To achieve the stability of grain production, it is necessary to analyze the impact of groundwater change on crop yield. In this paper, the AquaCrop model was established in the Shijin irrigation district. First, the meteorological data, crop data, management data, soil data and simulation settings were collected as the input data. Second, the calibration and validation of the model were carried out. The RE was all within ±20%, the standard root mean square error (NRMSE) and the coefficient of residual mass (CRM) of cotton were 0.0593 and −0.0173, those of summer maize were 0.0892 and 0.0266 and those of winter wheat were 0.0779 and 0.0273, respectively. The model was accurate and successfully applied. The effect of groundwater change on crop yield in the whole growth period and different growth stages was studied. The results showed that summer maize and winter wheat were less sensitive to groundwater. The minimum groundwater depth for maize and wheat was 1.1 and 1.0 m, respectively. Cotton was more sensitive to groundwater. The suitable groundwater depth of cotton was 1.1–2.0 m. The maximum suitable groundwater depth of cotton in the sowing and seedling stage was 1.5 m. The minimum suitable groundwater depth of cotton in the bud stage and the floweri20ng and boll stage was 1.1 m.

  • Compared with previous studies using fixed groundwater depth, this paper studied the effect of the dynamic groundwater fluctuation process on the crop growth process.

  • The response of crop yield to the groundwater depth change process in the whole growth was analyzed.

  • Cotton, which is most sensitive to groundwater, was selected from three crops and its response to groundwater at different growth stages was analyzed.

Graphical Abstract

Graphical Abstract
Graphical Abstract

Crop growth is affected by many factors, such as natural conditions, growth environment, management measures and simulation settings. Groundwater depth is one of the factors. In the process of crop growth, there is bound to be a certain connection with groundwater. Groundwater depth not only affects the absorption of water but also the transportation of salt, nitrogen, etc. Groundwater depth will further influence the photosynthesis of the crop canopy by affecting the root system and finally affecting the crop yield (Benyamini et al., 2005).

The research on the relationship between groundwater depth and crop response mainly focuses on crop yield. Mejia et al. (2000) investigated the effect of groundwater depth on soybean and maize yield by comparing the groundwater control with the free drainage scenario and found that the crop yield under the groundwater control of 0.5 and 0.75 m was higher than that under the free drainage scenario. Xiao et al. (2010) experimented on groundwater depth at the Erfeng irrigation station in Anhui Province. The results showed that the groundwater depth in the early stage of jointing was more than 0.6 m or in the late stage of jointing was less than 0.4 m, which would have a serious negative impact on the yield of summer maize. Kahlown et al. (2005) carried out a groundwater experiment on various crops (wheat, sugarcane, corn, sorghum, etc.) in Pakistan. The findings showed that corn and sorghum were waterlogging-prone crops, and their yield decreased with the increase in the groundwater level. When the groundwater depth was 0.5 m, most of the water needed for the growth of wheat and sunflower has been obtained with the groundwater depth achieving 0.5 m. The most suitable groundwater depth for sugarcane was about 2.0 m. Among all crops, the suitable groundwater depth should be controlled at around 1.5–2.0 m. Liu et al. (2011) quantitatively studied the effect of shallow groundwater level on the winter wheat yield under rain-fed conditions by using a lysimeter. The results suggested that the groundwater depth in the range of 0.4–1.5 m met more than 65% of the potential evapotranspiration of winter wheat. Zhao et al. (2020) discussed the impact of the groundwater depth on summer maize growth. The results indicated that the suitable groundwater table depth for summer maize growth in the Shijin irrigation district was 1.5 m, and the groundwater contribution was 58.8%.

The response of crop yield to groundwater in different stages of crop growth was also studied. Fang et al. (2012) studied the suitable groundwater depth of wheat during the filling period in the Jianghan Plain. The findings showed that the groundwater depth within 0.75 m would cause waterlogging damage. Shallow groundwater depth would cause flooding stress to cotton. The suitable groundwater depth of the cotton bud stage was 0.7–0.8 m, and a groundwater depth of less than 0.5 m may make cotton face a waterlogging risk (Zhu et al., 2003; Zhang & Dong, 2015).

The method based on model simulation can provide a powerful tool for the study of crop response to different groundwater depths (Ahmed et al., 2013). Xu et al. (2013) used the improved Soil Water Atmosphere Plant (SWAP) model to study the response of wheat yield to groundwater variations in the Qingtongxia irrigation district and found that the suitable groundwater depth of wheat was 1–1.5 m. Ramos et al. (2017) used the MOHID-Land model to simulate the soil water dynamics and corn growth in southern Portugal. The simulation suggested the importance of shallow groundwater on corn growth. Soylu et al. (2014) combined the Agro-IBIS model with the Hydrus-1D model to analyze the impact of groundwater on crops. The studies indicated that shallow groundwater played an important role in crop growth, but the groundwater too close to the surface would have a negative impact on crop photosynthesis. Han et al. (2015) used the SWAT model and Hydrous-1D model to study the relationship between cotton growth and groundwater in Aksu, Xinjiang. When the groundwater depth was 1.84 m, the contribution of groundwater to crop transpiration reached 23%, which was the most suitable groundwater depth. Groundwater had positive and negative effects on the growth of cotton. Kandil & Willardson (1992) conducted a regression analysis on the relationship between groundwater depth and cotton yield and found that both deep groundwater and shallow groundwater would reduce the yield. Karimov et al. (2014) showed that the yield of crops increased with the increase of groundwater depth, but there was a most suitable groundwater depth, and beyond this threshold, the yield would decrease with the increase of groundwater depth.

In most of the above studies, a fixed groundwater depth was used to study the effect of groundwater depth on crop yield. In fact, groundwater depth is a continuous dynamic process during crop growth, so it is necessary to study the effect of groundwater fluctuation on the crop growth process. Frequently used crop models, such as Hydrous-1D, SWAP and SWAT, are the main tools adopted by most researchers. However, these models often contain complicated parameters and equations. Therefore, this paper used the AquaCrop model to evaluate the impact of the groundwater fluctuation process on crop growth, which was simple and convenient.

Study area

The Shijin irrigation district is a large-scale irrigation district in Hebei Province of China. The Shijin irrigation district borders are Hengshui in the East, Xingtai in the South and Shijiazhuang in the West. It mainly irrigates the south of the downstream of Hutuo River, and north and west of Fuyang River. The irrigation district has a total agricultural population of 1.08 million, a cultivated area of 290,000 ha, and a control area of 4,144 km2. The Shijin irrigation district is a typical temperate continental monsoon climate, which is suitable for planting cotton, summer maize, winter wheat and other crops. The study area is shown in Figure 1.
Fig. 1

The location of the Shijin irrigation district.

Fig. 1

The location of the Shijin irrigation district.

Close modal

Data

The input data of the AquaCrop model mainly included meteorological data, crop data, management data, soil data and simulation settings.

The meteorological data from 1951 to 2017 included daily maximum temperature, daily minimum temperature, rainfall, wind speed, sunshine hours, atmospheric CO2 concentration and reference crop evapotranspiration (ET0). The meteorological data were collected from China's meteorological data sharing service system (http://cdc.cma.gov.cn/home.do). The atmospheric CO2 concentration was based on the default data of the model. The reference crop evapotranspiration (ET0) was calculated with the ET0 calculator developed by the Food and Agriculture Organization of the United Nations (FAO) (EI-Shirbeny et al., 2021; Valipour et al., 2021). The growth period of cotton in the Shijin irrigation district was about 110–140 days, that of summer maize was about 100 days, and that of winter wheat was about 200–220 days. Each stage of the crop growth period is shown in Tables 1,23. The production data for 2000–2014 were provided by the Hebei Statistical Bureau.

Table 1

The growth period of cotton.

Growth periodSowing stageSeedling stageBud stageFlower and boll stageBoll opening stage
Time Mid-April Mid-April–Late-May Late-May–Late-June Late-June–Mid-August Late-August–September 
Growth periodSowing stageSeedling stageBud stageFlower and boll stageBoll opening stage
Time Mid-April Mid-April–Late-May Late-May–Late-June Late-June–Mid-August Late-August–September 
Table 2

The growth period of summer maize.

Growth periodSowing and seedling stageJointing stageFlowering stageFilling stageMature stage
Time Mid-April–Late-June Late-June–Late-July Early-August–Mid-August Mid-August–Late-August Late-August–Mid-September 
Growth periodSowing and seedling stageJointing stageFlowering stageFilling stageMature stage
Time Mid-April–Late-June Late-June–Late-July Early-August–Mid-August Mid-August–Late-August Late-August–Mid-September 
Table 3

The growth period of winter wheat.

Growth periodFrom seedling stage to rising stageFrom rising stage to flowering stageFrom flowering stage to mature stage
Time Late-June to Mid-March Mid-March to Mid-April Mid-April to Late-May 
Growth periodFrom seedling stage to rising stageFrom rising stage to flowering stageFrom flowering stage to mature stage
Time Late-June to Mid-March Mid-March to Mid-April Mid-April to Late-May 

The soil database needed to be established according to the soil properties. The soil data mainly consisted of the number of soil layers, the thickness of soil layers, the saturated water content (SAT) of each layer, the field water capacity (FC), the wilting point (WP) and the saturated water conductivity (Ksat). The soil types of the Shijin irrigation district could be roughly divided into two categories according to the east and west areas, which could be found in the research from Xiang, (2009). The soil properties of each area are shown in Tables 4 and 5.

Table 4

Soil characteristics in the east area.

Soil textureDepth (m)WPFC (%)SAT (%)Ksat (mm/day)
Silty clay 0–0.50 16 40 50 15 
Silty 0.50–5.00 33 43 50 
Soil textureDepth (m)WPFC (%)SAT (%)Ksat (mm/day)
Silty clay 0–0.50 16 40 50 15 
Silty 0.50–5.00 33 43 50 
Table 5

Soil characteristics in the west area.

Soil textureDepth (m)WPFC (%)SAT (%)Ksat (mm/day)
Silty 0–5.00 33 43 50 
Soil textureDepth (m)WPFC (%)SAT (%)Ksat (mm/day)
Silty 0–5.00 33 43 50 

Management data mainly involved irrigation data and field management data. Irrigation data mainly included irrigation mode, irrigation time and irrigation quota. According to the study area conditions, the irrigation quota of the three crops is shown in Table 6 (Wei, 2017). Field management data mainly consisted of soil fertility, surface coverage and weeding management. According to the actual measures in the field, the management parameter was determined.

Table 6

Crop irrigation quota unit: m3/ha.

CropRainy yearNormal yearDry year
Cotton 450 1,350 1,800 
Summer maize 360 840 1,260 
Winter wheat 600 1,800 2,850 
CropRainy yearNormal yearDry year
Cotton 450 1,350 1,800 
Summer maize 360 840 1,260 
Winter wheat 600 1,800 2,850 

The groundwater data of monitoring sites from 2001 to 2014 were provided by the Water Resources Department of Hebei Province.

Introduction of the AquaCrop model

The AquaCrop model is a crop model developed by the FAO in 2009. The model consists of three modules: distinction of crop transpiration and soil evaporation, the crop growth simulation and yield response to water. The model evaluates the response of yield to water by calculating the water-use efficiency of crops and analyzing effective water use in soil and crop yield (Raes et al., 2009). In the study of crop response to water deficit, the empirical equation (Equation (1)) was often used. Yet, the potential evapotranspiration and actual evapotranspiration were used in the equation, which confused soil evaporation and crop transpiration, resulting in an increased deviation. The AquaCrop model optimizes the equation from two aspects. The first aspect is to divide the crop evapotranspiration (ET) into soil evaporation (E) and crop transpiration (Tr). The final crop yield (Y) is expressed as the product of biomass (B) and harvest index (HI). The biomass (B) is composed of water production efficiency (WP) and cumulative crop transpiration. The evolution of the equation is shown in Figure 2, and the improved equations are shown in Equations (2) and (3) (Steduto et al., 2008).
formula
(1)
formula
(2)
formula
(3)
where and are potential yield (kg/m2) and actual yield (kg/m2), respectively; and are potential evapotranspiration (mm) and actual evapotranspiration (mm) , respectively; is the proportion factor of crop yield response to water; Y is the final yield (kg/m2); B is biomass (kg/m2); HI is harvest index (%); WP is water production efficiency [kg/(m2 mm)]; and Tr is crop transpiration (mm).
Fig. 2

The evolution of the AquaCrop equation.

Fig. 2

The evolution of the AquaCrop equation.

Close modal

The second aspect is to use canopy coverage (CC) instead of leaf area index (LAI) to describe the growth of crops, which is shown in Equations (46). Canopy growth coefficient (CGC) and canopy decline coefficient (CDC) are used to express the crop growth and senescence process, which is helpful to expand the application scale of the model.

When
formula
(4)
When
formula
(5)
formula
(6)
where t is the number of days from the seedling stage(days); is the initial canopy coverage (%); is the maximum canopy coverage (%); CC is the canopy coverage at the time of t (%); CGC is the canopy growth coefficient; and CDC is the canopy decline coefficient.

Methods

The localization of the AquaCrop model

Sensitivity analysis of parameters
Sensitivity analysis of parameters is an important part of model localization by changing variables in turn to evaluate the change of output variables, which was of great significance for model calibration (Klepper, 1997). The maximum canopy coverage, the maximum effective root depth, the date of reaching the maximum effective root depth, reference harvest index and planting density in the AquaCrop model were selected for parameter sensitivity by the one time at a time (OTA) method (Daniel, 1973). Relative sensitivity (RS) was used to express the sensitivity of each parameter:
formula
(7)
where RS is the relative sensitivity; x is the input parameter of the model; is the change of the input parameter; and and are the output value before and after the parameter change, respectively. The larger the RS, the more sensitive the parameters are.
The calibration and validation of the AquaCrop model

After the sensitivity analysis of model parameters, the model needs to be calibrated and verified. The model data series was divided into three parts: one was used for model preheating, one was used for parameter calibration and the other was used for model validation. The preheating period was 2000–2002, the calibration period was 2003–2010, and the validation period was 2011–2014. Due to the lack of data, the preheating period of winter wheat was 2003–2004, the calibration period was 2005–2010 and the validation period was 2011–2014. The AquaCrop model parameters could be grouped into two categories: parameters calibrated by FAO and other parameters. The parameters calibrated by FAO are conservative parameters, which did not change with time and place (Chen et al., 2005). These parameters remained unchanged in the process of calibration. Other parameters needed to be adjusted in the range of parameters given by FAO through the trial-and-error method, gradually reducing the difference between the simulated value and the measured value until the agreement between the simulated value and the measured value was good. In this paper, the crop yield was selected as the target.

After the parameters of the model were determined, the data should be verified to evaluate the applicability of the model in the Shijin irrigation district. To evaluate the simulation performance of the model, the following parameters were used: relative error (RE), standard root mean square error (NRMSE) and coefficient of residual mass (CRM). The equations are as follows (Salemi et al., 2011; García-Vila & Fereres, 2012):
formula
(8)
formula
(9)
formula
(10)
where n is the sample size, and are the simulation value and measured value (t/ha), respectively; and is the average of the measured value.

When RE is within , it is considered that the model has a better simulation performance; regarding RMSE, the smaller the value, the smaller the deviation between the simulated values and the measured values; CRM can be positive or negative, a positive value indicates a low simulation value, a negative value indicates a high simulation value and a value of 0 has the best simulation effect.

Effect of groundwater depth on crop yield in the Shijin irrigation district

Determination of different level years
The different level years (rainy, normal and dry) of rainfall should be determined according to the crop growth period. The data series (1951–2017) was divided into different level years through frequency calculation and line fitting. The rainfall frequency curves of cotton, summer maize and winter wheat are shown in Figure 3.
Fig. 3

Fitting results of the P-III frequency curve.

Fig. 3

Fitting results of the P-III frequency curve.

Close modal

The frequency of 25, 50 and 75% correspond to rainy year, normal year and dry year, respectively. The determination results of the typical year corresponding to different level years are shown in Table 7. The typical years of cotton in rainy, normal and dry years were 2008, 2012 and 2007, respectively. The typical years of summer maize were 2005, 2012 and 2007. The typical years of winter wheat were 2007, 2012 and 2005.

Table 7

Rainfall in a crop growth period and the determination of a typical year.

CropTypeRainfall/mmTypical year
Cotton Rainy year 453.49 2008 
Normal year 355.58 2012 
Dry year 278.45 2007 
Summer maize Rainy year 471.69 2005 
Normal year 370.66 2012 
Dry year 282.76 2007 
Winter wheat Rainy year 102.67 2007 
Normal year 77.98 2012 
Dry year 57.26 2005 
CropTypeRainfall/mmTypical year
Cotton Rainy year 453.49 2008 
Normal year 355.58 2012 
Dry year 278.45 2007 
Summer maize Rainy year 471.69 2005 
Normal year 370.66 2012 
Dry year 282.76 2007 
Winter wheat Rainy year 102.67 2007 
Normal year 77.98 2012 
Dry year 57.26 2005 

The effect of groundwater depth on crop yield in the whole growth period
To analyze the effect of different groundwater depths on the crop yield in the whole growth period, several groups of groundwater depth scenarios were set based on the actual groundwater depth series (Figure 4). First, the actual groundwater depth series was taken as the benchmark series, then the benchmark series were scaled according to the multiple proportion amplifying method. Finally, 16 groups of the groundwater depth scenarios were generated. At the same time, to analyze the crop growth without the groundwater supply, the groundwater depth of 10 m deep in the whole growth period was set as another group of hypothetical scenarios.
Fig. 4

The groundwater fluctuation process in different typical years.

Fig. 4

The groundwater fluctuation process in different typical years.

Close modal
The effect of groundwater depth on cotton yield in different growth stages

Cotton is most sensitive to groundwater among the three crops, so cotton was taken as an example to study the impact of groundwater depth on cotton yield in each stage of the growth period. Based on the results of the selected optimal groundwater depth in the whole growth period, the groundwater depth was further adjusted in different stages to study the demand for groundwater in different stages of the growth period. When setting the groundwater depth in one growth stage, the groundwater depth and growth conditions in the other stage should remain unchanged. At the same time, to analyze the crop growth without the groundwater supply in the different growth stages, the groundwater depth of 10.0 m deep in the different growth stages was set as a hypothetical scenario. When setting the groundwater depth as 10.0 m in one stage, the groundwater depth in the other stage should be consistent with the groundwater depth conditions under different typical years.

The localization of the AquaCrop model

Sensitivity analysis of parameters

According to Equation (7), the sensitivity analysis results of parameters are shown in Table 8.

Table 8

Results of sensitivity analysis.

CropVariableThe date from sowing to floweringLength of the flowering stageMaximum effective rooting depthThe date from sowing to maximum rooting depth
Cotton Biomass 0.1711 0.0000 1.5244 0.3388 
Yield 0.2178 0.0000 1.6164 0.3851 
Summer maize Biomass 0.0327 0.0138 0.1029 0.0489 
Yield 0.0167 0.0129 0.1065 0.0500 
Winter wheat Biomass 0.0988 0.0000 0.0000 0.0120 
Yield 0.0938 0.0007 0.0010 0.0142 
CropVariableMaximum canopy coverInitial canopy coverPlanting densityReference harvest index (HI0)
Cotton Biomass 0.2725 0.0450 0.0450 0.1858 
Yield 0.2931 0.0533 0.0533 1.2144 
Summer maize Biomass 0.5516 0.0539 0.0968 0.0459 
Yield 0.5448 0.0538 0.1006 0.9461 
Winter wheat Biomass 0.5230 0.0521 0.1508 0.0000 
Yield 0.5239 0.0525 0.1510 1.0004 
CropVariableThe date from sowing to floweringLength of the flowering stageMaximum effective rooting depthThe date from sowing to maximum rooting depth
Cotton Biomass 0.1711 0.0000 1.5244 0.3388 
Yield 0.2178 0.0000 1.6164 0.3851 
Summer maize Biomass 0.0327 0.0138 0.1029 0.0489 
Yield 0.0167 0.0129 0.1065 0.0500 
Winter wheat Biomass 0.0988 0.0000 0.0000 0.0120 
Yield 0.0938 0.0007 0.0010 0.0142 
CropVariableMaximum canopy coverInitial canopy coverPlanting densityReference harvest index (HI0)
Cotton Biomass 0.2725 0.0450 0.0450 0.1858 
Yield 0.2931 0.0533 0.0533 1.2144 
Summer maize Biomass 0.5516 0.0539 0.0968 0.0459 
Yield 0.5448 0.0538 0.1006 0.9461 
Winter wheat Biomass 0.5230 0.0521 0.1508 0.0000 
Yield 0.5239 0.0525 0.1510 1.0004 

Note: The parameters corresponding to the value in bold are the sensitivity parameters.

It could be seen that except for the reference harvest index, other parameters have a similar impact on biomass and yield. Among the above parameters, the most influential factors on cotton biomass and yield were: maximum effective root depth, the date from sowing to maximum rooting depth and reference harvest index, with the relative sensitivity varying from 0.18 to 1.62. The parameters that a have great influence on summer maize were maximum effective root depth, maximum canopy coverage and reference harvest index, and the relative sensitivity changed in the range of 0.05–0.95. The parameters that have a great influence on winter wheat were maximum canopy coverage, reference harvest index and planting density, and the relative sensitivity changed in the range of 0–1.0. Therefore, in the process of crop parameter calibration, the adjustment of the above sensitive parameters should be put in the first place.

The calibration and validation of the AquaCrop model

The calibrated parameters of the model for cotton, summer maize and winter wheat in the Shijin irrigation district are shown in Table 9. The yield of cotton, summer maize and winter wheat simulated by the model are shown in Tables 10,1112.

Table 9

Main crop parameters.

CropParametersValueUnit
Cotton Soil surface covered by an individual seedling at 90% emergence cm2 
Planting density 60,000 plant/ha 
Maximum effective root depth 1.3 
Maximum canopy coverage 70 
Reference harvest index (HI025 
Summer maize Soil surface covered by an individual seedling at 90% emergence 6.5 cm2 
Planting density 100,000 plant/ha 
Maximum effective root depth 
Maximum canopy coverage 85 
Reference harvest index (HI047 
Winter wheat Soil surface covered by an individual seedling at 90% emergence 6.5 cm2 
Planting density 2,000,000 plant/ha 
Maximum effective root depth 2.4 
Maximum canopy coverage 90 
Reference harvest index (HI048 
CropParametersValueUnit
Cotton Soil surface covered by an individual seedling at 90% emergence cm2 
Planting density 60,000 plant/ha 
Maximum effective root depth 1.3 
Maximum canopy coverage 70 
Reference harvest index (HI025 
Summer maize Soil surface covered by an individual seedling at 90% emergence 6.5 cm2 
Planting density 100,000 plant/ha 
Maximum effective root depth 
Maximum canopy coverage 85 
Reference harvest index (HI047 
Winter wheat Soil surface covered by an individual seedling at 90% emergence 6.5 cm2 
Planting density 2,000,000 plant/ha 
Maximum effective root depth 2.4 
Maximum canopy coverage 90 
Reference harvest index (HI048 
Table 10

Simulation results of cotton.

YearYield (kg/ha)
RE (%)
Measured valueSimulation value
Preheating period 2000 915 878 −4.04 
2001 930 1,182 27.10 
2002 950 1,230 29.47 
Calibration period 2003 971 1,084 11.64 
2004 1,017 960 −5.60 
2005 1,048 1,053 0.48 
2006 1,047 1,194 14.04 
2007 1,060 1,208 13.87 
2008 1,134 1,179 3.97 
2009 1,081 1,183 9.44 
2010 1,080 1,014 −6.11 
Validation period 2011 1,237 1,166 −5.74 
2012 1,146 1,132 −1.22 
2013 1,138 1,231 8.17 
2014 1,151 1,224 6.34 
YearYield (kg/ha)
RE (%)
Measured valueSimulation value
Preheating period 2000 915 878 −4.04 
2001 930 1,182 27.10 
2002 950 1,230 29.47 
Calibration period 2003 971 1,084 11.64 
2004 1,017 960 −5.60 
2005 1,048 1,053 0.48 
2006 1,047 1,194 14.04 
2007 1,060 1,208 13.87 
2008 1,134 1,179 3.97 
2009 1,081 1,183 9.44 
2010 1,080 1,014 −6.11 
Validation period 2011 1,237 1,166 −5.74 
2012 1,146 1,132 −1.22 
2013 1,138 1,231 8.17 
2014 1,151 1,224 6.34 

Note: The values in bold are the minimum and maximum relative error of cotton in calibration and validation periods.

Table 11

Simulation results of summer maize.

YearYield (kg/ha)
RE (%)
Measured valueSimulation value
Preheating period 2000 7,654 8,074 5.49 
2001 6,997 7,239 3.46 
2002 7,587 6,880 −9.32 
Calibration period 2003 7,005 6,366 −9.12 
2004 7,020 8,027 14.34 
2005 7,046 8,137 15.48 
2006 7,093 6,441 −9.19 
2007 7,221 7,404 2.53 
2008 7,653 7,392 −3.41 
2009 7,515 6,124 −18.51 
2010 6,195 7,267 17.30 
Validation period 2011 7,416 6,765 −8.78 
2012 7,860 8,265 5.15 
2013 8,085 8,525 5.44 
2014 7,845 6,822 −13.04 
YearYield (kg/ha)
RE (%)
Measured valueSimulation value
Preheating period 2000 7,654 8,074 5.49 
2001 6,997 7,239 3.46 
2002 7,587 6,880 −9.32 
Calibration period 2003 7,005 6,366 −9.12 
2004 7,020 8,027 14.34 
2005 7,046 8,137 15.48 
2006 7,093 6,441 −9.19 
2007 7,221 7,404 2.53 
2008 7,653 7,392 −3.41 
2009 7,515 6,124 −18.51 
2010 6,195 7,267 17.30 
Validation period 2011 7,416 6,765 −8.78 
2012 7,860 8,265 5.15 
2013 8,085 8,525 5.44 
2014 7,845 6,822 −13.04 

Note: The values in bold are the minimum and maximum relative error of summer maize in calibration and validation periods.

Table 12

Simulation results of winter wheat.

YearYield (kg/ha)
RE (%)
Measured valueSimulation value
Preheating period 2003 7,380 7,875 6.71 
2004 7,275 6,343 −12.81 
Calibration period 2005 7,515 6,513 −13.33 
2006 7,523 7,489 −0.45 
2007 7,440 7,532 1.24 
2008 6,938 6,679 −3.73 
2009 7,650 7,788 1.80 
2010 7,710 7,073 −8.26 
Validation period 2011 7,463 7,379 −1.13 
2012 7,583 7,100 −6.37 
2013 7,605 8,223 8.13 
2014 7,521 6,647 −11.62 
YearYield (kg/ha)
RE (%)
Measured valueSimulation value
Preheating period 2003 7,380 7,875 6.71 
2004 7,275 6,343 −12.81 
Calibration period 2005 7,515 6,513 −13.33 
2006 7,523 7,489 −0.45 
2007 7,440 7,532 1.24 
2008 6,938 6,679 −3.73 
2009 7,650 7,788 1.80 
2010 7,710 7,073 −8.26 
Validation period 2011 7,463 7,379 −1.13 
2012 7,583 7,100 −6.37 
2013 7,605 8,223 8.13 
2014 7,521 6,647 −11.62 

Note: The values in bold are the minimum and maximum relative error of winter wheat in calibration and validation periods.

The results showed that the RE in the calibration and validation period were all within , and the simulation values given by the AquaCrop model were basically consistent with the measured values. NRMSE and CRM of cotton were 0.0593 and −0.0173, that of summer maize were 0.0892 and 0.0266, and that of winter wheat were 0.0779 and 0.0273, respectively. The AquaCrop model was suitable for the Shijin irrigation district and could be used to simulate the growth of crops.

Effect of groundwater depth on crop yield in the Shijin irrigation district

Effect of groundwater depth on crop yield in the whole growth period

The crop yields under the groundwater depth of 10.0 m (hypothetical scenario) in different typical years of crops are shown in Table 13, and the yields in different typical years under different groundwater depth conditions are shown in Figure 5.
Table 13

The yields under the groundwater depth of 10 m in the whole growth period (unit: kg/ha).

CropRainy yearNormal yearDry year
Cotton 1,181 836 1,173 
Summer maize 6,849 7,727 6,053 
Winter wheat 6,066 6,329 5,580 
CropRainy yearNormal yearDry year
Cotton 1,181 836 1,173 
Summer maize 6,849 7,727 6,053 
Winter wheat 6,066 6,329 5,580 

Note: The values in bold are the maximum yields under the groundwater depth of 10 m in the whole growth period.

Fig. 5

The yields under different groundwater depths in the whole growth period.

Fig. 5

The yields under different groundwater depths in the whole growth period.

Close modal

The suitable groundwater depth of cotton in the rainy year was 1.2–2.1 m, and the yield reached the maximum value of 1,665 kg/ha around 1.6 m, while the yield under current conditions (benchmark groundwater depth series) was 1,330 kg/ha. When the average groundwater depth was less than 0.9 m, the groundwater would have a remarkable negative effect on the growth of cotton. Compared with the hypothetical scenario, the yield of cotton would be reduced by at least 30%. The suitable groundwater depth of cotton was 1.0–2.2 m in the normal year and the maximum yield was 1,732 kg/ha at about 1.2 m and 1,132 kg/ha at present. When the average groundwater depth was less than 0.5 m during the growth period, the groundwater would have a remarkable negative effect on the growth of cotton. Compared with the hypothetical scenario, the yield of cotton would be reduced by at least 30%. The suitable groundwater depth of cotton in the dry year was 1.1–1.6 m, and the yield reached the maximum value of about 1.4 m, which was 1,835 kg/ha, and the yield under current conditions was 1,208 kg/ha. When the groundwater depth reached 5.0 m or more, it can be considered that the groundwater did not affect the growth of cotton.

The suitable groundwater depth was 1.5–2.5 m in the growth period of summer maize in the rainy year. The maximum yield was 6,922 kg/ha at about 2.3 m and 6,878 kg/ha at present. When the average groundwater depth was less than 0.8 m, the groundwater would have a negative effect on the growth of summer maize. The yield of summer maize reduced remarkably by at least 30%, compared with the hypothetical scenario. The suitable groundwater depth was 1.8–3.0 m in the normal year. The maximum yield was 7,727 kg/ha at about 2.8 m and 7,712 kg/ha at present. When the average groundwater depth was less than 1.5 m, the groundwater would have a remarkable negative effect on the growth of summer maize. Compared with the hypothetical scenario, the yield would be reduced by about 30%. The suitable groundwater depth was 1.3–1.8 m in the dry year and the maximum yield was 7,435 kg/ha at about 1.5 m and 6,073 kg/ha at present. When the groundwater depth was below 1.0 m, the yield would reduce by more than 30%. When the groundwater depth reached 5.0 m or more, it can be considered that the groundwater had no effect on summer maize.

The suitable groundwater depth was 2.0–3.0 m of winter wheat in the rainy year, and the yield reached the maximum value of 7,093 kg/ha at about 3.0 m, while the yield was 6,156 kg/ha under the current condition. When the average groundwater depth was less than 1.2 m during the growth period, the groundwater would have a remarkable negative effect on the growth of winter wheat. The yield would reduce by at least 30%, compared with the hypothetical scenario. The suitable groundwater depth was 2.3–3.5 m in the normal year, and the yield reached the maximum value at about 3.5 m, which was 7,188 kg/ha. Under the current condition, the yield was 7,188 kg/ha. When the average depth of groundwater was less than 1.8 m, the groundwater would have a notable negative effect on the growth of winter wheat. Compared with the hypothetical scenario, the yield would reduce by more than 30%. The suitable depth of groundwater was 1.5–2.5 m in the dry year, and the yield reached the maximum value at about 2.5 m, which was 6,959 kg/ha. Under the current condition, the yield was 5,604 kg/ha. When the groundwater depth was below 1.0 m, the yield would reduce by more than 30%. When the groundwater depth reached 5.0 m or more, it can be considered that the groundwater did not affect the growth of winter wheat.

Some research showed that the soil environment could be improved with an appropriate groundwater depth range, and the crop yield would increase (Walia et al., 2007). When the groundwater depth was above or below a value, the positive effect of groundwater on crops was obviously weakened (Sepaskhah et al., 2003; Athar et al., 2008). In this paper, the suitable groundwater depths of three crops in different typical years were obtained under given rainfall and irrigation conditions, which was a dynamic index. The dynamic index varied with rainfall, irrigation volume and actual groundwater depth process, so it was difficult to guide the actual crop production activities. However, it can be found from the research results that (1) the yield of winter wheat and summer maize tended to be stable with the increase of groundwater depth, which was close to the yield under the current conditions of groundwater depth. Also, the groundwater depth was basically more than 3 m in the current condition (Figure 4), indicating that the groundwater produced a little impact on crops. The yield of winter wheat and summer maize depended more on rainfall and irrigation than on groundwater. So, summer maize and winter wheat were less sensitive to the depth of groundwater, especially in rainy and normal years. However, when the groundwater depth was shallow, it would cause a significant reduction in crop production. Therefore, the key index of groundwater control for summer maize and winter wheat was minimum groundwater depth. When the groundwater depth was below the minimum groundwater depth, the yield would reduce significantly. Since the minimum groundwater depth was also affected by the selection of different typical years, the average mean value of different typical years (rainy, normal and dry) was taken into comprehensive consideration. The minimum groundwater depth for summer maize and winter wheat was 1.1 and 1 m, respectively; (2) cotton yield increased at first and then decreased as groundwater depths increased, indicating that cotton was more sensitive to the groundwater depth, so it was necessary to further analyze the response of different growth periods to groundwater depth. Similarly, to eliminate the influence of different typical year selections on the determination of suitable groundwater depth, the average value was taken as the final reference. The suitable groundwater depth of cotton was 1.1–2.0 m. (3) The yield of different crops varies under different groundwater depths because the yield of crops is mainly affected by the water supply. The main factors affecting the water supply are rainfall, irrigation and groundwater depth, and the difference in the supply conditions of different crops often leads to differences in the grain yield. Sometimes, the yields are higher in dry years than in rainy years, mainly because irrigation or groundwater provide more water for crop growth in dry years.

Effect of groundwater depth on cotton yield in different growth stages

The yields under the groundwater depth of 10 m (hypothetical scenario) are shown in Table 14, and the yields under different groundwater depth conditions in typical years are shown in Figure 6.
Table 14

The yields under the groundwater depth of 10 m in different growth stages (unit: kg/ha).

TypeSowing and seedling stageBud stageFlower and boll stageBoll opening stage
Rainy year 1,089 1,712 1,762 1,668 
Normal year 1,153 1,618 1,865 1,748 
Dry year 1,251 1,875 1,781 1,834 
TypeSowing and seedling stageBud stageFlower and boll stageBoll opening stage
Rainy year 1,089 1,712 1,762 1,668 
Normal year 1,153 1,618 1,865 1,748 
Dry year 1,251 1,875 1,781 1,834 

Note: The values in bold are the maximum yields under the groundwater depth of 10 m in different growth stages.

Fig. 6

The crop under different groundwater depths in different growth stages.

Fig. 6

The crop under different groundwater depths in different growth stages.

Close modal

During the sowing and seedling stage, the yield decreased rapidly with the groundwater depth reaching a certain depth. Therefore, the groundwater should not be too deep in the growing period; otherwise, it would affect the yield of cotton. So, the suitable groundwater depth of cotton in the rainy year, normal year and dry year was less than 1.5, 1.6 and 1.4 m, respectively, in the sowing and seedling stage. Cotton was sensitive to groundwater in the bud stage. Compared with the hypothetical scenario, when the groundwater depth was below 0.6 m in the rainy year, the yield could be reduced by at least 30%. The suitable groundwater depth of cotton in the bud stage was no lower than 1.5 m in the rainy year. Compared with the hypothetical scenario, when the groundwater depth was below 0.9 m in the normal year, the yield could be reduced by at least 30%. The suitable groundwater depth of cotton in the bud stage was no lower than 1.2 m in the normal year. Compared with the hypothetical scenario, when the groundwater depth was about 0.4 m in the dry year, the yield could be reduced by 30%. The suitable groundwater depth of cotton in the bud stage was no lower than 0.6 m in the dry year. The cotton in the flowering and boll stage was sensitive to the low groundwater depth as well as in the bud stage. Compared with the hypothetical scenario, when the groundwater depth was about 1.0 m in the rainy year, the yield could be reduced by 30%. The suitable groundwater depth in the rainy year was more than 1.8 m in the rainy year. Compared with the hypothetical scenario, when the groundwater depth was about 0.5 m in the normal year, the yield could be reduced by 30%. The suitable groundwater depth in the normal year was more than 0.8 m. Compared with the hypothetical scenario, when the groundwater depth was about 0.6 m in the dry year, the yield could be reduced by 30%. The suitable groundwater depth in the dry year was more than 0.8 m. The cotton in the boll opening stage was not sensitive to groundwater. Groundwater depth varied from 0 to 6.0 m, and cotton yield change is less than 10%.

Except for the boll opening stage, the cotton in other growing stages was sensitive to the groundwater depth. The cotton in the sowing and seedling stage was sensitive to the high groundwater depth, while the cotton in the bud stage and flowering and boll stage was sensitive to the low groundwater depth. Similarly, to eliminate the influence of different typical year selections on the determination of suitable groundwater depth, the average value was taken as the final reference. The maximum suitable groundwater depth of cotton in the sowing and seedling stage was 1.5 m. The minimum suitable groundwater depth of cotton in the bud stage and the flowering and boll stage were 1.1 m.

As the determination of the typical year was based on the total rainfall, the rainfall distribution process was not considered fully, and the growth of crops in each stage was closely related to the rainfall distribution process. So, the process of selecting a typical year in this paper produced a certain impact on the crop yield and the analysis of groundwater on yield. The most direct factor affecting crop yield was soil moisture, which was affected by rainfall, groundwater and irrigation water during crop growth. Taking the sowing and seedling stage as an example, the soil water content and rainfall in typical years are shown in Figure 7 (no irrigation in this stage). The left figure showed the soil water content without groundwater recharge, and the right figure displayed the soil water content under the suitable groundwater depth.
Fig. 7

Soil water content and rainfall in typical years.

Fig. 7

Soil water content and rainfall in typical years.

Close modal

The soil water content was closely related to rainfall and groundwater. Also, crop yield was strongly related to soil water content. The most direct way to control crop yield was to monitor soil moisture, which was also a comprehensive indicator of rainfall, irrigation and groundwater. Through the real-time monitoring of soil moisture content, crop dynamic regulation and irrigation time setting could be realized. The real-time monitoring could be achieved in the facility agriculture, but not in large-scale irrigation areas where the soil moisture content could not be monitored in a large range. In this situation, controlling the groundwater level in a reasonable range could effectively guarantee and improve crop yield, especially cotton, which was sensitive to groundwater.

In this paper, the AquaCrop model was used to study the effect of the groundwater fluctuation process on the crop growth process. The main results were as follows:

  • The AquaCrop model driven by the meteorological data, crop data, soil data, groundwater and management data was used to simulate the crop growth in the Shijin irrigation district. Based on the sensitivity analysis of parameters, the model was calibrated and verified. The calibration and validation results showed that the RE in the calibration period (2003–2010) and validation period (2011–2014) were all within , the NRMSE and CRM in the validation period were within a reasonable range, and the AquaCrop model was applicable in the Shijin irrigation district.

  • Summer maize and winter wheat were less sensitive to the depth of groundwater, especially in the rainy and normal years. The key index of the groundwater control for summer maize and winter wheat was the minimum groundwater depth. The minimum groundwater depth for summer maize and winter wheat was 1.1 and 1.0 m, respectively. The cotton yield increased at first and then decreased as groundwater depths increased, indicating that cotton was more sensitive to the groundwater depth. The suitable groundwater depth of cotton was 1.1–2.0 m.

  • Except for the boll opening stage, the cotton in the other growing stages was sensitive to the groundwater depth. The cotton in the sowing and seedling stage was sensitive to the high groundwater depth, while the cotton in the bud stage and flowering and boll stage were sensitive to the low groundwater depth. The maximum suitable groundwater depth of cotton in the sowing and seedling stage was 1.5 m. The minimum suitable groundwater depth of cotton in the bud stage and the flowering and boll stage was 1.1 m.

  • In this paper, the effects of different groundwater depths on crop yield were evaluated from the perspective of the whole growth period. Taking cotton as an example, the effects of different groundwater depths in different growth stages on crop yield were further analyzed, and the appropriate groundwater depths were finally obtained. The dynamic characteristics of groundwater are fully considered in this study, instead of using fixed groundwater depth. However, the suitable groundwater depth for crop growth was influenced by the rainfall process, irrigation process, solar radiation process, etc., especially the difference in the rainfall distribution process, and there may be a water deficit in some stages of a rainy year and water surplus in some stages of a dry year, which caused the change of suitable groundwater depth. So, the suitable groundwater depth determined in this paper was obtained under the given rainfall process, irrigation process and weather conditions.

  • The suitable groundwater depth method proposed in this paper has wide applicability to other areas with different suitable groundwater depths. The method proposes the definition of the effect of the groundwater on crop growth with a dynamic process, which is more in line with estimating the actual field conditions that prevail on crop growth than fixed groundwater depths. Finally, the present study promotes the effective cultivation of crops under different climate events for regions with similar characteristics.

The authors acknowledge the financial support for this work provided by the National Natural Science Foundation of China (Grant no. 51879181) and the State Key Laboratory of Hydraulic Engineering Simulation and Safety (Tianjin University) (Grant no. HESS-2109).

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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