Quantitative evaluation of the influence of rainfall changes and groundwater exploitation on the groundwater level: a case study of the northern Huangqihai Basin, China

Groundwater plays important roles in ecosystem health and socioeconomic development, especially for endorheic basins in semiarid and arid areas. Climate changes and anthropogenic activities have changed the quantity, quality and spatiotemporal distribution of groundwater resources (Yihdego et al. 2017; Berdimbetov et al. 2020; Goni et al. 2021; Abbas et al. 2022), and the evolution of the groundwater environment has changed from a single naturally driven model to a dual naturally–artificially driven model (Wang et al. 2006). Research on the influence of climate change and anthropogenic activities on groundwater is a hot but challenging topic in the field of hydrology and water resources and is of great significance for revealing the mechanism of the water cycle and protecting water resources.

Many scholars have studied the influence of climate change and anthropogenic activities from different perspectives. Goni et al. (2021) examined the intensification of tropical rainfall favored groundwater recharge in the semiarid, southwestern Lake Chad basin of Nigeria. Alghamdi et al. (2022) noted that groundwater depletion coupled with climate change, increasing temperature, and decreasing precipitation has led to groundwater quality deterioration and diminishing groundwater quantity, subsequently affecting agricultural productivity in arid environments. Abbas et al. (2022) used Soil and Water Assessment Tool-Modular Three-Dimensional Finite Difference Groundwater Flow Model- Water Evaluation and Planning (SWAT-MODFLOW-WEAP) to assess the impact of climate change on water resources and noted that water resource management should adapt to climate change. Research by Anurag & Crystal Ng (2022) showed that abundant precipitation increased groundwater recharge, and moisture feedbacks on evapotranspiration should be considered when quantifying climate change impacts on recharge. Additionally, anthropogenic activities had a remarkable impact on groundwater, and the impacts of these different activities on groundwater are overlaying and complex. Wei et al. (2016) analyzed the response relationship between a decrease in the groundwater level and the irrigation area in the Jiansanjiang region of Heilongjiang Province. Hao et al. (2017) indicated a decline in the groundwater depth of the Minqin Oasis with long-term overextraction. As the rapid socioeconomic development occurred, the population, grain yield, farmland area and GDP increased, and these factors were the primary forces leading to the decline in the groundwater level (Li et al. 2018). Cui et al. (2020) considered the largest impact on groundwater depth to be rainfall and mining volume. Abubaker et al. (2020) considered that water scarcity would become a major concern under global climate change and pressure from human activities. According to the research by Li et al. (2020), human factors such as exploitation amount, irrigated area, and population quantity have increased substantially since the development of the oasis in the 1970s, and these factors have influenced groundwater level reduction, accounting for 87.79%. Malakar et al. (2021) considered groundwater exploitation as the dominant factor in the groundwater level change based on mathematical statistical methods. Qin et al. (2022) concluded that groundwater dynamics were influenced by climatic factors and that human activities gradually played a dominant role in increasing groundwater withdrawal. Chen et al. (2022) studied the change in the groundwater level in Chahanaoer where agriculture was well developed, and found that groundwater extraction had the largest contribution to the groundwater level, followed by precipitation, evaporation and runoff. The North China Plain is an important grain-producing area, agricultural irrigation relying on groundwater accounts for more than 80% of the total groundwater extraction, and groundwater exploitation has become the dominant factor influencing the groundwater level in the North China Plain (Zhang et al. 2022).

At present, previous studies were mainly focused on qualitative analyses (Wang et al. 2006; Goni et al. 2021), but few studies of quantitative analyses, which are essential for facilitating groundwater management to cope with future challenges, have been reported. Studies (Wang et al. 2006; Cui et al. 2020; Wilopo et al. 2021; Abbas et al. 2022) have indicated that the influence of climate change and anthropogenic activities, such as exploitation, land use, population growth, urbanization and irrigation, cannot be ignored and may have changed the hydrological process. However, the challenge is that the multiple climatic and anthropogenic factors influence groundwater in a complicated manner, and the influencing degrees of each factor are difficult to distinguish. It is not clear how to quantitatively analyze the influence of climate change and anthropogenic activities on groundwater.

From the perspective of groundwater balance, rainfall is an important meteorological factor and plays a crucial role in groundwater recharge, especially in endorheic basins. Human activities, such as exploitation, irrigation, industrial development and population growth, have profoundly changed the groundwater environment (Braud et al. 2013; Kuroda et al. 2017). The effects of these activities can be classified into two types: direct and indirect effects (Li & Hou 2020). Q_GE is the most direct way to influence groundwater quantity and level, and the changes in some other anthropogenic activities can be reflected in the amount of exploitation to some extent (Li et al. 2018). For example, population growth, industrial development and farmland expansion lead to an increase in groundwater consumption. With this in mind, the northern Hauangqihai Basin (a typical endorheic basin in northern China) was selected as the study area, and the influence of rainfall and Q_GE on groundwater was qualitatively and quantitatively analyzed. This work aimed to (1) reveal the temporal variation characteristics of rainfall, Q_GE and W, by using statistical methods and the Mann–Kendall (M–K) trend test; (2) qualitatively analyze the relationships among rainfall, Q_GE and W; (3) determine the mutation time and (4) quantitatively evaluate the contributions of the influencing factors to groundwater based on the slope-changing ratio of cumulative quantity (SCRCQ) method and the elastic coefficient method. The study is expected to offer scientific references for the rational exploitation of groundwater resources and help explore effective measures to promote the sustainable utilization of groundwater resources.

According to previous studies (Ding 2018), the main recharge sources of groundwater are rainfall infiltration and groundwater lateral inflow. Rainfall infiltration, which recharges groundwater, directly accounts for approximately 60% of the total recharge. Affected by the topography, the lateral inflow is mainly from the surrounding mountains and recharges the Huangqihai Basin; in other words, rainfall recharges the bedrock fissure water in the surrounding mountains, and then the bedrock fissure water recharges the groundwater via lateral inflow. The infiltrating recharge of seasonal rivers is fundamentally from rainfall. Thus, rainfall is the main recharge source of groundwater in the study area, whether directly or indirectly. The discharge pathways include exploitation, evaporation and discharge into the lake, and the Q_GE accounts for approximately 50% of the total discharge. Farmland is widely distributed, and the irrigation consumption of groundwater is high. The groundwater depths in most areas are deeper than the limited evaporation depth (5 m). According to Aver'yanov's evaporation equations, evaporation has a slight influence on the groundwater level. Huangqihai Lake is the only lake in the study area and its area has declined in the past decade. Based on Darcy's Law, the discharge from groundwater to Huangqihai Lake decreased because of the shrinkage of the contact area between groundwater and the lake. In recent years, the water area of Huangqihai Lake has basically been maintained at 30 km² due to ecological restoration.

The data of groundwater level, Q_GE and rainfall were collected in this study. Groundwater monitoring in the study area started in the 1990s and has been gradually strengthened. The groundwater level data (1996–2020) were based on monitoring data and collected from the Geological Environment Monitoring Institute of Inner Mongolia. The distribution of the monitoring wells is shown in Figure 1. The W value is the groundwater level in December minus the groundwater level in January. The Q_GE value means the exploitation quantity in a year. The Q_GE data from 2011 to 2020 were derived from the Water Resources Bulletins of Right Chahar County, and the data from 1998 to 2010 were calculated by the different proportions of the total groundwater exploitation of Wulanchabu City in wet, normal and dry years. There were no available Q_GE data from 1996 to 1997. The study area and its nearby region are located in a semiarid continental climate zone with similar climatic characteristics. There are two meteorological stations near the study area, and the Jining meteorological station is closest to the study area; thus, the climatic data collected from the station were selected to represent the climatic characteristics of the study area. The data of annual rainfall and monthly rainfall since monitoring (1966–2020) were obtained from the China Meteorological Data Network (http://data.cma.cn).

If the statistical value (Z) is positive, it indicates that the trend of the analyzing factor is increasing, whereas if Z is negative, it indicates that the trend is decreasing. Given the level of significance (α), if the absolute value of Z (|Z|) is greater than Z_1−α/2, the trend is significant; if |Z| is less than Z_1−α/2, the trend is insignificant. When α is 0.05, the threshold corresponding to the level of significance is 1.96.

• (2)

  M–K mutation test

The M–K mutation test is a simple and effective nonparametric statistical method used to recognize mutations and is widely utilized in meteorology, hydrology and other research fields (Cui et al. 2020). The specific steps are as follows.

The UF and UB curves are drawn by plotting all values of UF_k and UB_k in chronological order. When α is 0.05, the threshold corresponding to the level of significance is 1.96. If the intersection point of the UF and UB curves is located between the significant horizontal lines, the time corresponding to the intersection is considered as the mutation time.

• (3)

  Cumulative anomaly method

As shown in Figure 4, the W values gradually changed from positive to negative from 1996 to 2020, and the W values obviously decreased since 2004. The W value changed with the changes in annual rainfall and Q_GE. In 2011, the W value was the highest and the Q_GE value was the lowest. After 2014, the Q_GE values generally increased, and the W values increased but still remained negative, indicating a continuous decrease in the groundwater level.

The Q_GE values showed an unobvious upward trend from 1998 to 2020 (Z = 1.58), and the insignificance was mainly due to the uncertainty of human activities, which were influenced by many factors, such as changes in policy, population and agriculture. Based on previous studies (Ding 2018), agricultural irrigation played an important role in the Q_GE. The Q_GE values were relatively low in 2003, 2004 and 2012 (Figure 4) when the rainfalls and agriculture exploitations were abundant. The regional water administrative departments strengthened the rational management of groundwater exploitation from 2017 to 2020, and the Q_GE values decreased year by year.

The Q_GE values in 1999 and 2000 were 0.4276 and 0.4214 billion m³, respectively, and the relative difference between them was less than 1.45%; thus, the Q_GE values in 1999 and 2000 were considered the same exploitation conditions. Similarly, the exploitation conditions in 2015 and 2016 were considered equivalent. The two identical exploitation conditions from 1999–2000 and 2015–2016, named as conditions A and B, respectively, were chosen to perform the following research (Table 2).

As shown in Table 2, the W values increased with the increase in annual rainfall under the same exploitation conditions. According to the Pearson correlation analysis, r was +0.9710 at the level of significance α of 0.05, and there was a positive correlation between the annual rainfall and W, meaning that rainfall had a positive influence on the groundwater level. Although the abundant rainfall was beneficial to recovering the groundwater level, it was noticeable that the W values were still negative in the normal years and under the condition that the Q_GE values were more than 0.4200 billion m³. It was indicated that the recharge of groundwater in a normal year was not enough to keep the groundwater balance under such exploitation conditions, which led to the negative W. The W value changed from negative to positive in wet years.

The influence of Q_GE on W was discussed under the same rainfall conditions in wet years (A′), normal years (B′) and dry years (C′). The comparisons of Q_GE and W under the three rainfall conditions are shown in Table 3.

Under condition A′ (wet years), the annual rainfalls were abundant, the Q_GE values were relatively low and the W values were positive. The Q_GE value in 2016 was higher than that in 1998 and 2003, and the W value in 2016 was correspondingly lower than that in the other 2 years. Under condition B′ (normal years), the rainfalls were at the middle level; the W values were negative and decreased due to an increase in Q_GE. The Q_GE values in the 3 normal years were larger than the Q_GE values in the wet years. Under condition C′ (dry years), the rainfalls were the lowest; and the Q_GE values in these 2 years were larger than the Q_GE values in wet years. The W values sharply decreased due to an increase in Q_GE. Moreover, W was more sensitive to the change in Q_GE with the insufficient recharge of rainfall. In the dry year, the Q_GE value in 2006 was 1.11% larger than that in 2001, but the corresponding W was 489.43% larger in 2006 than that in 2001.

Based on the above analysis, it was indicated that W decreased due to an increase in Q_GE under the same rainfall conditions. The Pearson correlation analysis (Table 3) further proved that there was a negative correlation between Q_GE and W. Accompanied by the decrease in annual rainfall, the correlation coefficient r’ between Q_GE and W changed from −0.4690 to −1.000, which indicated that the relationship between Q_GE and W became closer because of the decrease in rainfall recharge.

Figure 7 shows that W_mon differed among the monitoring wells and varied by year. Overall, the W_mon values were low from January to March but higher in April. This was mainly attributed to the lagging recharge from rainfall and the melting of ice and snow. Although the rainfall obviously increased in summer, the W_mon did not immediately change to be positive but remained negative, and some W_mon values further decreased, such as the W_mon values at M017, M034, and M030 in July 2003. Summer was the peak period of Q_GE and the consumption of irrigation was high. The negative influence of the Q_GE on the groundwater level was greater than the positive influence of rainfall recharge. Regardless of where the monitoring wells were located, the change characteristics of the W_mon upstream and downstream were similar, indicating that the influence of Q_GE on W_mon was greater than that of rainfall for the whole area. Because of the decreasing Q_GE after irrigation and the lagging of rainfall recharge, the W_mon became greater and even became positive after irrigation.

Combined with Figures 3 and 4, the annual rainfall in the period of 1996–2020 showed an increasing trend, but the W values remained negative for many years. The contrasting characteristics of the annual rainfall and W further affirmed that the positive influence of annual rainfall on the groundwater level was weaker than the negative influence of Q_GE. By comparing the groundwater levels in different years (Figure 6), the upstream and downstream groundwater levels showed a continuous downward trend overall, which once again indicated that the unreasonable exploitation caused the groundwater imbalance. The Q_GE values decreased from 2017 to 2020, and the rainfalls in these years were abundant, the W values increased but were still negative. There were three probable reasons: one is that the infiltration of rainfall to groundwater decreases due to the thickened vadose zones caused by the declining of groundwater level, the second reason is that the groundwater system is under a negative balance and the third reason is the lagging recharge from rainfall. This topic needs further research.

The average annual rainfall in the period before the mutation was 375.20 mm, the proportion of wet years was 55.55%, the proportion of normal years was 22.22% and the proportion of dry years was 22.22%. The W values ranged from +0.3558 to −0.2771 m, and the average was +0.0407 m. Although the Q_GE in this period increased, the W value was positive when the year was a wet one with abundant rainfall. After the mutation, the average annual rainfall in the period decreased to 350.36 mm, the proportion of wet years was 31.25%, the proportion of normal years was 37.50% and the proportion of dry years was 31.25%. The W values ranged from +0.0946 to −0.8830 m, and the average was −0.3656 m. Comparing these changes, the W values decreased after the mutation. Although the ratios of wet years, normal years and dry years were even in the period after the mutation, there were several extreme rainfall events. The continuous dry water years (2005–2007) and the increase in exploitation led to a negative equilibrium in the groundwater system and the W values quickly changed from positive to negative. Moreover, 2009 was an extremely dry year with a lower annual rainfall of 184.5 mm, which led to less recharge from rainfall infiltration, and the W value sharply declined. In 2012, the annual rainfall was 547.7 mm, the year was an extremely wet one and the Q_GE value was significantly reduced compared with the previous few years. Under such favorable conditions, the W value should increase significantly but that was not the case in fact. The monthly rainfall in July 2012 reached 202.4 mm, but the W value was only +0.0414 m, and the positive effects of the abundant rainfall on groundwater were not ideal compared to those in 2003 and 2004. The probable reason was that the concentrated and strong rainfall directly flowed away as surface runoff and was not conducive to the infiltration recharge of groundwater (Ma 2018). Moreover, due to the continuous decline in the groundwater level and the thickening of vadose zones, the recharge amount of rainfall infiltration decreased and the time lag became remarkable. Research on the reduction and lagging of rainfall infiltration requires further study based on rainfall events, moisture content of vadose zones, daily groundwater level and other aspects.

The elastic coefficient method was adopted to quantitatively evaluate the influence of rainfall changes and Q_GE on the groundwater level for the first time, and the SCRCQ method was applied to evaluate the influence and to verify the results obtained from the elastic coefficient method. The results of the two methods are shown below.

According to Table 4, the elastic coefficient (ε_R) of W to the annual rainfall was 1.50, indicating that there was a 15% change in W if the annual rainfall changed by 10%. The average annual rainfall after the mutation was reduced by 6.62% compared to that before the mutation; thus, the corresponding change in W based on ε_R after the mutation should have been −0.07 m. However, the actual change in W before and after the mutation (ΔW) was −0.33 m. Based on this, the contributions of rainfall changes and Q_GE to W were calculated to be 22.64 and 77.36%, respectively.

Table 4

Contributions of rainfall and Q_GE to W based on the elastic coefficient method

Period .	(mm) .	(m) .	ΔR (%) .	εR .	ΔWa (m) .	Δ W (m) .	CR′ (%) .	CGE′ (%) .
1996–2004	375.20	0.75	− 6.62	1.50	− 0.07	− 0.33	22.64	77.36
2005–2020	350.36	0.42

Period .	(mm) .	(m) .	ΔR (%) .	εR .	ΔWa (m) .	Δ W (m) .	CR′ (%) .	CGE′ (%) .
1996–2004	375.20	0.75	− 6.62	1.50	− 0.07	− 0.33	22.64	77.36
2005–2020	350.36	0.42

View Large

The SCRCQ method is not influenced by subjective weight assignment, and the quantitative results have been verified to be objective and reasonable by previous studies (Kong et al. 2018; Zhao et al. 2019). Comparing the results obtained from the two methods, the relative difference between them was less than 5%, which indicated that the results obtained by using the elastic coefficient method were reliable. Moreover, the elastic coefficient method was easier to calculate, and the results were consistent with those obtained from other methods and qualitative analysis. Therefore, the elastic coefficient method can be used to quantitatively evaluate the influence on groundwater. The averages of the calculated results based on the two methods were determined to be the quantitative results, that is, the contribution of rainfall changes to W was 22.08%, and the contribution of Q_GE was 77.92%.

This study was the first to apply the elastic coefficient method to quantitatively evaluate the influence of rainfall changes and Q_GE on the groundwater level, and the results were basically consistent with those of the SCRCQ method. The influence of groundwater exploitation on the groundwater level in the study area was greater than that of precipitation, and this result was consistent with research in other arid and semiarid areas. For example, agricultural irrigation relying on groundwater was the key factor affecting groundwater level changes in the Songnen and Sanjiang Plains (Liu et al. 2021). Chen et al. (2022) concluded that groundwater extraction had the largest contribution to the groundwater level in the Chahannaoer Basin, followed by precipitation, evaporation and runoff. The change in the groundwater level in the Hexi Corridor had the highest correlation with the irrigated area, followed by population and groundwater supply (Song et al. 2022). All these studies corroborated that the results based on the elastic coefficient method and the SCRCQ method were reliable and in line with the actual situation of the study area.

As shown in Figure 4, the W values in the study area were always negative, which means that the groundwater level continued to decrease. The contribution of rainfall changes to W was much less than that of Q_GE to W, meaning that the positive influence of increased rainfall on groundwater recharge was not enough to offset the negative influence of Q_GE.

According to Table 4, when Q_GE was greater than 0.42 billion m³, the W values in the normal and dry years were always negative, and the groundwater level decreased. Based on the perspective of groundwater balance, the two factors (rainfall and Q_GE) that had a significant impact on groundwater balance from two aspects of recharge and discharge were selected in this study. However, the impacts of other human activities, such as population growth, farmland expansion and industrial development, on groundwater can be reflected in the amount of Q_GE to some extent more or less, that is, Q_GE can reflect the intensity of other human activities on groundwater to a certain extent. Under the current exploitation conditions, the recharge of groundwater in normal and dry years is insufficient, but the Q_GE is excessive, which is not conducive to the sustainable development of the groundwater environment. The climate is hard to control and has a relatively slight influence on groundwater. Thus, more attention should be given to the reasonable management of the exploitation amount, exploitation intensity and exploitation density. In addition, when the groundwater level changes due to disturbance by external factors, the influencing degrees of rainfall and groundwater exploitation on groundwater may change. Previous studies (Wang et al. 2022) have shown that the groundwater level declined after coal mining, and the vadose zone was thickened, which led to the relationship between rainfall infiltrating recharge and the groundwater level becoming unobvious. When the external environment changes, the groundwater recharge, discharge and underground hydrological processes may also correspondingly change, and the relationships among rainfall, groundwater exploitation and groundwater level are in a dynamic state (Li & Hou 2020). This reminds us that the management and measures of protecting groundwater should be adjusted due to these dynamic relationships.

From the perspective of groundwater balance, the rainfall changes (the main recharge source of groundwater) and Q_GE (the main discharge way of groundwater) were selected as the influencing factors, and their influence on groundwater level was qualitatively and quantitatively studied by using multiple methods.

The annual rainfall (1996–2020) showed an unobvious upward trend based on the M–K trend test, and the monthly rainfall distribution was uneven and mainly concentrated from June to August. The trend of Q_GE was insignificantly increasing. W was always negative in the period from 1996 to 2020. Through the comparative analysis of the W under different conditions, there was a positive correlation between the annual rainfall and W and a negative correlation between Q_GE and W, and the positive influence of rainfall on W was less than the negative influence of Q_GE.

According to the M–K mutation test and the cumulative anomaly method, 2004 was determined as the mutation year. The averages of annual rainfall before and after the mutation were 375.20 and 350.36 mm, respectively, and continuous drought and extreme rainfall occurred after the mutation. The average of W deceased after the mutation. Under favorable conditions with abundant rainfall and less exploitation, the W should significantly increase but that was not the case in fact. This result was probably because most of the strongly concentrated rainfall directly ran off as surface runoff, rather than infiltrating and recharging the groundwater. Moreover, due to the continuous decline in groundwater level and the thickening of vadose zones, the recharge amount of rainfall infiltration decreased and the time lag became remarkable. Further research on these problems needs to be performed.

The quantitative evaluation of the influence of rainfall changes and Q_GE on W was performed by applying the elastic coefficient method and the SCRCQ method. This was the first to use the elastic coefficient method in the influence study of groundwater, and the results were proven to be reliable and reasonable. The contribution of rainfall changes to W was 22.08%, and the contribution of Q_GE was 77.92%. The negative influence of Q_GE on W was much greater than that of rainfall changes, which was consistent with the qualitative analysis. Based on this, more attention should be given to the scientific management of Q_GE.

The integration of the qualitative analysis and quantitative evaluation provides an effective way to identify the influence of external factors on groundwater level, and the application of the elastic coefficient method made the quantitative evaluation easy to obtain. This study is significant for the sustainable development of groundwater and for fully understanding the groundwater cycle.

This research was funded by the Project of Collaborative Innovation Center for Grassland Ecological Security (Ecohydrological Characteristics and Ecosystem Services Assessment in the Tabu River Watershed, MK0143A032021), Basic Scientific Research Foundation Special Project of the China Institute of Water Resources and Hydropower Research (MK2021J07), Inner Mongolia Science and Technology planning project (2022YFHH0024) and Science and Technology Planning Project of Inner Mongolia (MK0143A012022).

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

The authors declare there is no conflict.