in producing agricultural productions in this country.
neoclassical concept of efficiency, introduced by Keller and Keller (1995), all water losses are not considered to be unnecessary losses. Keller and Keller (1995) believed that all water losses should not be considered as losses, because some of them are returned to the hydrological cycle again. The important point is that the return flow during this process has a different quality from the initial source. Therefore, it is necessary to consider water quality of return flows, which may be reused, in calculating neoclassical efficiencies. In other words, return flow may not have the same value relative to irrigation water due to its lower quality (e.g. higher salinity). Keller and Keller (1995) developed a new relationship with considering the return flow and created an evolution in the calculations of efficiency. In this relationship, leaching has been described as a qualityrelated parameter. They evaluated this relationship in the Grand Valley at the downstream of the Colorado River and the Imperial Irrigation District at the upstream of the Colorado River in the United States and Egypt's Nile Valley Irrigation System. Haie and Keller (2008) described a new relationship for the efficiency and introduced a coefficient (W) for the water quality in the relationship. In order to calculate this coefficient, the amount of leaching requirement (LR) is used. The obtained relationship was tested in three regions of Grand Valley at downstream of the Colorado River, the Imperial Irrigation District at the upstream of the Colorado River in the United States, and the Nile River in Egypt. In that research, the coefficient of W was considered for the inflow water to the region and outflow from the region, but the calculation method was not mentioned. Molden et al. (1998, 2003, and 2010) presented many approaches to increase water productivity and efficiency. They stated that the effect of water quality on the calculation of water productivity and efficiency is one of the basic issues to be considered but did not examine how it was affected. Haie and Keller (2011) presented a relationship for the calculation of the efficiency based on the principle of mass conservation and by considering the dimension of the usefulness of the parameters. They investigated their relationship in the Great Region downstream of the Colorado River, Nile River in Egypt and a hypothetical city and a hypothetical agricultural area. They estimated the quality and beneficial coefficients for a number of parameters but did not provide a methodology for calculating these coefficients. They also calculated the efficiency at three macro, meso, and micro levels, with and without considering the return flow, and two states of simultaneous consideration of the beneficial and quality dimensions and taking into account only the beneficial dimension. Mokari ghahroodi et al. (2015) evaluated irrigation systems in Qazvin plain in Iran by determining the classical and neoclassical irrigation efficiencies (net and effective efficiencies). In this research, different components of water balance were measured in different irrigation methods and details of the calculation method for efficiency were presented. However, the water quality was not considered in the calculation of irrigation efficiency. Haie (2016) introduced the concept of sustainable efficiency (Sefficiency) and stated this concept in based on food, energy, and water nexus and related the calculation of efficiency to food security. All of the aforementioned studies have proved the superiority of neoclassical efficiencies to the classical efficiency. All researchers believed that the water quality must be considered in efficiency and productivity calculations. In the studies on the neoclassical efficiencies, the water quality and its importance have been pointed out. Also, in the concept of water efficiency with considering the quality and beneficial dimensions, it is explicitly referred to the necessity of considering the dimension of water quality in efficiency calculations; however, the methodology for this calculation is not presented. Iran has always encountered water scarcity; increasing the irrigation efficiency is one of the solutions that have been addressed to solve this problem. Increasing efficiency requires the correct calculation of the current efficiency in irrigation and drainage networks. The Sefficiency provides a highly accurate estimation of irrigation efficiency, and it should be calculated at different levels, by taking into
2. Materials and methods 2.1. Study area The Moghan irrigation and drainage network is located at the northwest of Iran and the west of the Caspian Sea. It has 72,000 ha agricultural lands and the Aras River is the main water resource. The Aras River is from the Bingol-Dagh Mountains of Turkey, its catchment area is 10,020 km2, and Iran's share is 39 %. The rest of the Aras catchment in Turkey is 23 % and in Azerbaijan and Armenia is 38 %. The maximum and minimum flow rates of Aras River in normal and dry years in the Milo Moghan dam is 2600 and 180 m3.s−1, respectively (on average 400 m3/s). The main canal of the Moghan irrigation and drainage network is supplied by the Milo-Moghan diversion dam and eight sluice gates with a maximum discharge of 80 m3.s−1. This canal, which acts as a mother canal, has a predominantly earth body and its total length of the main canal is 113 km. Table 1 shows the characteristics of the network canals. In addition, excessive water is collected through existing drains and transferred to the Aras River after transferring to the boundary drain. A portion of the excess water is also entered to the Aras River through a natural drainage. The Milo-Moghan Dam was constructed at a distance of 260 km downstream of the Aras reservoir dam and 53 km in the west of Parsabad Moghan city in Aslanezou area on the Aras River. It was jointly operated by the two governments of the Islamic Republic of Iran and the Republic of Azerbaijan. Fig. 1 shows the location of the Moghan irrigation and drainage network and the Milo-Moghan diversion dam. The average rainfall and relative humidity in the region are 279 mm and 72 %, respectively. The average annual minimum, average and maximum temperatures in the region were reported as 9.6, 15 and 20.5 °C, respectively. The total area of agricultural land in 2015–2016 was 91,243 ha, of which 40,150 ha were cultivated in autumn and the rest in the summer. The highest cultivation is related to wheat with an area of 34,183 ha and the lowest is related to olive orchards with a cultivated area of 531 ha. Most of the agricultural land is irrigated by surface irrigation and the average water consumption is 10,000 m3. year−1. ha−1.
Table 1 Properties of canals in Moghan irrigation and drainage network.
2
The length of the main and first level canals Kilometer
The length of the second level canals
The length of the main and secondary drains
Number of valves
Irrigation areas
35 37.09 60 44.4 176.49
65.525 95.527 115.76 83.033 358.845
45.5 86.7 161.9 123.81 417.91
171 200 605 270 1246
Aslandooz Shahrak Pars abad Bileh savar total
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Fig. 1. Location of Moghan irrigation and drainage network and the Milo-Moghan diversion dam.
source (e.g. if the main source is the river, the amount of water extracted from the underground aquifer through the wells can be considered as OS), PP is total rainfall and SE is Sefficiency. The two coefficients i (inflow models) and c (consumptive models) correspond to two water balances: useful inflow and effective consumption. Each coefficient is either zero or one, with their sum equal to one. The subscribe "s" indicates that the useful part of the parameters is considered in the calculation of SE. One of the most basic points in the Eq. (1) is usefulness dimensions of the parameters. In order to define the practical indices, such as the efficiency, the usefulness of the parameters should be determined. In Eq. (1), both beneficial and quality dimensions are considered for each parameter in order to be useful, which are respectively denoted by "b" and "q". Quality dimension is related to the water quality and it is about a system in which the water flows; while the beneficial dimension depends on the place of consumption and the purpose of the water consumption. Given these two dimensions, the useful part of a parameter (XS) which used in Eq. (1), is defined based on Eqs. 2 to 4 (Haie and Keller, 2011).
2.2. Efficiency based on water balance Efficiency based on the water balance, Sefficiency (SE), is calculated by Eq. (1) (Haie and Keller, 2011).
ET + NR + i (V 2 + RP ) ⎤ SE = ⎡ ⎢ + OS + PP − c (V 2 + RP ) ⎥ V 1 ⎣ ⎦s
(1)
where ET is evapotranspiration and NR is water which completely depleted from the system and cannot be reused (non-reusable) such as wind draft and water evaporation losses in sprinkler irrigation, water entering the salt lake, water evaporated from the canals or reservoirs). V2 is the output water which can be RF or VD based on the studied level. As shown in Fig. 2, RF is the water released from the area and returns to the main source and VD is the volume of water at downstream of the area where RF returned to the main source. The water balance parameters which used in Eq. (1) are illustrated in Fig. 2. RP is the potential return flow which can return but has not returned yet. For example, the water volume remaining in the soil storage capacity can either be removed by drains from the area, or it can return to the main source as deep percolation (as described for RF), but has not yet returned, however, it has the potential to be returned. V1 is the input volume of water which can be VU or VA according to the studied level. As shown in Fig. 2 VA is the abstracted water from the main source for irrigating the area and VU is the volume of water at upstream. OS is the inflow water from additional sources other than the main
Xq = WqX . X
(2)
Xb = WbX . X
(3)
Xs = WqX . WbX . X
(4)
Xq indicates the effective value of parameter X in terms of quality and WqX is quality weight. This coefficient depends on the water
Fig. 2. Parameters of efficiency based on water balance equation (Eq. 1). 3
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generally important for the operators of a water resource (for example, it is important for a farmer).
compound and the path through which the water passes and determined by the frameworks and indices of the water quality in each region. WqX is calculated based on downstream conditions. For example, when salt-resistant crops are cultivated in downstream lands, WqX is higher and when the salt-sensitive crops are cultivated, it decreases. Xb shows the beneficial part of the parameter X and WbX is beneficial weight defined according to the purpose of the project and divides the water consumption into two beneficial and non-beneficial parts. The value of the beneficial weight is obtained by dividing the beneficial part of a parameter by the total value of that parameter. Haie and Keller (2011) were estimated the coefficient of Wb and Wq for a number of parameters, however, the method of calculation was not presented. In the study of irrigation efficiency based on the water balance in the irrigation and drainage networks, the accurate estimation of WbX and WqX is of great importance. Since geographic scale may affect the estimation of efficiency, three levels of macro, meso, and micro were considered as the levels of Sefficiency estimation and analysis (Haie and Keller, 2011). Macro Sefficiency (Eq. 5) shows the relationship between the useful outflow and total inflow to a water resource (such as a river). At the macro level the water resource may have several outlets or branches. For example, a river can have several outlets for different irrigation networks (Fig. 3). The macro Sefficiency (MacroSE) is generally used for managers and authorities to decide on the water allocation and transfer (Eq. 5).
Macro SE = [
ET + NR + i (VD + RP ) ]s VU + OS + PP − c (VD + RP )
Micro SE = [
ET + NR + i (RF + RP ) ]s VA + OS + PP − c (RF + RP )
(7)
All three levels of efficiency analysis are necessary for the management of the water resources. This may lead to multi-objective planning and multi-purpose management by managers and operators (). According to the definition of efficiency levels, Moghan irrigation and drainage network is considered as a meso level (Fig. 3). 2.3. Data collection and calculation of parameters In order to calculate irrigation efficiency of the Moghan irrigation and drainage network, all parameters of Eq. 6 and the quality and beneficial coefficients associated with the parameters were calculated. For this purpose, the data related to the cropping pattern (Table 2), water and soil quality of the region, characteristics and dimensions of the water conveyance canals, inflow rate into the network, outflow rate from the artificial and natural drainages and meteorological data were collected. Using the meteorological data and ETo calculator software (Raes, 2009), the reference evapotranspiration of the area was calculated and the monthly evapotranspiration of each crop was determined according to the Eq. (8) (Allen et al., 1998):
ET = ETo*K c
(5)
(8)
in which, ET is the crop evapotranspiration (mm), ETo is the reference evapotranspiration (mm), and Kc (-) is the crop coefficient. As mentioned before, in order to calculate the coefficient Wb (-), the beneficial and non- beneficial parts of the parameter must be determined. The beneficial part of the ET is the transpiration and nonbeneficial part is evaporation. According to the Eq. (9), the transpiration rate of different crops was calculated, and the evaporation rate was derived by subtracting the transpiration from the evapotranspiration on a monthly basis:
Meso Sefficiency (Eq. 6) is the relation between the useful outflow and the total inflow within one of the branches in the macro level, such as an irrigation network that abstracted the required water from a river (Fig. 3). Meso Sefficiency (MesoSE) is commonly used by managers and authorities to operate a part of the water resource.
Meso SE = [
ET + NR ]s VA + OS + PP
(6)
Micro Sefficiency (Eq. 7) is to examine the relationship between the useful outflow and the total inflow within one of the branches in the meso level (for example, a farm or a part of the irrigation network) (Fig. 3). In fact, the micro Sefficiency (MicroSE) is the calculation of efficiency in a field, regardless of the return flows. This efficiency is
Tc = K cb*ETo
(9)
where, Tc (mm) is transpiration and Kcb (-) is the basal crop coefficient due to the transpiration. The amount of crop coefficients, basal coefficients and growth
Fig. 3. Efficiency at three levels of macro, meso, and micro in the study area. 4
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Table 2 Cropping pattern in the study area. Cultivation area (ha)
Crop
Cultivation area (ha)
Crop
Cultivation area (ha)
Crop
34183 2653 2108
Wheat Sugar beet Forage maize
2118 531 2297
cotton Soybean (first cultivation) Watermelon
2467 1888 6438
3863 1576 1921
Rapeseed tomato Nectarine
16543 538 2687
Soybean (second cultivation) Olive Other fruit gardens
8198 1081 153
Barley Maize Maize (second cultivation) Alfalfa Almond Other crops
period of each crop were extracted from FAO Report No. 56 (Allen et al., 1998) and adjusted with the study area. With summation of evapotranspiration rates of various crops based on the value of cultivation area, evapotranspiration and transpiration of the study area were calculated regionally. In addition, the evaporation rate was calculated by subtracting the transpiration from the evapotranspiration rate of the study area. The coefficient WbET for the study area is derived from Eq. (10):
WbET =
Tc ET
WbVA =
(10)
P (125 − 0.02P ) 125
Ya b = 1 − (ECe − ECethreshold ) Ym 100
(11)
Peff (12)
PP
(14)
where, Ya is the actual crop yield (kg), Ym is the maximum expected crop yield (kg) for ECe < ECethreshold, b is the gradient of the reduction in the crop yield (%), for increasing 1 dS m−1electrical conductivity of the soil saturated extract, ECe is the soil saturated extract salinity, which is twice the salinity of the inflow water into the area (dS. m−1), ECethreshold is the threshold soil saturated extract. The values of ECethreshold and b were extracted from the FAO Technical Reports No. 29 and 48 (FAO, 1992, 1989) for the crops of the study area. According to the efficiency level in this study, the outflow (V2) is denoted by RF which consists of the return flow to the Aras River through the artificial drainage network and natural drainage and also the recycle of a part of drained water in Moghan irrigation and drainage network. Data related to the outflow of artificial drainage and their monthly salinity and also the amount of natural drainage of the area (average of it is 0.065 L/s/ha) were obtained from the regional water authority of Ardabil province (Table 3). There was not recycling of drainage water in 2015–2016. By entering the return flow (drainage water) to the Aras River, the water quality for downstream users is changed, and these changes indicate the usefulness of the return flow in terms of water quality. Considering the cropping pattern at the downstream, the relative crop yield due to the reduction in the water salinity of the Aras River was obtained using Eq. (14) and considered as WqRF. Therefore, WqRF is calculated based on the downstream conditions. When salt-resistant crops are cultivated in downstream lands, WqRF is higher (in other words, return flow is useful) and when the salt-sensitive crops are cultivated, it decreases (in other words, return flow isn’t useful). The salinity of natural drainage was considered equivalent to average salinity of underground drains (4.7 dS m−1) and the salinity of the artificial drainage at the entrance to the river is 2.9 dS m−1 (Regional Water
where Peff is the effective rainfall (mm) and P is the monthly rainfall (mm). Peff and PP was calculated regionally based on the values of cultivation area related to various crops and the coefficient WbPP was determined from Eq. (12):
WbPP =
(13)
The coefficient WqVA was calculated proportional to the reduction in crop yield due to the water salinity. The reduction in crop yield due to the water salinity was calculated for each crop using Eq. (14). WqVA was considered equivalent to the relative crop yield expected under ECe > ECethreshold for each crop. The river salinity has little fluctuations in different months of the year and its average salinity was 1.12 dS m−1. By weighting the WqVA related to each crop based on the values of cultivation area, the WqVA was obtained regionally for the studied area.
Due to the nature of the evapotranspiration, and since the water transpiration due to the plant and the evaporation from the soil surface enter the atmosphere, the coefficient WqET (-) is considered as one. The value of daily rainfall in the study area was obtained from Parsabad Meteorological Station. Due to the good quality of the rainwater, its WqPP value was considered one. The beneficial part of the rainfall is directly consumed by the plant and it provides a portion of the required water, and the rest is considered to be non- beneficial part of the rainfall. The beneficial part of the rainfall is in fact the effective rainfall. Hence, the monthly effective rainfall was determined for each plant using the USDA’s method (Dastane, 1974) in months when the plant requires water (Eq. 11):
Peff =
VF VA
The study area is defined at the meso level (Fig. 3); hence, the inflow is denoted by VA (m3 ). The amount of VA is the volume of water entering the Moghan irrigation and drainage network for agricultural use. Data related to the VA and the volume of water delivered to the farmers’ fields (VF) (m3 ) was obtained monthly for 2015–2016 from the regional water authority of Ardabil province (Table 3). In order to calculate the coefficient WbVA, the beneficial part is the water delivered to the farmers’ fields for irrigation and the non-beneficial part is the water losses in the transfer path. The WbVA was calculated using Eq. (13):
Table 3 Amount of allocated water (VA) and delivered water (VF) in Moghan irrigation and drainage network and return flow to the Aras River through natural and artificial drains in 2015–2016 (1000 m3). Month
October
November
March
April
May
June
July
August
September
Allocated Water Delivered water Artificial drainage Natural drainage
45520 43120 15860 12131
16330 14750 9940 12131
46020 41200 12730 11726
119330 106500 22060 12535
139690 127740 18990 12535
125530 115190 17600 12535
156500 139990 12690 12535
179040 159950 11700 12535
83030 74860 10210 12535
5
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Fig. 4. A flowchart of the proposed methodology for the calculating Sefficiency.
where, Qr is the Aras River flow rate (m3.s−1), QRF is the return flow rate (m3.s−1) (including discharge of artificial and natural drainages), ECr is the water salinity of Aras river (dS. m−1), ECRF is the water salinity of the return flow (dS. m−1) (artificial and natural drainages) and EC* is the water salinity of Aras River after entering the return flows (dS. m−1). It should be noted since downstream lands are in
Authority of Ardabil Province, 2015). By using Eq. (15) and assuming rapid mixing (Sharma and Ahmad, 2014), the changes in the salinity of the river after entering the return flow (drainage water) were estimated, as follows:
EC * =
(Qr . ECr ) + (QRF . ECRF ) Qr + QRF
(15) 6
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Azerbaijan and no data was available in this region, it was assumed that the cropping pattern of downstream lands is the same as those of upstream. It was also assumed that the return flow including artificial and natural drainages had no loss until reaching the Aras River. Therefore, the coefficient WbRF was considered equal to 1. There are three types of non-reusable flow (NR) in the study area, including evaporation from the water surface of canals, deep percolation and evaporation from the soil surface. The evaporation rate from the canals was calculated according to Eq. (16) (Allen et al., 1998):
Ec = K (Epan )
Ie =
ECw 5ECe − ECw
Since the calculation of physical water productivity (WP) is desirable in this research, the average yield for each crop (Ya ) was obtained from Organization of Agriculture Jihad of Ardabil province. Then, based on the calculated efficiency and gross irrigation requirement, WP was calculated at the meso and micro levels based on Eq. (20) and compared with calculated WP according to conventional method by taking into account the classical efficiency in calculating the gross irrigation requirement.
(16)
WP= Ya/(
LR DP
ET − Peff Ie / MicroSE /MesoSE
)
(20)
3. Results and discussion 3.1. Efficiencies The values related to the parameters of the water balance equation, the associated coefficients of Wq and Wb and the useful part of each parameter are given in Table 4. The coefficient WqVA had different values in different months, because of the difference between crops cultivated in different months (Table 4). The WqVA was equal to 1 in November and March; this is due to the fact that during these months, only wheat and barley were cultivated on the farm land, whose salinity threshold was higher than that of the inflow. While in other months, this coefficient was less than one, due to the cultivation of crops whose salinity thresholds were lower than the salinity of irrigation water, therefore crop yield was less than its potential, and the coefficient WqVA decreased. Regarding coefficient WbVA, the difference among the values in the various months indicated the difference in the volume of inflow rate and the water allocated to the farmers. In October, 95 % of the inflow rate was allocated to the farmers, and there were 5 % losses, while coefficient WbVA reached 89 % in March (11 % losses). The high value of WbVA indicated the proper water allocation to the network according to the needs of the operators (on-demand allocation of irrigation water). About the coefficient WbPP, increasing this coefficient indicates that more rainfall is used. The low values in some months are due to the low level of cultivation in that month; this coefficient in June is equivalent to 0.96 and in November is 0.53, while in the March all crops are on the farm land and use the rainfall, while in November only autumn crops are on the farm land. As a result, the major part of precipitation in this month is non-beneficial for our purpose (irrigation). The low value of WbET associated with the ET in November is due to the low cultivation area in this month and low air temperature. The reason for high rate of this coefficient in September is high cultivation area and high air temperature and consequently the transpiration rate is increased. Since the main purpose in the irrigation and drainage network is to irrigate the farms and produce the crops, the evaporation from the soil surface and irrigation canals is non-beneficial; therefore, the coefficient Wb in these two components will be zero. In other words, the evaporation from the soil surface and irrigation canals was non-beneficial. The coefficient WbDP was calculated based on the leaching requirement. In the months with high cultivation area, leaching requirement is increased and a greater amount of DP is used for leaching and as a result, the coefficient WbDP is increased. The highest value of this coefficient is occurred in September, which all crops are on the farm land, and the smallest is occurred in November, which the wheat and barley are just cultivated.
(17)
in which, LR is leaching requirement ratio (no dimension), and ECw is the irrigation water salinity (dS. m−1). The LR was obtained regionally for the studied area and WbDP was calculated based on Eq. (18).
WbDP =
(19)
VA
2.5. Water productivity
where, Ec is the evaporation from the water surface, Epan is the evaporation from the surface of the pan, and K is the pan coefficient. Since irrigation of the lands is carried out only through irrigation networks, and groundwater does not interfere with the irrigation of the land, therefore, the flowing water percolated deeply out of the area is considered as non-reusable flow. Deep percolation (DP) rate was calculated based on the water balance equation in the study area. Evaporation from the soil surface was also estimated by subtracting the transpiration from evapotranspiration. Due to the fact that the purpose of the project is to produce the crop, the evaporation from the canals and the soil surface were considered to be completely non-beneficial and the coefficient Wb for them were considered to be zero. Regarding the amount of water percolated deeply from the area, a part of deep percolation which leaches the soil profile was considered beneficial, the rest was non-beneficial. Eq. (17) was used to calculate the leaching water (Rhoades and Merrill, 1976).
LR =
ET − Peff
(18)
For evaporation from the canal and soil surfaces, the quality will not be affected and therefore, Wq coefficient for them was considered to be one. Due to high quality of water entering the study area, the coefficient WqDP was also considered to be one. In order to facilitate the calculation, the area was considered as a separated region. Therefore, the value of OS was zero. In addition, in the study area, RP can be the volume of water stored in the soil storage capacity at the end of a period and can be used in the next period. For the following period, this flow can be considered as a flow from other sources (OS). Since the calculation of efficiency is based on a water balance, if some water remains in the soil capacity at the end of the year 2014–2015, it can be considered as OS for the years 2015–2016. On the other hand, the amount of water remaining at the end of the year 2015–2016 is considered as RP. Since these two parameters will be equal and deducted from each other in the calculation of efficiency (based on Eq. 6), their calculation will not change in the efficiency. Therefore, in the research, the values of this flow were considered to be zero. Fig. 4 presents a flowchart of the proposed methodology for computing Sefficiency and different water balance parameters and their quality and beneficial coefficients.
2.4. Classical efficiency In order to better understand the difference in the calculation of the efficiency, the classical efficiency (Ie ) was calculated according to the Eq. (19) and was compared with the Sefficiency at the meso and micro levels. 7
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Table 4 Value of parameters of the water balance equation (1000 m3) and the quality and beneficial coefficients associated with them. Flow type
Parameter
October
November
March
April
May
June
July
August
September
VA
value Wq Wb Usefulness coefficient Useful part value Wq Wb Usefulness coefficient Useful part value Wq Wb Usefulness coefficient Useful part value Wq Wb Usefulness coefficient Useful part value Wq Wb Usefulness coefficient Useful part value Wq Wb Usefulness coefficient Useful part value Wq Wb Usefulness coefficient Useful part value Wq Wb Usefulness coefficient Useful part
45520 0.97 0.95 0.92
16330 1.00 0.90 0.90
46020 1.00 0.90 0.90
119330 0.98 0.89 0.87
139690 0.98 0.91 0.89
125530 0.98 0.92 0.90
156500 0.97 0.89 0.86
179040 0.93 0.89 0.83
83030 0.97 0.90 0.87
41947 13250 1.00 0.58 0.58
14697 25598 1.00 0.53 0.53
41418 2328 1.00 0.63 0.63
104080 2253 1.00 0.86 0.86
124576 22465 1.00 0.90 0.90
113178 11352 1.00 0.96 0.96
135106 2321 1.00 0.72 0.72
148191 1911 1.00 0.72 0.72
72485 12512 1.00 0.66 0.66
7685 21490 1.00 0.88 0.88
13567 8591 1.00 0.29 0.29
1467 18619 1.00 0.84 0.84
1938 42568 1.00 0.90 0.90
20219 76231 1.00 0.91 0.91
10898 69930 1.00 0.88 0.88
1671 78363 1.00 0.86 0.86
1376 81890 1.00 0.94 0.94
8258 56540 1.00 0.95 0.95
18911 2669 1.00 0.00 0.00
2491 6077 1.00 0.00 0.00
15640 2986 1.00 0.00 0.00
38311 4164 1.00 0.00 0.00
69370 6781 1.00 0.00 0.00
61538 8064 1.00 0.00 0.00
67392 10756 1.00 0.00 0.00
76977 4962 1.00 0.00 0.00
53713 3107 1.00 0.00 0.00
0.00 392 1.00 0.00 0.00
0.00 199 1.00 0.00 0.00
0.00 276 1.00 0.00 0.00
0.00 400 1.00 0.00 0.00
0.00 623 1.00 0.00 0.00
0.00 908 1.00 0.00 0.00
0.00 1271 1.00 0.00 0.00
0.00 1384 1.00 0.00 0.00
0.00 716 1.00 0.00 0.00
0.00 8897 1.00 0.28 0.28
0.00 11068 1.00 0.03 0.03
0.00 4996 1.00 0.12 0.12
0.00 44020 1.00 0.05 0.05
0.00 53776 1.00 0.1 0.1
0.00 35909 1.00 0.21 0.21
0.00 53963 1.00 0.22 0.22
0.00 73443 1.00 0.14 0.14
0.00 15541 1.00 0.45 0.45
2491 15860 0.88 1.00 0.88
332 9940 1.00 1.00 1.00
600 12730 1.00 1.00 1.00
2201 22060 0.90 1.00 0.90
5378 18990 0.90 1.00 0.90
7541 17600 0.91 1.00 0.91
11872 12690 0.88 1.00 0.88
10282 11700 0.85 1.00 0.85
6993 10210 0.90 1.00 0.90
13957 12131 0.88 1.00 0.88
9940 12131 1.00 1.00 1.00
12730 11726 1.00 1.00 1.00
19854 12535 0.90 1.00 0.90
17091 12535 0.90 1.00 0.90
16016 12535 0.91 1.00 0.91
11167 12353 0.88 1.00 0.88
9945 12535 0.85 1.00 0.85
9189 12535 0.90 1.00 0.90
10675
12131
11726
11282
11282
11407
11031
10655
11282
PP
ET
NR
Evaporation
Evaporation from the channel
Deep percolation
RF
RF to the Aras River through the artificial drainage network
RF to the Aras River through the natural drainage network
VA: Abstracted water from the main source, PP: Total rainfall, ET: Evapotranspiration, NR: Non-Reusable, water consumption, RF: Return Flows, Wq: quality coefficient, Wb: beneficial coefficient.
The coefficient WqRF in different months has different values, because of the difference in cultivation area in different months and crop response to the salinity. In November and December, this coefficient is equal to one (i.e. during these months all water has good quality for consumers). The reason is that in these two months wheat and barley are cultivated, in which, the salinity threshold are higher than the salinity threshold for irrigation water (mixing the return water with river water). However, in August, the lowest value of WqRF is occurred, which is due to the cultivation of summer crops (salinity thresholds below the salinity of the irrigation water). Table 5 shows the percent of usefulness of each parameter per year (in the years 2015–2016). As mentioned before, the usefulness of each parameter indicates how the parameter was useful in the study area in terms of water quality and beneficially. The highest amount of usefulness in the study area (at the meso level) is related to the RF and the lowest is related to the NR (Table 6). Our results demonstrated that about 87 % of inflow, 91 % of the rainfall, 89 % of the ET, 13 % of the NR, and 91 % of the RF were useful in the study area.
Table 5 The value and usefulness of water balance parameters per year (1000 m3). Parameter
VA
PP
ET
NR
RF
Value Useful part Percentage of usefulness
910990 795678 87
93990 67079 91
454222 404343 89
357348 47690 13
242796 221360 91
VA: Abstracted water from the main source, PP: Total Precipitation, ET: Evapotranspiration, NR: Non-Reusable, water consumption, RF: Return Flows.
The highest and the lowest efficiencies are respectively occurred in September and November (Fig. 5). The average Sefficiency at the meso level in the study area is 72 %. High efficiency in some months like September is due to the proper water allocation. This means that in months with high water efficiency, the water is allocated to the needs of the area, but in the months with lower water efficiency, more water is allocated than the water needs of the area. In addition, in the months with high rainfall, this is more evident, because a part of the needs for 8
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Table 6 Water productivity at different concepts of efficiency. Gross irrigation requirement
Productivity
Crop
ET Meso Sefficiency Cubic meters per hectare
Micro Sefficiency
Classical efficiency
Meso Sefficiency Micro Sefficiency Kilograms to cubic meters
Classical efficiency
Wheat Cotton Barley Sugar beet Soybean (first cultivation) Maize Forage corn Watermelon Forage corn (second cultivation) Rapeseed Soybean (second cultivation) olive Almond tomato Nectarine alfalfa
4650 7170 3760 8980 7140 7520 6200 3690 4270 3650 4540 5690 7020 6630 7250 8580
9800 15090 7913 18901 15030 15831 13056 7778 8985 7686 9561 13881 14774 13963 15258 18057
12282 18913 9917 23689 18837 19841 16363 9748 11261 9633 11983 17397 18516 17500 19123 22631
0.58 0.24 0.51 4.30 0.22 0.65 4.28 6.07 6.21 0.48 0.35 0.13 0.17 3.65 1.12 0.71
0.30 0.13 0.27 2.26 0.12 0.34 2.25 3.20 3.27 0.25 0.18 0.07 0.09 1.92 0.59 0.38
6465 9956 5220 12469 9916 10444 8614 5131 5928 5071 6307 9157 9747 9212 10066 11913
0.38 0.16 0.34 2.84 0.15 0.43 2.82 4.01 4.10 0.32 0.23 0.09 0.11 2.41 0.74 0.47
than the micro and meso Sefficiencies. According to the results of Keller and Keller (1995) in the Grand Valley at the downstream of the Colorado River and Egypt's Nile Valley Irrigation System, the reason for the difference between classical, micro and meso Sefficiencies in the region is the existence of the return flows. Of course, the difference between the classical efficiency and meso Sefficiency is greater than the difference between the classical efficiency and micro Sefficiency, due to the presence of the return flows in the meso level. 3.2. Water productivity The water productivity at the different concepts of efficiencyare presented in Table 6. The maximum and minimum values of WP were obtained for meso Sefficiency and the classical efficiency, respectively. According to the special situations of the Aras River in terms of geographical location, micro Sefficiency will be representative efficiency for the study area. Therefore, WP at the micro level in the study area represents the water productivity of the region. The reason of higher WP at the micro Sefficiency compared to the classical efficiency is that in classical efficiency all losses of the system are considered as non-reusable losses, while in the micro Sefficiency the entire non-reusable flow, which includes losses of the system, is not considered as losses, and the portion required for leaching is deducted from the system losses.
Fig. 5. Meso and micro Sefficiencies and classical efficiency in different irrigation months (in percent).
water is supplied by rainfall and it is not necessary to allocate the water. The highest and the lowest values of micro Sefficiency are occurred on September and November, respectively. The average micro Sefficiency of the irrigation network is 47.5 %. The reduction in the efficiency, especially in the seasons where there is a lower water requirement, is clearly evident; the reason for sharp reduction in the efficiency in November is low water requirement for irrigation in the region that could be compensated by effective rainfall (Table 4); however, for mismanagement reasons (the water allocation to the area without considering the values of irrigation water requirement and effective rainfall), water enters to the network and the volume of drainage water increases in this month. Since the volume of outflow in terms of the study’s level (micro level) is not useful, the efficiency decreases sharply, while in high consumption months such as September due to the high water requirement in the area and the low effective rainfall, the volume of drainage water decreases. As a result, there will be a lower difference between the micro and meso Sefficiencies. The difference between the classical efficiency, meso and micro Sefficiencies are presented in Fig. 5. The highest and lowest classical efficiency is related to September and November, respectively. The average classical efficiency is obtained as 37.9 %. The reason for zero value of the classical efficiency in November is high effective rainfall in this month which supplies all water requirements, while water is allocated to the network in this month due to mismanagement reasons. As seen, there is a large difference between the classical and micro and meso Sefficiencies, especially in the months with high water consumption. In all months, the classical efficiency shows a lower amount
4. Conclusion Considering the assessment level of the efficiency in management decisions is very important. The Aras River is an International Border River and is exited out of the country after the arrival of return flows, and so the return flows are practically non-useful for the country. Therefore, micro Sefficiency is the representative efficiency of the study area, and making management decisions is necessary in the region based on micro Sefficiency to increase the efficiency. It can be noted that if the representative efficiency of the area is considered as the meso Sefficiency, the non-reusable flow needs to be reduced. However, if the micro Sefficiency is considered as the region's representative efficiency, it is necessary to avoid the return flow losses out of the area. This suggests that the correct calculation of the efficiency in the region can affect management decisions. The effect of the return flow quality on the efficiency calculations represents itself with WqRF coefficient. The higher the coefficient WqRF, the greater usefulness of the return flow. In the study area, the coefficient WqRF was calculated on a monthly basis. The difference in the 9
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value of coefficient WqRF in different months is due to the different cropping pattern at the downstream. For example, in November and March, the coefficient WqRF is equal to one, due to the cultivation of salt-resistant crops with higher salinity thresholds than the return flow salinity. While in the following months, the coefficient WqRF decreased due to the cultivation of crops with lower salinity thresholds. If some crops are cultivated in downstream reaches with more salinity thresholds than the salinity of return flow, the coefficient WqRF and consequently the efficiency will increase. The average classical efficiency in the Moghan irrigation and drainage network was 37.9 percent. Furthermore, the meso and micro Sefficiencies were obtained as 72.0 and 47.5 percent, respectively. The reason for the high meso Sefficiency compared to micro Sefficiency and the classical efficiency is the lack of considering the return flows as losses. In other words, in meso Sefficiency, the return flow is considered to be a useful outflow, hence, water loss is reduced and efficiency is increased. The reason for high micro Sefficiency as compared to the classical efficiency is the lack of taking into account the leaching requirement as losses in micro Sefficiency. In other words, a part of deep percolation used for leaching is considered as a useful outflow of the system (increasing the efficiency), while in the classical efficiency, this part of the losses is considered non-useful. Considering the assessment level of the water productivity in management decisions is very important. Since the micro Sefficiency is a representative efficiency of the region, the water productivity at the micro level can be considered as water productivity in the study area. Therefore, water productivity at the micro level is the representative water productivity of the study area, and making management decisions is necessary in the region based on it. This recommends that the correct calculation of the efficiency and consequently, water productivity in the region can affect management decisions. Also, uncertainty and sensitivity analyses of input data for calculating the Sefficiency at different levels (macro, meso, and micro) are recommend to be considered in future studies.
for Computing Crop Water Requirements. Food and Agriculture Organization of the United Nations, Rome, Italy (FAO Irrigation and Drainage Paper No. 56). Dastane, N.G., 1974. Effective Rainfall in Irrigated Agriculture. Food and Agriculture Organization of the United Nations, Rome, Italy (FAO Irrigation and Drainage Paper No. 25). Food and agriculture organization, 1989. Water Quality for Agriculture. FAO Irrigation and Drainage Paper 29. Food and agriculture organization, 1992. The Use of Saline Waters for Crop Production. FAO Irrigation and Drainage Paper 48. Haie, N., 2016. Sefficiency (sustainable efficiency) of water-energy-food entangled system. Int. J. Water Resource Dev. 32, 721–737. Haie, N., Keller, A.A., 2008. Effective efficiency as a tool for sustainable water resources management. J. Am. Water Resour. Assoc. 44 (4), 961–968. Haie, N., Keller, A.A., 2011. Macro, meso, and micro-efficiencies in water resources management: a new framework using water balance. Am. Water Resources Assoc. 48 (2), 235–243. Huffaker, R., 2008. Conservation potential of agricultural water conservation subsidies. Water Resour. Res. 44. Israelsen, O.W., 1932. Irrigation Principles and Practices. John Wiley and Sons, NewYork. Jensen, M.E., 2007. Beyond irrigation efficiency. Irrig. Sci. 25 (3), 233–245. Keller, A.A., Keller, J., 1995. Effective Efficiency: A Water Use Efficiency Concept forAllocating Freshwater Resources. Center for Economic Policy Studies. Winrock International Discussion Paper 22, January. Keller, A.A., Keller, J., Seckler, D., 1996. Integrated Water Resource Systems: Theory and Policy Implications. International Water Management Institute, Sri Lanka, Colombo Research Report 3. Mateos, L., 2008. Identifying a new paradigm for assessing irrigation system performance. Irrigation Sci. 27, 25–34. Mokari ghahroodi, E., Noory, H., Liaghat, A.M., 2015. Performance evaluation study and hydrologic and productive analysis of irrigation system at Qazvin irrigation network (Iran). Agric. Water Manag. 148, 189–195. Molden, D.J., Murray-Rust, H., Sakthivadivel, R., Makin, I., 2003. A water productivity framework for understanding and action. In: Kijne, W., Barkers, R., Molden, D. (Eds.), Water Productivity in Agriculture: Limits and Opportunities for Improvements. CAB International, Wallingford, United Kingdom, pp. 368. Molden, D., Oweis, T., Steduto, P., Bindraban, P., Hanjra, M.A., Kijne, J., 2010. Improving agricultural water productivity: between optimism and caution. Agric. Water Manag. 97 (4), 528–534. Molden, D.J., Sakthivadivel, R., Perry, C.J., Fraiture, C., Kloezen, W.H., 1998. Indicators for Comparing Performance of Irrigated Agricultural Systems. International Water Management Institute, Colombo, Sri Lanka pp. 26 (Research Report 20). Perry, C.J., 1999. The IWMI water resources paradigm-definitions and implications. Agric. Water Manag. 40 (1), 45–50. Raes, D., 2009. The ETo Calculator: Reference Manual. FAO, Land and Water Division, Rome, Italy 38 pp. Rhoades, J.D., Merrill, S.D., 1976. Assessing the suitability of water for irrigation: theoretical and empirical approaches. Prognosis of Salinity and Alkalinity. FAO Soils Bulletin 31. FAO, Rome. Seckler, D., 1996. The New Era of Water Resources Management: From Dry to Wet Water Savings. International Water Management Institute, Colombo, Sri Lanka pp. 18 (Research Report 1). Seckler, D., Molden, D., Sakthivadivel, R., 2003. The concept of efficiency in water resources management and policy. In: Kijne, W., Barkers, R., Molden, D. (Eds.), Water Productivity in Agriculture: Limits and Opportunities for Improvement. CAB International, Wallingford, United Kingdom, pp. 37–51. Sharma, H., Ahmad, Z., 2014. Transverse mixing of pollutants in streams: a review. Am. J. Civ. Eng. Archit. 41, 472–482. Ward, F.A., Pulido Velázquez, M., 2008. Water conservation in irrigation can increase water use. Proc. Natl. Acad. Sci. 105, 18215–18220. Willardson, L.S., Allen, R.G., Frederiksen, H.D., 1994. Elimination of irrigation efficiencies. In: 13th Technical Conference (October). USCID, Denver, Colorado.
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.agwat.2020.106025. References Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop Evapotranspiration. Guidelines
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Efficiency and productivity of irrigation water based on water balance considering quality of return flows
T
Hasti Kazem Attar, Hamideh Noory*, Hamed Ebrahimian, Abdol-Majid Liaghat Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
A R T I C LE I N FO
A B S T R A C T
Keywords: Sefficiency Productivity Return flow Water quality Water usefulness
Efficiency is one of the most important assessment indicators in irrigation systems. Classical efficiency (CE) is not an exact index due to the lack of consideration of the return flows. Therefore, the neoclassical concepts of the efficiency are considered to take a part of losses of irrigation water as a return flow into account. Quality of the return flows may change in their path and it must be considered in evaluating the efficiency and productivity of irrigation water. This research was carried out to investigate this challenge. Sustainable efficiency (SE) was applied based on the water balance and quality of return flows. The methodology and detail for computing different parameters and their quality and beneficial coefficients in water balance equation were presented. Moghan irrigation and drainage network in the northwest of Iran was selected as the study area and CE and SE were calculated in meso and micro levels using the meteorological data, cropping pattern, irrigation water volume, natural and artificial drainages, infiltration and return flow quality. In addition, the irrigation water productivity was calculated by considering the volume of water based on the different concepts of efficiency. Quality coefficient related to return flow had different values in different months (0.85 in August and 1 in November and December). The results showed that about 87 % of inflow, 91 % of the rainfall, 89 % of the evapotranspiration, 13 % of the non-reusable water, and 91 % of the return flow were useful in the study area. The highest and the lowest efficiencies are occurred in September and November, respectively. The average of meso and micro Sefficiencies were 72 % and 47.5 %, respectively, and the CE was 37.9 %. The results showed that water productivity based on the SE is more than that of the CE. The water productivity at the meso level also showed a higher value than at the micro level.
1. Introduction As water resources systems become more complex, competition among different water users increases and need to food increases in the world, simple efficiency indicators have recognized insufficient in supporting an effective water resources systems design and evaluation. The efficiency is one of the most important evaluation indicators in the irrigation systems. Israelsen (1932) presented a concept that is nowadays regarded as the classical efficiency (CE). Many researchers (Willardson et al., 1994; Seckler, 1996; Perry, 1999; Jensen, 2007; Molden, et al., 2010; Huffaker, 2008; Ward and Pulido Velázquez, 2008) reported examples of misunderstandings in water management activities and water conservation programs due to the inadequacy of CE concept because of the lack of attention to the return flows. In irrigation, there are three sources of water losses including evaporation from the surfaces of land, water and plants that do not contribute to cultivated crop evapotranspiration, drainage losses (runoff
⁎
and deep percolation) and spillage losses (due to mismatches between water supply and demand). In CE, they are considered water losses to the system as a whole. CE at the farm can provide a meaningful concept of the efficiency, however, applying classical efficiency for water basins as a general principle, may guide us to wrong decisions (Keller and Keller, 1995). Various attempts have been made to solve these errors and mistakes, which has eventually led to the concept of neoclassical efficiency. Several scholars have proposed a distinction between the concept of classical and neoclassical irrigation efficiency (Keller et al., 1996; Seckler et al., 2003; Haie and Keller, 2008; Mateos, 2008). The concept of neoclassical efficiency has led to a shift in irrigation perspective from the water delivery systems to a wider perspective that considers irrigation planning and management along with all the water resources within a particular basin. In neoclassical efficiencies, the water losses of CE are not necessarily real water losses to the system as a whole and many of these losses are captured and recycled elsewhere in the system which called return flows (Seckler et al., 2003). Also in the
Corresponding author. E-mail address: [email protected] (H. Noory).
https://doi.org/10.1016/j.agwat.2020.106025 Received 21 July 2019; Received in revised form 8 January 2020; Accepted 8 January 2020 0378-3774/ © 2020 Elsevier B.V. All rights reserved.
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account the beneficial and quality dimensions. Due to the novelty of Sefficiency indicator based on water balance, the complexity of its calculations and the lack of its application in different study areas, the main purpose of this study was to provide a methodology and details for computing various water balance components and Sefficiency with considering the quality of return flows. Another purpose is evaluation of irrigation water productivity by taking into account the different concepts of efficiency. For this purpose, Moghan irrigation and drainage network in the northwest of Iran was selected as the study area because of its great importance in producing agricultural productions in this country.
neoclassical concept of efficiency, introduced by Keller and Keller (1995), all water losses are not considered to be unnecessary losses. Keller and Keller (1995) believed that all water losses should not be considered as losses, because some of them are returned to the hydrological cycle again. The important point is that the return flow during this process has a different quality from the initial source. Therefore, it is necessary to consider water quality of return flows, which may be reused, in calculating neoclassical efficiencies. In other words, return flow may not have the same value relative to irrigation water due to its lower quality (e.g. higher salinity). Keller and Keller (1995) developed a new relationship with considering the return flow and created an evolution in the calculations of efficiency. In this relationship, leaching has been described as a qualityrelated parameter. They evaluated this relationship in the Grand Valley at the downstream of the Colorado River and the Imperial Irrigation District at the upstream of the Colorado River in the United States and Egypt's Nile Valley Irrigation System. Haie and Keller (2008) described a new relationship for the efficiency and introduced a coefficient (W) for the water quality in the relationship. In order to calculate this coefficient, the amount of leaching requirement (LR) is used. The obtained relationship was tested in three regions of Grand Valley at downstream of the Colorado River, the Imperial Irrigation District at the upstream of the Colorado River in the United States, and the Nile River in Egypt. In that research, the coefficient of W was considered for the inflow water to the region and outflow from the region, but the calculation method was not mentioned. Molden et al. (1998, 2003, and 2010) presented many approaches to increase water productivity and efficiency. They stated that the effect of water quality on the calculation of water productivity and efficiency is one of the basic issues to be considered but did not examine how it was affected. Haie and Keller (2011) presented a relationship for the calculation of the efficiency based on the principle of mass conservation and by considering the dimension of the usefulness of the parameters. They investigated their relationship in the Great Region downstream of the Colorado River, Nile River in Egypt and a hypothetical city and a hypothetical agricultural area. They estimated the quality and beneficial coefficients for a number of parameters but did not provide a methodology for calculating these coefficients. They also calculated the efficiency at three macro, meso, and micro levels, with and without considering the return flow, and two states of simultaneous consideration of the beneficial and quality dimensions and taking into account only the beneficial dimension. Mokari ghahroodi et al. (2015) evaluated irrigation systems in Qazvin plain in Iran by determining the classical and neoclassical irrigation efficiencies (net and effective efficiencies). In this research, different components of water balance were measured in different irrigation methods and details of the calculation method for efficiency were presented. However, the water quality was not considered in the calculation of irrigation efficiency. Haie (2016) introduced the concept of sustainable efficiency (Sefficiency) and stated this concept in based on food, energy, and water nexus and related the calculation of efficiency to food security. All of the aforementioned studies have proved the superiority of neoclassical efficiencies to the classical efficiency. All researchers believed that the water quality must be considered in efficiency and productivity calculations. In the studies on the neoclassical efficiencies, the water quality and its importance have been pointed out. Also, in the concept of water efficiency with considering the quality and beneficial dimensions, it is explicitly referred to the necessity of considering the dimension of water quality in efficiency calculations; however, the methodology for this calculation is not presented. Iran has always encountered water scarcity; increasing the irrigation efficiency is one of the solutions that have been addressed to solve this problem. Increasing efficiency requires the correct calculation of the current efficiency in irrigation and drainage networks. The Sefficiency provides a highly accurate estimation of irrigation efficiency, and it should be calculated at different levels, by taking into
2. Materials and methods 2.1. Study area The Moghan irrigation and drainage network is located at the northwest of Iran and the west of the Caspian Sea. It has 72,000 ha agricultural lands and the Aras River is the main water resource. The Aras River is from the Bingol-Dagh Mountains of Turkey, its catchment area is 10,020 km2, and Iran's share is 39 %. The rest of the Aras catchment in Turkey is 23 % and in Azerbaijan and Armenia is 38 %. The maximum and minimum flow rates of Aras River in normal and dry years in the Milo Moghan dam is 2600 and 180 m3.s−1, respectively (on average 400 m3/s). The main canal of the Moghan irrigation and drainage network is supplied by the Milo-Moghan diversion dam and eight sluice gates with a maximum discharge of 80 m3.s−1. This canal, which acts as a mother canal, has a predominantly earth body and its total length of the main canal is 113 km. Table 1 shows the characteristics of the network canals. In addition, excessive water is collected through existing drains and transferred to the Aras River after transferring to the boundary drain. A portion of the excess water is also entered to the Aras River through a natural drainage. The Milo-Moghan Dam was constructed at a distance of 260 km downstream of the Aras reservoir dam and 53 km in the west of Parsabad Moghan city in Aslanezou area on the Aras River. It was jointly operated by the two governments of the Islamic Republic of Iran and the Republic of Azerbaijan. Fig. 1 shows the location of the Moghan irrigation and drainage network and the Milo-Moghan diversion dam. The average rainfall and relative humidity in the region are 279 mm and 72 %, respectively. The average annual minimum, average and maximum temperatures in the region were reported as 9.6, 15 and 20.5 °C, respectively. The total area of agricultural land in 2015–2016 was 91,243 ha, of which 40,150 ha were cultivated in autumn and the rest in the summer. The highest cultivation is related to wheat with an area of 34,183 ha and the lowest is related to olive orchards with a cultivated area of 531 ha. Most of the agricultural land is irrigated by surface irrigation and the average water consumption is 10,000 m3. year−1. ha−1.
Table 1 Properties of canals in Moghan irrigation and drainage network.
2
The length of the main and first level canals Kilometer
The length of the second level canals
The length of the main and secondary drains
Number of valves
Irrigation areas
35 37.09 60 44.4 176.49
65.525 95.527 115.76 83.033 358.845
45.5 86.7 161.9 123.81 417.91
171 200 605 270 1246
Aslandooz Shahrak Pars abad Bileh savar total
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Fig. 1. Location of Moghan irrigation and drainage network and the Milo-Moghan diversion dam.
source (e.g. if the main source is the river, the amount of water extracted from the underground aquifer through the wells can be considered as OS), PP is total rainfall and SE is Sefficiency. The two coefficients i (inflow models) and c (consumptive models) correspond to two water balances: useful inflow and effective consumption. Each coefficient is either zero or one, with their sum equal to one. The subscribe "s" indicates that the useful part of the parameters is considered in the calculation of SE. One of the most basic points in the Eq. (1) is usefulness dimensions of the parameters. In order to define the practical indices, such as the efficiency, the usefulness of the parameters should be determined. In Eq. (1), both beneficial and quality dimensions are considered for each parameter in order to be useful, which are respectively denoted by "b" and "q". Quality dimension is related to the water quality and it is about a system in which the water flows; while the beneficial dimension depends on the place of consumption and the purpose of the water consumption. Given these two dimensions, the useful part of a parameter (XS) which used in Eq. (1), is defined based on Eqs. 2 to 4 (Haie and Keller, 2011).
2.2. Efficiency based on water balance Efficiency based on the water balance, Sefficiency (SE), is calculated by Eq. (1) (Haie and Keller, 2011).
ET + NR + i (V 2 + RP ) ⎤ SE = ⎡ ⎢ + OS + PP − c (V 2 + RP ) ⎥ V 1 ⎣ ⎦s
(1)
where ET is evapotranspiration and NR is water which completely depleted from the system and cannot be reused (non-reusable) such as wind draft and water evaporation losses in sprinkler irrigation, water entering the salt lake, water evaporated from the canals or reservoirs). V2 is the output water which can be RF or VD based on the studied level. As shown in Fig. 2, RF is the water released from the area and returns to the main source and VD is the volume of water at downstream of the area where RF returned to the main source. The water balance parameters which used in Eq. (1) are illustrated in Fig. 2. RP is the potential return flow which can return but has not returned yet. For example, the water volume remaining in the soil storage capacity can either be removed by drains from the area, or it can return to the main source as deep percolation (as described for RF), but has not yet returned, however, it has the potential to be returned. V1 is the input volume of water which can be VU or VA according to the studied level. As shown in Fig. 2 VA is the abstracted water from the main source for irrigating the area and VU is the volume of water at upstream. OS is the inflow water from additional sources other than the main
Xq = WqX . X
(2)
Xb = WbX . X
(3)
Xs = WqX . WbX . X
(4)
Xq indicates the effective value of parameter X in terms of quality and WqX is quality weight. This coefficient depends on the water
Fig. 2. Parameters of efficiency based on water balance equation (Eq. 1). 3
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generally important for the operators of a water resource (for example, it is important for a farmer).
compound and the path through which the water passes and determined by the frameworks and indices of the water quality in each region. WqX is calculated based on downstream conditions. For example, when salt-resistant crops are cultivated in downstream lands, WqX is higher and when the salt-sensitive crops are cultivated, it decreases. Xb shows the beneficial part of the parameter X and WbX is beneficial weight defined according to the purpose of the project and divides the water consumption into two beneficial and non-beneficial parts. The value of the beneficial weight is obtained by dividing the beneficial part of a parameter by the total value of that parameter. Haie and Keller (2011) were estimated the coefficient of Wb and Wq for a number of parameters, however, the method of calculation was not presented. In the study of irrigation efficiency based on the water balance in the irrigation and drainage networks, the accurate estimation of WbX and WqX is of great importance. Since geographic scale may affect the estimation of efficiency, three levels of macro, meso, and micro were considered as the levels of Sefficiency estimation and analysis (Haie and Keller, 2011). Macro Sefficiency (Eq. 5) shows the relationship between the useful outflow and total inflow to a water resource (such as a river). At the macro level the water resource may have several outlets or branches. For example, a river can have several outlets for different irrigation networks (Fig. 3). The macro Sefficiency (MacroSE) is generally used for managers and authorities to decide on the water allocation and transfer (Eq. 5).
Macro SE = [
ET + NR + i (VD + RP ) ]s VU + OS + PP − c (VD + RP )
Micro SE = [
ET + NR + i (RF + RP ) ]s VA + OS + PP − c (RF + RP )
(7)
All three levels of efficiency analysis are necessary for the management of the water resources. This may lead to multi-objective planning and multi-purpose management by managers and operators (). According to the definition of efficiency levels, Moghan irrigation and drainage network is considered as a meso level (Fig. 3). 2.3. Data collection and calculation of parameters In order to calculate irrigation efficiency of the Moghan irrigation and drainage network, all parameters of Eq. 6 and the quality and beneficial coefficients associated with the parameters were calculated. For this purpose, the data related to the cropping pattern (Table 2), water and soil quality of the region, characteristics and dimensions of the water conveyance canals, inflow rate into the network, outflow rate from the artificial and natural drainages and meteorological data were collected. Using the meteorological data and ETo calculator software (Raes, 2009), the reference evapotranspiration of the area was calculated and the monthly evapotranspiration of each crop was determined according to the Eq. (8) (Allen et al., 1998):
ET = ETo*K c
(5)
(8)
in which, ET is the crop evapotranspiration (mm), ETo is the reference evapotranspiration (mm), and Kc (-) is the crop coefficient. As mentioned before, in order to calculate the coefficient Wb (-), the beneficial and non- beneficial parts of the parameter must be determined. The beneficial part of the ET is the transpiration and nonbeneficial part is evaporation. According to the Eq. (9), the transpiration rate of different crops was calculated, and the evaporation rate was derived by subtracting the transpiration from the evapotranspiration on a monthly basis:
Meso Sefficiency (Eq. 6) is the relation between the useful outflow and the total inflow within one of the branches in the macro level, such as an irrigation network that abstracted the required water from a river (Fig. 3). Meso Sefficiency (MesoSE) is commonly used by managers and authorities to operate a part of the water resource.
Meso SE = [
ET + NR ]s VA + OS + PP
(6)
Micro Sefficiency (Eq. 7) is to examine the relationship between the useful outflow and the total inflow within one of the branches in the meso level (for example, a farm or a part of the irrigation network) (Fig. 3). In fact, the micro Sefficiency (MicroSE) is the calculation of efficiency in a field, regardless of the return flows. This efficiency is
Tc = K cb*ETo
(9)
where, Tc (mm) is transpiration and Kcb (-) is the basal crop coefficient due to the transpiration. The amount of crop coefficients, basal coefficients and growth
Fig. 3. Efficiency at three levels of macro, meso, and micro in the study area. 4
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Table 2 Cropping pattern in the study area. Cultivation area (ha)
Crop
Cultivation area (ha)
Crop
Cultivation area (ha)
Crop
34183 2653 2108
Wheat Sugar beet Forage maize
2118 531 2297
cotton Soybean (first cultivation) Watermelon
2467 1888 6438
3863 1576 1921
Rapeseed tomato Nectarine
16543 538 2687
Soybean (second cultivation) Olive Other fruit gardens
8198 1081 153
Barley Maize Maize (second cultivation) Alfalfa Almond Other crops
period of each crop were extracted from FAO Report No. 56 (Allen et al., 1998) and adjusted with the study area. With summation of evapotranspiration rates of various crops based on the value of cultivation area, evapotranspiration and transpiration of the study area were calculated regionally. In addition, the evaporation rate was calculated by subtracting the transpiration from the evapotranspiration rate of the study area. The coefficient WbET for the study area is derived from Eq. (10):
WbET =
Tc ET
WbVA =
(10)
P (125 − 0.02P ) 125
Ya b = 1 − (ECe − ECethreshold ) Ym 100
(11)
Peff (12)
PP
(14)
where, Ya is the actual crop yield (kg), Ym is the maximum expected crop yield (kg) for ECe < ECethreshold, b is the gradient of the reduction in the crop yield (%), for increasing 1 dS m−1electrical conductivity of the soil saturated extract, ECe is the soil saturated extract salinity, which is twice the salinity of the inflow water into the area (dS. m−1), ECethreshold is the threshold soil saturated extract. The values of ECethreshold and b were extracted from the FAO Technical Reports No. 29 and 48 (FAO, 1992, 1989) for the crops of the study area. According to the efficiency level in this study, the outflow (V2) is denoted by RF which consists of the return flow to the Aras River through the artificial drainage network and natural drainage and also the recycle of a part of drained water in Moghan irrigation and drainage network. Data related to the outflow of artificial drainage and their monthly salinity and also the amount of natural drainage of the area (average of it is 0.065 L/s/ha) were obtained from the regional water authority of Ardabil province (Table 3). There was not recycling of drainage water in 2015–2016. By entering the return flow (drainage water) to the Aras River, the water quality for downstream users is changed, and these changes indicate the usefulness of the return flow in terms of water quality. Considering the cropping pattern at the downstream, the relative crop yield due to the reduction in the water salinity of the Aras River was obtained using Eq. (14) and considered as WqRF. Therefore, WqRF is calculated based on the downstream conditions. When salt-resistant crops are cultivated in downstream lands, WqRF is higher (in other words, return flow is useful) and when the salt-sensitive crops are cultivated, it decreases (in other words, return flow isn’t useful). The salinity of natural drainage was considered equivalent to average salinity of underground drains (4.7 dS m−1) and the salinity of the artificial drainage at the entrance to the river is 2.9 dS m−1 (Regional Water
where Peff is the effective rainfall (mm) and P is the monthly rainfall (mm). Peff and PP was calculated regionally based on the values of cultivation area related to various crops and the coefficient WbPP was determined from Eq. (12):
WbPP =
(13)
The coefficient WqVA was calculated proportional to the reduction in crop yield due to the water salinity. The reduction in crop yield due to the water salinity was calculated for each crop using Eq. (14). WqVA was considered equivalent to the relative crop yield expected under ECe > ECethreshold for each crop. The river salinity has little fluctuations in different months of the year and its average salinity was 1.12 dS m−1. By weighting the WqVA related to each crop based on the values of cultivation area, the WqVA was obtained regionally for the studied area.
Due to the nature of the evapotranspiration, and since the water transpiration due to the plant and the evaporation from the soil surface enter the atmosphere, the coefficient WqET (-) is considered as one. The value of daily rainfall in the study area was obtained from Parsabad Meteorological Station. Due to the good quality of the rainwater, its WqPP value was considered one. The beneficial part of the rainfall is directly consumed by the plant and it provides a portion of the required water, and the rest is considered to be non- beneficial part of the rainfall. The beneficial part of the rainfall is in fact the effective rainfall. Hence, the monthly effective rainfall was determined for each plant using the USDA’s method (Dastane, 1974) in months when the plant requires water (Eq. 11):
Peff =
VF VA
The study area is defined at the meso level (Fig. 3); hence, the inflow is denoted by VA (m3 ). The amount of VA is the volume of water entering the Moghan irrigation and drainage network for agricultural use. Data related to the VA and the volume of water delivered to the farmers’ fields (VF) (m3 ) was obtained monthly for 2015–2016 from the regional water authority of Ardabil province (Table 3). In order to calculate the coefficient WbVA, the beneficial part is the water delivered to the farmers’ fields for irrigation and the non-beneficial part is the water losses in the transfer path. The WbVA was calculated using Eq. (13):
Table 3 Amount of allocated water (VA) and delivered water (VF) in Moghan irrigation and drainage network and return flow to the Aras River through natural and artificial drains in 2015–2016 (1000 m3). Month
October
November
March
April
May
June
July
August
September
Allocated Water Delivered water Artificial drainage Natural drainage
45520 43120 15860 12131
16330 14750 9940 12131
46020 41200 12730 11726
119330 106500 22060 12535
139690 127740 18990 12535
125530 115190 17600 12535
156500 139990 12690 12535
179040 159950 11700 12535
83030 74860 10210 12535
5
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Fig. 4. A flowchart of the proposed methodology for the calculating Sefficiency.
where, Qr is the Aras River flow rate (m3.s−1), QRF is the return flow rate (m3.s−1) (including discharge of artificial and natural drainages), ECr is the water salinity of Aras river (dS. m−1), ECRF is the water salinity of the return flow (dS. m−1) (artificial and natural drainages) and EC* is the water salinity of Aras River after entering the return flows (dS. m−1). It should be noted since downstream lands are in
Authority of Ardabil Province, 2015). By using Eq. (15) and assuming rapid mixing (Sharma and Ahmad, 2014), the changes in the salinity of the river after entering the return flow (drainage water) were estimated, as follows:
EC * =
(Qr . ECr ) + (QRF . ECRF ) Qr + QRF
(15) 6
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Azerbaijan and no data was available in this region, it was assumed that the cropping pattern of downstream lands is the same as those of upstream. It was also assumed that the return flow including artificial and natural drainages had no loss until reaching the Aras River. Therefore, the coefficient WbRF was considered equal to 1. There are three types of non-reusable flow (NR) in the study area, including evaporation from the water surface of canals, deep percolation and evaporation from the soil surface. The evaporation rate from the canals was calculated according to Eq. (16) (Allen et al., 1998):
Ec = K (Epan )
Ie =
ECw 5ECe − ECw
Since the calculation of physical water productivity (WP) is desirable in this research, the average yield for each crop (Ya ) was obtained from Organization of Agriculture Jihad of Ardabil province. Then, based on the calculated efficiency and gross irrigation requirement, WP was calculated at the meso and micro levels based on Eq. (20) and compared with calculated WP according to conventional method by taking into account the classical efficiency in calculating the gross irrigation requirement.
(16)
WP= Ya/(
LR DP
ET − Peff Ie / MicroSE /MesoSE
)
(20)
3. Results and discussion 3.1. Efficiencies The values related to the parameters of the water balance equation, the associated coefficients of Wq and Wb and the useful part of each parameter are given in Table 4. The coefficient WqVA had different values in different months, because of the difference between crops cultivated in different months (Table 4). The WqVA was equal to 1 in November and March; this is due to the fact that during these months, only wheat and barley were cultivated on the farm land, whose salinity threshold was higher than that of the inflow. While in other months, this coefficient was less than one, due to the cultivation of crops whose salinity thresholds were lower than the salinity of irrigation water, therefore crop yield was less than its potential, and the coefficient WqVA decreased. Regarding coefficient WbVA, the difference among the values in the various months indicated the difference in the volume of inflow rate and the water allocated to the farmers. In October, 95 % of the inflow rate was allocated to the farmers, and there were 5 % losses, while coefficient WbVA reached 89 % in March (11 % losses). The high value of WbVA indicated the proper water allocation to the network according to the needs of the operators (on-demand allocation of irrigation water). About the coefficient WbPP, increasing this coefficient indicates that more rainfall is used. The low values in some months are due to the low level of cultivation in that month; this coefficient in June is equivalent to 0.96 and in November is 0.53, while in the March all crops are on the farm land and use the rainfall, while in November only autumn crops are on the farm land. As a result, the major part of precipitation in this month is non-beneficial for our purpose (irrigation). The low value of WbET associated with the ET in November is due to the low cultivation area in this month and low air temperature. The reason for high rate of this coefficient in September is high cultivation area and high air temperature and consequently the transpiration rate is increased. Since the main purpose in the irrigation and drainage network is to irrigate the farms and produce the crops, the evaporation from the soil surface and irrigation canals is non-beneficial; therefore, the coefficient Wb in these two components will be zero. In other words, the evaporation from the soil surface and irrigation canals was non-beneficial. The coefficient WbDP was calculated based on the leaching requirement. In the months with high cultivation area, leaching requirement is increased and a greater amount of DP is used for leaching and as a result, the coefficient WbDP is increased. The highest value of this coefficient is occurred in September, which all crops are on the farm land, and the smallest is occurred in November, which the wheat and barley are just cultivated.
(17)
in which, LR is leaching requirement ratio (no dimension), and ECw is the irrigation water salinity (dS. m−1). The LR was obtained regionally for the studied area and WbDP was calculated based on Eq. (18).
WbDP =
(19)
VA
2.5. Water productivity
where, Ec is the evaporation from the water surface, Epan is the evaporation from the surface of the pan, and K is the pan coefficient. Since irrigation of the lands is carried out only through irrigation networks, and groundwater does not interfere with the irrigation of the land, therefore, the flowing water percolated deeply out of the area is considered as non-reusable flow. Deep percolation (DP) rate was calculated based on the water balance equation in the study area. Evaporation from the soil surface was also estimated by subtracting the transpiration from evapotranspiration. Due to the fact that the purpose of the project is to produce the crop, the evaporation from the canals and the soil surface were considered to be completely non-beneficial and the coefficient Wb for them were considered to be zero. Regarding the amount of water percolated deeply from the area, a part of deep percolation which leaches the soil profile was considered beneficial, the rest was non-beneficial. Eq. (17) was used to calculate the leaching water (Rhoades and Merrill, 1976).
LR =
ET − Peff
(18)
For evaporation from the canal and soil surfaces, the quality will not be affected and therefore, Wq coefficient for them was considered to be one. Due to high quality of water entering the study area, the coefficient WqDP was also considered to be one. In order to facilitate the calculation, the area was considered as a separated region. Therefore, the value of OS was zero. In addition, in the study area, RP can be the volume of water stored in the soil storage capacity at the end of a period and can be used in the next period. For the following period, this flow can be considered as a flow from other sources (OS). Since the calculation of efficiency is based on a water balance, if some water remains in the soil capacity at the end of the year 2014–2015, it can be considered as OS for the years 2015–2016. On the other hand, the amount of water remaining at the end of the year 2015–2016 is considered as RP. Since these two parameters will be equal and deducted from each other in the calculation of efficiency (based on Eq. 6), their calculation will not change in the efficiency. Therefore, in the research, the values of this flow were considered to be zero. Fig. 4 presents a flowchart of the proposed methodology for computing Sefficiency and different water balance parameters and their quality and beneficial coefficients.
2.4. Classical efficiency In order to better understand the difference in the calculation of the efficiency, the classical efficiency (Ie ) was calculated according to the Eq. (19) and was compared with the Sefficiency at the meso and micro levels. 7
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Table 4 Value of parameters of the water balance equation (1000 m3) and the quality and beneficial coefficients associated with them. Flow type
Parameter
October
November
March
April
May
June
July
August
September
VA
value Wq Wb Usefulness coefficient Useful part value Wq Wb Usefulness coefficient Useful part value Wq Wb Usefulness coefficient Useful part value Wq Wb Usefulness coefficient Useful part value Wq Wb Usefulness coefficient Useful part value Wq Wb Usefulness coefficient Useful part value Wq Wb Usefulness coefficient Useful part value Wq Wb Usefulness coefficient Useful part
45520 0.97 0.95 0.92
16330 1.00 0.90 0.90
46020 1.00 0.90 0.90
119330 0.98 0.89 0.87
139690 0.98 0.91 0.89
125530 0.98 0.92 0.90
156500 0.97 0.89 0.86
179040 0.93 0.89 0.83
83030 0.97 0.90 0.87
41947 13250 1.00 0.58 0.58
14697 25598 1.00 0.53 0.53
41418 2328 1.00 0.63 0.63
104080 2253 1.00 0.86 0.86
124576 22465 1.00 0.90 0.90
113178 11352 1.00 0.96 0.96
135106 2321 1.00 0.72 0.72
148191 1911 1.00 0.72 0.72
72485 12512 1.00 0.66 0.66
7685 21490 1.00 0.88 0.88
13567 8591 1.00 0.29 0.29
1467 18619 1.00 0.84 0.84
1938 42568 1.00 0.90 0.90
20219 76231 1.00 0.91 0.91
10898 69930 1.00 0.88 0.88
1671 78363 1.00 0.86 0.86
1376 81890 1.00 0.94 0.94
8258 56540 1.00 0.95 0.95
18911 2669 1.00 0.00 0.00
2491 6077 1.00 0.00 0.00
15640 2986 1.00 0.00 0.00
38311 4164 1.00 0.00 0.00
69370 6781 1.00 0.00 0.00
61538 8064 1.00 0.00 0.00
67392 10756 1.00 0.00 0.00
76977 4962 1.00 0.00 0.00
53713 3107 1.00 0.00 0.00
0.00 392 1.00 0.00 0.00
0.00 199 1.00 0.00 0.00
0.00 276 1.00 0.00 0.00
0.00 400 1.00 0.00 0.00
0.00 623 1.00 0.00 0.00
0.00 908 1.00 0.00 0.00
0.00 1271 1.00 0.00 0.00
0.00 1384 1.00 0.00 0.00
0.00 716 1.00 0.00 0.00
0.00 8897 1.00 0.28 0.28
0.00 11068 1.00 0.03 0.03
0.00 4996 1.00 0.12 0.12
0.00 44020 1.00 0.05 0.05
0.00 53776 1.00 0.1 0.1
0.00 35909 1.00 0.21 0.21
0.00 53963 1.00 0.22 0.22
0.00 73443 1.00 0.14 0.14
0.00 15541 1.00 0.45 0.45
2491 15860 0.88 1.00 0.88
332 9940 1.00 1.00 1.00
600 12730 1.00 1.00 1.00
2201 22060 0.90 1.00 0.90
5378 18990 0.90 1.00 0.90
7541 17600 0.91 1.00 0.91
11872 12690 0.88 1.00 0.88
10282 11700 0.85 1.00 0.85
6993 10210 0.90 1.00 0.90
13957 12131 0.88 1.00 0.88
9940 12131 1.00 1.00 1.00
12730 11726 1.00 1.00 1.00
19854 12535 0.90 1.00 0.90
17091 12535 0.90 1.00 0.90
16016 12535 0.91 1.00 0.91
11167 12353 0.88 1.00 0.88
9945 12535 0.85 1.00 0.85
9189 12535 0.90 1.00 0.90
10675
12131
11726
11282
11282
11407
11031
10655
11282
PP
ET
NR
Evaporation
Evaporation from the channel
Deep percolation
RF
RF to the Aras River through the artificial drainage network
RF to the Aras River through the natural drainage network
VA: Abstracted water from the main source, PP: Total rainfall, ET: Evapotranspiration, NR: Non-Reusable, water consumption, RF: Return Flows, Wq: quality coefficient, Wb: beneficial coefficient.
The coefficient WqRF in different months has different values, because of the difference in cultivation area in different months and crop response to the salinity. In November and December, this coefficient is equal to one (i.e. during these months all water has good quality for consumers). The reason is that in these two months wheat and barley are cultivated, in which, the salinity threshold are higher than the salinity threshold for irrigation water (mixing the return water with river water). However, in August, the lowest value of WqRF is occurred, which is due to the cultivation of summer crops (salinity thresholds below the salinity of the irrigation water). Table 5 shows the percent of usefulness of each parameter per year (in the years 2015–2016). As mentioned before, the usefulness of each parameter indicates how the parameter was useful in the study area in terms of water quality and beneficially. The highest amount of usefulness in the study area (at the meso level) is related to the RF and the lowest is related to the NR (Table 6). Our results demonstrated that about 87 % of inflow, 91 % of the rainfall, 89 % of the ET, 13 % of the NR, and 91 % of the RF were useful in the study area.
Table 5 The value and usefulness of water balance parameters per year (1000 m3). Parameter
VA
PP
ET
NR
RF
Value Useful part Percentage of usefulness
910990 795678 87
93990 67079 91
454222 404343 89
357348 47690 13
242796 221360 91
VA: Abstracted water from the main source, PP: Total Precipitation, ET: Evapotranspiration, NR: Non-Reusable, water consumption, RF: Return Flows.
The highest and the lowest efficiencies are respectively occurred in September and November (Fig. 5). The average Sefficiency at the meso level in the study area is 72 %. High efficiency in some months like September is due to the proper water allocation. This means that in months with high water efficiency, the water is allocated to the needs of the area, but in the months with lower water efficiency, more water is allocated than the water needs of the area. In addition, in the months with high rainfall, this is more evident, because a part of the needs for 8
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Table 6 Water productivity at different concepts of efficiency. Gross irrigation requirement
Productivity
Crop
ET Meso Sefficiency Cubic meters per hectare
Micro Sefficiency
Classical efficiency
Meso Sefficiency Micro Sefficiency Kilograms to cubic meters
Classical efficiency
Wheat Cotton Barley Sugar beet Soybean (first cultivation) Maize Forage corn Watermelon Forage corn (second cultivation) Rapeseed Soybean (second cultivation) olive Almond tomato Nectarine alfalfa
4650 7170 3760 8980 7140 7520 6200 3690 4270 3650 4540 5690 7020 6630 7250 8580
9800 15090 7913 18901 15030 15831 13056 7778 8985 7686 9561 13881 14774 13963 15258 18057
12282 18913 9917 23689 18837 19841 16363 9748 11261 9633 11983 17397 18516 17500 19123 22631
0.58 0.24 0.51 4.30 0.22 0.65 4.28 6.07 6.21 0.48 0.35 0.13 0.17 3.65 1.12 0.71
0.30 0.13 0.27 2.26 0.12 0.34 2.25 3.20 3.27 0.25 0.18 0.07 0.09 1.92 0.59 0.38
6465 9956 5220 12469 9916 10444 8614 5131 5928 5071 6307 9157 9747 9212 10066 11913
0.38 0.16 0.34 2.84 0.15 0.43 2.82 4.01 4.10 0.32 0.23 0.09 0.11 2.41 0.74 0.47
than the micro and meso Sefficiencies. According to the results of Keller and Keller (1995) in the Grand Valley at the downstream of the Colorado River and Egypt's Nile Valley Irrigation System, the reason for the difference between classical, micro and meso Sefficiencies in the region is the existence of the return flows. Of course, the difference between the classical efficiency and meso Sefficiency is greater than the difference between the classical efficiency and micro Sefficiency, due to the presence of the return flows in the meso level. 3.2. Water productivity The water productivity at the different concepts of efficiencyare presented in Table 6. The maximum and minimum values of WP were obtained for meso Sefficiency and the classical efficiency, respectively. According to the special situations of the Aras River in terms of geographical location, micro Sefficiency will be representative efficiency for the study area. Therefore, WP at the micro level in the study area represents the water productivity of the region. The reason of higher WP at the micro Sefficiency compared to the classical efficiency is that in classical efficiency all losses of the system are considered as non-reusable losses, while in the micro Sefficiency the entire non-reusable flow, which includes losses of the system, is not considered as losses, and the portion required for leaching is deducted from the system losses.
Fig. 5. Meso and micro Sefficiencies and classical efficiency in different irrigation months (in percent).
water is supplied by rainfall and it is not necessary to allocate the water. The highest and the lowest values of micro Sefficiency are occurred on September and November, respectively. The average micro Sefficiency of the irrigation network is 47.5 %. The reduction in the efficiency, especially in the seasons where there is a lower water requirement, is clearly evident; the reason for sharp reduction in the efficiency in November is low water requirement for irrigation in the region that could be compensated by effective rainfall (Table 4); however, for mismanagement reasons (the water allocation to the area without considering the values of irrigation water requirement and effective rainfall), water enters to the network and the volume of drainage water increases in this month. Since the volume of outflow in terms of the study’s level (micro level) is not useful, the efficiency decreases sharply, while in high consumption months such as September due to the high water requirement in the area and the low effective rainfall, the volume of drainage water decreases. As a result, there will be a lower difference between the micro and meso Sefficiencies. The difference between the classical efficiency, meso and micro Sefficiencies are presented in Fig. 5. The highest and lowest classical efficiency is related to September and November, respectively. The average classical efficiency is obtained as 37.9 %. The reason for zero value of the classical efficiency in November is high effective rainfall in this month which supplies all water requirements, while water is allocated to the network in this month due to mismanagement reasons. As seen, there is a large difference between the classical and micro and meso Sefficiencies, especially in the months with high water consumption. In all months, the classical efficiency shows a lower amount
4. Conclusion Considering the assessment level of the efficiency in management decisions is very important. The Aras River is an International Border River and is exited out of the country after the arrival of return flows, and so the return flows are practically non-useful for the country. Therefore, micro Sefficiency is the representative efficiency of the study area, and making management decisions is necessary in the region based on micro Sefficiency to increase the efficiency. It can be noted that if the representative efficiency of the area is considered as the meso Sefficiency, the non-reusable flow needs to be reduced. However, if the micro Sefficiency is considered as the region's representative efficiency, it is necessary to avoid the return flow losses out of the area. This suggests that the correct calculation of the efficiency in the region can affect management decisions. The effect of the return flow quality on the efficiency calculations represents itself with WqRF coefficient. The higher the coefficient WqRF, the greater usefulness of the return flow. In the study area, the coefficient WqRF was calculated on a monthly basis. The difference in the 9
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value of coefficient WqRF in different months is due to the different cropping pattern at the downstream. For example, in November and March, the coefficient WqRF is equal to one, due to the cultivation of salt-resistant crops with higher salinity thresholds than the return flow salinity. While in the following months, the coefficient WqRF decreased due to the cultivation of crops with lower salinity thresholds. If some crops are cultivated in downstream reaches with more salinity thresholds than the salinity of return flow, the coefficient WqRF and consequently the efficiency will increase. The average classical efficiency in the Moghan irrigation and drainage network was 37.9 percent. Furthermore, the meso and micro Sefficiencies were obtained as 72.0 and 47.5 percent, respectively. The reason for the high meso Sefficiency compared to micro Sefficiency and the classical efficiency is the lack of considering the return flows as losses. In other words, in meso Sefficiency, the return flow is considered to be a useful outflow, hence, water loss is reduced and efficiency is increased. The reason for high micro Sefficiency as compared to the classical efficiency is the lack of taking into account the leaching requirement as losses in micro Sefficiency. In other words, a part of deep percolation used for leaching is considered as a useful outflow of the system (increasing the efficiency), while in the classical efficiency, this part of the losses is considered non-useful. Considering the assessment level of the water productivity in management decisions is very important. Since the micro Sefficiency is a representative efficiency of the region, the water productivity at the micro level can be considered as water productivity in the study area. Therefore, water productivity at the micro level is the representative water productivity of the study area, and making management decisions is necessary in the region based on it. This recommends that the correct calculation of the efficiency and consequently, water productivity in the region can affect management decisions. Also, uncertainty and sensitivity analyses of input data for calculating the Sefficiency at different levels (macro, meso, and micro) are recommend to be considered in future studies.
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Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.agwat.2020.106025. References Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop Evapotranspiration. Guidelines
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