Simulation soil water-salt flux and irrigation quota for summer maize based on SWAP model in the Northwest China

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Methods The SWAP (Soil-Water-Atmosphere-Plant) model parameters were calibrated and validated based on field experiments data to simulate soil water-salt flux and soil water-salt cumulative flux under different scenarios in the Northwest China. Results Soil water flux, soil water cumulative flux, soil salt flux and soil salt cumulative flux decreased with the decrease on irrigation quota at the lower interface of crop root zone and storage zone under different scenarios. The soil water cumulative flux and soil salt cumulative flux changed small, when the irrigation quota was reduced to 70%IQ (Irrigation Quota) and 60% IQ. Soil water could be stably stored in 0-100 cm soil layer to meet the growth requirements of summer maize, which brought in by irrigation and rainfall. When the irrigation quota of summer maize was 70% IQ (3500 m 3 ·ha − 1 ), soil water-salt flux and soil water-salt cumulative flux were minimal at the lower interface of crop root zone and storage zone. The yield reduction of summer maize was only 10%. Conclusions 3500 m 3 ·ha − 1 was the optimal irrigation quota for summer maize from the perspective of soil water-salt balance and crop growth. It was to provide technical support for the efficient utilization of water resources and also guided agricultural production practice in the Northwest China. Soil water flux soil salt flux irrigation quota summer maize SWAP model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Soil salinization stands as a significant global challenge and a primary factor impacting agricultural productivity and the ecological health of farmlands in the world. According to the statistic analysis, the salinization area of cultivated land has reached 9.21×10 6 ha, accounting for 6.62% of the total cultivated area in China (Wang et al., 2019 ; Feng et al., 2020 ; Li et al., 2024 ). The rational utilization of salinization land is of great significance to the development of comprehensive agriculture in China. Lubotan is a closed structural depression, which located in Shaanxi Province of Northwest China. Soil salt has accumulated in Lubotan for a long time, which was affected by natural and human activities. Although it was reclaimed as farmland in modern times, due to the geographical low-lying land characteristics of the Lubotan, the drainage of farmland was not smooth, the groundwater level rose, and the soil salinization was gradually aggravated in Lubotan. Most farmland suffered from soil salinization to varying degrees, which seriously restricted local agriculture proceeding development (Li et al., 2021 ; Li et al., 2023 ). The local relevant departments carried out large-scale development and renovation of the Lubotan through land leveling, drainage and alkali control measures at the end of the 20 century. The cultivated land was not salinized in large areas, and the crops planted were less affected by salt and alkali in Lubotan. However, the flood irrigation method was widely used in the Lubotan, which not only caused the waste of water resources, but also leaded to the current rise in groundwater levels and soil secondary salinization (Sun et al., 2019 ; Li et al., 2021 ; Jing et al., 2021 ). In order to reduce soil secondary salinization and save water resources, water-saving irrigation measures should be adopted for summer maize in the Lubotan. After the implementation of water-saving irrigation measures for summer maize, soil water-salt transport and water-salt balance will change. The relationship between soil water-salt flux change and irrigation quota was still not clear, and the optimal irrigation quota for summer maize was also not clear in the study area. Therefore, it was necessary to study the change of soil water-salt flux and the irrigation quota of summer maize in Lubotan. Field experiment was a method to study soil water-salt transport and crop irrigation quota, but there were many factors affecting field experiment, which required a long cycle and high cost (Dewedar et al., 2020 ; Létourneau and Caron, 2021). Mathematical models were widely used to simulate soil water-salt transport and crop irrigation quota based on field experiments. In these mathematical models, the SWAP model was extensively utilized to simulate soil water-salt transport and crop irrigation quota in the world (Zhao et al., 2020 ; Ravensbergen et al., 2024 ). It had been widely accepted and recognized (Heinen et al., 2024 ). The SWAP model is a physically model that simulates soil water flow, solute transport, heat transfer and crop growth processes at the field scale through the entire growing seasons (van Dam et al., 2008 ; Hu et al., 2019 ).. Therefore, SWAP model can be used to simulate the changes of soil water-salt transport and irrigation quota of summer maize under different water-saving irrigation in the study area. The SWAP model provided an effective method for the formulation and evaluation of crop irrigation system, the prediction of groundwater depth change, the prediction of soil water-salt transport, and the prediction of crop growth and yield (Verma et al., 2014 ; Chen et al., 2019 ; Alavi et al., 2022 ). Shafiei et al. 2014 used SWAP model to simulate soil water distribution and water flow flux of two different types of fields in the arid area of Iran. It showed that the water flow flux of farmland soil deep layer downward leakage changed greatly and was more sensitive to the influence of soil hydraulic conductivity. Jiang et al.2016 used the SWAP model to simulate the soil water-salt transport of spring wheat in Shiyang River Basin of Gansu Province of Northwest China. It showed that soil salt could remain stable after five years of continuous saline water irrigation, and the SWAP model could predict soil water-salt dynamic changes over extended periods. Chen et al. 2019 used SWAP model to simulate soil water movement in paddy fields under four water-saving irrigation modes. It showed that the controlled irrigation and drainage modes under different hydrological years had obvious water-saving and labor-saving effects, and did not cause a reduction in rice yields, which was conducive to guiding the practice of rice water-saving irrigation. The researchers used the SWAP model to simulate the changes of soil water-salt transport, crop irrigation management, which provided a theoretical basis and scientific basis for controlling soil salinization (Li et al., 2020; Kramer and Mau, 2020). These publications show that, the SWAP model could be used to deeply study soil water-salt transport and crop irrigation management under crop growth conditions in salinized regions. However, the above studies did not characterize the relationship between soil water-salt flux changes and irrigation quotas under different water-saving irrigation. The relationship between soil water-salt fluxes, soil water-salt cumulative fluxes and irrigation quota of summer maize had been reported rarely in Northwest China. The SWAP model parameters were calibrated and validated based on field experiments data. Three different irrigation quotas were set for summer maize, and the SWAP model was used to simulate the soil water-salt fluxes, soil water-salt cumulative fluxes and summer maize yield for different scenarios under summer maize growth conditions. The purpose of this study was to provide technical support for the efficient utilization of water resources and to guide agricultural production practice in Lupotan of Northwest China. The objectives of this study were: (1) to calibrate and validate the SWAP model parameters, simulated results were compared with measured results obtained under field experiment conditions; (2) to simulate soil water flux and soil water cumulative flux under different scenarios; (3) to simulate soil salt flux and soil salt cumulative flux under different scenarios. Material and methods Design of field experiments Field experiments were carried out from June 2018 to September 2019 in Lupotan area (109°22′E, 34°48′N, and altitude 490 m) of Northwest China. A standardized farmland was selected as the experimental field in Lupotan, with an area of 4×10 4 m 2 (Fig. 1 ). The farming system was mainly winter wheat and summer maize rotation in Lupotan. There were a large number of saline alkali lands in Lupotan. The soil is characterized as typical sulfate saline-alkali soil, with a pH ranging from 8.3 to 8.6 in experiment region. The soil particle size composition was measured by laser particle size analyzer (Master sizer 2000, UK). According to the international classification standard of soil texture, soil texture of the experimental area was determined. Soil physical properties for different soil layers in experiment region are shown in Table 1 . The experiment crop was summer maize ("Zhengdan 958") with 25 cm plant spacing and 35 cm row spacing in 2018 and 2019. Summer maize was sown in early June and harvested at the end of September with whole growth period of about 120 days. Summer maize was irrigated with border irrigation method and was irrigated four times during the growth period, consistent with the actual conditions of local summer maize cultivation. Actual evapotranspiration (ET c ) of summer maize during the growth period was 500 mm (Pan et al., 2020 ). Irrigation water quota of summer maize growth period as follows: 1200 m 3 ·ha − 1 (June 25), 1300 m 3 ·ha − 1 (July 15), 1300 m 3 ·ha − 1 (August 22) and 1200 m 3 ·ha − 1 (September 10). Irrigation water salinity was about 0.4 g·L − 1 . Due to the current irrigation conditions of summer maize, three summer maize fields were set up as three experimental plots in a standardized farmland, and the irrigation method and system were consistent with three replicates. Summer maize needed to be fertilized before sowing. The amount of fertilization was 600 kg·ha − 1 for diammonium phosphate, 300 kg·ha − 1 for urea, and 225 kg·ha − 1 for potassium. Herbicides were sprayed before sowing summer maize. Other agronomic measures were consistent with the local actual situation. Table 1 Soil physical properties for different soil layers in experiment region Soil depths (cm) Clay (<0.002mm, %) Silt (0.002 ~ 0.02mm, %) Sand (0.02 ~ 2.00mm, %) Soil bulk density (g·cm − 3 ) Soil texture 0–20 4.58 49.43 46.00 1.46 Silty sandy loam 20–40 3.23 46.81 44.05 1.48 40–60 4.51 45.26 43.24 1.49 60–100 4.26 45.74 45.27 1.50 Experiments observation items and methods The observation section was located in the center of the standardized experimental field. Three TRIME pipes of 1 metre long were arranged and buried on the observation section. The TRIME-PICO of portable soil water content meter was used to measure 0–20, 20–40, 40–60, 60–80 and 80–100 cm, respectively. The observation period was from June 2018 to September 2019. Soil samples were obtained by soil drills in layers near each TRIME pipes, with the depth of 0–20, 20–40, 40–60, 60–80 and 80–100 cm, respectively. Electrical conductivity, EC 1:5 (mS·cm − 1 ) was measured by conductivity meter and translated into soil salinity (g·kg − 1 ) using the calculation equation ( S = 0.2813EC 1:5 -0.0056 , where S refers to soil salinity) (Pan et al., 2020 ). There was a 3 metre deep groundwater observation well in the middle of the standardized experiment farmland, which was observed the change of groundwater table depth. The groundwater table depth was between 1.85 and 2.25 meters in experiment region. The initial soil water content, soil salt content and hydraulic characteristic parameters for different soil layers were obtained before sowing summer maize. The parameters of soil water characteristic curve were measured using a centrifuge, and the hydraulic characteristic parameters of VG (van Genuchten) model were fitted using RETC software. The initial soil hydraulic characteristic parameters for different soil layers are shown in Table 2 . The initial measured molecular diffusion coefficient was 3.5 cm 2 ·d − 1 , and the dispersion length was 19.0 cm. Table 2 Soil hydraulic parameters for different soil layers in experiment region Soil depths (cm) θ r (cm 3 ·cm − 3 ) θ s (cm 3 ·cm − 3 ) K s (cm·d − 1 ) α n γ 0–20 0.025 0.44 120.00 0.200 1.574 0.5 20–40 0.028 0.42 79.46 0.089 1.574 0.5 40–60 0.026 0.39 100.20 0.089 1.574 0.5 60–100 0.029 0.41 89.56 0.089 1.574 0.5 Note: θ r is residual water content; θ s is saturated water content; K s is saturated hydraulic conductivity; α, n, γ are shape factor. The same as below. After emergence of summer maize, the plant height (H), leaf length (L) and width (B) of summer maize under difference growth stages were measured every 7–10 days with a steel tape. The leaf area index (LAI) was calculated with the equation ( LAI = ( k × L × B )· A − 1 , k is a fitting coefficient (0.75 for summer maize), A is the area covered by summer maize leaves) (Korzukhin and Grabovsky, 2020 ). The root drill with a diameter of 8 cm was used to take samples using the "cross method" (5 drills were taken from each corn plant, each drill was divided into 5 layers, each layer was 20 cm, and 100 cm was taken to remove most of the maize roots) to obtain the root length data of summer maize. The root length and density distribution data of summer maize were obtained by scanning the root scanner and using WinRHIZO root analysis software, and repeated three times. The summer maize yield was measured after harvest, and the yield was the yield of dried seeds (kg·ha − 1 ). Meteorological data was obtained by the automatic weather station in experiment region. The rainfall in 2018 and 2019 was 442.2, 454.0 mm, respectively. Both 2018 and 2019 were normal years in study area. The monthly average meteorological data are shown in Table 3 . Table 3 Average meteorological data every monthly in 2018 and 2019 Month Maximum temperature (°C) Minimum temperature (°C) Average temperature (°C) Average humidity (%) Average wind speed (m·s − 1 ) Average air pressure (KPa) Rainfall (mm) 1 -0.51 -10.75 -2.54 56.34 1.17 90.82 2.40 2 1.56 -9.70 -1.67 48.46 1.27 90.33 4.40 3 11.61 -1.89 6.68 46.54 1.40 90.15 2.30 4 20.58 4.18 14.68 46.98 1.63 89.68 26.70 5 23.11 9.09 18.41 44.61 2.25 89.50 93.20 6 32.42 15.50 26.82 56.24 1.26 89.48 17.40 7 36.45 15.18 28.01 73.58 0.38 89.35 147.30 8 35.30 16.35 28.30 78.72 0.66 89.45 63.20 9 28.20 12.59 19.40 68.69 0.56 90.24 50.70 10 20.24 6.19 12.42 56.72 1.17 90.32 15.40 11 9.96 -2.01 3.19 58.83 1.26 90.69 31.00 12 0.77 -12.71 -3.17 62.84 1.37 90.59 0.00 SWAP Model The SWAP (Soil-Water-Atmosphere-Plant) model is a comprehensive model that simulates soil water movement, solute transport, heat transfer, and crop growth processes at the field scale by integrating the theoretical research results of the current SPAC (Soil-Plant-Atmosphere Continuum) system (van Dam et al., 2008 ; Hu et al., 2019 ). The meteorological data used in the model were obtained from the automatic weather station installed in experiment region in 2018 and 2019. Based on the actual situation of soil texture and the depth of the summer maize root active layer in the growth period, the depth of the simulated soil profile was 0-100 cm. The 0-100 cm soil layer of the simulated soil profile was divided into 34 soil layers. The initial soil water content and soil salt content data were used in SWAP model. The upper boundary conditions of the model were rainfall, evaporation, crop transpiration and irrigation determined by meteorological factors. The bottom boundary condition of the model adopted a kind of boundary, which was groundwater table depth of the groundwater observation well in the study area. The actual irrigation quota was used for summer irrigation in SWAP model. Other data required for model inputs were experimental measured data. For an exhaustive and unparalleled introduction to the SWAP model, refer to the SWAP model theory book (van Dam et al., 1997 ). The RMSE (Root Mean Square Error) and MRE (Mean Relative Error) were utilized to quantify the deviation of the simulated results from the measured results. Results Model calibration and validation of SWAP model parameters The comparison between the simulated and measured soil water content for different soil layers are shown in Fig.2 and Fig. 3. The simulated values were agreed precisely with the measured values in each soil layers during summer maize growth periods. The RMSE values﹤0.05 cm 3 ·cm -3 , and the MRE values﹤15%. The simulation effect of soil water content was feasible. The parameters of soil water characteristic curve after model calibration and validation are shown in Table 4. Soil salt content was the percentage of the mass of salt in the soil to the dry soil mass. The comparison between simulated and measured soil salt content for different soil layers are shown in Fig. 4 and Fig. 5. The simulated values were agreed precisely with the measured values in each soil layers during summer maize growth periods, and slightly worse than soil water content calibration process. The RMSE values﹤0.10 mg·cm -3 , and the MRE values﹤20%. The simulation effect of soil salt content was feasible. The molecular diffusion coefficient was 0.85 cm 2 ·d -1 , and the dispersion was 10.0 cm after model calibration and validation. Table 4 Soil hydraulic parameters for different soil layers after calibration and validation Soil depths (cm) θ r (cm 3 ·cm -3 ) θ s (cm 3 ·cm -3 ) K s (cm·d -1 ) α n γ 0-20 0.020 0.44 140.00 0.200 1.574 0.5 20-40 0.028 0.39 99.86 0.089 1.574 0.5 40-60 0.024 0.38 120.00 0.089 1.574 0.5 60-100 0.028 0.38 99.86 0.089 1.574 0.5 The comparison between the simulated and measured plant height of summer maize are shown in Fig. 6. The simulated values were agreed precisely with the measured values. The RMSE values﹤10 cm, and the MRE values﹤15%. The comparison between the simulated and measured LAI of summer maize are shown in Fig. 7. The simulated values were in agreement well with the measured values. The RMSE values of LAI﹤1.0 cm 2 ·cm -2 , and the MRE values﹤15%. The simulation effect of plant height and LAI were feasible. The simulation yield of summer maize were 7902.4, 7803.6 kg·ha -1 in 2018 and 2019, respectively. The actual yield of summer maize in field experiments were 8516.4, 8507.0 kg·ha -1 in 2018 and 2019, respectively. The simulated values were agreed precisely with the measured values (Fig. 8). The RMSE values of summer maize yield﹤800.0 kg·ha -1 , and the MRE values﹤8.0 % . The simulation effect of summer maize yield was feasible. The minimum canopy resistance, critical level EC max , decline root water uptake per unit EC slope of summer maize were 70 s·m -1 , 1.7 dS·m -1 and 12% after calibration and validation, respectively. The above results demonstrated that SWAP model can accurately simulated soil water content, soil salt content, summer maize growth and yield under crop growth conditions after calibration and validation in the study area. Simulation soil water flux and soil water cumulative flux under different scenarios The flood irrigation with canal water for summer maize was an extensive irrigation method in the Lupotan of Shaanxi Province, which was easy to waste water resources and leaded to secondary salinization of soil (Xu et al., 2019). Proper adjustment and optimization of crop irrigation quota could promote the efficient utilization of water resources. Based on the meteorological data from 1961 to 2023, the rainfall of 25%、50% and 75% hydrologic years was 521, 454, 405 mm. There were minimal differences among the difference hydrologic year levels, thus only 50% hydrologic year was used in model simulation. Irrigation quota (IQ) was 5000 m 3 ·ha -1 under the actual planting condition of summer maize in Lupotan. This simulation set three irrigation quotas: 80% IQ (4000 m 3 ·ha -1 ), 70% IQ (3500 m 3 ·ha -1 ) and 60% IQ (3000 m 3 ·ha -1 ). At the same time, the irrigation quota in each summer maize growth period was also adjusted according to the corresponding proportion. The initial soil water-salt content and summer maize growth data were consistent with the field experiments measured data in 2018. The upper boundary conditions were rainfall, evaporation, crop transpiration and irrigation determined by meteorological factors. The bottom boundary condition adopted a kind of boundary, which was groundwater table depth of the groundwater observation well in 2018. The simulated soil layer thickness was 0-100 cm. Soil water flux and salt flux for different soil layers were simulated by SWAP model. On this basis, it determined the optimal irrigation quota for summer maize in Lupotan. Soil water flux simulation results of different soil profiles under different scenarios are shown in Fig. 9. The 60 cm profile was the soil profile of summer maize main root system layer, which was expressed as the water flux of the lower interface of the root zone. The 100 cm profile was the soil profile of the largest root depth of summer maize, which was expressed as the water flux of the lower interface of the storage zone. Soil water flux was negative downward and positive upward (the same as below). Soil water flux change was closely related to irrigation and rainfall in the lower interface of root zone. Soil water leakage in root zone mainly occurred after four times irrigation, and decreased with the decrease of irrigation quota. Soil water flux was below -7.0 mm·d -1 under 80% IQ, and below -2.0 mm·d -1 under 70% IQ and 60% IQ. It was shown that the reduction of irrigation quota could reduce the amount of soil water leakage. It could avoid soil water loss, and most of the water could be retained in the root soil layer of summer maize for crop use, which brought in by irrigation and rainfall. Variation law of soil water flux at the lower interface of the storage zone was similar to that of at the lower interface of the root zone. Soil water flux was below -2.0 mm·d -1 under 80% IQ, and soil water flux was below -1.0 mm·d -1 under 70% IQ and 60% IQ scenarios. Three scenarios could reduce soil water flux at the lower interface of storage zone, reduce the downward leakage loss of soil water, and make the soil water stored in the storage zone. Soil water in the storage zone exchanged with soil water in crop root zone. Table 5 shows soil water cumulative flux in different soil profiles under different scenarios. If the soil water cumulative flux was negative, it meant that soil water leaks downward. The soil water cumulative flux at the lower interface of root zone and the lower interface of water storage zone decreased with the decrease of irrigation quota. The soil water cumulative flux at the interface under the root zone decreased by 18.36 mm, 29.53 mm and 31.62 mm compared with 100% IQ under 80% IQ, 70% IQ and 60% IQ, respectively. The reduction of irrigation quota could reduce soil water leakage in root zone and improved crop water use efficiency. The soil water cumulative flux at the lower interface of the storage zone was 4.24 mm, 6.57 mm and 7.8 mm under 80% IQ, 70% IQ and 60% IQ scenarios, which were less than that of under 100% IQ, respectively. The reduction of irrigation quota could make soil water stored in the storage zone. At the same time, when irrigation quota was reduced to 70% IQ and 60% IQ, the soil water cumulative flux at the lower interface of root zone and the lower interface of storage zone was small, and water brought in by irrigation and rainfall could be stably stored in 0-100 cm soil layer for summer maize growth. Table 5 Soil water cumulative flux of 60 and 100 com soil profile under different scenarios Soil profile (cm) Soil water cumulative flux (mm) 100%IQ 80%IQ 70%IQ 60%IQ 60 -38.89 -20.53 -9.36 -7.27 100 -15.57 -11.33 -9.00 -7.77 Simulation soil salt flux and soil salt cumulative flux under different scenarios Soil salt flux simulation results in different soil profiles under different scenarios are shown in Fig. 10. Soil salt flux exhibited a similar pattern with soil water flux, and soil salt flux in the lower interface of root zone decreased with the decrease of irrigation quota. Soil salt flux was below -9.5 mg·(cm 2 ·d) -1 under 80% IQ, below -3.0 mg·(cm 2 ·d) -1 under 70% IQ, and below -1.5 mg·(cm 2 ·d) -1 under 60% IQ, respectively. Soil salt flux was below -1.8 mg·(cm 2 ·d) -1 at the lower interface of the storage zone under 80% IQ, 70% IQ and 60% IQ scenarios. It had little difference between 80% IQ and 60% IQ scenarios, respectively. Soil salt flux decreased with the decrease of soil water flux, which fully explained that water salt transport characteristics of "salt comes with water and salt goes with water" (Wang et al., 2019). The soil salt cumulative flux in different soil profiles under different scenarios was shown in Table 6. If soil salt cumulative flux was negative, it meant that soil salt was leached downward. The soil salt cumulative flux at the lower interface of root zone and the lower interface of water storage zone decreased with the decrease of irrigation quota. The soil salt cumulative flux at the interface under the root zone decreased by 14.25, 23.39 and 25.54 mg·cm -2 under 80% IQ, 70% IQ and 60% IQ compared with 100% IQ, respectively. The soil salt cumulative flux at the lower interface of storage zone decreased by 3.77, 4.75 and 5.22 mg·cm -2 under 80% IQ, 70% IQ and 60% IQ compared with 100% IQ, respectively. When irrigation quota was reduced to 70% IQ and 60% IQ, the change of soil salt cumulative flux at the lower interface of root zone and the lower interface of storage zone was small, and less soil salt brought by irrigation was accumulated in the 0-100 cm soil layer. In addition, the simulated summer maize yields were 7408.50, 7112.16and 6914.60 kg·ha -1 under 80% IQ, 70% IQ and 60% IQ scenarios, respectively, which were 6.25%, 10.0% and 12.5% lower than those under 100% IQ (7902.4 kg·ha -1 ) scenarios. Through comprehensive analysis, compared with the situation that soil water flux, soil salt flux and soil water-salt cumulative flux at the lower interface of root zone and storage zone were small, and the yield reduction of summer maize was small. 70% IQ (3500 m 3 ·ha -1 ) irrigation scenario could be used as the optimal irrigation quota for summer maize in the study area. Table 6 Soil salt cumulative flux of 60 and 100 com soil profile under different scenarios Soil profile (cm) Soil salt cumulative flux (mg·cm -2 ) 100%IQ 80%IQ 70%IQ 60%IQ 60 -35.05 -20.80 -11.66 -9.51 100 -17.65 -13.88 -12.90 -12.43 Discussion The SWAP model simulated soil water and solute transport of the vertical one-dimensional soil, without considering the flow relationship between soil water and groundwater in different regions (Hu et al., 2019 ). Due to study area was selected as a standardized farmland as experiments field, the area was large and there was an exchange between soil water and groundwater. The SWAP model simulation did not take into account the exchange of soil water and groundwater. Soil hydraulic characteristic parameters were relatively easier to be calibrated accurately and model accuracy was higher under calibration of soil hydraulic characteristic parameters and solute transport parameters. The accuracy of solute transport parameter rate was relatively low, which was mainly due to the fact that the solute parameters such as molecular diffusion coefficient, dispersion, and solute exchange rate between free water and adsorbed water were difficult to adjust to the most appropriate value (Kargas et al., 2016 ; Zhang et al., 2021 ). Furthermore, the solute not only moved with the movement of soil moisture, but also moved under the action of its own concentration gradient (Lei et al., 2023 ). When calibrating the parameters of the SWAP model, the parameter estimation was carried out by the trial-and-error method (Fattori and Marin, 2023 ; Wang et al., 2024 ). This method had prominent problems such as large amount of computation, long time-consuming and strong subjectivity. Moreover, SWAP model also lacked a nested parameter sensitivity analysis module, which did not accurately identify the parameters that had a greater impact on the simulation results (Zhao et al., 2020 ; Huang et al., 2024 ). Although there were some limitations between the SWAP model and the actual situation in simulating soil water and salt transport and crop growth, the theoretical basis of the SWAP model was mature and reliable (Camargo and Kemanian, 2016 ; Yu et al., 2020 ). Data of model simulation was easy to obtain through field experimental determination, and the model was relatively easy to operate and use. It had been widely accepted and recognized in practice. The SWAP model was widely used to simulate soil water and salt transport in arid or semi-arid areas around the world (Ravensbergen et al., 2024 ). Soil water and salt transport was complex and changeable. SWAP model simulation was an effective method to study the change of soil water-salt transport and crop growth (Chen et al., 2019 ; Alavi et al., 2022 ). In this study, the SWAP model was used to simulate soil water-salt flux and soil water-salt cumulative flux under different scenarios, and optimal irrigation quota for summer maize was determined. The results demonstrated that soil water-salt flux and soil water-salt cumulative flux decreased with the decrease on irrigation quota at lower interface of the crop root zone and the storage zone under different water-saving irrigation quota. 3500 m 3 ·ha − 1 was the optimal irrigation quota for summer maize in the study area. When irrigation quota was reduced to 3500 m 3 ·ha − 1 , soil water-salt flux and water-salt cumulative flux were minimal at the lower interface of the crop root zone and the storage zone, and less soil salt was accumulated in the 0-100 cm soil layer. It was still of certain application value to clarify soil water and salt movement and the efficient utilization of water resources in Lubotan of Northwest China. Conclusions The SWAP model parameters were calibrated and validated to simulate soil water-salt flux, soil water-salt soil water-salt cumulative flux and irrigation quota under different scenarios in the Northwest China. The model parameters were obtained after calibration and validation. Soil water flux, soil water cumulative flux, soil salt flux and soil salt cumulative flux decreased with the decrease on irrigation quota at the lower interface of crop root zone and storage zone under different scenarios. The soil water cumulative flux and soil salt cumulative flux changed small, when the irrigation quota was reduced to 70%IQ and 60% IQ. Soil water could be stably stored in 0-100 cm soil layer for summer maize growth requirement, which brought in by irrigation and rainfall. When the irrigation quota of summer maize was 70% IQ (3500 m 3 ·ha − 1 ), soil water-salt flux and soil water-salt cumulative flux were minimal at the lower interface of the crop root zone and the storage zone. The yield reduction of summer maize was only 10%. It was the optimal irrigation quota for summer maize from the perspective of soil water-salt balance and crop growth in the study area. This study was to provide technical support for the efficient utilization of water resources and also guided agricultural production practice in Lubotan of Northwest China. Declarations Competing interests The authors have no relevant financial or non-financial interests to disclose. Funding This research was financially supported by National Natural Science Foundation of China (52169009) and Jiangxi Students’ Platform for innovation and entrepreneurship training program (202410410013X and S202510410009). Author contributions Chengfu Yuan: Conceptualization, Methodology, Formal analysis, Software, Project administration, Funding acquisition, Writing-original draft, Writing-review and editing. Yanxin Pan: Software, Formal analysis, Data curation, Investigation. Siyuan Jing: Software, Data curation, Investigation. Data availability The data that supports the findings of this study are available in the supplementary material of this article. References Alavi SA, Naseri AA, Ritzema H, Van Dam J., Hellegers, P (2022) A combined model approach to optimize surface irrigation practice: SWAP and WinSRFR. Agric Water Manage 271: 107741. http://dx.doi.org/10.1016/j.agwat.2022.107741. Camargo GGT, Kemanian A R (2016) Six crop models differ in their simulation of water uptake. Agric for Meteorol 220: 116-129. https://doi.org/10.1016/j. agrformet.2016.01.013. Chen K, Yu S, Li Q, Zhang M, Wang Y, Liu Z (2019) Simulation and Evaluation of Technical Schemes for Water-saving Irrigation of Rice in Different Hydrological Years. Trans CSAE 50: 268-277. (in Chinese with English abstract). doi:10.6041/j.issn.1000-1298.2019.12.031. Chen S, Mao X, David A, Yang J (2019) Model of crop growth, water flow, and solute transport in layered soil. Agric Water Manage 22: 160-174. http://dx.doi.org/10.1016/j.agwat.2019.04.031. Dewedar O, Plauborg F, Marwa M, El-shafie A, Ragab R. (2020) Improving water saving, yield, and water productivity of bean under deficit drip irrigation: Field and modelling study using the SALTMED mode. Irrig Drain 70: 224-242. http://dx.doi.org/10.1002/ird.2539. Fattori IM, Marin FR (2023) Assessing the influence of crop model structure on the performance of data assimilation for sugarcane. Comput Electron Agric 209: 1-11. https://doi.org/10.1016/j.compag.2023.107848. Feng W, Gao J, Cen R, Yang F, He Z, Wu J, Miao Q, Liao H (2020) Effects of polyacrylamide-based super absorbent polymer and corn straw biochar on the arid and semi-arid salinized soil. Agric 11: 1-17. http://dx.doi.org/10.3390/agriculture10110519. Huang X, Zhao Y, Guo T, Mao X. (2024) Enhancing SWAP simulation accuracy via assimilation of leaf area index and soil moisture under different irrigation, film mulching and maize varieties conditions. Comput Electron Agric 218: 108625. http://dx.doi.org/10.1016/j.compag.2024.108625. Heinen M, Mulder M, Dam J V, Bartholomeus R, Lier QDJV, Wit J D, Broehe M H (2024) SWAP 50 years: advances in modelling soil-water-atmosphere-plant interactions. Agric Water Manage 298: 108883. http://dx.doi.org/10.1016/j.agwat.2024.108883. Hu S, Shi L, Huang K, Zha Y, Hu X, Ye H (2019) Improvement of sugarcane crop simulation by SWAP-WOFOST model via data assimilation. Field Crop Res 232: 49-61. http://dx.doi.org/10.1016/j.fcr.2018.12.009. Jiang J, Feng S, Ma J, Huo Z, Zhang C (2016) Irrigation management for spring maize grown on saline soil based on SWAP model. Field Crops Res 196: 85-97. http://dx.doi.org/10.1016/j.fcr.2016.06.011. Jing B, Yan B, Li J, Liu R (2021) Comprehensive evaluation and analysis of soil fertility of saline-alkaline land in Weibei Lubotan area in the middle reaches of Yellow River. J Northwest U (Nat Sci Edi) 52: 371-379. (in Chinese with English abstract). doi:10.16152/j.cnki.xdxbzr.2022-03-002. Kargas G, Kerkides P, Sotirakoglou K, Poulovassilis A (2016) Temporal variability of surface soil hydraulic properties under various tillage systems. Soil Tillage Res 158: 22-31. https://doi.org/10.1016/j.still.2015.11.011. Korzukhin M, Grabovsky V (2020) Estimation of Leaf Area Index (LAI) of Russian forests using a mechanical model and forest inventory data. Environ Sci 18: 341-346. http://dx.doi.org/10.1007/978-3-030-37421-1_18. Kramer I, Mau Y (2023) Review: modeling the effects of salinity and sodicity in agricultural systems. Water Resour Res 59: 034750. http://dx.doi.org/10.1029/2023WR034750. Lei G, Zeng W, Yu J, Huang J (2023) A comparison of physical-based and machine learning modeling for soil salt dynamics in crop fields. Agric Water Manage 277: 108115. http://dx.doi.org/10.1016/j.agwat.2022.108115. Létourneau G, Caron J (2019) Irrigation Management Scale and Water Application Method to Improve Yield and Water Productivity of Field-Grown Strawberries. Agronomy 9: 286. http://dx.doi.org/10.3390/agronomy9060286. Li A, Wu Y, Cao S (2024) Effects of land use-land cover on soil water and salinity contents. Acta Ecol Sinica 2: 307-314. http://dx.doi.org/10.1016/j.chnaes.2023.07.002. Li P, Ren L (2020) Evaluating the saline water irrigation schemes using a distributed agro-hydrological model. J Hydrol 594: 125688. http://dx.doi.org/10.1016/j.jhydrol.2020.125688. Li S, Wang Q, Fan Q, Wu M, Zhang L, Fei L, Xue R, Li L (2023) Relationship between vegetation spatial distribution pattern and soil factors in drainage ditches in arid irrigation area. Chin. J Ecol 42: 607-616. (in Chinese with English abstract). doi:10.13292/j.1000-4890.202302.023. Li S, Wang Q, Wu M, Fang W, Zhang H, Yang G, Li J (2021) Simulation of desalination process in dredger fill soil under natural and engineering measures. Trans CSAE 37: 91-98. (in Chinese with English abstract). doi:10.11975/j.issn.1002-6819.2021.06.012. Li S, Wu M, Jia Z, Luo W, Fei L, Li J (2021) Influence of different controlled drainage strategies on the water and salt environment of ditch wetland: A model-based study. Soil Till Res 208: 104894. http://dx.doi.org/10.1016/j.still.2020.104894. Pan Y, Yuan C, Jing S (2020) Simulation and optimization of irrigation schedule for summer maize based on SWAP model in saline region. Int J Agr Biol Eng 13: 117-122. http://dx.doi.org/10.25165/j.ijabe.20201303.5218. Ravensbergen APP, van Ittersum MK, Kempenaar C, Ramsebner N, De Wit D, Reidsma P (2024) Coupling field monitoring with crop growth modelling provides detailed insights on yield gaps at field level: a case study on ware potato production in the Netherlands. Field Crop Res 308: 109295. http://dx.doi.org/10.1016/j.fcr.2024.109295. Shafiei M, Ghahraman B, Saghafian B, Davary K, Pande S, Vazifedoust M (2014) Uncertainty assessment of the agro-hydrological SWAP model application at field scale: A case study in a dry region. Agric Water Manage 146: 324-334. http://dx.doi.org/10.1016/j.agwat.2014.09.008. Sun S, Luo W, Zou J, Wu W, Jia Z, Zhang Z, Zhu W (2019) Types of drainage network impact attenuation of dissolved contaminants. J Irrig Drain 38: 122-128. (in Chinese with English abstract). doi:10.13522/j.cnki.ggps.20180404. van Dam JC, Huygen J, Wesseling JG, Feddes RA, Kabat P, van Walsum PEV, Groenendijk P, van Diepen CA (1997) Theory of SWAP version 2.0: simulation of water flow, solute transport and plant growth in the soil-water-atmosphere-plant environment. Wageningen: DLO Winand Staring Centre. 287-295. https://www.swap.alterra.nl/. van Dam JC, Groenendijk P, Hendriks RFA, Kroes JG (2008) Advances of modeling water flow in variably saturated soils with SWAP. Vadose Zone J 7: 640-653. http://dx.doi.org/10.2136/vzj2007.0060. Verma AK, Gupta SK, Isaac RK (2014) Application of soil-water-atmosphere-plant model to assess performance of subsurface drainage system under semi-arid monsoon climate. Irrig Drain 63: 93-101. http://dx.doi.org/10.1002/ird.1783. Wang G, Shi H, Li X, Zheng Q, Guo J, Wang W (2019) Analysis of water and salt transportation and balance during cultivated land, waste land and lake system in Hetao Irrigation Area. J Hydraul Eng 50: 1518-1528. (in Chinese with English abstract). doi:10.13243/j.cnki.slxb.20190593. Wang J, Wang Y, Qi Z (2024) Remote sensing data assimilation in crop growth modeling from an agricultural perspective: new insights on challenges and prospects. Agronomy 14: 1920. http://dx.doi.org/10.3390/agronomy14091920. Wang X, Zhang D, Qi Q, Tong S, An Y, Lu X, Liu Y (2019) The restoration feasibility of degraded Carex Tussock in soda-salinization area in arid region. Ecol Indic 3: 131-136. http://dx.doi.org/10.1016/j.ecolind.2018.08.066. Xu J, Cai H, Wang X, Ma C, Saddique Q (2019) Exploring optimal irrigation and nitrogen fertilization in a winter wheat-summer maize rotation system for improving crop yield and reducing water and nitrogen leaching. Agric Water Manage 228: 105904. http://dx.doi.org/10.1016/j.agwat.2019.105904. Yu DY, Zha YY, Shi LS, Jin XL, Hu S, Yang Q, Huang K, Zeng WZ (2020) Improvement of sugarcane yield estimation by assimilating UAV-derived plant height observations. Eur J Agron 121: 1-16. https://doi.org/10.1016/j. eja.2020.126159. Zhang X, Ma F, Yin S, Wallace CD, Soltanian MR, Dai Z, Ritzi RW, Ma Z, Zhan C, Lü X (2021) Application of upscaling methods for fluid flow and mass transport in multi-scale heterogeneous media: a critical review. Appl Energy 303: 117603. https://doi.org/10.1016/j.apenergy.2021.117603. Zhao Y, Mao X, Shukla M (2020) A modified SWAP model for soil water and heat dynamics and seed–maize growth under film mulching. Agr Forest Meteorol 292: 108127. http://dx.doi.org/10.1016/j.agrformet.2020.108127. 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Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYDAC5gMgUkKOn735wIEPP4jRwpYAIi2MJXuOJR6c2UO8lorEDTdyjA9zsBGhg7+Nx0zi5w6JxJkzcj4cZuBhkOcXO4Bfi8QxHjPJ3jMSxv08bzccLrBgMJw5OwG/FgP5HjMJ3jYJ2ZntuRsOz+BhSDC4TUgLG9CWv20SjBsO5Dw4zMNGpBZpoC2KG07kMBCnReIYW7G1bJsEKJANgIEsQdgv/G3MG2++basDReXjDx9+2MjzSxPQwsDAYYBiKyHlIMD+gBhVo2AUjIJRMJIBADxSRL5wlxjIAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-1909-7338","institution":"Jiangxi Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Chengfu","middleName":"","lastName":"Yuan","suffix":""},{"id":501799418,"identity":"6731288f-a203-49f1-92ec-6ed3a12f205a","order_by":1,"name":"Yanxin Pan","email":"","orcid":"","institution":"Nanchang Institute of 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farmland in the study area\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6922081/v1/16e1c22e1bde7dbff7ecd40d.jpg"},{"id":89908603,"identity":"73070e99-81cc-405f-945e-277b12a94fe3","added_by":"auto","created_at":"2025-08-26 10:27:19","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":92741,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the simulated and measured soil water content for different soil layers in calibration (\u003cstrong\u003e2018\u003c/strong\u003e)\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6922081/v1/c03361e7497558c5ec79b0c5.jpg"},{"id":89908600,"identity":"e768fd30-4b0e-43e0-82b4-fd93658c3686","added_by":"auto","created_at":"2025-08-26 10:27:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":91605,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the simulated and measured soil water content for different soil layers in validation (\u003cstrong\u003e2019\u003c/strong\u003e)\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6922081/v1/26695fcd1a4ca5bc611915d0.jpg"},{"id":89908602,"identity":"e53025fb-3dec-4aef-8496-665210d3b272","added_by":"auto","created_at":"2025-08-26 10:27:19","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":88581,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the simulated and measured soil salt content for different soil layers in calibration (\u003cstrong\u003e2018\u003c/strong\u003e)\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6922081/v1/376149dd320ccb5f6412cb1c.jpg"},{"id":89909065,"identity":"4aa6b18a-24b6-405d-a7ea-f01ca96720ef","added_by":"auto","created_at":"2025-08-26 10:35:19","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":86625,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the simulated and measured soil salt content for different soil layers in validation (\u003cstrong\u003e2019\u003c/strong\u003e)\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6922081/v1/81c44b3c5b1893e8bf3fe26b.jpg"},{"id":89908601,"identity":"e28e007e-998e-4715-bbd7-39de3a695b86","added_by":"auto","created_at":"2025-08-26 10:27:19","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":50085,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the simulated and measured plant height of summer maize in calibration (\u003cstrong\u003ea\u003c/strong\u003e) and validation (\u003cstrong\u003eb\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6922081/v1/2f42a24da7d3e0885c29c62b.jpg"},{"id":89908611,"identity":"11121001-4bdb-4d3c-9b29-6b265f0c43eb","added_by":"auto","created_at":"2025-08-26 10:27:19","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":49086,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the simulated and measured LAI of summer maize in calibration (\u003cstrong\u003ea\u003c/strong\u003e) and validation (\u003cstrong\u003eb\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6922081/v1/7afb7123d521b7370f385d01.jpg"},{"id":89908607,"identity":"dbdad59a-54f7-460b-86d6-86b7b4ccb930","added_by":"auto","created_at":"2025-08-26 10:27:19","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":27550,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the simulated and measured summer maize yield in calibration (\u003cstrong\u003e2018\u003c/strong\u003e) and validation (\u003cstrong\u003e2019\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6922081/v1/4fcfe866bc5c298c00558406.jpg"},{"id":89909072,"identity":"914b5297-9d0e-4fb3-8ef1-8967c3590a0d","added_by":"auto","created_at":"2025-08-26 10:35:19","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":108498,"visible":true,"origin":"","legend":"\u003cp\u003eSimulated soil water flux under different scenarios: (\u003cstrong\u003ea\u003c/strong\u003e) 100%IQ; (\u003cstrong\u003eb\u003c/strong\u003e) 80%IQ; (\u003cstrong\u003ec\u003c/strong\u003e) 70%IQ and (\u003cstrong\u003ed\u003c/strong\u003e) 60%IQ.\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6922081/v1/8eb58d11a93c2fd476c7e4dd.jpg"},{"id":89909070,"identity":"f68bb915-9ecf-4812-88d7-09a0f24ba44c","added_by":"auto","created_at":"2025-08-26 10:35:19","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":114159,"visible":true,"origin":"","legend":"\u003cp\u003eSimulated soil salt flux under different scenarios: (\u003cstrong\u003ea\u003c/strong\u003e) 100%IQ; (\u003cstrong\u003eb\u003c/strong\u003e) 80%IQ; (\u003cstrong\u003ec\u003c/strong\u003e) 70%IQ and (\u003cstrong\u003ed\u003c/strong\u003e) 60%IQ.\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6922081/v1/c9a8f3c8106d77d75982b3bf.jpg"},{"id":94102516,"identity":"a016f290-f7ba-49ec-921a-2c7ac5b2778c","added_by":"auto","created_at":"2025-10-22 11:31:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1692853,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6922081/v1/2bf4b8cf-1cc3-46a4-83fa-db1b17c8dc9f.pdf"},{"id":89908609,"identity":"db157a02-f8ba-47dc-b0ec-5c44e552354c","added_by":"auto","created_at":"2025-08-26 10:27:19","extension":"xls","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":103936,"visible":true,"origin":"","legend":"","description":"","filename":"Electronicsupplementarymaterial.xls","url":"https://assets-eu.researchsquare.com/files/rs-6922081/v1/cb7acaf7f19fca853d6198ef.xls"}],"financialInterests":"","formattedTitle":"Simulation soil water-salt flux and irrigation quota for summer maize based on SWAP model in the Northwest China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSoil salinization stands as a significant global challenge and a primary factor impacting agricultural productivity and the ecological health of farmlands in the world. According to the statistic analysis, the salinization area of cultivated land has reached 9.21\u0026times;10\u003csup\u003e6\u003c/sup\u003e ha, accounting for 6.62% of the total cultivated area in China (Wang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Feng et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The rational utilization of salinization land is of great significance to the development of comprehensive agriculture in China. Lubotan is a closed structural depression, which located in Shaanxi Province of Northwest China. Soil salt has accumulated in Lubotan for a long time, which was affected by natural and human activities. Although it was reclaimed as farmland in modern times, due to the geographical low-lying land characteristics of the Lubotan, the drainage of farmland was not smooth, the groundwater level rose, and the soil salinization was gradually aggravated in Lubotan. Most farmland suffered from soil salinization to varying degrees, which seriously restricted local agriculture proceeding development (Li et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The local relevant departments carried out large-scale development and renovation of the Lubotan through land leveling, drainage and alkali control measures at the end of the 20 century. The cultivated land was not salinized in large areas, and the crops planted were less affected by salt and alkali in Lubotan. However, the flood irrigation method was widely used in the Lubotan, which not only caused the waste of water resources, but also leaded to the current rise in groundwater levels and soil secondary salinization (Sun et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jing et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In order to reduce soil secondary salinization and save water resources, water-saving irrigation measures should be adopted for summer maize in the Lubotan. After the implementation of water-saving irrigation measures for summer maize, soil water-salt transport and water-salt balance will change. The relationship between soil water-salt flux change and irrigation quota was still not clear, and the optimal irrigation quota for summer maize was also not clear in the study area. Therefore, it was necessary to study the change of soil water-salt flux and the irrigation quota of summer maize in Lubotan.\u003c/p\u003e\u003cp\u003eField experiment was a method to study soil water-salt transport and crop irrigation quota, but there were many factors affecting field experiment, which required a long cycle and high cost (Dewedar et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; L\u0026eacute;tourneau and Caron, 2021). Mathematical models were widely used to simulate soil water-salt transport and crop irrigation quota based on field experiments. In these mathematical models, the SWAP model was extensively utilized to simulate soil water-salt transport and crop irrigation quota in the world (Zhao et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ravensbergen et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It had been widely accepted and recognized (Heinen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The SWAP model is a physically model that simulates soil water flow, solute transport, heat transfer and crop growth processes at the field scale through the entire growing seasons (van Dam et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).. Therefore, SWAP model can be used to simulate the changes of soil water-salt transport and irrigation quota of summer maize under different water-saving irrigation in the study area.\u003c/p\u003e\u003cp\u003eThe SWAP model provided an effective method for the formulation and evaluation of crop irrigation system, the prediction of groundwater depth change, the prediction of soil water-salt transport, and the prediction of crop growth and yield (Verma et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Alavi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Shafiei et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e used SWAP model to simulate soil water distribution and water flow flux of two different types of fields in the arid area of Iran. It showed that the water flow flux of farmland soil deep layer downward leakage changed greatly and was more sensitive to the influence of soil hydraulic conductivity. Jiang et al.2016 used the SWAP model to simulate the soil water-salt transport of spring wheat in Shiyang River Basin of Gansu Province of Northwest China. It showed that soil salt could remain stable after five years of continuous saline water irrigation, and the SWAP model could predict soil water-salt dynamic changes over extended periods. Chen et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e used SWAP model to simulate soil water movement in paddy fields under four water-saving irrigation modes. It showed that the controlled irrigation and drainage modes under different hydrological years had obvious water-saving and labor-saving effects, and did not cause a reduction in rice yields, which was conducive to guiding the practice of rice water-saving irrigation. The researchers used the SWAP model to simulate the changes of soil water-salt transport, crop irrigation management, which provided a theoretical basis and scientific basis for controlling soil salinization (Li et al., 2020; Kramer and Mau, 2020). These publications show that, the SWAP model could be used to deeply study soil water-salt transport and crop irrigation management under crop growth conditions in salinized regions. However, the above studies did not characterize the relationship between soil water-salt flux changes and irrigation quotas under different water-saving irrigation. The relationship between soil water-salt fluxes, soil water-salt cumulative fluxes and irrigation quota of summer maize had been reported rarely in Northwest China. The SWAP model parameters were calibrated and validated based on field experiments data. Three different irrigation quotas were set for summer maize, and the SWAP model was used to simulate the soil water-salt fluxes, soil water-salt cumulative fluxes and summer maize yield for different scenarios under summer maize growth conditions.\u003c/p\u003e\u003cp\u003eThe purpose of this study was to provide technical support for the efficient utilization of water resources and to guide agricultural production practice in Lupotan of Northwest China. The objectives of this study were: (1) to calibrate and validate the SWAP model parameters, simulated results were compared with measured results obtained under field experiment conditions; (2) to simulate soil water flux and soil water cumulative flux under different scenarios; (3) to simulate soil salt flux and soil salt cumulative flux under different scenarios.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eDesign of field experiments\u003c/p\u003e\u003cp\u003eField experiments were carried out from June 2018 to September 2019 in Lupotan area (109\u0026deg;22\u0026prime;E, 34\u0026deg;48\u0026prime;N, and altitude 490 m) of Northwest China. A standardized farmland was selected as the experimental field in Lupotan, with an area of 4\u0026times;10\u003csup\u003e4\u003c/sup\u003e m\u003csup\u003e2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The farming system was mainly winter wheat and summer maize rotation in Lupotan. There were a large number of saline alkali lands in Lupotan. The soil is characterized as typical sulfate saline-alkali soil, with a pH ranging from 8.3 to 8.6 in experiment region. The soil particle size composition was measured by laser particle size analyzer (Master sizer 2000, UK). According to the international classification standard of soil texture, soil texture of the experimental area was determined. Soil physical properties for different soil layers in experiment region are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe experiment crop was summer maize (\"Zhengdan 958\") with 25 cm plant spacing and 35 cm row spacing in 2018 and 2019. Summer maize was sown in early June and harvested at the end of September with whole growth period of about 120 days. Summer maize was irrigated with border irrigation method and was irrigated four times during the growth period, consistent with the actual conditions of local summer maize cultivation. Actual evapotranspiration (ET\u003csub\u003ec\u003c/sub\u003e) of summer maize during the growth period was 500 mm (Pan et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Irrigation water quota of summer maize growth period as follows: 1200 m\u003csup\u003e3\u003c/sup\u003e\u0026middot;ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (June 25), 1300 m\u003csup\u003e3\u003c/sup\u003e\u0026middot;ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (July 15), 1300 m\u003csup\u003e3\u003c/sup\u003e\u0026middot;ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (August 22) and 1200 m\u003csup\u003e3\u003c/sup\u003e\u0026middot;ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (September 10). Irrigation water salinity was about 0.4 g\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Due to the current irrigation conditions of summer maize, three summer maize fields were set up as three experimental plots in a standardized farmland, and the irrigation method and system were consistent with three replicates. Summer maize needed to be fertilized before sowing. The amount of fertilization was 600 kg\u0026middot;ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for diammonium phosphate, 300 kg\u0026middot;ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for urea, and 225 kg\u0026middot;ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for potassium. Herbicides were sprayed before sowing summer maize. Other agronomic measures were consistent with the local actual situation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSoil physical properties for different soil layers in experiment region\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil depths\u003c/p\u003e\u003cp\u003e(cm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClay\u003c/p\u003e\u003cp\u003e(\u0026lt;0.002mm, %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSilt\u003c/p\u003e\u003cp\u003e(0.002\u0026thinsp;~\u0026thinsp;0.02mm, %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSand\u003c/p\u003e\u003cp\u003e(0.02\u0026thinsp;~\u0026thinsp;2.00mm, %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSoil bulk density\u003c/p\u003e\u003cp\u003e(g\u0026middot;cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSoil texture\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u0026ndash;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eSilty sandy loam\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60\u0026ndash;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eExperiments observation items and methods\u003c/p\u003e\u003cp\u003eThe observation section was located in the center of the standardized experimental field. Three TRIME pipes of 1 metre long were arranged and buried on the observation section. The TRIME-PICO of portable soil water content meter was used to measure 0\u0026ndash;20, 20\u0026ndash;40, 40\u0026ndash;60, 60\u0026ndash;80 and 80\u0026ndash;100 cm, respectively. The observation period was from June 2018 to September 2019. Soil samples were obtained by soil drills in layers near each TRIME pipes, with the depth of 0\u0026ndash;20, 20\u0026ndash;40, 40\u0026ndash;60, 60\u0026ndash;80 and 80\u0026ndash;100 cm, respectively. Electrical conductivity, EC\u003csub\u003e1:5\u003c/sub\u003e (mS\u0026middot;cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was measured by conductivity meter and translated into soil salinity (g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) using the calculation equation (\u003cem\u003eS\u0026thinsp;=\u0026thinsp;0.2813EC\u003c/em\u003e\u003csub\u003e\u003cem\u003e1:5\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-0.0056\u003c/em\u003e, where \u003cem\u003eS\u003c/em\u003e refers to soil salinity) (Pan et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). There was a 3 metre deep groundwater observation well in the middle of the standardized experiment farmland, which was observed the change of groundwater table depth. The groundwater table depth was between 1.85 and 2.25 meters in experiment region. The initial soil water content, soil salt content and hydraulic characteristic parameters for different soil layers were obtained before sowing summer maize. The parameters of soil water characteristic curve were measured using a centrifuge, and the hydraulic characteristic parameters of VG (van Genuchten) model were fitted using RETC software. The initial soil hydraulic characteristic parameters for different soil layers are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The initial measured molecular diffusion coefficient was 3.5 cm\u003csup\u003e2\u003c/sup\u003e\u0026middot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and the dispersion length was 19.0 cm.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSoil hydraulic parameters for different soil layers in experiment region\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil depths (cm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e (cm\u003csup\u003e3\u003c/sup\u003e\u0026middot;cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e (cm\u003csup\u003e3\u003c/sup\u003e\u0026middot;cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eK\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e (cm\u0026middot;d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eα\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eγ\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u0026ndash;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e120.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e79.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60\u0026ndash;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e89.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: \u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e is residual water content; \u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e is saturated water content; \u003cem\u003eK\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e is saturated hydraulic conductivity; α, n, γ are shape factor. The same as below.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAfter emergence of summer maize, the plant height (H), leaf length (L) and width (B) of summer maize under difference growth stages were measured every 7\u0026ndash;10 days with a steel tape. The leaf area index (LAI) was calculated with the equation (\u003cem\u003eLAI\u003c/em\u003e = (\u003cem\u003ek\u003c/em\u003e \u0026times; \u003cem\u003eL\u003c/em\u003e \u0026times; \u003cem\u003eB\u003c/em\u003e)\u0026middot;\u003cem\u003eA\u003c/em\u003e\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, \u003cem\u003ek\u003c/em\u003e is a fitting coefficient (0.75 for summer maize), \u003cem\u003eA\u003c/em\u003e is the area covered by summer maize leaves) (Korzukhin and Grabovsky, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The root drill with a diameter of 8 cm was used to take samples using the \"cross method\" (5 drills were taken from each corn plant, each drill was divided into 5 layers, each layer was 20 cm, and 100 cm was taken to remove most of the maize roots) to obtain the root length data of summer maize. The root length and density distribution data of summer maize were obtained by scanning the root scanner and using WinRHIZO root analysis software, and repeated three times. The summer maize yield was measured after harvest, and the yield was the yield of dried seeds (kg\u0026middot;ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Meteorological data was obtained by the automatic weather station in experiment region. The rainfall in 2018 and 2019 was 442.2, 454.0 mm, respectively. Both 2018 and 2019 were normal years in study area. The monthly average meteorological data are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAverage meteorological data every monthly in 2018 and 2019\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonth\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaximum temperature\u003c/p\u003e\u003cp\u003e(\u0026deg;C)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMinimum temperature (\u0026deg;C)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAverage temperature (\u0026deg;C)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAverage humidity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAverage wind speed (m\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAverage air pressure (KPa)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRainfall\u003c/p\u003e\u003cp\u003e(mm)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-10.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e56.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e90.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-9.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e90.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e46.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e90.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e46.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e89.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e26.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e44.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e89.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e93.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e56.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e89.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e17.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e73.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e89.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e147.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e78.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e89.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e63.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e68.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e90.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e50.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e56.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e90.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e15.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e58.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e90.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e31.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-12.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e62.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e90.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSWAP Model\u003c/p\u003e\u003cp\u003eThe SWAP (Soil-Water-Atmosphere-Plant) model is a comprehensive model that simulates soil water movement, solute transport, heat transfer, and crop growth processes at the field scale by integrating the theoretical research results of the current SPAC (Soil-Plant-Atmosphere Continuum) system (van Dam et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The meteorological data used in the model were obtained from the automatic weather station installed in experiment region in 2018 and 2019. Based on the actual situation of soil texture and the depth of the summer maize root active layer in the growth period, the depth of the simulated soil profile was 0-100 cm. The 0-100 cm soil layer of the simulated soil profile was divided into 34 soil layers. The initial soil water content and soil salt content data were used in SWAP model. The upper boundary conditions of the model were rainfall, evaporation, crop transpiration and irrigation determined by meteorological factors. The bottom boundary condition of the model adopted a kind of boundary, which was groundwater table depth of the groundwater observation well in the study area. The actual irrigation quota was used for summer irrigation in SWAP model. Other data required for model inputs were experimental measured data. For an exhaustive and unparalleled introduction to the SWAP model, refer to the SWAP model theory book (van Dam et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). The RMSE (Root Mean Square Error) and MRE (Mean Relative Error) were utilized to quantify the deviation of the simulated results from the measured results.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eModel calibration and validation of SWAP model parameters\u003c/p\u003e\n\u003cp\u003eThe comparison between the simulated and measured soil water content\u0026nbsp;for different soil layers\u0026nbsp;are shown in Fig.2 and Fig. 3. The simulated values were\u0026nbsp;agreed precisely\u0026nbsp;with the measured values in each soil layers during summer maize growth periods. The RMSE values﹤0.05 cm\u003csup\u003e3\u003c/sup\u003e\u0026middot;cm\u003csup\u003e-3\u003c/sup\u003e, and the MRE values﹤15%. The simulation effect of soil water content was feasible. The parameters of soil water characteristic curve after model calibration and validation are shown in Table 4.\u003c/p\u003e\n\u003cp\u003eSoil salt content was the percentage of the mass of salt in the soil to the dry soil mass. The comparison between simulated and measured soil salt content\u0026nbsp;for different soil layers\u0026nbsp;are shown in Fig. 4 and Fig. 5. The simulated values were\u0026nbsp;agreed precisely\u0026nbsp;with the measured values in each soil layers during summer maize growth periods, and slightly worse than soil water content calibration process. The RMSE values﹤0.10 mg\u0026middot;cm\u003csup\u003e-3\u003c/sup\u003e, and the MRE values﹤20%. The simulation effect of soil salt content was feasible. The molecular diffusion coefficient\u0026nbsp;was 0.85 cm\u003csup\u003e2\u003c/sup\u003e\u0026middot;d\u003csup\u003e-1\u003c/sup\u003e, and the dispersion was 10.0 cm after model calibration and validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003eSoil hydraulic parameters\u0026nbsp;for different soil layers after calibration and validation\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"636\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003eSoil depths (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e\u003cem\u003e\u0026theta;\u003csub\u003er\u003c/sub\u003e\u003c/em\u003e (cm\u003csup\u003e3\u003c/sup\u003e\u0026middot;cm\u003csup\u003e-3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e\u003cem\u003e\u0026theta;\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e(cm\u003csup\u003e3\u003c/sup\u003e\u0026middot;cm\u003csup\u003e-3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e\u003cem\u003e\u0026nbsp;K\u003csub\u003es\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e(cm\u0026middot;d\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e\u0026gamma;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e0-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e140.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e1.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e20-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e99.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e1.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e40-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e120.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e1.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e60-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e99.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e1.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe comparison between the simulated and measured plant height of summer maize are shown in Fig. 6. The simulated values were agreed precisely with the measured values. The RMSE values﹤10 cm, and the MRE values﹤15%. The comparison between the simulated and measured LAI of summer maize are shown in Fig. 7. The simulated values were in agreement well with the measured values. The RMSE values of LAI﹤1.0 cm\u003csup\u003e2\u003c/sup\u003e\u0026middot;cm\u003csup\u003e-2\u003c/sup\u003e, and the MRE values﹤15%.\u0026nbsp;The simulation effect of\u0026nbsp;plant height\u0026nbsp;and LAI were feasible.\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;simulation\u0026nbsp;yield of summer maize were 7902.4, 7803.6 kg\u0026middot;ha\u003csup\u003e-1\u0026nbsp;in\u003c/sup\u003e 2018 and 2019, respectively. The actual yield of summer maize in field experiments were 8516.4, 8507.0 kg\u0026middot;ha\u003csup\u003e-1\u0026nbsp;in\u003c/sup\u003e 2018 and 2019, respectively. The simulated values were agreed precisely with the measured values (Fig. 8). The RMSE values of summer maize yield﹤800.0 kg\u0026middot;ha\u003csup\u003e-1\u003c/sup\u003e, and the MRE values﹤8.0 % .\u0026nbsp;The simulation effect of\u0026nbsp;summer maize yield\u0026nbsp;was feasible.\u0026nbsp;The minimum canopy resistance, critical level \u003cem\u003eEC\u003csub\u003emax\u003c/sub\u003e\u003c/em\u003e, decline root water uptake per unit\u003cem\u003e\u0026nbsp;EC\u003csub\u003eslope\u003c/sub\u003e\u003c/em\u003e of summer maize were\u0026nbsp;70 s\u0026middot;m\u003csup\u003e-1\u003c/sup\u003e, 1.7 dS\u0026middot;m\u003csup\u003e-1\u003c/sup\u003e and\u0026nbsp;12%\u0026nbsp;after calibration and\u0026nbsp;validation, respectively.\u003c/p\u003e\n\u003cp\u003eThe above results demonstrated that\u0026nbsp;SWAP model can accurately simulated soil water content, soil salt content, summer maize growth and yield\u0026nbsp;under crop growth conditions\u0026nbsp;after calibration and validation in the study area.\u003c/p\u003e\n\u003cp\u003eSimulation soil water flux and soil water cumulative flux under different scenarios\u003c/p\u003e\n\u003cp\u003eThe flood irrigation with canal water for summer maize was an extensive irrigation method in the Lupotan of Shaanxi Province, which was easy to waste water resources and leaded to secondary salinization of soil\u0026nbsp;(Xu et al., 2019). Proper adjustment and optimization of crop irrigation quota could promote the efficient utilization of water resources. Based on the meteorological data from 1961 to 2023, the rainfall of 25%、50% and 75% hydrologic years was 521, 454, 405 mm. There were minimal differences among the difference hydrologic year levels, thus only 50% hydrologic year was used in model simulation. Irrigation quota (IQ) was 5000 m\u003csup\u003e3\u003c/sup\u003e\u0026middot;ha\u003csup\u003e-1\u003c/sup\u003e under the actual planting condition of summer maize in Lupotan. This simulation set three irrigation quotas: 80% IQ (4000 m\u003csup\u003e3\u003c/sup\u003e\u0026middot;ha\u003csup\u003e-1\u003c/sup\u003e), 70% IQ (3500 m\u003csup\u003e3\u003c/sup\u003e\u0026middot;ha\u003csup\u003e-1\u003c/sup\u003e) and 60% IQ (3000 m\u003csup\u003e3\u003c/sup\u003e\u0026middot;ha\u003csup\u003e-1\u003c/sup\u003e). At the same time, the irrigation quota in each summer maize growth period was also adjusted according to the corresponding proportion. The initial soil water-salt content and summer maize growth data were consistent with the field experiments measured data in 2018. The upper boundary conditions were rainfall, evaporation, crop transpiration and irrigation determined by meteorological factors. The bottom boundary condition adopted a kind of boundary, which was groundwater table depth of the groundwater observation well in 2018. The simulated soil layer thickness was 0-100 cm. Soil water flux and salt flux for different soil layers were simulated by SWAP model. On this basis, it determined the optimal irrigation quota for summer maize in Lupotan.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSoil water flux simulation results of different soil profiles under different scenarios are shown in Fig. 9.\u0026nbsp;The\u0026nbsp;60 cm\u0026nbsp;profile was the soil profile of summer maize main root system layer, which was expressed as the water flux of the lower interface of the root zone. The 100 cm profile was the soil profile of the largest root depth of summer maize, which was expressed as the water flux of the lower interface of the storage zone.\u0026nbsp;Soil water flux was negative downward and positive upward (the same as below). Soil water flux change was closely related to irrigation and rainfall in the lower interface of root zone. Soil water leakage in root zone mainly occurred after four times irrigation, and decreased with the decrease of irrigation quota. Soil water flux was below -7.0 mm\u0026middot;d\u003csup\u003e-1\u003c/sup\u003e under 80% IQ, and below -2.0 mm\u0026middot;d\u003csup\u003e-1\u003c/sup\u003e under 70% IQ and 60% IQ. It was shown that the reduction of irrigation quota could reduce the amount of soil water leakage. It could avoid soil water loss, and most of the water could be retained in the root soil layer of summer maize for crop use, which brought in by irrigation and rainfall. Variation law of soil water flux at the lower interface of the storage zone was similar to that of at the lower interface of the root zone. Soil water flux was below -2.0 mm\u0026middot;d\u003csup\u003e-1\u003c/sup\u003e under 80% IQ, and soil water flux was below -1.0 mm\u0026middot;d\u003csup\u003e-1\u003c/sup\u003e under 70% IQ and 60% IQ scenarios. Three scenarios could reduce soil water flux at the lower interface of storage zone, reduce the downward leakage loss of soil water, and make the soil water stored in the storage zone. Soil water in the storage zone exchanged with soil water in crop root zone.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;5 shows soil water cumulative flux in different soil profiles under different scenarios. If the soil water cumulative flux was negative, it meant that soil water leaks downward. The soil water cumulative flux at the lower interface of root zone and the lower interface of water storage zone decreased with the decrease of irrigation quota. The soil water cumulative flux at the interface under the root zone decreased by 18.36 mm, 29.53 mm and 31.62 mm compared with 100% IQ under 80% IQ, 70% IQ and 60% IQ, respectively. The reduction of irrigation quota could reduce soil water leakage in root zone and improved crop water use efficiency. The soil water cumulative flux at the lower interface of the storage zone was 4.24 mm, 6.57 mm and 7.8 mm under 80% IQ, 70% IQ and 60% IQ scenarios, which were less than that of under 100% IQ, respectively. The reduction of irrigation quota could make soil water stored in the storage zone. At the same time, when irrigation quota was reduced to 70% IQ and 60% IQ, the soil water cumulative flux at the lower interface of root zone and the lower interface of storage zone was small, and water brought in by irrigation and rainfall could be stably stored in 0-100 cm soil layer for summer maize growth.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e Soil water cumulative flux of 60 and 100 com soil profile under different scenarios\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp align=\"center\"\u003eSoil profile (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp align=\"center\"\u003eSoil water cumulative flux (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e100%IQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e80%IQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e70%IQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e60%IQ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e-38.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e-20.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e-9.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e-7.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e-15.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e-11.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e-9.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp align=\"center\"\u003e-7.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSimulation soil salt flux and soil salt cumulative flux under different scenarios\u003c/p\u003e\n\u003cp\u003eSoil salt flux simulation results in different soil profiles under different scenarios are shown in Fig. 10. Soil salt flux exhibited a similar pattern with soil water flux, and soil salt flux in the lower interface of root zone decreased with the decrease of irrigation quota. Soil salt flux was below -9.5 mg\u0026middot;(cm\u003csup\u003e2\u003c/sup\u003e\u0026middot;d)\u003csup\u003e-1\u003c/sup\u003e under 80% IQ, below -3.0 mg\u0026middot;(cm\u003csup\u003e2\u003c/sup\u003e\u0026middot;d)\u003csup\u003e-1\u003c/sup\u003e under 70% IQ, and below -1.5 mg\u0026middot;(cm\u003csup\u003e2\u003c/sup\u003e\u0026middot;d)\u003csup\u003e-1\u003c/sup\u003e under 60% IQ, respectively. Soil salt flux was below -1.8 mg\u0026middot;(cm\u003csup\u003e2\u003c/sup\u003e\u0026middot;d)\u003csup\u003e-1\u003c/sup\u003e at the lower interface of the storage zone under 80% IQ, 70% IQ and 60% IQ scenarios. It had little difference between 80% IQ and 60% IQ scenarios, respectively. Soil salt flux decreased with the decrease of soil water flux, which fully explained that water salt transport characteristics of \u0026quot;salt comes with water and salt goes with water\u0026quot;\u0026nbsp;(Wang et al., 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;soil salt cumulative flux\u0026nbsp;in different soil profiles under different scenarios was shown in Table 6. If\u0026nbsp;soil salt cumulative flux\u0026nbsp;was negative, it meant that soil salt was leached downward. The\u0026nbsp;soil salt cumulative flux\u0026nbsp;at the lower interface of root zone and the lower interface of water storage zone decreased with the decrease of irrigation quota. The\u0026nbsp;soil salt cumulative flux\u0026nbsp;at the interface under the root zone decreased by 14.25, 23.39 and 25.54 mg\u0026middot;cm\u003csup\u003e-2\u003c/sup\u003e under 80% IQ, 70% IQ and 60% IQ compared with 100% IQ, respectively. The\u0026nbsp;soil salt cumulative flux\u0026nbsp;at the lower interface of storage zone decreased by 3.77, 4.75 and 5.22 mg\u0026middot;cm\u003csup\u003e-2\u003c/sup\u003e under 80% IQ, 70% IQ and 60% IQ compared with 100% IQ, respectively. When irrigation quota was reduced to\u0026nbsp;70% IQ and 60% IQ, the change of\u0026nbsp;soil salt cumulative flux\u0026nbsp;at the lower interface of root zone and the lower interface of storage zone was small, and less soil salt brought by irrigation was accumulated in the 0-100 cm soil layer.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, the simulated summer maize yields were 7408.50, 7112.16and 6914.60 kg\u0026middot;ha\u003csup\u003e-1\u003c/sup\u003e under 80% IQ, 70% IQ and 60% IQ scenarios, respectively, which were 6.25%, 10.0% and 12.5% lower than those under 100% IQ (7902.4 kg\u0026middot;ha\u003csup\u003e-1\u003c/sup\u003e) scenarios. Through comprehensive analysis, compared with the situation that soil water flux, soil salt flux and\u0026nbsp;soil water-salt cumulative flux\u0026nbsp;at the lower interface of root zone and storage zone were small, and the yield reduction of summer maize was small. 70% IQ (3500 m\u003csup\u003e3\u003c/sup\u003e\u0026middot;ha\u003csup\u003e-1\u003c/sup\u003e) irrigation scenario could be used as the optimal irrigation quota for summer maize in the study area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e Soil salt cumulative flux of 60 and 100 com soil profile under different scenarios\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 208px;\"\u003e\n \u003cp\u003eSoil profile (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 392px;\"\u003e\n \u003cp\u003eSoil salt cumulative flux (mg\u0026middot;cm\u003csup\u003e-2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e100%IQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e80%IQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e70%IQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e60%IQ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-35.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-20.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-11.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-9.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e-17.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-13.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-12.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-12.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe SWAP model simulated soil water and solute transport of the vertical one-dimensional soil, without considering the flow relationship between soil water and groundwater in different regions (Hu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Due to study area was selected as a standardized farmland as experiments field, the area was large and there was an exchange between soil water and groundwater. The SWAP model simulation did not take into account the exchange of soil water and groundwater. Soil hydraulic characteristic parameters were relatively easier to be calibrated accurately and model accuracy was higher under calibration of soil hydraulic characteristic parameters and solute transport parameters. The accuracy of solute transport parameter rate was relatively low, which was mainly due to the fact that the solute parameters such as molecular diffusion coefficient, dispersion, and solute exchange rate between free water and adsorbed water were difficult to adjust to the most appropriate value (Kargas et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, the solute not only moved with the movement of soil moisture, but also moved under the action of its own concentration gradient (Lei et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). When calibrating the parameters of the SWAP model, the parameter estimation was carried out by the trial-and-error method (Fattori and Marin, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This method had prominent problems such as large amount of computation, long time-consuming and strong subjectivity. Moreover, SWAP model also lacked a nested parameter sensitivity analysis module, which did not accurately identify the parameters that had a greater impact on the simulation results (Zhao et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough there were some limitations between the SWAP model and the actual situation in simulating soil water and salt transport and crop growth, the theoretical basis of the SWAP model was mature and reliable (Camargo and Kemanian, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Data of model simulation was easy to obtain through field experimental determination, and the model was relatively easy to operate and use. It had been widely accepted and recognized in practice. The SWAP model was widely used to simulate soil water and salt transport in arid or semi-arid areas around the world (Ravensbergen et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Soil water and salt transport was complex and changeable. SWAP model simulation was an effective method to study the change of soil water-salt transport and crop growth (Chen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Alavi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this study, the SWAP model was used to simulate soil water-salt flux and soil water-salt cumulative flux under different scenarios, and optimal irrigation quota for summer maize was determined. The results demonstrated that soil water-salt flux and soil water-salt cumulative flux decreased with the decrease on irrigation quota at lower interface of the crop root zone and the storage zone under different water-saving irrigation quota. 3500 m\u003csup\u003e3\u003c/sup\u003e\u0026middot;ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e was the optimal irrigation quota for summer maize in the study area. When irrigation quota was reduced to 3500 m\u003csup\u003e3\u003c/sup\u003e\u0026middot;ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, soil water-salt flux and water-salt cumulative flux were minimal at the lower interface of the crop root zone and the storage zone, and less soil salt was accumulated in the 0-100 cm soil layer. It was still of certain application value to clarify soil water and salt movement and the efficient utilization of water resources in Lubotan of Northwest China.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe SWAP model parameters were calibrated and validated to simulate soil water-salt flux, soil water-salt soil water-salt cumulative flux and irrigation quota under different scenarios in the Northwest China. The model parameters were obtained after calibration and validation. Soil water flux, soil water cumulative flux, soil salt flux and soil salt cumulative flux decreased with the decrease on irrigation quota at the lower interface of crop root zone and storage zone under different scenarios. The soil water cumulative flux and soil salt cumulative flux changed small, when the irrigation quota was reduced to 70%IQ and 60% IQ. Soil water could be stably stored in 0-100 cm soil layer for summer maize growth requirement, which brought in by irrigation and rainfall. When the irrigation quota of summer maize was 70% IQ (3500 m\u003csup\u003e3\u003c/sup\u003e\u0026middot;ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), soil water-salt flux and soil water-salt cumulative flux were minimal at the lower interface of the crop root zone and the storage zone. The yield reduction of summer maize was only 10%. It was the optimal irrigation quota for summer maize from the perspective of soil water-salt balance and crop growth in the study area. This study was to provide technical support for the efficient utilization of water resources and also guided agricultural production practice in Lubotan of Northwest China.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was financially supported by National Natural Science Foundation of China (52169009) and Jiangxi Students\u0026rsquo; Platform for innovation and entrepreneurship training program (202410410013X and S202510410009).\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e\u003cp\u003eChengfu Yuan: Conceptualization, Methodology, Formal analysis, Software, Project administration, Funding acquisition, Writing-original draft, Writing-review and editing. Yanxin Pan: Software, Formal analysis, Data curation, Investigation. Siyuan Jing: Software, Data curation, Investigation.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eThe data that supports the findings of this study are available in the supplementary material of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlavi SA, Naseri AA, Ritzema H, Van Dam J., Hellegers, P (2022) A combined model approach to optimize surface irrigation practice: SWAP and WinSRFR. Agric Water Manage 271: 107741. http://dx.doi.org/10.1016/j.agwat.2022.107741.\u003c/li\u003e\n\u003cli\u003eCamargo GGT, Kemanian A R (2016) Six crop models differ in their simulation of water uptake. Agric for Meteorol 220: 116-129. https://doi.org/10.1016/j. agrformet.2016.01.013.\u003c/li\u003e\n\u003cli\u003eChen K, Yu S, Li Q, Zhang M, Wang Y, Liu Z (2019) Simulation and Evaluation of Technical Schemes for Water-saving Irrigation of Rice in Different Hydrological Years. Trans CSAE 50: 268-277. (in Chinese with English abstract). doi:10.6041/j.issn.1000-1298.2019.12.031.\u003c/li\u003e\n\u003cli\u003eChen S, Mao X, David A, Yang J (2019) Model of crop growth, water flow, and solute transport in layered soil. Agric Water Manage 22: 160-174. http://dx.doi.org/10.1016/j.agwat.2019.04.031.\u003c/li\u003e\n\u003cli\u003eDewedar O, Plauborg F, Marwa M, El-shafie A, Ragab R. (2020) Improving water saving, yield, and water productivity of bean under deficit drip irrigation: Field and modelling study using the SALTMED mode. Irrig Drain 70: 224-242. http://dx.doi.org/10.1002/ird.2539.\u003c/li\u003e\n\u003cli\u003eFattori IM, Marin FR (2023) Assessing the influence of crop model structure on the performance of data assimilation for sugarcane. Comput Electron Agric 209: 1-11. https://doi.org/10.1016/j.compag.2023.107848.\u003c/li\u003e\n\u003cli\u003eFeng W, Gao J, Cen R, Yang F, He Z, Wu J, Miao Q, Liao H (2020) Effects of polyacrylamide-based super absorbent polymer and corn straw biochar on the arid and semi-arid salinized soil. Agric 11: 1-17. http://dx.doi.org/10.3390/agriculture10110519.\u003c/li\u003e\n\u003cli\u003eHuang X, Zhao Y, Guo T, Mao X. (2024) Enhancing SWAP simulation accuracy via assimilation of leaf area index and soil moisture under different irrigation, film mulching and maize varieties conditions. Comput Electron Agric 218: 108625. http://dx.doi.org/10.1016/j.compag.2024.108625.\u003c/li\u003e\n\u003cli\u003eHeinen M, Mulder M, Dam J V, Bartholomeus R, Lier QDJV, Wit J D, Broehe M H (2024) SWAP 50 years: advances in modelling soil-water-atmosphere-plant interactions. Agric Water Manage 298: 108883. http://dx.doi.org/10.1016/j.agwat.2024.108883.\u003c/li\u003e\n\u003cli\u003eHu S, Shi L, Huang K, Zha Y, Hu X, Ye H (2019) Improvement of sugarcane crop simulation by SWAP-WOFOST model via data assimilation. Field Crop Res 232: 49-61. http://dx.doi.org/10.1016/j.fcr.2018.12.009.\u003c/li\u003e\n\u003cli\u003eJiang J, Feng S, Ma J, Huo Z, Zhang C (2016) Irrigation management for spring maize grown on saline soil based on SWAP model. Field Crops Res 196: 85-97. http://dx.doi.org/10.1016/j.fcr.2016.06.011.\u003c/li\u003e\n\u003cli\u003eJing B, Yan B, Li J, Liu R (2021) Comprehensive evaluation and analysis of soil fertility of saline-alkaline land in Weibei Lubotan area in the middle reaches of Yellow River. J Northwest U (Nat Sci Edi) 52: 371-379. (in Chinese with English abstract). doi:10.16152/j.cnki.xdxbzr.2022-03-002.\u003c/li\u003e\n\u003cli\u003eKargas G, Kerkides P, Sotirakoglou K, Poulovassilis A (2016) Temporal variability of surface soil hydraulic properties under various tillage systems. Soil Tillage Res 158: 22-31. https://doi.org/10.1016/j.still.2015.11.011. \u003c/li\u003e\n\u003cli\u003eKorzukhin M, Grabovsky V (2020) Estimation of Leaf Area Index (LAI) of Russian forests using a mechanical model and forest inventory data. Environ Sci 18: 341-346. http://dx.doi.org/10.1007/978-3-030-37421-1_18.\u003c/li\u003e\n\u003cli\u003eKramer I, Mau Y (2023) Review: modeling the effects of salinity and sodicity in agricultural systems. Water Resour Res 59: 034750. http://dx.doi.org/10.1029/2023WR034750.\u003c/li\u003e\n\u003cli\u003eLei G, Zeng W, Yu J, Huang J (2023) A comparison of physical-based and machine learning modeling for soil salt dynamics in crop fields. Agric Water Manage 277: 108115. http://dx.doi.org/10.1016/j.agwat.2022.108115.\u003c/li\u003e\n\u003cli\u003eL\u0026eacute;tourneau G, Caron J (2019) Irrigation Management Scale and Water Application Method to Improve Yield and Water Productivity of Field-Grown Strawberries. Agronomy 9: 286. http://dx.doi.org/10.3390/agronomy9060286.\u003c/li\u003e\n\u003cli\u003eLi A, Wu Y, Cao S (2024) Effects of land use-land cover on soil water and salinity contents. Acta Ecol Sinica 2: 307-314. http://dx.doi.org/10.1016/j.chnaes.2023.07.002.\u003c/li\u003e\n\u003cli\u003eLi P, Ren L (2020) Evaluating the saline water irrigation schemes using a distributed agro-hydrological model. J Hydrol 594: 125688. http://dx.doi.org/10.1016/j.jhydrol.2020.125688.\u003c/li\u003e\n\u003cli\u003eLi S, Wang Q, Fan Q, Wu M, Zhang L, Fei L, Xue R, Li L (2023) Relationship between vegetation spatial distribution pattern and soil factors in drainage ditches in arid irrigation area. Chin. J Ecol 42: 607-616. (in Chinese with English abstract).\u003cbr\u003e doi:10.13292/j.1000-4890.202302.023.\u003c/li\u003e\n\u003cli\u003eLi S, Wang Q, Wu M, Fang W, Zhang H, Yang G, Li J (2021) Simulation of desalination process in dredger fill soil under natural and engineering measures. Trans CSAE 37: 91-98. (in Chinese with English abstract). doi:10.11975/j.issn.1002-6819.2021.06.012.\u003c/li\u003e\n\u003cli\u003eLi S, Wu M, Jia Z, Luo W, Fei L, Li J (2021) Influence of different controlled drainage strategies on the water and salt environment of ditch wetland: A model-based study. Soil Till Res 208: 104894. http://dx.doi.org/10.1016/j.still.2020.104894.\u003c/li\u003e\n\u003cli\u003ePan Y, Yuan C, Jing S (2020) Simulation and optimization of irrigation schedule for summer maize based on SWAP model in saline region. Int J Agr Biol Eng 13: 117-122. http://dx.doi.org/10.25165/j.ijabe.20201303.5218.\u003c/li\u003e\n\u003cli\u003eRavensbergen APP, van Ittersum MK, Kempenaar C, Ramsebner N, De Wit D, Reidsma P (2024) Coupling field monitoring with crop growth modelling provides detailed insights on yield gaps at field level: a case study on ware potato production in the Netherlands. Field Crop Res 308: 109295. http://dx.doi.org/10.1016/j.fcr.2024.109295.\u003c/li\u003e\n\u003cli\u003eShafiei M, Ghahraman B, Saghafian B, Davary K, Pande S, Vazifedoust M (2014) Uncertainty assessment of the agro-hydrological SWAP model application at field scale: A case study in a dry region. Agric Water Manage 146: 324-334. http://dx.doi.org/10.1016/j.agwat.2014.09.008.\u003c/li\u003e\n\u003cli\u003eSun S, Luo W, Zou J, Wu W, Jia Z, Zhang Z, Zhu W (2019) Types of drainage network impact attenuation of dissolved contaminants. J Irrig Drain 38: 122-128. (in Chinese with English abstract). doi:10.13522/j.cnki.ggps.20180404.\u003c/li\u003e\n\u003cli\u003evan Dam JC, Huygen J, Wesseling JG, Feddes RA, Kabat P, van Walsum PEV, Groenendijk P, van Diepen CA (1997) Theory of SWAP version 2.0: simulation of water flow, solute transport and plant growth in the soil-water-atmosphere-plant environment. Wageningen: DLO Winand Staring Centre. 287-295. https://www.swap.alterra.nl/.\u003c/li\u003e\n\u003cli\u003evan Dam JC, Groenendijk P, Hendriks RFA, Kroes JG (2008) Advances of modeling water flow in variably saturated soils with SWAP. Vadose Zone J 7: 640-653. http://dx.doi.org/10.2136/vzj2007.0060.\u003c/li\u003e\n\u003cli\u003eVerma AK, Gupta SK, Isaac RK (2014) Application of soil-water-atmosphere-plant model to assess performance of subsurface drainage system under semi-arid monsoon climate. Irrig Drain 63: 93-101. http://dx.doi.org/10.1002/ird.1783.\u003c/li\u003e\n\u003cli\u003eWang G, Shi H, Li X, Zheng Q, Guo J, Wang W (2019) Analysis of water and salt transportation and balance during cultivated land, waste land and lake system in Hetao Irrigation Area. J Hydraul Eng 50: 1518-1528. (in Chinese with English abstract). doi:10.13243/j.cnki.slxb.20190593.\u003c/li\u003e\n\u003cli\u003eWang J, Wang Y, Qi Z (2024) Remote sensing data assimilation in crop growth modeling from an agricultural perspective: new insights on challenges and prospects. Agronomy 14: 1920. http://dx.doi.org/10.3390/agronomy14091920.\u003c/li\u003e\n\u003cli\u003eWang X, Zhang D, Qi Q, Tong S, An Y, Lu X, Liu Y (2019) The restoration feasibility of degraded Carex Tussock in soda-salinization area in arid region. Ecol Indic 3: 131-136. http://dx.doi.org/10.1016/j.ecolind.2018.08.066.\u003c/li\u003e\n\u003cli\u003eXu J, Cai H, Wang X, Ma C, Saddique Q (2019) Exploring optimal irrigation and nitrogen fertilization in a winter wheat-summer maize rotation system for improving crop yield and reducing water and nitrogen leaching. Agric Water Manage 228: 105904. http://dx.doi.org/10.1016/j.agwat.2019.105904.\u003c/li\u003e\n\u003cli\u003eYu DY, Zha YY, Shi LS, Jin XL, Hu S, Yang Q, Huang K, Zeng WZ (2020) Improvement of sugarcane yield estimation by assimilating UAV-derived plant height observations. Eur J Agron 121: 1-16. https://doi.org/10.1016/j. eja.2020.126159.\u003c/li\u003e\n\u003cli\u003eZhang X, Ma F, Yin S, Wallace CD, Soltanian MR, Dai Z, Ritzi RW, Ma Z, Zhan C, L\u0026uuml; X (2021) Application of upscaling methods for fluid flow and mass transport in multi-scale heterogeneous media: a critical review. Appl Energy 303: 117603. https://doi.org/10.1016/j.apenergy.2021.117603.\u003c/li\u003e\n\u003cli\u003eZhao Y, Mao X, Shukla M (2020) A modified SWAP model for soil water and heat dynamics and seed\u0026ndash;maize growth under film mulching. Agr Forest Meteorol 292: 108127. http://dx.doi.org/10.1016/j.agrformet.2020.108127.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Soil water flux, soil salt flux, irrigation quota, summer maize, SWAP model","lastPublishedDoi":"10.21203/rs.3.rs-6922081/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6922081/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and aims\u003c/h2\u003e\u003cp\u003eTo investigate the effects of different water-saving irrigation quota on soil water and salt flux under the growth conditions of crop, providing a theoretical basis for the prevention of soil salinization and the efficient utilization of water resources in arid area.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThe SWAP (Soil-Water-Atmosphere-Plant) model parameters were calibrated and validated based on field experiments data to simulate soil water-salt flux and soil water-salt cumulative flux under different scenarios in the Northwest China.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSoil water flux, soil water cumulative flux, soil salt flux and soil salt cumulative flux decreased with the decrease on irrigation quota at the lower interface of crop root zone and storage zone under different scenarios. The soil water cumulative flux and soil salt cumulative flux changed small, when the irrigation quota was reduced to 70%IQ (Irrigation Quota) and 60% IQ. Soil water could be stably stored in 0-100 cm soil layer to meet the growth requirements of summer maize, which brought in by irrigation and rainfall. When the irrigation quota of summer maize was 70% IQ (3500 m\u003csup\u003e3\u003c/sup\u003e\u0026middot;ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), soil water-salt flux and soil water-salt cumulative flux were minimal at the lower interface of crop root zone and storage zone. The yield reduction of summer maize was only 10%.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003e3500 m\u003csup\u003e3\u003c/sup\u003e\u0026middot;ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e was the optimal irrigation quota for summer maize from the perspective of soil water-salt balance and crop growth. It was to provide technical support for the efficient utilization of water resources and also guided agricultural production practice in the Northwest China.\u003c/p\u003e","manuscriptTitle":"Simulation soil water-salt flux and irrigation quota for summer maize based on SWAP model in the Northwest China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-26 10:27:14","doi":"10.21203/rs.3.rs-6922081/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bc75f194-1b31-4382-80b2-b19b9500d146","owner":[],"postedDate":"August 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-22T11:23:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-26 10:27:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6922081","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6922081","identity":"rs-6922081","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}