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Border irrigation performance with distance-based cut-off
Agricultural Water Management 201 (2018) 27–37
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Border irrigation performance with distance-based cut-off ⁎
T
Mohamed Khaled Salahou , Xiyun Jiao, Haishen Lü State Key Laboratory of Hydrology–Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Surface irrigation Border irrigation Inflow rate Cut-off ratio Irrigation performance
Border irrigation is widely practised for winter wheat production on the North China Plain. Winter wheat is mainly irrigated with groundwater as a supplement to insufficient precipitation to maintain high agricultural production. As a result of the increased demands for water, groundwater levels have declined. Therefore, improvements to border irrigation performance and water use efficiency are urgently needed. The objective of this study was to determine the optimal distance at which to cut off inflow under different inflow rate conditions in closed-ended border systems. The experimental treatments included three inflow rates (high, moderate, and low, with average rates of 6.91 l s−1 m−1, 4.95 l s−1 m−1, and 2.81 l s−1 m−1, respectively) and three cut-off ratios (CRs) arranged in three replications at the CAS Ecological Agricultural Experiment Station in Nanpi, Hebei Province, China. The surface irrigation hydraulic simulation model WinSRFR was used to examine the sensitivity of the existing design to a range of bottom slopes, surface roughness values, and inflow rates to demonstrate the robustness of the solutions in terms of their application efficiency and low-quarter distribution uniformity. The results present the optimum CR values for different inflow rate conditions to maximize irrigation performance. The results indicate that irrigation performance above the optimum CR values for high, moderate, and low inflow rates is not very sensitive to bottom slope, and no substantial changes in performance were noted when Manning’s roughness coefficient was between 0.04 and 0.09. A set of inflow rate ranges that corresponds to the recommended CRs that could achieve high irrigation performance is presented.
1. Introduction Winter wheat is one of the two major crops grown in Hebei Province, China. Irrigation with groundwater is necessary for maintaining sustainable crop production because less than 30% of precipitation falls in the winter, which is much lower than the water requirements of winter wheat (Lv et al., 2013). Today, approximately 70% of the water resources extracted for agriculture (of which approximately 70% are used for wheat irrigation) are pumped from groundwater in Hebei Province on the North China Plain (Lv et al., 2013). Groundwater levels have declined as a result of climate change, human activities, and increasing demands for water, resulting in the formation of perennial shallow groundwater depression cones over a wide area in Hebei Province. Therefore, the development of an irrigation system to increase the efficiency of irrigation water use and to prevent further over-exploitation of groundwater is urgently needed. Border irrigation is widely practised for wheat and corn production on the North China Plain and is the most common irrigation method in this region, relying on gravity and the field surface to distribute water within a field (Morris et al., 2015). Assessing the performance of border irrigation requires knowledge
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of the management methods used by farmers and system variable measures (i.e., border dimensions (length and width), inflow rate (Q), slope (S), Manning’s roughness coefficient (n), cut-off time, and soil infiltration properties) (Pereira et al., 2002). Researchers have achieved improved border irrigation performance by optimizing border dimensions (Anwar et al., 2016; Chen et al., 2012), adopting appropriate inflow rates and cut-off times (Morris et al., 2015), improving inflow rate, and measuring the field elevations carefully (Bautista et al., 2009a). Additionally, Manning’s roughness coefficient has been shown to affect the performance of border irrigation (Bautista et al., 2002); therefore, the optimization of the design and/or operation of a border irrigation system needs to account for the potential range of infiltration and Manning’s roughness coefficient conditions that could be encountered (Bautista et al., 2009a). Zhang et al. (2006), Bautista et al. (2009a), and Anwar et al. (2016) recommended field roughness coefficients of 0.02–0.40, 0.04–0.10, and 0.04–0.16, respectively. Border irrigation performance depends greatly on the cut-off time. In a well-designed and operated system, the volume of applied water is precisely controlled. Thus, for a given inflow rate, the cut-off time is determined from the prescribed application depth. However, the inflow
Corresponding author. E-mail addresses: [email protected] (M.K. Salahou), [email protected] (X. Jiao), [email protected] (H. Lü).
https://doi.org/10.1016/j.agwat.2018.01.014 Received 13 November 2016; Received in revised form 8 January 2018; Accepted 14 January 2018 0378-3774/ © 2018 Elsevier B.V. All rights reserved.
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Fig. 1. Longitudinal slopes and ordinary least squares regression equations of (a) border 8, (b) border 12, and (c) border 20.
based cut-off criterion may also help compensate for uncertainties in other field variables, such as slope, roughness, and infiltration. The recommended CRs for open-ended border irrigation with loamy sand soil and lengths of 200 and 400 m were 0.77 and 0.85, respectively (Mailhol and Merot, 2007), and the recommended CR for block-end furrow irrigation with loamy soil and moderate slope (0.005 m m−1) was 0.89 (Cahoon et al., 1995). Previous studies have addressed the problem of cut-off time in level basins, open-ended borders, or furrow systems. However, the longitudinal slope (graded basin or border) has significant effects on cut-off time and irrigation performance (González et al., 2011). Additionally, closed-ended border irrigation systems currently prevail on the North China Plain, and the most popular irrigation cut-off method adopted by farmers is to stop irrigation when the water reaches the lower end of the field (CR = 1). Under this method, the irrigation water often reaches depths in excess of the required depth (Pereira et al., 2002), and water accumulates at the end of the field, significantly increasing the overflow
is not measured routinely except at the farm turnout. Additionally, under this method, the water overflow may occur at the end of the field or the water may not reach the end of the field, particularly in level basins; thus, in many counties, the adopted cut-off time in level basins is at the point of completion of the advance phase (Clyma and Ali, 1977; Wattenburger and Clyma, 1989). In level basins, this approach is more robust than the limiting length approach, particularly when a farmer has poor control over the water (Reddy et al., 2013). Clemmens (1998) presented a new procedure for the design of level basins based on cutoff time and concluded that the cut-off distance could be adjusted depending on the Manning roughness coefficient. For the previous reasons, instead of determining the cut-off time from the application depth, the researchers adopted the cut-off ratio (CR), defined as the ratio of the advance distance at cut-off to the total field length. The distance-based cut-off criterion can help compensate for inflow uncertainties where the inflow can be expected to vary but cannot be readily measured. Furthermore, to some degree, the distance28
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Fig. 2. Measured (dots) and simulated (lines) advance and recession curves of (a) border 2, (b) border 15, and (c) border 20.
2. Materials and methods
risk. Furthermore, some crops cannot tolerate excessive ponding. Hence, the objectives of this study were as follows: (1) to determine the optimal distance at which to cut off inflow under different inflow rate conditions in closed-ended border systems in the North China Plain (three inflow rates in three blocks, considering the infiltration values for each border); (2) to establish different scenarios incorporating different slopes, n values, and inflow rates to assess the sensitivity ranges of these variables on the irrigation performance when optimum cut-off distance values are used; and (3) to provide recommendations for improving the performance of border irrigation systems on the North China Plain.
2.1. Field measurements 2.1.1. Study area Field studies were conducted at the CAS Ecological Agricultural Experiment Station in the town of Nanpi in Hebei Province, China, at a longitude, latitude, and elevation of 116°40′E, 38°06′N and 20 m, respectively. This area is located in a monsoon climate zone with an annual evaporation range between 1500 and 1800 mm. The mean annual precipitation and temperature at the study site are approximately 567.4 mm and 12.3 °C, respectively. Rainfall generally occurs during the summer, with 73%, 11%, 13%, and 3% of the annual precipitation 29
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Table 1 Inflow rate, average slope, Manning’s roughness, inflow duration, advance time, CR, infiltrated volume, and KE equation coefficients for each irrigated border. Block
Border number
Average slope
Average inflow rate (l s−1/m)
Average inflow rate in each block (l s−1/m)
Inflow duration (min)
Advance duration (min)
CR
Kostiakov coefficient a
k mm/hra
Manning coefficient n
Infiltrated volume (m3)
A
1 2 3 4 5 6 7 8 9
0.0023 0.0014 0.0022 0.0023 0.0016 0.0015 0.0011 0.0017 0.0023
6.88 6.87 7.08 6.91 6.85 6.78 6.87 6.84 7.10
6.91
18.80 18.80 17.10 20.70 21.00 22.40 22.80 23.00 23.50
27.32 27.03 24.27 25.93 26.65 25.63 26.13 26.67 27.08
0.8 0.8 0.8 0.85 0.85 0.9 0.9 0.9 0.9
– 0.77 0.77 0.77 0.77 0.77 0.77 0.77 0.77
– 143.541 136.168 163.205 134.902 140.11 147.039 166.224 166.224
– 0.09 0.09 0.09 0.09 0.095 0.09 0.09 0.095
28.71 28.67 26.88 31.75 31.93 33.72 34.77 34.93 37.04
B
10 11 12 13 14 15 16 17 18
0.0022 0.0023 0.0023 0.0024 0.0025 0.0022 0.0014 0.0024 0.0027
4.87 4.82 4.84 5.00 4.93 4.98 5.06 5.13 4.90
4.95
21.20 24.80 25.00 26.20 23.70 20.50 24.10 24.60 23.10
28.92 32.7 31.25 30.5 29.3 26.02 27.58 27.83 26.97
0.8 0.8 0.8 0.85 0.85 0.85 0.9 0.9 0.9
0.68 0.68 0.68 0.68 0.68 0.68 0.68 0.68 0.68
104.607 134.966 130.123 130.067 122.236 111.16 122.771 119.34 110.402
0.095 0.06 0.065 0.065 0.06 0.06 0.06 0.075 0.06
22.92 26.54 26.86 29.08 25.94 22.66 27.07 28.02 25.13
C
19 20 21 22 23 24 25 26 27
0.0027 0.0027 0.0026 0.0029 0.003 0.003 0.0029 0.0029 0.0025
2.78 2.77 2.73 2.84 2.78 – 2.88 2.85 2.84
2.81
42.40 38.70 44.70 50.50 43.90 – 51.30 48.10 47.00
48.23 43.9 51.83 52.67 46.47 – 51.33 48.13 47
0.9 0.9 0.9 0.95 0.95 0.95 1 1 1
0.57 0.57 0.57 0.57 0.57 – 0.57 0.57 0.57
90.835 83.729 92.39 99.226 92.4 – 100.717 95.337 92.962
0.06 0.06 0.06 0.06 0.06 – 0.06 0.06 0.06
26.17 23.80 27.09 31.84 27.09 – 32.80 30.43 29.63
available at a site, farmers can extend the border width without affecting their irrigation practices. However, smaller water supplies are available at most sites such that farmers limit the border widths to accelerate surface water flow and to accomplish each irrigation event quickly. Therefore, the border applied in the experimental field measured 100 m in length and 3.7 m in width, similar to the border dimensions used by local farmers. The winter wheat is irrigated two times per season in the study area (pre-sowing and spring irrigation), the experiment was conducted during the spring irrigation event, and the required application water depth was 60 mm (Jiao and Wang, 2012). 2.1.3. Inflow The inflow rate plays a principal role in irrigation performance, and its values greatly depend on the water source and pump performance during irrigation. In practise, the amount of discharge supplied varies among the sites in the study area, with averages of 6.91 l s−1 m−1, 4.95 l s−1 m−1, and 2.81 l s−1 m−1. The inflows for all the irrigation events were set at these values. These inflow rates and different CRs were investigated to determine the optimal values to achieve high irrigation performance. The CRs for both of the first two inflow rates were 0.8, 0.85, and 0.90. However, because the last inflow rate was low, the CRs reached 0.90, 0.95, and 1.00, respectively. Generally, each (Q-CR) combination was replicated three times. It was assumed that only one border was irrigated at a time, that the full amount of inflow through the service point was applied to that border, and that the buffering capacity of the farm channel removed most of the short-term variation. Consequently, the time-averaged inflow rate was used in all the evaluations.
Fig. 3. Average inflow rates in blocks A, B, and C. The vertical bars indicate the standard deviations of the inflow rate; for some points, the vertical bars are too small to be seen.
occurring during the summer, spring, autumn, and winter, respectively. A field was planted with winter wheat on October 15, 2014, and was harvested in the middle of June 2015. The soil at the site is classified as silt loam (67.02% silt, 25.19% sand, and 7.79% clay on average), and the groundwater depth is approximately 4 m. The volumetric soil water content at field capacity before irrigation ranged from 15.8% to 19.6%. The bulk densities were 1.21 g/cm3 in the surface soil layer (0–5 cm), 1.51 g/cm3 in the subsurface soil layer (60–80 cm), and 1.45 g/cm3 at a depth of 1 m.
2.1.2. Experimental design and management A field experiment was conducted during the 2015 irrigation season with three replications. Three inflow rates were combined with three CRs. The experimental site was divided into three blocks (A, B, and C). Each block was divided by nine borders, resulting in a total of 27 separate irrigation assessments. According to farm household surveys, most farmers prefer to practise simple irrigation management with large borders to minimize labour costs. When large water supplies are
2.1.4. Irrigation performance measures The indicators that were used in this study to evaluate the performance of closed-ended border irrigation were application efficiency (AE), requirement efficiency (RE), distribution uniformity (DUlq), water deficit (Dd), and deep percolation (Dp) (Burt et al., 1997). These indicators were defined as follows: 30
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Fig. 4. The average advance times for different cut-off ratios in blocks A, B, and C.
AE =
average depth of infiltrated water stored in the root zone depth of total water applied
RE =
average depth of infiltrated water stored in the root zone water required in the root zone
2.2. Modelling (1)
(2)
average of the lowest 25% of infiltration depths average infiltration depth in the whole field
(3)
Dp =
average depth of water drained beyond the root zone depth of total water applied
(4)
Dd =
average depth of water deficit water required in the root zone
(5)
DUlq =
2.2.1. Field characteristic parameters The field characteristic parameters (Manning n and infiltration parameters) were required to evaluate the irrigation events. When estimating the soil infiltration parameters, the analysis was conducted using the simple post-irrigation volume balance (PIVB) (Merriam and Keller, 1987; U.S. Dept. of Agriculture –Natural Resources Conservation Service (USDA-NRCS), 1997) to solve for the k parameter in the Kostiakov equation, Z = kτa, in which the exponent is independently determined, Z is the cumulative depth of infiltration in m, τ is the intake opportunity time in min, and k is a coefficient with units of m/mina. The field characteristic parameter values were established with the aid of the hydraulic surface irrigation simulation model WinSRFR 31
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and n parameters. This procedure was repeated until a good fit between the two curves was achieved. In this manner, the values of n, a, and k were obtained. The input data for WinSRFR for each irrigation event included the irrigation requirement, the advance and recession times at each measurement distance within each border (every 5 m along the border length), the border dimensions (measured using a measuring tape in the field), and the elevations along the border length (measured using an optical level). Fig. 1 shows the measured field elevations for the longitudinal section and the ordinary least square regression for borders 8, 12, and 20 in blocks A, B, and C, respectively. The coefficients of the ordinary least squares regression equations are reported as the slopes (S) of the borders in this study. All 28 borders slope in the downstream direction. The first border recession time was not observed, and some other data were not obtained from border 24 due to the field conditions. Therefore, the elevations of borders 1 and 24 were not included in the analysis. The hydraulic simulation model WinSRFR 4.1.3 was calibrated for each individual irrigation event, resulting in soil infiltration values for each event.
Fig. 5. The average inflow rate vs cut-off ratio in blocks A, B, and C. The vertical bars represent the standard deviations of the total applied water; for some points, the vertical bars are too small to be seen.
(Bautista et al., 2009a,b, 2014) and trial-and-error as follows. Initial values were set for the a and n parameters, and an initial estimate for the k parameter was calculated using the WinSRFR model. The parameter set was then fed into the WinSRFR simulation model. The simulated advance and recession curves were compared with the observed curves, and if the fit was poor, new values were assigned to the a
Fig. 6. The percentages of the (a) DUlq, (b) AE, (c) RE, (d) DP, and (e) Dd for an application depth of 60 mm and different CRs in blocks A, B, and C. The vertical bars represent the standard deviations of the irrigation performance indexes; for some points, the vertical bars are too small to be seen.
32
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Fig. 7. Sensitivity to slope in (a) border 2, (b) border 15, and (c) border 20.
Table 1 provides details of the inflow rate, average slope, Manning’s n, inflow duration, advance time, CR, and input volume to borders. The estimated parameters of the KE equation using the WinSRFR hydraulic simulation module are listed in Table 1. The advance curve can be adjusted by modifying parameter a; thus, when the soil conditions were similar in each block, parameter a was expected to be similar. The values of parameter a in the KE equation were 0.77, 0.68, and 0.57 in blocks A, B, and C, respectively, with average inflow rates of 6.91, 4.95, and 2.81 l s−1 m−1, respectively. However, the K parameter values were different over the borders in each block. Manning’s n ranged from 0.06 to 0.09, which is within the basic recommended range of the field roughness coefficient (Bautista et al., 2009a; Zhang et al., 2006).
2.2.2. Sensitivity analyses Because of the uncertainty of the field properties (a and k) and system inputs (S, n, and Q), systematic sensitivity analyses must be conducted to assess the robustness of any operation or design recommendation (Bautista et al., 2009a). In this study, the hydraulic simulation model WinSRFR was used for sensitivity analysis of the performance. The sensitivity of the performance to the inflow rate was examined to provide a clearer understanding of the performance at different inflow rates. Manning’s n varies throughout the irrigation season. The average reported slope in each block can also vary from border to border and throughout the irrigation season. Hence, the existing design was tested over ranges of roughness (Manning’s n values of 0.10 ± 60%), slope (from 0.0005 to 0.004), and discharge, and the range of the tested discharge is discussed in the following section.
3.2. Inflow rate and irrigation deficit In the field experiments, statistical analysis of the inflow rate yielded standard deviations (σ) of 0.12, 0.10, and 0.05 for blocks A, B, and C, respectively, and coefficients of variation (CV) of 1.7, 2.1, and 1.8%, respectively. These results indicate that no large variation in the inflow distribution occurred within each block (Fig. 3). The effects of different inflow rates and CRs on irrigation performance in each block were then investigated. In block A (experiments B1 through B9), the average final advance time was not sensitive to the CR values under different field conditions. The final advance time remained stable (26 min) with increasing CR (Fig. 4a); however, the total water applied increased rapidly with
3. Results and discussion 3.1. Model fitting As described above, the infiltration parameters that were input into the WinSRFR model were selected through comparison of the advance and recession curves produced from the measured advance and recession data with those produced by the WinSRFR model. The simulated results for the advance and recession curves for borders 2, 15, and 20 (Fig. 2) are presented here. 33
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Fig. 8. Sensitivity to Manning’s roughness in (a) border 2, (b) border 15, and (c) border 20.
increasing CR (Fig. 5). The average applied water increased from 28 m3 to 35 m3 (indicating average infiltrations of 75 and 95 mm of water, respectively) as the CR value increased from 0.80 to 0.90. The applied volumes were reasonable considering the minimum volume needed to satisfy the irrigation requirement (60 mm). For block B (experiments B10 through B18), the average final advance times were 30, 28, and 27 min for CR values of 0.80, 0.85, and 0.90, respectively (Fig. 4b). The total water applied was between 23 and 28 m3, which satisfied the irrigation requirement. In this block, at the moderate inflow rate (4.95 l s−1 m−1), increasing the CR did not consistently increase the total applied volume (Fig. 5). For example, in borders 10 and 15, the total applied water in each was approximately 23 m3, with CRs of 0.8 and 0.85, respectively, whereas in borders 14 and 15, the total water in each was approximately 25 m3, with CRs of 0.85 and 0.9, respectively. For block C (experiments B19 through B27), the average final advance times were approximately equal (48 min) for different CR values (0.90, 0.95, and 1.00) and field conditions (Fig. 4c). Although the total water applied satisfied the irrigation requirement, the water amount increased significantly with increasing CR value (Fig. 5). In general, when the inflow rate is low (2.81 l s−1 m−1 in our case), a CR less than 0.9 may result in an incomplete advance phase. To avoid such a
scenario, the irrigation time should be lengthy, and the total water applied may be higher than irrigation requirement, particularly if the advance phase is completed. In general, under the field conditions and applied inflow rates, when the total water applied is between 26 m3 and 28 m3, the water reaches the end of the field, resulting in satisfactory irrigation (Fig. 5).
3.3. Irrigation performance indexes for each irrigation event Fig. 6 shows the percentages of DUlq, AE, RE, Dp, and Dd for an application depth of 60 mm. In Fig. 6a, for the average discharge of 6.91 l s−1 m−1 in block A, the optimum cut-off time was 18 min when the CR value was 0.80, which resulted in maximum DUlq values of 84% and 94% in borders 2 and 3 (CR = 0.80), respectively. The same optimum CR value was obtained when AE was used as the determining performance indicator, i.e., maximizing AE rather than DUlq (Fig. 6b). Typically, AE and DUlq are highly correlated in closed-ended borders. Moderately high uniformity values for DUlq resulted in a CR value of 0.85 in block B, where the average inflow rate was 4.95 l s−1 m−1 (Fig. 6a). The DUlq values were 86%, 93%, and 88% in borders 13, 14, and 15 (CR = 0.85), respectively. In those borders, the cut-off times were 26, 24, and 21 min, respectively. The DUlq values obtained from 34
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Fig. 9. Sensitivity to inflow rate in (a) border 2, (b) border 15, and (c) border 20.
identify the management strategies along the field. These points are located at 0.8, 0.85, and 0.90 of the field length. When the advance wave takes approximately 18 min to reach the first monitoring point, it indicates that the inflow rate is high (6.91 l s−1 m−1), with a recommended CR of 0.8; when the advance wave takes between 21 and 26 min to reach the second point, it indicates that the inflow rate is moderate (4.95 l s−1 m−1), with a recommended CR of 0.85; when the advance wave takes between 39 and 45 min to reach the third monitoring point, it indicates that the inflow rate is small (2.81 l s−1 m−1), with a recommended CR of 0.9.
Table 2 Maximum and minimum slope (S), roughness (n), and inflow rate (Q) values in blocks A, B, and C. Block
CR
Slope range
Manning range
Inflow range (l s−1 m−1)
A B C
0.80 0.85 0.90
0.0020–0.004 0.0010–0.004 0.0010–0.004
0.04–0.09 0.04–0.10 0.04–0.10
6.5–7.8 4.5–6.5 2.6–4.5
borders 16, 17, and 18 (CR = 0.90) were higher than those obtained from borders 13, 14, and 15 (CR = 0.85). However, the Dp values of borders 16, 17, and 18 were also higher than those of borders 13, 14, and 15, whereas the AE values of borders 16, 17, and 18 were lower (Fig. 6b and d). Additionally, an acceptable irrigation performance was achieved at CR = 0.85 (Fig. 6c and d), and a longer inflow rate duration was not needed (CR > 0.85). In block C, at an average inflow rate of 2.81 l s−1 m−1, the maximum DUlq achieved a value of 85%, and the AE values were 83, 92, and 80% at CR = 0.9 in borders 19, 20, and 21, respectively (Fig. 6a and c). In those borders, the cut-off times were 42, 39, and 45 min, respectively. In this block, continuing the supply of water until completion of the advance phase (CR = 1) resulted in decreasing AE and increasing Dp values (Fig. 6b and d). Therefore, the recommended CR values for the average inflow rates of 6.91 l s−1 m−1, 4.95 l s−1 m−1, and 2.81 l s−1 m−1 are 0.8, 0.85, and 0.9, respectively. Under the field conditions, three monitoring points were needed to
3.4. Sensitivity analysis In the interest of brevity, the results of the analyses of sensitivity to inflow rate, slope, and Manning’s roughness are reported without any adjustments to the recommended CRs for borders 2 (block A), 15 (block B), and 20 (block C). AE and DUlq were used as performance indicators (AE, DUlq, and RE were plotted on the following figures, and the same results were obtained when other irrigation performance indicators were used as the determining performance indicators). Generally, an improved surface irrigation system can achieve acceptable performance with minimum distribution uniformity and an AE of 80% (Pereira et al., 2007). The sensitivity of the existing solutions was tested over a range of slopes (from 0.0005 to 0.004). Fig. 7 shows the values of the 35
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values with CR values of 0.8, 0.85, and 0.9 in blocks A, B, and C, respectively. The maximum and minimum S, n, and Q values that corresponded to the high-performance indicators in blocks A, B, and C are summarized in Table 2. The boundary values above are currently recommended for achieving high AE and DUlq values.
performance indicators over a range of slopes. The high inflow rate with CR = 0.8 in block A (Fig. 7a) resulted in AE and DUlq values greater than 80% for slopes between 0.002 and 0.004. A high slope at CR = 0.8 (comparatively early) contributed to water advancement; the water reached the end of the field after the water was cut off, and high performance was achieved. In block B, the inflow rate and CR were 4.95 l s−1 m−1 and 0.85, respectively, and the optimum slope range was between 0.0010 and 0.004, with performance indicator values greater than 85% (Fig. 7b). However, obtaining performance indicator (AE and DUlq) values greater than 85% was difficult at the low inflow rate and CR of 0.9 that were applied in block C. For slopes between 0.0010 and 0.004, the DUlq and AE values were greater than 80% in block C (Fig. 7c). These results indicate that over the recommended CRs, the AE and DUlq increased as the slope increased (the AE and DUlq were less sensitive to slope changes); thereafter, the DUlq values began to decrease for higher slope values. Additionally, even if the slope is increased (which decreases the volume stored in the surface at cut-off time), the irrigation events in block A, block B, and block C satisfy the irrigation requirement at the recommended slope ranges (Fig. 7). The sensitivities of the average field characteristics were tested over a range of n values (from 0.04 to 0.16). Fig. 8 shows the values of the performance indicators over a range of Manning’s n values without any adjustments in the recommended CR values in each block. Neither performance indicator was very sensitive to variation in roughness. In blocks A, B, and C, high performance indicators were obtained for Manning’s roughness ranges of 0.04–0.09, 0.04–0.10, and 0.04–0.10, respectively (Fig. 8a–c). These findings indicate that over these ranges of roughness values, the irrigation performances of the recommended CRs in each block were robust. Fig. 9 shows the results of the sensitivity analyses over a range of inflow rates without any adjustments of the CRs in each block. For the larger decreases in the average inflow rate applied in each block, the DUlq values decreased significantly. However, for larger increases in discharge, the DUlq values were less sensitive to changes. In block C (border 20), lower performance indicator values were obtained when the inflow rate decreased to less than 2.6 l s−1 m−1, and the DUlq was less than 70% (Fig. 9a). As discharge increased, the performance indicator values increased, and when discharge decreased, the performance indicators decreased. For larger increases in the average applied inflow in block C (+52.5%) at CR = 0.9, the performance indicator values were greater than 80%. This inflow rate (4.5 l s−1 m−1) corresponded to a decrease in the average applied inflow (−14.5%) in block B (border 15), where the CR was 0.85, and lower discharge resulted in high performance indicator values in block B (Fig. 9b). Similarly, larger increases in the average inflow applied in block B (+27.5%) resulted in performance indicator values greater than 85%, and this inflow rate value (6.5 l s−1 m−1) approximately corresponded to the decrease of the average applied inflow (−2.5%) in block A, where the CR was 0.8. The inflow rate of 6.5 l s−1 m−1 in block A resulted in a DUlq value of 84% and an AE value of 77%. In block A, the inflow range of 6.5 l s−1 m−1–7.8 l s−1 m−1 corresponded to DUlq values greater than 80% and AE values of 75 or 77%. Notably, as the inflow rate increased in block A, the DUlq increased and the AE decreased (Fig. 9c). This result occurred because DUlq is calculated as a ratio of the average depth of infiltration in a quarter of the field and because more water accumulated above the soil surface and infiltrated deeper at the end of the border than it did at the other positions. Therefore, when the same high inflow rates as those applied in Block A are used, farmers can accomplish each irrigation event quickly with a CR of 0.8, whereas when the same moderate or low inflow rates as those applied in blocks B and C are used, farmers can efficiently accomplish irrigation events by setting CR values to 0.85 and 0.90, respectively. The low inflow rates applied in block C required a long irrigation time (approximately 50 min), even at CR = 0.9. Hence, a range of inflow rates results in high performance indicator
4. Conclusions To maintain high agricultural production with sustainable utilization of limited water resources for a particular area, recommendations of specific inflow rates and inflow durations are necessary. Under the field conditions in this study, high performance indicator values were achieved using CRs below the currently adopted value (CR = 1). A set of inflow rate ranges and corresponding CRs for achieving high performance indicator values are recommended. The following conclusions were drawn from the results. (1) Establishing a cut-off time before the advance phase is completed when the inflow rate is high (> 6.5 l s−1 m−1) is necessary to prevent the accumulation of water at the end of the field, particularly when the slope is high. The recommended CR for an average inflow rate of 6.91 l s−1 m−1 is 0.80. (2) Acceptable performance indicator values can be achieved using average moderate and low inflow rates of 4.95 and 2.81 l s−1 m−1, with CRs of 0.85 and 0.9, respectively. (3) Irrigation performance indicators at levels above the currently recommended CRs for high, moderate, and low inflow rates show low sensitivity to the bottom slope and Manning’s n; no substantial changes in performance were noted at Manning’s n values between 0.04 and 0.09. (4) The minimum recommended inflow rate is 2.6 l s−1 m−1 for irrigation events when the CR is 0.90. (5) The sensitivity analysis showed that high performance indicator values are achievable for high, moderate, and low inflow rates of 7.8–6.5 l s−1 m−1, 6.5–4.5 l s−1 m−1, and 4.5–2.6 l s−1 m−1 at CRs of 0.80, 0.85, and 0.90, respectively. Therefore, from a farmer’s perspective, the inflow rate is adjustable, and the recommended CR remains unchanged within each recommended range of inflow rate. The border irrigation tests presented here provide a range of different inflow rate levels and CRs that could be encountered across a field. However, although the optimal CRs were calculated for a fixed required depth (60 mm, which is the most frequent required depth in the study area), the required depth can vary depending on the needs of the crop and the soil. Additionally, the evaluations involved short fields, and some experiments applied high inflow rates and, therefore, short opportunity times. Under such conditions, the infiltration parameters are sensitive. For these reasons, further research should be conducted to determine the optimal CRs in soils with infiltration properties or requirements different from those considered in the present study. Acknowledgments The authors would like to thank the reviewers for their valuable suggestions. This work was financially supported by the National Natural Science Foundation of China (50979025). References Anwar, A.A., Ahmad, W., Bhatti, M.T., Ul Haq, Z., 2016. The potential of precision surface irrigation in the Indus Basin Irrigation System. Irrig. Sci. 34, 379–396. Bautista, E., Strelkoff, T.S., Clemmens, A.J., 2002. Sensitivity of surface irrigation to infiltration parameters: implications for management. In: USCID/EWRI Conference, Energy, Climate, Environment and Water. Issues and Opportunities for Irrigation and Drainage. July 10–13, ASCE, San Luis Obispo. pp. 475–485.
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Lv, L., Yao, Y., Zhang, L., Dong, Z., Jia, X., Liang, S., Ji, J., 2013. Winter wheat grain yield and its components in the North China Plain: irrigation management, cultivation, and climate. Chil. J. Agric. Res. 73, 233–242. Mailhol, J.C., Merot, A., 2007. SPFC: a tool to improve water management and hay production in the Crau region. Irrig. Sci. 26, 289–302. Merriam, J.L., Keller, J., 1987. Farm Irrigation System Evaluation: A Guide for Management. Department of Agricultural and Irrigation Engineering, Utah State University, Logan. Morris, M.R., Hussain, A., Gillies, M.H., O’Halloran, N.J., 2015. Inflow rate and border irrigation performance. Agric. Water Manag. 155, 76–86. Pereira, L.S., Oweis, T.Y., Zairi, A., 2002. Irrigation water management under scarcity. Agric. Water Manag. 57, 175–206. Pereira, L.S., Gonçalves, J.M., Dong, B., Mao, Z., Fang, S.X., 2007. Assessing basin irrigation and scheduling strategies for saving irrigation water and controlling salinity in the upper Yellow River Basin, China. Agric. Water Manag. 93, 109–122. Reddy, J.M., Jumaboev, K., Matyakubov, B., Eshmuratov, D., 2013. Evaluation of furrow irrigation practices in Fergana Valley of Uzbekistan. Agric. Water Manag. 117, 133–144. U.S. Dept. of Agriculture−Natural Resources Conservation Service (USDA-NRCS), 1997. National Irrigation Handbook, Part 652, Irrigation Guide. National Technical Information Service, Washington. Wattenburger, P.L., Clyma, W., 1989. Level basin design and management in the absence of water control. Part I: evaluation of completion-of-advance irrigation. Trans. ASAE 32, 838–843. Zhang, S.H., Xu, D., Li, Y.N., Cai, L.G., 2006. An optimized inverse model used to estimate Kostiakov infiltration parameters and Manning’s roughness coefficient based on SGA and SRFR model: (I) establishment. J. Hydraul. Eng. 37, 1297–1302.
Bautista, E., Clemmens, A.J., Strelkoff, T.S., Niblack, M., 2009a. Analysis of surface irrigation systems with WinSRFR—example application. Agric. Water Manag. 96, 1162–1169. Bautista, E., Clemmens, A.J., Strelkoff, T.S., Schlegel, J., 2009b. Modern analysis of surface irrigation systems with WinSRFR. Agric. Water Manag. 96, 1146–1154. Bautista, E., Warrick, A.W., Schlegel, J.L., 2014. Wetted-perimeter dependent furrow infiltration and its implication for the hydraulic analysis of furrow irrigation systems. In: World Environmental and Water Resources Congress. ASCE, Portland. pp. 1727–1735. Burt, C.M., Clemmens, A.J., Strelkoff, T.S., Solomon, K.H., Bliesner, R.D., Hardy, L.A., Howell, T.A., Eisenhauer, D.E., 1997. Irrigation performance measures: efficiency and uniformity. J. Irrig. Drain. Eng. 123, 431. Cahoon, J.E., Mandel, P., Eisenhauer, D.E., 1995. Management recommendations for sloping blocked-end furrow irrigation. Appl. Eng. Agric. 11, 527–538. Chen, B., Ouyang, Z., Sun, Z., Wu, L., Li, F., 2012. Evaluation on the potential of improving border irrigation performance through border dimensions optimization: a case study on the irrigation districts along the lower Yellow River. Irrig. Sci. 31, 715–728. Clemmens, A.J., 1998. Level basin design based on cutoff criteria. Irrig. Drain. Syst. 12, 85–113. Clyma, W., Ali, A., 1977. Traditional and improved irrigation practices in Pakistan. In: Proceedings of ASCE Conference on Water Management for Irrigation and Drainage Practices. July 20–22, ASCE, Reno. pp. 201–216. González, C., Cervera, L., Moret-Fernández, D., 2011. Basin irrigation design with longitudinal slope. Agric. Water Manag. 98, 1516–1522. Jiao, X.Y., Wang, W.H., 2012. Robust Design of Border Irrigation and Furrow Irrigation. Hohai University Press, Nanjing, China.
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Border irrigation performance with distance-based cut-off ⁎
T
Mohamed Khaled Salahou , Xiyun Jiao, Haishen Lü State Key Laboratory of Hydrology–Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Surface irrigation Border irrigation Inflow rate Cut-off ratio Irrigation performance
Border irrigation is widely practised for winter wheat production on the North China Plain. Winter wheat is mainly irrigated with groundwater as a supplement to insufficient precipitation to maintain high agricultural production. As a result of the increased demands for water, groundwater levels have declined. Therefore, improvements to border irrigation performance and water use efficiency are urgently needed. The objective of this study was to determine the optimal distance at which to cut off inflow under different inflow rate conditions in closed-ended border systems. The experimental treatments included three inflow rates (high, moderate, and low, with average rates of 6.91 l s−1 m−1, 4.95 l s−1 m−1, and 2.81 l s−1 m−1, respectively) and three cut-off ratios (CRs) arranged in three replications at the CAS Ecological Agricultural Experiment Station in Nanpi, Hebei Province, China. The surface irrigation hydraulic simulation model WinSRFR was used to examine the sensitivity of the existing design to a range of bottom slopes, surface roughness values, and inflow rates to demonstrate the robustness of the solutions in terms of their application efficiency and low-quarter distribution uniformity. The results present the optimum CR values for different inflow rate conditions to maximize irrigation performance. The results indicate that irrigation performance above the optimum CR values for high, moderate, and low inflow rates is not very sensitive to bottom slope, and no substantial changes in performance were noted when Manning’s roughness coefficient was between 0.04 and 0.09. A set of inflow rate ranges that corresponds to the recommended CRs that could achieve high irrigation performance is presented.
1. Introduction Winter wheat is one of the two major crops grown in Hebei Province, China. Irrigation with groundwater is necessary for maintaining sustainable crop production because less than 30% of precipitation falls in the winter, which is much lower than the water requirements of winter wheat (Lv et al., 2013). Today, approximately 70% of the water resources extracted for agriculture (of which approximately 70% are used for wheat irrigation) are pumped from groundwater in Hebei Province on the North China Plain (Lv et al., 2013). Groundwater levels have declined as a result of climate change, human activities, and increasing demands for water, resulting in the formation of perennial shallow groundwater depression cones over a wide area in Hebei Province. Therefore, the development of an irrigation system to increase the efficiency of irrigation water use and to prevent further over-exploitation of groundwater is urgently needed. Border irrigation is widely practised for wheat and corn production on the North China Plain and is the most common irrigation method in this region, relying on gravity and the field surface to distribute water within a field (Morris et al., 2015). Assessing the performance of border irrigation requires knowledge
⁎
of the management methods used by farmers and system variable measures (i.e., border dimensions (length and width), inflow rate (Q), slope (S), Manning’s roughness coefficient (n), cut-off time, and soil infiltration properties) (Pereira et al., 2002). Researchers have achieved improved border irrigation performance by optimizing border dimensions (Anwar et al., 2016; Chen et al., 2012), adopting appropriate inflow rates and cut-off times (Morris et al., 2015), improving inflow rate, and measuring the field elevations carefully (Bautista et al., 2009a). Additionally, Manning’s roughness coefficient has been shown to affect the performance of border irrigation (Bautista et al., 2002); therefore, the optimization of the design and/or operation of a border irrigation system needs to account for the potential range of infiltration and Manning’s roughness coefficient conditions that could be encountered (Bautista et al., 2009a). Zhang et al. (2006), Bautista et al. (2009a), and Anwar et al. (2016) recommended field roughness coefficients of 0.02–0.40, 0.04–0.10, and 0.04–0.16, respectively. Border irrigation performance depends greatly on the cut-off time. In a well-designed and operated system, the volume of applied water is precisely controlled. Thus, for a given inflow rate, the cut-off time is determined from the prescribed application depth. However, the inflow
Corresponding author. E-mail addresses: [email protected] (M.K. Salahou), [email protected] (X. Jiao), [email protected] (H. Lü).
https://doi.org/10.1016/j.agwat.2018.01.014 Received 13 November 2016; Received in revised form 8 January 2018; Accepted 14 January 2018 0378-3774/ © 2018 Elsevier B.V. All rights reserved.
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Fig. 1. Longitudinal slopes and ordinary least squares regression equations of (a) border 8, (b) border 12, and (c) border 20.
based cut-off criterion may also help compensate for uncertainties in other field variables, such as slope, roughness, and infiltration. The recommended CRs for open-ended border irrigation with loamy sand soil and lengths of 200 and 400 m were 0.77 and 0.85, respectively (Mailhol and Merot, 2007), and the recommended CR for block-end furrow irrigation with loamy soil and moderate slope (0.005 m m−1) was 0.89 (Cahoon et al., 1995). Previous studies have addressed the problem of cut-off time in level basins, open-ended borders, or furrow systems. However, the longitudinal slope (graded basin or border) has significant effects on cut-off time and irrigation performance (González et al., 2011). Additionally, closed-ended border irrigation systems currently prevail on the North China Plain, and the most popular irrigation cut-off method adopted by farmers is to stop irrigation when the water reaches the lower end of the field (CR = 1). Under this method, the irrigation water often reaches depths in excess of the required depth (Pereira et al., 2002), and water accumulates at the end of the field, significantly increasing the overflow
is not measured routinely except at the farm turnout. Additionally, under this method, the water overflow may occur at the end of the field or the water may not reach the end of the field, particularly in level basins; thus, in many counties, the adopted cut-off time in level basins is at the point of completion of the advance phase (Clyma and Ali, 1977; Wattenburger and Clyma, 1989). In level basins, this approach is more robust than the limiting length approach, particularly when a farmer has poor control over the water (Reddy et al., 2013). Clemmens (1998) presented a new procedure for the design of level basins based on cutoff time and concluded that the cut-off distance could be adjusted depending on the Manning roughness coefficient. For the previous reasons, instead of determining the cut-off time from the application depth, the researchers adopted the cut-off ratio (CR), defined as the ratio of the advance distance at cut-off to the total field length. The distance-based cut-off criterion can help compensate for inflow uncertainties where the inflow can be expected to vary but cannot be readily measured. Furthermore, to some degree, the distance28
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Fig. 2. Measured (dots) and simulated (lines) advance and recession curves of (a) border 2, (b) border 15, and (c) border 20.
2. Materials and methods
risk. Furthermore, some crops cannot tolerate excessive ponding. Hence, the objectives of this study were as follows: (1) to determine the optimal distance at which to cut off inflow under different inflow rate conditions in closed-ended border systems in the North China Plain (three inflow rates in three blocks, considering the infiltration values for each border); (2) to establish different scenarios incorporating different slopes, n values, and inflow rates to assess the sensitivity ranges of these variables on the irrigation performance when optimum cut-off distance values are used; and (3) to provide recommendations for improving the performance of border irrigation systems on the North China Plain.
2.1. Field measurements 2.1.1. Study area Field studies were conducted at the CAS Ecological Agricultural Experiment Station in the town of Nanpi in Hebei Province, China, at a longitude, latitude, and elevation of 116°40′E, 38°06′N and 20 m, respectively. This area is located in a monsoon climate zone with an annual evaporation range between 1500 and 1800 mm. The mean annual precipitation and temperature at the study site are approximately 567.4 mm and 12.3 °C, respectively. Rainfall generally occurs during the summer, with 73%, 11%, 13%, and 3% of the annual precipitation 29
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Table 1 Inflow rate, average slope, Manning’s roughness, inflow duration, advance time, CR, infiltrated volume, and KE equation coefficients for each irrigated border. Block
Border number
Average slope
Average inflow rate (l s−1/m)
Average inflow rate in each block (l s−1/m)
Inflow duration (min)
Advance duration (min)
CR
Kostiakov coefficient a
k mm/hra
Manning coefficient n
Infiltrated volume (m3)
A
1 2 3 4 5 6 7 8 9
0.0023 0.0014 0.0022 0.0023 0.0016 0.0015 0.0011 0.0017 0.0023
6.88 6.87 7.08 6.91 6.85 6.78 6.87 6.84 7.10
6.91
18.80 18.80 17.10 20.70 21.00 22.40 22.80 23.00 23.50
27.32 27.03 24.27 25.93 26.65 25.63 26.13 26.67 27.08
0.8 0.8 0.8 0.85 0.85 0.9 0.9 0.9 0.9
– 0.77 0.77 0.77 0.77 0.77 0.77 0.77 0.77
– 143.541 136.168 163.205 134.902 140.11 147.039 166.224 166.224
– 0.09 0.09 0.09 0.09 0.095 0.09 0.09 0.095
28.71 28.67 26.88 31.75 31.93 33.72 34.77 34.93 37.04
B
10 11 12 13 14 15 16 17 18
0.0022 0.0023 0.0023 0.0024 0.0025 0.0022 0.0014 0.0024 0.0027
4.87 4.82 4.84 5.00 4.93 4.98 5.06 5.13 4.90
4.95
21.20 24.80 25.00 26.20 23.70 20.50 24.10 24.60 23.10
28.92 32.7 31.25 30.5 29.3 26.02 27.58 27.83 26.97
0.8 0.8 0.8 0.85 0.85 0.85 0.9 0.9 0.9
0.68 0.68 0.68 0.68 0.68 0.68 0.68 0.68 0.68
104.607 134.966 130.123 130.067 122.236 111.16 122.771 119.34 110.402
0.095 0.06 0.065 0.065 0.06 0.06 0.06 0.075 0.06
22.92 26.54 26.86 29.08 25.94 22.66 27.07 28.02 25.13
C
19 20 21 22 23 24 25 26 27
0.0027 0.0027 0.0026 0.0029 0.003 0.003 0.0029 0.0029 0.0025
2.78 2.77 2.73 2.84 2.78 – 2.88 2.85 2.84
2.81
42.40 38.70 44.70 50.50 43.90 – 51.30 48.10 47.00
48.23 43.9 51.83 52.67 46.47 – 51.33 48.13 47
0.9 0.9 0.9 0.95 0.95 0.95 1 1 1
0.57 0.57 0.57 0.57 0.57 – 0.57 0.57 0.57
90.835 83.729 92.39 99.226 92.4 – 100.717 95.337 92.962
0.06 0.06 0.06 0.06 0.06 – 0.06 0.06 0.06
26.17 23.80 27.09 31.84 27.09 – 32.80 30.43 29.63
available at a site, farmers can extend the border width without affecting their irrigation practices. However, smaller water supplies are available at most sites such that farmers limit the border widths to accelerate surface water flow and to accomplish each irrigation event quickly. Therefore, the border applied in the experimental field measured 100 m in length and 3.7 m in width, similar to the border dimensions used by local farmers. The winter wheat is irrigated two times per season in the study area (pre-sowing and spring irrigation), the experiment was conducted during the spring irrigation event, and the required application water depth was 60 mm (Jiao and Wang, 2012). 2.1.3. Inflow The inflow rate plays a principal role in irrigation performance, and its values greatly depend on the water source and pump performance during irrigation. In practise, the amount of discharge supplied varies among the sites in the study area, with averages of 6.91 l s−1 m−1, 4.95 l s−1 m−1, and 2.81 l s−1 m−1. The inflows for all the irrigation events were set at these values. These inflow rates and different CRs were investigated to determine the optimal values to achieve high irrigation performance. The CRs for both of the first two inflow rates were 0.8, 0.85, and 0.90. However, because the last inflow rate was low, the CRs reached 0.90, 0.95, and 1.00, respectively. Generally, each (Q-CR) combination was replicated three times. It was assumed that only one border was irrigated at a time, that the full amount of inflow through the service point was applied to that border, and that the buffering capacity of the farm channel removed most of the short-term variation. Consequently, the time-averaged inflow rate was used in all the evaluations.
Fig. 3. Average inflow rates in blocks A, B, and C. The vertical bars indicate the standard deviations of the inflow rate; for some points, the vertical bars are too small to be seen.
occurring during the summer, spring, autumn, and winter, respectively. A field was planted with winter wheat on October 15, 2014, and was harvested in the middle of June 2015. The soil at the site is classified as silt loam (67.02% silt, 25.19% sand, and 7.79% clay on average), and the groundwater depth is approximately 4 m. The volumetric soil water content at field capacity before irrigation ranged from 15.8% to 19.6%. The bulk densities were 1.21 g/cm3 in the surface soil layer (0–5 cm), 1.51 g/cm3 in the subsurface soil layer (60–80 cm), and 1.45 g/cm3 at a depth of 1 m.
2.1.2. Experimental design and management A field experiment was conducted during the 2015 irrigation season with three replications. Three inflow rates were combined with three CRs. The experimental site was divided into three blocks (A, B, and C). Each block was divided by nine borders, resulting in a total of 27 separate irrigation assessments. According to farm household surveys, most farmers prefer to practise simple irrigation management with large borders to minimize labour costs. When large water supplies are
2.1.4. Irrigation performance measures The indicators that were used in this study to evaluate the performance of closed-ended border irrigation were application efficiency (AE), requirement efficiency (RE), distribution uniformity (DUlq), water deficit (Dd), and deep percolation (Dp) (Burt et al., 1997). These indicators were defined as follows: 30
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Fig. 4. The average advance times for different cut-off ratios in blocks A, B, and C.
AE =
average depth of infiltrated water stored in the root zone depth of total water applied
RE =
average depth of infiltrated water stored in the root zone water required in the root zone
2.2. Modelling (1)
(2)
average of the lowest 25% of infiltration depths average infiltration depth in the whole field
(3)
Dp =
average depth of water drained beyond the root zone depth of total water applied
(4)
Dd =
average depth of water deficit water required in the root zone
(5)
DUlq =
2.2.1. Field characteristic parameters The field characteristic parameters (Manning n and infiltration parameters) were required to evaluate the irrigation events. When estimating the soil infiltration parameters, the analysis was conducted using the simple post-irrigation volume balance (PIVB) (Merriam and Keller, 1987; U.S. Dept. of Agriculture –Natural Resources Conservation Service (USDA-NRCS), 1997) to solve for the k parameter in the Kostiakov equation, Z = kτa, in which the exponent is independently determined, Z is the cumulative depth of infiltration in m, τ is the intake opportunity time in min, and k is a coefficient with units of m/mina. The field characteristic parameter values were established with the aid of the hydraulic surface irrigation simulation model WinSRFR 31
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and n parameters. This procedure was repeated until a good fit between the two curves was achieved. In this manner, the values of n, a, and k were obtained. The input data for WinSRFR for each irrigation event included the irrigation requirement, the advance and recession times at each measurement distance within each border (every 5 m along the border length), the border dimensions (measured using a measuring tape in the field), and the elevations along the border length (measured using an optical level). Fig. 1 shows the measured field elevations for the longitudinal section and the ordinary least square regression for borders 8, 12, and 20 in blocks A, B, and C, respectively. The coefficients of the ordinary least squares regression equations are reported as the slopes (S) of the borders in this study. All 28 borders slope in the downstream direction. The first border recession time was not observed, and some other data were not obtained from border 24 due to the field conditions. Therefore, the elevations of borders 1 and 24 were not included in the analysis. The hydraulic simulation model WinSRFR 4.1.3 was calibrated for each individual irrigation event, resulting in soil infiltration values for each event.
Fig. 5. The average inflow rate vs cut-off ratio in blocks A, B, and C. The vertical bars represent the standard deviations of the total applied water; for some points, the vertical bars are too small to be seen.
(Bautista et al., 2009a,b, 2014) and trial-and-error as follows. Initial values were set for the a and n parameters, and an initial estimate for the k parameter was calculated using the WinSRFR model. The parameter set was then fed into the WinSRFR simulation model. The simulated advance and recession curves were compared with the observed curves, and if the fit was poor, new values were assigned to the a
Fig. 6. The percentages of the (a) DUlq, (b) AE, (c) RE, (d) DP, and (e) Dd for an application depth of 60 mm and different CRs in blocks A, B, and C. The vertical bars represent the standard deviations of the irrigation performance indexes; for some points, the vertical bars are too small to be seen.
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Fig. 7. Sensitivity to slope in (a) border 2, (b) border 15, and (c) border 20.
Table 1 provides details of the inflow rate, average slope, Manning’s n, inflow duration, advance time, CR, and input volume to borders. The estimated parameters of the KE equation using the WinSRFR hydraulic simulation module are listed in Table 1. The advance curve can be adjusted by modifying parameter a; thus, when the soil conditions were similar in each block, parameter a was expected to be similar. The values of parameter a in the KE equation were 0.77, 0.68, and 0.57 in blocks A, B, and C, respectively, with average inflow rates of 6.91, 4.95, and 2.81 l s−1 m−1, respectively. However, the K parameter values were different over the borders in each block. Manning’s n ranged from 0.06 to 0.09, which is within the basic recommended range of the field roughness coefficient (Bautista et al., 2009a; Zhang et al., 2006).
2.2.2. Sensitivity analyses Because of the uncertainty of the field properties (a and k) and system inputs (S, n, and Q), systematic sensitivity analyses must be conducted to assess the robustness of any operation or design recommendation (Bautista et al., 2009a). In this study, the hydraulic simulation model WinSRFR was used for sensitivity analysis of the performance. The sensitivity of the performance to the inflow rate was examined to provide a clearer understanding of the performance at different inflow rates. Manning’s n varies throughout the irrigation season. The average reported slope in each block can also vary from border to border and throughout the irrigation season. Hence, the existing design was tested over ranges of roughness (Manning’s n values of 0.10 ± 60%), slope (from 0.0005 to 0.004), and discharge, and the range of the tested discharge is discussed in the following section.
3.2. Inflow rate and irrigation deficit In the field experiments, statistical analysis of the inflow rate yielded standard deviations (σ) of 0.12, 0.10, and 0.05 for blocks A, B, and C, respectively, and coefficients of variation (CV) of 1.7, 2.1, and 1.8%, respectively. These results indicate that no large variation in the inflow distribution occurred within each block (Fig. 3). The effects of different inflow rates and CRs on irrigation performance in each block were then investigated. In block A (experiments B1 through B9), the average final advance time was not sensitive to the CR values under different field conditions. The final advance time remained stable (26 min) with increasing CR (Fig. 4a); however, the total water applied increased rapidly with
3. Results and discussion 3.1. Model fitting As described above, the infiltration parameters that were input into the WinSRFR model were selected through comparison of the advance and recession curves produced from the measured advance and recession data with those produced by the WinSRFR model. The simulated results for the advance and recession curves for borders 2, 15, and 20 (Fig. 2) are presented here. 33
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Fig. 8. Sensitivity to Manning’s roughness in (a) border 2, (b) border 15, and (c) border 20.
increasing CR (Fig. 5). The average applied water increased from 28 m3 to 35 m3 (indicating average infiltrations of 75 and 95 mm of water, respectively) as the CR value increased from 0.80 to 0.90. The applied volumes were reasonable considering the minimum volume needed to satisfy the irrigation requirement (60 mm). For block B (experiments B10 through B18), the average final advance times were 30, 28, and 27 min for CR values of 0.80, 0.85, and 0.90, respectively (Fig. 4b). The total water applied was between 23 and 28 m3, which satisfied the irrigation requirement. In this block, at the moderate inflow rate (4.95 l s−1 m−1), increasing the CR did not consistently increase the total applied volume (Fig. 5). For example, in borders 10 and 15, the total applied water in each was approximately 23 m3, with CRs of 0.8 and 0.85, respectively, whereas in borders 14 and 15, the total water in each was approximately 25 m3, with CRs of 0.85 and 0.9, respectively. For block C (experiments B19 through B27), the average final advance times were approximately equal (48 min) for different CR values (0.90, 0.95, and 1.00) and field conditions (Fig. 4c). Although the total water applied satisfied the irrigation requirement, the water amount increased significantly with increasing CR value (Fig. 5). In general, when the inflow rate is low (2.81 l s−1 m−1 in our case), a CR less than 0.9 may result in an incomplete advance phase. To avoid such a
scenario, the irrigation time should be lengthy, and the total water applied may be higher than irrigation requirement, particularly if the advance phase is completed. In general, under the field conditions and applied inflow rates, when the total water applied is between 26 m3 and 28 m3, the water reaches the end of the field, resulting in satisfactory irrigation (Fig. 5).
3.3. Irrigation performance indexes for each irrigation event Fig. 6 shows the percentages of DUlq, AE, RE, Dp, and Dd for an application depth of 60 mm. In Fig. 6a, for the average discharge of 6.91 l s−1 m−1 in block A, the optimum cut-off time was 18 min when the CR value was 0.80, which resulted in maximum DUlq values of 84% and 94% in borders 2 and 3 (CR = 0.80), respectively. The same optimum CR value was obtained when AE was used as the determining performance indicator, i.e., maximizing AE rather than DUlq (Fig. 6b). Typically, AE and DUlq are highly correlated in closed-ended borders. Moderately high uniformity values for DUlq resulted in a CR value of 0.85 in block B, where the average inflow rate was 4.95 l s−1 m−1 (Fig. 6a). The DUlq values were 86%, 93%, and 88% in borders 13, 14, and 15 (CR = 0.85), respectively. In those borders, the cut-off times were 26, 24, and 21 min, respectively. The DUlq values obtained from 34
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Fig. 9. Sensitivity to inflow rate in (a) border 2, (b) border 15, and (c) border 20.
identify the management strategies along the field. These points are located at 0.8, 0.85, and 0.90 of the field length. When the advance wave takes approximately 18 min to reach the first monitoring point, it indicates that the inflow rate is high (6.91 l s−1 m−1), with a recommended CR of 0.8; when the advance wave takes between 21 and 26 min to reach the second point, it indicates that the inflow rate is moderate (4.95 l s−1 m−1), with a recommended CR of 0.85; when the advance wave takes between 39 and 45 min to reach the third monitoring point, it indicates that the inflow rate is small (2.81 l s−1 m−1), with a recommended CR of 0.9.
Table 2 Maximum and minimum slope (S), roughness (n), and inflow rate (Q) values in blocks A, B, and C. Block
CR
Slope range
Manning range
Inflow range (l s−1 m−1)
A B C
0.80 0.85 0.90
0.0020–0.004 0.0010–0.004 0.0010–0.004
0.04–0.09 0.04–0.10 0.04–0.10
6.5–7.8 4.5–6.5 2.6–4.5
borders 16, 17, and 18 (CR = 0.90) were higher than those obtained from borders 13, 14, and 15 (CR = 0.85). However, the Dp values of borders 16, 17, and 18 were also higher than those of borders 13, 14, and 15, whereas the AE values of borders 16, 17, and 18 were lower (Fig. 6b and d). Additionally, an acceptable irrigation performance was achieved at CR = 0.85 (Fig. 6c and d), and a longer inflow rate duration was not needed (CR > 0.85). In block C, at an average inflow rate of 2.81 l s−1 m−1, the maximum DUlq achieved a value of 85%, and the AE values were 83, 92, and 80% at CR = 0.9 in borders 19, 20, and 21, respectively (Fig. 6a and c). In those borders, the cut-off times were 42, 39, and 45 min, respectively. In this block, continuing the supply of water until completion of the advance phase (CR = 1) resulted in decreasing AE and increasing Dp values (Fig. 6b and d). Therefore, the recommended CR values for the average inflow rates of 6.91 l s−1 m−1, 4.95 l s−1 m−1, and 2.81 l s−1 m−1 are 0.8, 0.85, and 0.9, respectively. Under the field conditions, three monitoring points were needed to
3.4. Sensitivity analysis In the interest of brevity, the results of the analyses of sensitivity to inflow rate, slope, and Manning’s roughness are reported without any adjustments to the recommended CRs for borders 2 (block A), 15 (block B), and 20 (block C). AE and DUlq were used as performance indicators (AE, DUlq, and RE were plotted on the following figures, and the same results were obtained when other irrigation performance indicators were used as the determining performance indicators). Generally, an improved surface irrigation system can achieve acceptable performance with minimum distribution uniformity and an AE of 80% (Pereira et al., 2007). The sensitivity of the existing solutions was tested over a range of slopes (from 0.0005 to 0.004). Fig. 7 shows the values of the 35
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values with CR values of 0.8, 0.85, and 0.9 in blocks A, B, and C, respectively. The maximum and minimum S, n, and Q values that corresponded to the high-performance indicators in blocks A, B, and C are summarized in Table 2. The boundary values above are currently recommended for achieving high AE and DUlq values.
performance indicators over a range of slopes. The high inflow rate with CR = 0.8 in block A (Fig. 7a) resulted in AE and DUlq values greater than 80% for slopes between 0.002 and 0.004. A high slope at CR = 0.8 (comparatively early) contributed to water advancement; the water reached the end of the field after the water was cut off, and high performance was achieved. In block B, the inflow rate and CR were 4.95 l s−1 m−1 and 0.85, respectively, and the optimum slope range was between 0.0010 and 0.004, with performance indicator values greater than 85% (Fig. 7b). However, obtaining performance indicator (AE and DUlq) values greater than 85% was difficult at the low inflow rate and CR of 0.9 that were applied in block C. For slopes between 0.0010 and 0.004, the DUlq and AE values were greater than 80% in block C (Fig. 7c). These results indicate that over the recommended CRs, the AE and DUlq increased as the slope increased (the AE and DUlq were less sensitive to slope changes); thereafter, the DUlq values began to decrease for higher slope values. Additionally, even if the slope is increased (which decreases the volume stored in the surface at cut-off time), the irrigation events in block A, block B, and block C satisfy the irrigation requirement at the recommended slope ranges (Fig. 7). The sensitivities of the average field characteristics were tested over a range of n values (from 0.04 to 0.16). Fig. 8 shows the values of the performance indicators over a range of Manning’s n values without any adjustments in the recommended CR values in each block. Neither performance indicator was very sensitive to variation in roughness. In blocks A, B, and C, high performance indicators were obtained for Manning’s roughness ranges of 0.04–0.09, 0.04–0.10, and 0.04–0.10, respectively (Fig. 8a–c). These findings indicate that over these ranges of roughness values, the irrigation performances of the recommended CRs in each block were robust. Fig. 9 shows the results of the sensitivity analyses over a range of inflow rates without any adjustments of the CRs in each block. For the larger decreases in the average inflow rate applied in each block, the DUlq values decreased significantly. However, for larger increases in discharge, the DUlq values were less sensitive to changes. In block C (border 20), lower performance indicator values were obtained when the inflow rate decreased to less than 2.6 l s−1 m−1, and the DUlq was less than 70% (Fig. 9a). As discharge increased, the performance indicator values increased, and when discharge decreased, the performance indicators decreased. For larger increases in the average applied inflow in block C (+52.5%) at CR = 0.9, the performance indicator values were greater than 80%. This inflow rate (4.5 l s−1 m−1) corresponded to a decrease in the average applied inflow (−14.5%) in block B (border 15), where the CR was 0.85, and lower discharge resulted in high performance indicator values in block B (Fig. 9b). Similarly, larger increases in the average inflow applied in block B (+27.5%) resulted in performance indicator values greater than 85%, and this inflow rate value (6.5 l s−1 m−1) approximately corresponded to the decrease of the average applied inflow (−2.5%) in block A, where the CR was 0.8. The inflow rate of 6.5 l s−1 m−1 in block A resulted in a DUlq value of 84% and an AE value of 77%. In block A, the inflow range of 6.5 l s−1 m−1–7.8 l s−1 m−1 corresponded to DUlq values greater than 80% and AE values of 75 or 77%. Notably, as the inflow rate increased in block A, the DUlq increased and the AE decreased (Fig. 9c). This result occurred because DUlq is calculated as a ratio of the average depth of infiltration in a quarter of the field and because more water accumulated above the soil surface and infiltrated deeper at the end of the border than it did at the other positions. Therefore, when the same high inflow rates as those applied in Block A are used, farmers can accomplish each irrigation event quickly with a CR of 0.8, whereas when the same moderate or low inflow rates as those applied in blocks B and C are used, farmers can efficiently accomplish irrigation events by setting CR values to 0.85 and 0.90, respectively. The low inflow rates applied in block C required a long irrigation time (approximately 50 min), even at CR = 0.9. Hence, a range of inflow rates results in high performance indicator
4. Conclusions To maintain high agricultural production with sustainable utilization of limited water resources for a particular area, recommendations of specific inflow rates and inflow durations are necessary. Under the field conditions in this study, high performance indicator values were achieved using CRs below the currently adopted value (CR = 1). A set of inflow rate ranges and corresponding CRs for achieving high performance indicator values are recommended. The following conclusions were drawn from the results. (1) Establishing a cut-off time before the advance phase is completed when the inflow rate is high (> 6.5 l s−1 m−1) is necessary to prevent the accumulation of water at the end of the field, particularly when the slope is high. The recommended CR for an average inflow rate of 6.91 l s−1 m−1 is 0.80. (2) Acceptable performance indicator values can be achieved using average moderate and low inflow rates of 4.95 and 2.81 l s−1 m−1, with CRs of 0.85 and 0.9, respectively. (3) Irrigation performance indicators at levels above the currently recommended CRs for high, moderate, and low inflow rates show low sensitivity to the bottom slope and Manning’s n; no substantial changes in performance were noted at Manning’s n values between 0.04 and 0.09. (4) The minimum recommended inflow rate is 2.6 l s−1 m−1 for irrigation events when the CR is 0.90. (5) The sensitivity analysis showed that high performance indicator values are achievable for high, moderate, and low inflow rates of 7.8–6.5 l s−1 m−1, 6.5–4.5 l s−1 m−1, and 4.5–2.6 l s−1 m−1 at CRs of 0.80, 0.85, and 0.90, respectively. Therefore, from a farmer’s perspective, the inflow rate is adjustable, and the recommended CR remains unchanged within each recommended range of inflow rate. The border irrigation tests presented here provide a range of different inflow rate levels and CRs that could be encountered across a field. However, although the optimal CRs were calculated for a fixed required depth (60 mm, which is the most frequent required depth in the study area), the required depth can vary depending on the needs of the crop and the soil. Additionally, the evaluations involved short fields, and some experiments applied high inflow rates and, therefore, short opportunity times. Under such conditions, the infiltration parameters are sensitive. For these reasons, further research should be conducted to determine the optimal CRs in soils with infiltration properties or requirements different from those considered in the present study. Acknowledgments The authors would like to thank the reviewers for their valuable suggestions. This work was financially supported by the National Natural Science Foundation of China (50979025). References Anwar, A.A., Ahmad, W., Bhatti, M.T., Ul Haq, Z., 2016. The potential of precision surface irrigation in the Indus Basin Irrigation System. Irrig. Sci. 34, 379–396. Bautista, E., Strelkoff, T.S., Clemmens, A.J., 2002. Sensitivity of surface irrigation to infiltration parameters: implications for management. In: USCID/EWRI Conference, Energy, Climate, Environment and Water. Issues and Opportunities for Irrigation and Drainage. July 10–13, ASCE, San Luis Obispo. pp. 475–485.
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