Introduction

Surface irrigation is the oldest and most prevalent irrigation method because it is less expensive and requires less energy than sprinkler and drip irrigation systems. Thus, several studies have been conducted to boost the efficiency of surface irrigation systems [1,2,3,4]. Furthermore, precise design and sensible management of surface irrigation can result in higher water use efficiency and planted area [5, 6]. Furrow irrigation is the most effective type of surface irrigation due to adequate root zone aeration [7]. However, furrow irrigation has some problems such as low efficiency, poor distribution uniformity, and high deep percolation [8, 9]. The inappropriate management, design, and implementation are important reasons for the poor performance of surface irrigation systems [5].

Numerous investigations have been performed to examine the irrigation border irrigation performance in different soils using the SURDEV model [10, 11] and furrow irrigation performance with different raised bed sizes and different lengths using WinSRFR model [12, 13] and management draw depletion method or comparative assessment furrow and border irrigation methods [14]. Furrow irrigation design factors, including furrow length, inflow rate, and irrigation time (time of cutoff), were estimated to reduce irrigation costs as an objective function and achieve optimum application efficiency [15, 16]. The studies also apply a dual modelling approach to simulate surface and subsurface flow using WinSRFR and SWAP models [17, 18], and examine the impact of micro-furrow bottom width and depth on water flow [19]. Being the dominant factors, water conservation and irrigation performance vary spatially because of topographical and geographical variations and environmental changes, researchers emphasized evaluating the irrigation efficacy of a particular agriculture field depending upon the available water and irrigation engineering prospects [19,20,21].

An efficient irrigation system in developing countries can boost the economy [22] specifically in Pakistan where 84% of the cultivable land falls within arid to semi-arid climatic zones, and 90% of the country’s food production relies on irrigation. Moreover, the country stands near to declare water-scarce country since the per capita water availability in Pakistan stands at less than 1000 m3. Also, it is reported that a 32% water shortage is expected in Pakistan by 2025, resulting in a 70 million-ton decline in food production [23]. Key factors contributing to current and projected water shortages are consisting on population growth, climate change impacts, inadequate surface water storage capacity, and inefficient performance characterized by substantial conveyance and application losses of the irrigation system [24]. For instance, Akbar et al. [25] revealed irrigation efficiencies with application efficiency (AE) at 41%, deep percolation (DP) losses at 89%, and distribution uniformity (DU) at 43% by traditional surface irrigation methods like border, furrow bed systems, and level basin.

Since, the presence of aeration in the root zone makes furrow irrigation the best one among other surface irrigation methods [7]. However, it has problems of high deep percolation losses, low efficiency, and poor uniformity of distribution where the design and inappropriate management remained causes of its poor performance [9]. Besides, the efficacy of diverse surface water irrigation methods, particularly focusing on border and furrow irrigation, by employing different models of SURDEV, CROPWAT, and WinSRFR, has been explored [12, 24,25,26]. However, the SURDEV model is incapable of evaluating the irrigation performance at minimum depth (at the end of the field) and lower quarter depth (depth at the ¾ distance of the length of the field) and the CROPWAT model investigates the only crop water requirement on depth basis without the performance of irrigation methods. Ismail et al. [12] and Akbar et al. [25] utilized the model WinSRFR; however, the irrigation performance concerning the lower quarter and minimum depth parameters was not evaluated. Evaluation of irrigation performance with crucial parameters for the required minimum depth highlights significance in this context [27, 28]. These parameters include the variability in topography, inflow rate, cutoff time, and field dimensions.

This study aimed to boost the irrigation performance of raised bed furrow by utilizing the WinSRFR model considering the optimal decision parameters (cutoff time, inflow rate, and field dimensions). Specifically, the objectives of the studies are (i) conducting a comprehensive assessment of the current irrigation performance of raised bed furrows through field evaluations and analyzing the factors such as deep percolation loss, distribution uniformity, adequacy, and application efficiency; (ii) utilizing the WinSRFR model to optimize key irrigation parameters, including flow rate, cutoff time, and field dimensions to improve overall irrigation performance based on the insights gained from the evaluation of existing raised bed furrows. This research gap underscores the need to investigate the influence of these depth criteria on the lowest quarter of the field, where a significant portion of water may fail to penetrate to the required depth, resulting in under-irrigation scenarios [29, 30]. Also, it aids in understanding the impact of depth criteria on the lowest quarter of the field, where insufficient water penetration may lead to under-irrigation.

Material and Methods

Experiment Site and Field Preparation

Field experiments were carried out in the local farm at Khanewal, located in south Punjab, which lies between 30.31° N latitude and 71.86°E longitude as shown in Fig. 1. Climatically, the experimental site has the characteristics of mild winter and dry summer. The mean yearly maximum and minimum temperatures are 41 °C and 6 °C, respectively. Khanewal receives an average of 197 mm of precipitation each year. The district is noted for its fertile areas and crops like wheat, sugar cane, and cotton. Cotton-wheat is the most important crop rotation. Maize, millet, and oil seed crops are also being introduced into current farming systems. The soils in the area are ranged from sandy loam to clay. The canal irrigation system and groundwater supply are the main contributors to the district’s agriculture needs. Moreover, the land at the experiment site of 45 m in length and width of 44 m underwent preparation procedures including moldboard plough followed by laser land leveling. This process involved using moldboard plough techniques to loosen the soil and create a favorable raised bed as shown in Fig. 1. Subsequently, laser land leveling was employed to ensure a uniform and even land surface, optimizing irrigation and agricultural practices.

Fig. 1
figure 1

Location of experimental site; a site location with country Pakistan; b District Khanewal; c prepared field of furrow; d furrow with irrigation

Full size image

Field Data

The soil samples were collected in accordance with the guidelines provided by [31], from random depths at 0–30 and 30–60 cm, and irrigation depth was calculated by using following:

$${Z}_{req =}\sum_{\text{i}=1}^{\text{n}}\left({\theta }_{fc}-{\theta }_{pwp}\right)\times BD\times {D}_{i}\times \text{dep}$$
(1)

In the equation, Zreq represents the water amount necessary to saturate the root zone up to field capacity in millimeters. This is calculated using fc, which denotes the moisture content at field capacity in percentage, pwp representing the moisture content at permanent wilting point in percentage, BD indicating the bulk density of the soil in grams per cubic centimeter, Di representing the effective root depth in meters, and n representing the number of sampling depths within the root zone.

The hydrometer method was used to determine soil texture, and the texture triangle created by the United States Department of Agriculture (USDA) was used to designate the textural class. The cylinder method was applied to calculate the bulk density of the soil. In the study, the bulk density of the soil was determined using the cylinder method, which is a widely employed technique in soil science. This method involves extracting soil cores with a cylindrical tool, measuring the core’s volume, and weighing. The application of this method ensures a precise assessment of soil density, offering valuable data for understanding soil structure and related properties. Also, soil moisture at field capacity (FC) and permanent wilting point (PWP) was evaluated using a pressure plate device. The assessments were conducted at specific pressure heads, with measurements taken at 33 kPa for FC and 1500 kPa for PWP. This method provides a precise characterization of soil moisture levels at critical points, contributing valuable data for understanding soil water dynamics. For the details of the adopted methodology, Ismail et al. [12] are referred to. The soil physical analyses are shown in Table 1.

Table 1 Physical analysis of soil sample collected from experimental site
Full size table

Moreover, furrow geometrical data including top, middle, and bottom widths as well as depth and furrow spacing measured at the field’s start, middle, and tail segments, and the averages of each were taken. The average sizes of all parameters are shown in Fig. 2.

Fig. 2
figure 2

Cross-sectional view of furrow

Full size image

The schematic layout view of the flow dynamic measured in the field is demonstrated in Fig. 3. The movement of water during irrigation by noting the time it entered designated stations near the raised bed irrigation system. According to our observations, water stopped flowing into the field after it reached the far end of the furrows. This method guaranteed that the central area of the raised bed received adequate moisture, which contributed to excellent irrigation output. Furthermore, to calculate the advance rate, wooden sticks were used at every 11.25 m throughout the furrow length of 45 m. The time when the advancing waterfront reached each of the previously indicated wooden sticks along the furrow was recorded. Later, the irrigation has been cut off by achieving the required depth. To measure the inflow rate of the raised bed furrow, we used the volumetric method of each siphon pipe for all irrigation events. In each furrow, the inflow rate was found to be 2 L/s and the cutoff time was 54 min, respectively. A similar methodology has been applied by [10, 31].

Fig. 3
figure 3

Field measurements; a overall measured parameters; b advance time measurement; c recession time measurement

Full size image

WinSRFR Model

The WinSRFR model, developed by [32], is a mathematical model utilized for surface irrigation analysis and simulation. The model includes four components for surface irrigation hydraulic analysis: (i) simulation, (ii) physical design, (iii) event analysis (field evaluation), and (iv) operation analysis. Therefore, this model has been applied to several surface irrigation management and design applications [33]. For the purpose of performing the necessary calculations, the model makes use of two models: a kinematic wave model, which applies to slopes ≥ 0.004, and a zero-inertia model, which applies to farms with closed ends and slopes < 0.004. To model this, the Saint–Venant equations, a set of partial differential equations, are employed. These Eqs. (2) and (3) consist of continuity and momentum equations [34], and are as follows;

$$\frac{\partial Q}{\partial x}+ \frac{\partial A}{\partial t} + {I}_{x} = 0$$
(2)
$$\frac{1}{g}\frac{\partial v}{\partial t} + \frac{v}{g} \frac{\partial v}{\partial x}+\frac{\partial y}{\partial x}= {S}_{0}-{S}_{f}+ \frac{v{I}_{x}}{gA}$$
(3)

where y is the depth of water (m); I is the infiltration rate \({({\text{m}}^{3}\text{s}}^{-1}{\text{m}}^{-1}\)); \({S}_{0}\) is the bed slope (m/m);g is the gravity acceleration (m/s); Q is the inflow (\({\text{m}}^{3}\)/s); v is the velocity of flow (m/s); \({S}_{f}\) is the energy line slope (m/m); t is the irrigation time (s); A is the low cross-section (\({\text{m}}^{2}\)); and x is the distance from the start of the furrow (m).

The Kostiakove (1932) was developed the infiltration model (Eq. 4) which is widely used to evaluate and design of surface irrigation system [35, 36].

$$Z ={kt}^{a}$$
(4)

where Z is the cumulative rate of infiltration (cm3/cm/s); t is the infiltration duration (min); and k and a are the equation’s empirical coefficients. In general, the two basic soil parameters were determined using the two-point technique. By analyzing the relationship between the waterfront’s advance in a furrow and the time it takes to reach the field’s downstream end, Elliot and Walker (1982) created a technique for figuring out the Kostiakov infiltration equation’s constants. In a two-point advance method, a fixed station was placed to measure the time it takes for water to advance during irrigation. In this investigation, the WinSRFR 4.1.3 model was used to determine the parameters of infiltration by taking into account two recorded points: one in the center of the furrow and another at its downstream end. These constants are frequently computed using Eqs. (5) and (7) by Streikoff et al. [32].

$$a= \frac{ln(\frac{VL}{V0.5L})}{ln(\frac{tL}{t0.5L})}$$
(5)
$${\sigma }_{z}=\frac{a+r\left(1-a\right)+1}{(1+a)(1+r)}$$
(6)
$$K=\frac{Vl}{{T}_{L}^{a}{\sigma }_{z}}$$
(7)
$${f}_{o}=\frac{{Q}_{in}-{Q}_{out}}{L}$$
(8)

where, in the equation, L represents the furrow’s length in meters;\({t}_{\text{L}}\) and \({t}_{0.5\text{L}}\) denote the advance time at distances of L and L/2, respectively, measured in minutes; Qout and Qin represent the inflow and outflow discharge (m3/min); \({\sigma }_{\text{z}}\) is the shape factor for subsurface and r represents the power of the advance trajectory in relation to time.

WinSRFR Model Setup

The WinSRFR model was applied in an existing furrow irrigation system. Inputting all the relevant physical operational data and boundary conditions from Table 2 into the event analysis tab, the Kostiakove infiltration parameters were determined applying the two-point method. Specifically, the boundary conditions including inflow rates as upstream and closed end as downstream were chosen since these conditions are commonly applied in the field of agriculture. Manning’s coefficient and infiltration parameters were entered into the model to generate simulated recession and advance curves. Manning’s roughness is a widely used factor for the assessment and design of surface irrigation systems, involving the measurement of flow resistance and the efficiency of water movement. Harun-ur-Rashid [37] reported that in furrow method, Manning’s roughness values typically fall within the range of 0.03 to 0.05, but it can vary depending on the specific location and time [38]. The simulated curves were juxtaposed to the measured curves and new values of infiltration parameters were tested within an approximate range upon weak resemblance between these curves [32, 33, 39, 40]. Figure 4 presents the evaluation and optimization by different modules in WinSRFR.

Table 2 Input parameters for numerical simulation of furrow
Full size table
Fig. 4
figure 4

Schematic diagram for model application

Full size image

According to the classification of performance indicator [41], the performance of the model was evaluated by statistical indicator the root mean square error (RMSE) following Eq. (9) that measures the overall difference between observed and simulated data. It ranges from a minimum value of zero and lower values indicate better model performance.

$$RMSE=\frac{\sqrt{{\sum }_{i=1}^{n}{(Si-Mi)}^{2}}}{n}$$
(9)

where Mi is the ith measured value; “n” is the number of observations; Si is the ith value of the simulated.

Optimization of Irrigation Performance

The model facilitated the optimization by providing the modules of operational analysis and physical design. Furthermore, the calibrated parameters were used in both modules to optimize the irrigation performance by developing four scenarios, shown in Table 3, based on parameters of physical design, including length and width of the furrow, while keeping other parameters (inflow rate, cutoff time, and slope) constant, and also four scenarios, shown in Table 3, based on the parameters of operational analysis, such as cutoff time and inflow rate of the furrow, while keeping other parameters (length, width, and slope) constant.

Table 3 Developed scenarios based on phasical design and operational analysis
Full size table

Irrigation Performance Indicators

The furrow irrigation efficiency was evaluated using performance indicators of distribution uniformity (DU), application efficiency (AE), adequacy (AD), and deep percolation (DP). Based on the recommendations of Bautista et al. [42], these irrigation efficiencies were computed by simulation modelling.

$$\text{AE}=\frac{{D}_{\text{ad}}}{{D}_{\text{ap}}} \times 100$$
(10)
$${\text{DU}}_{\text{min}}=\frac{{Z}_{\text{min}}}{{Z}_{\text{req}}} \times 100$$
(11)
$${\text{AD}}_{\text{min}}=\frac{{Z}_{\text{min}}}{{Z}_{\text{req}}} \times 100$$
(12)
$$\text{DP}=\frac{{D}_{\text{dp}}}{{D}_{\text{ap}}}\times 100$$
(13)

In the above equations, Dad represents the applied water depth to the furrow in millimeters; Dap denotes the water depth added to the root zone in millimeters; Zmin represents the minimum infiltrated depth in millimeters; Ddp represents the depth of deep percolated water in millimeters; Zreq represents the required depth to fill the root zone, which is determined from the root zone water balance, in millimeters.

Results

Parameters of Infiltration Equation

The infiltration parameters were found to be 120.978 mm/h for the hydraulic conductivity (K) and 0.404 for the shape parameter (a) for the first irrigation event in the furrow irrigation system by employing the zero-inertia model. The observed value of K 120.978 mm/h indicates the soil’s ability to allow water to penetrate, reflecting its hydraulic conductivity under the specific conditions of this study.

Figure 5 shows the observed advance, recession, and opportunity times with 2 L/s inflow rate and 50 min cutoff time. The highest advance time was found to be a distance of 28 min at 45 m distance, and the lowest advance time at a distance of 11.25 m was 5 min. The longest recession time was found to be 75 min at the upstream end and the minimum recession time was 60 min at distances of 33.5 m and 45 m from the water source. The greatest infiltration opportunity time was recorded at a distance of 11.25 m and was 70 min, whereas the shortest opportunity time was recorded at 32 min at a distance of 45 m.

Fig. 5
figure 5

Observed advanced, recession, and opportunity time

Full size image

Furrow Irrigation Performance

The root mean square error (RMSE) values were calculated to gauge the accuracy of the model. For the advance curve, the RMSE value was found to be 0.69 min, while for the recession curve, it was 1.19 min (Fig. 6). These lower RMSE values indicated a better match between predicted and observed data, signifying the model’s effectiveness. These RMSE values serve as indicators and validate WinSRFR's ability to evaluate the irrigation system’s performance. The calibration yielded the following values: 120.879 mm/h for parameter (k), 0.609 for parameter (a), and 0.043 for parameter (n).

Fig. 6
figure 6

Measured and simulated advance recession trajectories during calibration

Full size image

These results are in agreement with studies of [43,44,45,46,47] where it is reported that WinSRFR accuracy is reasonable for the simulation of trajectory curves of advance and recession. Also, Nie et al. [48] reported that considering changes in soil infiltration can increase the accuracy of computations of water advance and recession trajectories. Overall, the findings showed that WinSRFR performed satisfactorily and that the field could be optimized using the model to enhance irrigation efficiency.

Figure 7 indicates the average irrigation performance indicators namely DUmin (distribution uniformity at minimum depth), DUlq (distribution uniformity at low quarter depth), AE (application efficiency), and DP (deep percolation) at different levels. Specifically, the DUmin, DUlq, AE, DP, ADlq, and ADmin were at 80%, 83%, 40%, 60%, 1.51, and 1.56 respectively. Overall, the performance indicators suggested an unsatisfactory performance except for the DU.

Fig. 7
figure 7

Evaluating existing irrigation performance by different indicators

Full size image

Improving Irrigation Performance by Varying Length and Width

Figure 8 shows the performance indicators at different lengths and widths under constant inflow rate and cutoff time. Figure 8a and b present results of scenarios (S1) and (S2). The findings revealed that by increasing the furrow length, the efficiency and uniformity were found to be decreased while deep percolation losses were found to be increased. The highest DU and PAE and the lowest DP were observed at a furrow length of 45 m in (S1) and 20 m in (S2) respectively. On the other hand, the lowest performance occurred at the furrow length of 60 m in (S1) and 45 m in (S2), respectively. Furthermore, overflow conditions were observed at furrow lengths of 20 and 30 m in (S1), and the flow does not reach at downstream end of the field at 60 m length in (S2), under the given width, inflow, and cutoff times.

Fig. 8
figure 8

Performance evaluation at various lengths (20–60m) with a width 1.33 m, b width 2.66 m, c width 3.99 m, d width 5.32 m

Full size image

Figure 8c and d illustrate the outcomes of scenarios (S3) and (S4). The highest levels of performance in terms of DU, PAE, and DP were attained at a length of 20 m in (S3) and (S4), respectively. Conversely, the least performance was observed when the length was set at 30 m in (S3). Beyond a length of 20 m in (S4) and 30 m in (S3), the flow failed to reach the field’s downstream end. This pattern emphasizes the trend that increasing the furrow width and length causes the reduction in efficiency and uniformity, while also contributing to an increase in deep percolation losses.

Figure 9 illustrates the comparison of performance indicators for existing and optimized furrow design based on its width and length at a specified inflow rate and cutoff time. The results indicated that optimized design can improve potential application efficiency by up to 12% and the distribution uniformity up to 3% compared to the existing length and width settings. Additionally, a reduction in deep percolation losses of 12% can be achieved.

Fig. 9
figure 9

a Existing and b optimized efficiencies of furrow on the basis length and width

Full size image

Optimizing Irrigation Performance by Varying Cutoff Time and Inflow Rate

Figure 10 represents the performance indicators at various inflow rates and cutoff times with constant length and width (S5–S8). Results of scenarios S5 and S6 (Fig. 10a–b) indicated that by increasing the cutoff time, the deep percolation losses were found to be increased while efficiency and uniformity were found to be decreased. The maximum DU and AE and minimum DP were achieved on cutoff time of 54 min (S5) and 30 min (S6). The minimum distribution uniformity, application efficiency, and maximum deep percolation losses were achieved at 60 min cutoff time in S5 and S6. Moreover, the flow does not reach at the downstream end of the field cutoff time of 30 min in S5.

Fig. 10
figure 10

Performance evaluation at various cutoff times (30–80 min) with a inflow rate 2 L/s, b inflow rate 4L/s, c inflow rate 5 L/s, d inflow rate 6 L/s

Full size image

Figure 10c and d illustrate the outcomes of scenarios (S7) and (S8). The lowest levels of performance in terms of DU, AE, and DP were attained at cutoff time 60 min (S7) and 54 min (S4). The maximum performance was achieved at 30 min cutoff time in both scenarios. Beyond the cutoff time of 60 min in (S7) and 80 min in (S8), the overflow conditions were ensued on the given length and width. This pattern emphasizes the trend that increasing the furrow inflow and cutoff causes a reduction in efficiency and uniformity, while also contributing to an increase in deep percolation losses.

According to the findings presented in Fig. 11, modifying both parameters (inflow rate and cutoff time) based on current length and width can lead to significant improvements in distribution uniformity and application efficiency The results indicate that by adjusting these parameters, the application efficiency can be enhanced by approximately 12%, while distribution uniformity can be improved by approximately 3% compared to the current length and width settings. Furthermore, adjusting the inflow rate and cutoff time also helps in reducing deep percolation losses by approximately 12%. These improvements demonstrate the potential of optimizing the irrigation system design and operation to achieve better performance and minimize water losses in terms of deep percolation.

Fig. 11
figure 11

a Current and b optimized efficiencies of furrow based on inflow rate and cutoff time

Full size image

Discussion

This higher K value suggests that the soil can absorb water at a faster rate due to the first irrigation (where soil pores were dry). The shape parameter (a) of 0.404 provides insights into the furrow’s geometric characteristics and how water spreads within it. This smaller value of “a” indicates a narrower and deeper furrow shape. Xu et al. [43] and Mazarei et al. [49] demonstrated that the range of variation for k was larger for the first irrigation event than the later irrigation events, but depended upon the cultural practices and soil type which were consistent with the results as obtained by this study. The variations observed in advance, recession, and opportunity times along a surface furrow irrigation system. Notably, the advance time was found to be notably shorter at the upstream end as compared to the downstream end. The advance time gradually increased with longitudinal direction throughout the irrigation process along the furrow. The observation showed the differing advance times can be attributed to variations in soil moisture. The dry soil was found to be on the downstream side and the wet soil was found to be on the upstream side which resulted in higher advance time and lower recession time at the downstream end and lower advance time and higher recession time at the upstream end. This suggests efficient water distribution and utilization in the furrow, indicating that the irrigation system effectively delivers water to the crops. The infiltration opportunity time was notably longer at the upstream end when compared to the downstream end which indicated that a greater amount of time was required for water infiltration to occur at the upstream end, in contrast to the downstream end. The reason behind this prolonged infiltration time can be attributed to the soil profile in the vicinity of the nearby earthen water course. Specifically, the soil in this area was both wet and hard, which hindered the infiltration process.

In this context, the irrigation performance is impacted by the advance and recession times. It was noticed that with surface irrigation, the irrigation water did not reach every location along the longitudinal direction at the same time, leading to different window of opportunity periods [43]. The deep percolation, low water use efficiency, and poor distribution uniformity are caused by long advance and recession times, which cause greater infiltration in the front and less in the downstream end [46, 50]. Mazarei et al. [49] and Abbasi et al. [51] reported that opportunity time is reduced by length but advance and recession times depend on soil type and farming type and shape of furrow and vegetation within the field. The outcomes of this research study are comparable with aforementioned studies; however, these depend on field soil types majorly.

In this case, the (AE) indicates that a significant portion of the water applied was not effectively used in the case of furrow, by the crops, potentially leading to water wastage. The (DU) indicated relatively good uniformity suggesting that water was distributed more evenly compared to other aspects of irrigation performance. The DP indicated that the applied water, with a significant portion, is moving below the root zone causing the leaching down the nutrients and wasting water resources as well. Moreover, the admin (adequacy at minimum depth) and ADlq (adequacy at low quarter depth) indicated poor performance in these aspects. It is important to note that both values were greater than one indicating the over-irrigation in the field. This finding aligns with previous studies conducted by Ismail et al. [12] and Mazarei et al. [13], which also confirmed the presence of over-irrigation issues.

The low efficiency and high losses observed in existing irrigation performance evaluation by furrow irrigation can be attributed to a combination of factors including a low inflow rate, a lengthy cutoff time, and suboptimal field length. Because of this combination, the field received an excessive amount of water application that exceeded the necessary application rate. Besides, a significant portion of the applied water went unused by the crops leading to a decrease in application efficiency. The prolonged cutoff time and geometric parameters of the furrow extended the duration of water application which increased the problem of excessive water application. This inefficiency in water use negatively impacted the overall performance of the irrigation system. Furthermore, despite the distribution uniformity remained excellent as per defined categories by Diotto and Irmak [47] the other performance indicators require to be improved. Griffiths et al. [52] stated that well-designed and well-managed furrow irrigation systems typically aim for a recommended application efficiency ranging between 65 and 70%; however, this can be higher depending on specific soil types and effective management practices. In terms of deep percolation, a desirable range falls between 30 and 45%. However, it is important to avoid excessive deep percolation, as this significant irrigation water loss undermines the efficiency of the system. Effective design and management are crucial to strike the right balance and optimize water use in furrow irrigation. There are several categories into which furrow irrigation distribution homogeneity can be divided: DU is classified as bad if it is less than 70%, good if it is between 70 and 85%, very good if it is between 85 and 90%, and excellent if it is greater than 90% [53].

These findings highlight the importance of considering the length parameter in the design and its optimization. The geometry parameters (field slope, length, and width) can affect the irrigation performance [46, 54]. Overall, results agreed with those obtained by Akbar et al. [55] where more potential to rise PAE was observed by decreasing the width and length of the field at a given inflow rate and cutoff time. Bo et al. [50] also reported that application efficiency was decreased by increasing the length of the field and deep percolation losses were increased with increasing length.

The decision variables (inflow and cutoff time) play a vital role while optimizing and inflow is more influential than cutoff time [8, 47]. The results of the relationship between increased cutoff time and decreased application efficiency (AE) are consistent with the outcomes from [10, 25]. The current study’s findings parallel those of decision variables, particularly discharge relative to cutoff time, in determining the efficiency of furrow irrigation. Similarly, the results of increasing the inflow rate with cutoff time decrease the overall efficacy which aligns with the study conducted by ref. [8, 56], showing cutoff time had a less pronounced effect. Moreover, besides the decision variables, other factors such as mismanagement of irrigation water result in losses after entry into field ditches because of lateral flow into side furrows, improper timing of irrigation application, and a lack of consideration for soil type, as all soil types affect or contribute to less application efficiency. Results by Tadele et al. [57] present a higher application efficiency when varying the decision variable compared to our study and other related research. Our field observations in the current experiment contributed to lower application efficiency. In summary, our study’s results align with those of previous research, emphasizing the importance of discharge and cutoff time as critical decision variables in furrow irrigation management.

Conclusion

This study presents a comprehensive evaluation and improvement of irrigation performance for raised bed furrows, employing a combination of field experiments and WinSRFR model simulations. The optimization process focused on key parameters such as input rate, cutoff time, and physical field properties, while irrigation performance parameters, including application efficiency (AE), distribution uniformity (DU), and deep percolation (DP), were thoroughly assessed. The important conclusions are as follows:

The findings demonstrate the reliability of the WinSRFR model in simulating advance and recession times, showcasing its accuracy in comparison to field measurements.

Results also highlight the importance of avoiding extreme lengths to maintain efficiency and uniformity and minimize deep percolation losses. Optimal furrow dimensions of 30 m length and 2.32 m width are suggested to enhance performance, resulting in increased AE and DUmin and decreased deep percolation losses.

The operational analysis further recommends an inflow rate of 4 lps and a cutoff time of 30 min for existing field conditions, leading to notable improvements in AE and DUmin and reduced deep percolation losses. The overall impact of WinSRFR on designing physical and operational parameters is evident, providing a cost-effective and time-efficient approach to enhancing irrigation performance.

Overall, WinSRFR assisted in designing physical and operational parameters at the lowest cost and shortest time for the enhancement of irrigation performance. Therefore, it is recommended that WinSRFR be used for designing and managing surface irrigation systems to reduce irrigation water losses and increase distribution uniformity. This study would be helpful for farmers, irrigation, and water management experts, policy makers, agronomists, and researchers to achieve the highest productivity and water use application efficiency. However, discrepancies in application efficiency outcomes across studies underscore the need for careful consideration of local conditions and management practices to optimize water distribution and efficiency. Since, the WinSRFR model simulates the flow of furrow irrigation systems, based on the one-dimensional open channel flow equation coupled with an infiltration function; therefore, advanced subsurface flow aspects should also be integrated along the furrow or channel. The current study is conducted just for the furrow irrigation method with one irrigation event. However, future researchers can extend this for different crop growth stages, methods, and soil textures with multi-irrigation events and accordingly improve the irrigation performance after optimization.