GRACE/GRACE-FO Satellite Assessment of Sown Area Expansion Impacts on Groundwater Sustainability in Jilin Province

Abstract

Jilin Province, an important commodity grain base in China, relies on groundwater resources for its agricultural development. The implementation of a series of policies, including agricultural subsidies and food security policies, has led to a rapid expansion of the sowing area in recent decades, resulting in an increase in agricultural water demand. This has had a significant impact on the groundwater system. It is therefore imperative to understand the dynamics of the groundwater to ensure the security of water resources, ecological security, and food security. An evaluation of the sustainability of groundwater resources in Jilin Province was conducted through a quantitative analysis of the reliability, resilience, and vulnerability of groundwater. This analysis was informed by the inversion of changes in groundwater reserves over a period of 249 months, commencing from 2002-04 to 2022-12. The inversion process utilized data from the Gravity Recovery and Climate Experiment (GRACE) gravity satellite and Global Land Data Assimilation System (GLDAS), offering a comprehensive view of the temporal dynamics of groundwater reserves in the region. The results indicated the following: (1) Groundwater storage (total amount of water below the surface) in Jilin Province exhibited an overall decreasing trend, with the highest groundwater level recorded in June and the lowest in September on a monthly basis. (2) Prior to September 2010, groundwater reserves were in surplus most of the time. From October 2010 to August 2018, however, they began to fluctuate between surplus and deficit states. Since September 2018, the reserves have been in a long-term deficit, showing an overall downward trend. (3) Prior to 2005, the groundwater system was at a high/extremely high level of sustainability. However, following 2011, it fell to a very low level of sustainability and has continued to deteriorate. (4) The maximum information coefficient and correlation analysis indicate that the sown area is the most significant factor contributing to the decline in the sustainability of the groundwater system. This study reveals the spatial and temporal distribution pattern and evolution trend of groundwater resources sustainability in Jilin Province, and provides theoretical and data support for regional groundwater resources protection and management.

1. Introduction

Groundwater, an indispensable source of fresh water, plays a pivotal role in the realm of sustainable agricultural development and ensuring food security. Concurrently, groundwater emerges as a pivotal freshwater resource, instrumental in sustaining ecosystems and facilitating human adaptation to climate change. Approximately 50% of the world’s residential water use is attributed to groundwater, as is 25% of the water utilized for agricultural irrigation. It is estimated that groundwater contributes to the irrigation of 38% of the world’s irrigated land [1]. The accelerated extraction of groundwater, precipitated by population growth, social and agricultural development, and climate change, has precipitated a series of problems, including declining water tables, wetland degradation, and ground subsidence [2,3,4]. The concept of groundwater sustainability was first proposed by Alley et al. in 1999 [5], and has since been refined to be defined as the ability of a groundwater system to provide adequate quantity and quality of water resources for ecosystems and human societies on a sustained basis [6]. At present, research on groundwater is chiefly concerned with the following areas: groundwater quantity, water pollution, and impacts on ecosystems [7]. K Kusam et al. used machine learning techniques to explore groundwater sustainability from the perspective of the impact of human activities and natural factors on groundwater pollution [8]; Chetan Sharma et al. used counterfactual AI (CAI) to analyze the impact of mitigation implementation on the Edwards Aquifer system [9]; Guo et al. used a combination of GRACE data and groundwater monitoring data to analyze the impact of irrigation and afforestation on the groundwater system in the Yellow River Basin [10].

In the past, the primary approach to groundwater research involved the monitoring of observation wells. However, this method is subject to significant limitations, including the uneven distribution of observation wells, limited coverage, and a paucity of data [11]. The successful launch of the GRACE satellite in 2002 changed the way research is conducted, A growing body of researchers has come to recognize the potential of GRACE satellite data for large-scale groundwater monitoring. Wei Feng et al. assessed groundwater depletion in North China using GRACE data and surface measurements [12]; Brian F. Thomas et al. used GRACE to observe groundwater responses to climate change [13]; Liu et al. employed GRACE to evaluate groundwater dynamics and its drought potential in the Taihang Mountains region. Their findings indicated that human activities are the primary factor influencing the decline of groundwater levels [14].

China has been a prominent agricultural nation since ancient times, and in recent decades, it has undergone rapid development. Agriculture has played a substantial role in China’s economic growth, as it has transitioned from a poor and weak country to the world’s second-largest economy. Notably, China can currently meet 22% of the world’s population’s food needs using a mere 7% of the planet’s arable land [15,16]. In 2024, Jilin Province’s total grain output reached 42.66 billion kilograms, positioning it fourth among China’s provinces. The province’s grain yield per square kilometer attained 729.6 kg, a figure that led the nation. However, Jilin Province is part of the region experiencing moderate water scarcity [17]. The escalating demand for food has given rise to concerns regarding the security of groundwater resources. Consequently, the enhancement of groundwater resource management for the purpose of sustainable agricultural development has emerged as a shared priority among researchers and governments. The assessment of the impact of agricultural development on groundwater sustainability is a prerequisite for achieving sustainable use of regional water resources. This assessment necessitates an in-depth understanding of the spatial and temporal changes in the sustainability of groundwater resources under past and current conditions, especially in the context of expanding agricultural trends.

In this study, Jilin Province was selected as the study area to explore the evolution and drivers of groundwater sustainability in the context of sown area expansion. The specific objectives are as follows: (1) to investigate the spatiotemporal patterns of groundwater storage in Jilin Province; (2) to analyze the spatiotemporal characteristics of groundwater system sustainability; (3) to investigate the repercussions factors on the sustainability of groundwater systems.

2. Materials and Methods

2.1. Study Area

Jilin Province is located in the central part of Northeast China. It borders Russia and North Korea (Figure 1). Jilin Province is situated at the geographic center of Northeast Asia. The region is considered the core area of the world’s black soil belt, boasting vast arable land and suitable agroclimatic conditions. It is renowned for its distinction as the world-renowned Golden Corn Belt and Golden Rice Belt, owing to its unique agricultural production conditions. The region plays a significant role. It is traditionally known as the nation’s granary. Jilin Province’s total land area constitutes approximately 2% of the country’s total, with arable land accounting for 4.4% of the nation’s total. The primary crops cultivated in this region include corn, rice, and soybeans. As one of China’s 13 primary grain-producing provinces, it has demonstrated a commitment to augmenting its grain production capacity and ensuring a reliable grain supply. The primary aquifer categories in Jilin Province encompass loose rock pore aquifers, carbonate rock fissure aquifers, metamorphic rock fissure aquifers, clastic rock pore fissure aquifers, and igneous rock fissure aquifers. Groundwater is found at a depth ranging from 3 to 150 m below ground level, with aquifer thicknesses measuring between 5 and 40 m. Jilin Province is confronted with considerable challenges related to water scarcity, characterized by limited per capita freshwater availability. The province exhibits a pronounced heterogeneity in its water resource distribution, both spatially and temporally, which precipitates a critical mismatch between availability and demand in specific locales. The aforementioned factors have resulted in the pervasive presence of water scarcity, engineering water scarcity, and water quality issues, all of which are present to varying extents in Jilin Province [17].

2.2. Datasets

2.2.1. GRACE Data

Since its launch in March 2002, the GRACE (Gravity Recovery and Climate Experiment) gravity satellite has been continuously observing changes in the global gravity field for nearly two decades. The Center for Space Research (CSR) at the University of Texas has developed the CSR Mascon product, which is designed to provide a superior version of the spherical harmonic coefficient product. In this paper, the objective is to obtain more precise data. To this end, CSR Mascon RL06 data for 249 months from April 2002 to December 2022 are utilized for further study, with a spatial resolution of 0.25° × 0.25° (https://www2.csr.utexas.edu, accessed on 20 June 2025). The GRACE satellite is designed to monitor changes in the Earth’s gravitational field, which are subsequently converted into changes in terrestrial water storage. These changes are then combined with CLDAS data to infer changes in groundwater storage. A substantial body of research has validated the efficacy of GRACE data in Northeast China, as evidenced by their strong correlation with measured groundwater data from the region [18,19].

2.2.2. GLDAS Data

The Global Land Data Assimilation System (GLDAS) is a collaborative endeavor between the NASA Goddard Space Flight Center (GSFC) and the National Centers for Oceanic and Atmospheric Prediction (NCEP) [20]. The hydrologic data utilized in this study are derived from the GLDAS Noah land surface process model (https://disc.gsfc.nasa.gov/, accessed on 20 June 2025). The spatial resolution of the data is 0.25° × 0.25°, and the time span extends from April 2002 to December 2022, encompassing a total of 249 months. The dataset encompasses canopy water storage, snow water equivalent, and soil water content at varying depths, including 0–10 cm, 10–40 cm, 40–100 cm, and 100–200 cm.

2.2.3. Meteorological Data

The precipitation data were obtained from the National Tibetan Plateau/Third Pole Environment Data Center (https://data.tpdc.ac.cn, accessed on 22 June 2025), China 1 km resolution monthly precipitation dataset (1901–2024), which was generated by the Delta spatial downscaling scheme at the Chinese scale based on the global 0.5° climate dataset published by the CRU and the global high-resolution climate dataset published by WorldClim, and was validated using 496 independent meteorological observation point data for validation, and the validation results are credible [21]. The potential evapotranspiration data were obtained from the National Tibetan Plateau/Third Pole Environment Data Center (https://data.tpdc.ac.cn, accessed on 22 June 2025). The 1 km monthly potential evapotranspiration dataset (1901–2024) was based on the China 1 km monthly mean, minimum, and maximum temperature datasets. The dataset was obtained by using the Hargreaves potential evapotranspiration calculation formula [22].

2.2.4. Land Use Data

The annual China Land Cover Dataset is derived from 335,709 Landsat images accessed via Google Earth Engine, which were utilized to construct China’s inaugural Landsat-derived Annual Land Cover Product (CLCD) (https://zenodo.org, accessed on 15 July 2025). The dataset under consideration contains year-by-year land cover information of China from 1985 to 2024 [23].

2.2.5. Other Data

The satellite-based global irrigation water use dataset, China’s DMSP-OLS-like 1 km nighttime light remote sensing dataset, and sown area are, respectively, derived from the National Tibetan Plateau/Third Pole Environment Data Center (https://data.tpdc.ac.cn, accessed on 22 June 2025), the National Earth System Science Data Center, and the National Science & Technology Infrastructure of China (http://www.geodata.cn, accessed on 13 July 2025) and the Jilin Province Statistical Yearbook.

2.3. Methods

The present study employs singular spectrum analysis to interpolate CSR Mascon data, combined with the GLDAS hydrological model to estimate changes in groundwater storage (GWS) in Jilin Province from April to December 2002. The groundwater sustainability of the region was calculated, and further assessments were conducted using MIC and correlation analysis to evaluate the relevance of precipitation, potential evapotranspiration, sown area, and nighttime lights on groundwater sustainability.

2.3.1. SSA Interpolation Methods

Due to factors such as satellite maintenance and other considerations, a 33-month data gap exists in the CSR Mascon data for the study period. Consequently, the original data only encompasses a total of 216 months. A plethora of studies have previously put forth the utilization of SSA (Singular Spectrum Analysis) for the purpose of data filling. This approach boasts the merits of stable identification and reinforcement cycles. Concurrently, the analysis results indicate that this method exhibits high reliability for filling land water storage data [24]. Consequently, to guarantee the consistency and precision of the CSR Mascon data, this study employed publicly available packages to perform SSA spatial interpolation and fill the terrestrial water storage grid derived from GRACE data inversion. The window width was set to M = 48 and the number of RCs to K = 8 [25]. The SSA interpolation process is as follows:

Converting time series $X=\left[x_1,x_1,\cdots ,x_N\right]$ into a trajectory matrix [24]:

$$\begin{array}{c}Y=\left[\begin{array}{cccc}{x}_1& x_1& \cdots & x_L\\ x_1& x_1& \cdots & x_{L+1}\\ ⋮& ⋮& \ddots & ⋮\\ x_M& x_{M+1}& \cdots & x_N\end{array}\right]\end{array}$$

(1)

In the formula: the number of columns L = N + 1 − M, where N is the length of the time series and M is the window length.

Use matrix $Y$ to form the lagged covariance matrix $YY\prime$ [26]. Let ${\lambda }_1,{\lambda }_1,\cdots ,{\lambda }_m$ be the eigenvalues of $YY\prime$. The corresponding feature vectors are denoted by $U_1,U_2,\cdots ,U_m$, and it is assumed that $k=rank\left(Y\right)$.

$$Y=X_1+X_2+\cdots +X_k$$

Consequently, the diagonal averaging reconstruction of the original sequence $G$ for Y is to be performed, as illustrated below [27]:

$$G_i=\left\{\begin{array}{c}{\displaystyle \frac{1}{i}}{\displaystyle \sum _{m=1}^i}{Z}_{m,i-m+1}^{*}, 1\le i<M^{*}\\ {\displaystyle \frac{1}{M^{*}}}{\displaystyle \sum _{m=1}^{M^{*}}}{Z}_{m,i-m+1}^{*}, M^{*}\le i<L^{*}\\ {\displaystyle \frac{1}{N-i+1}}{\displaystyle \sum _{m=i-L^{*}+1}^{N-L^{*}+1}}{Z}_{m,i-m+1}^{*}, L^{*}\le i<N\end{array}\right.$$

(2)

G is the sequence interpolated by the SSA method at a certain point, filling a total of 249 months.

2.3.2. Groundwater Storage Anomaly

Groundwater, soil water, snow water equivalent, and canopy water are all included in land water reserves. Therefore, this study employs the following formula to estimate changes in groundwater storage [28]:

$$\begin{array}{c}{∆GWS}_i={∆TWS}_i-∆W_{soil,i}-∆W_{snow,i}-∆W_{can,i}\end{array}$$

(3)

In this equation, ${∆GWS}_i$ represents the monthly change in groundwater, ${∆TWS}_i$ represents the monthly change in terrestrial water storage, $∆W_{soil,i}$ represents the monthly change in soil water, $∆W_{snow,i}$ represents the monthly change in snow water equivalent, and $∆W_{can,i}$ represents the monthly change in canopy water.

2.3.3. GRACE Groundwater Drought Index (GGDI)

The GGDI is a normalized index employed to assess groundwater drought in aquifer systems. Its novelty lies in its assessment of groundwater drought by combining groundwater storage deficits and surpluses. In instances where GGDI > 0, this is indicative of a surplus state; conversely, GGDI < 0 signifies a deficit [29].

The initial step involves the calculation of the monthly climate index (Ci). In this context, the term “climate” does not refer to the climatological definition of climate. “climate” (Ci) refers to the long-term average monthly value based on a yearly cycle, used to remove seasonal effects. The formula is as follows [13]:

$$\begin{array}{c}{C}_i={\displaystyle \frac{{\sum }_1^{n_i}{∆GWS}_i}{n_i}},i=\mathrm{1,2},\dots 12\end{array}$$

(4)

The groundwater storage deviation (GSD) is derived by removing the monthly climate component from the monthly ΔGWS. The GSD is defined as the net deviation in △GWS. The final step in the calculation of the GGDI involves the subtraction of the mean of the groundwater storage deviation from the deviation itself, followed by the division of the result by the standard deviation of the deviation. The GGDI formula is expressed as follows [13]:

$$GGDI={\displaystyle \frac{\left({GSD}_t-\overline{GSD}\right)}{S_{GSD}}}$$

(5)

In the aforementioned equation, ${GSD}_t$ denotes the change in groundwater storage, which is derived by removing monthly climatic anomalies from the GRACE-derived groundwater storage change. $\overline{GSD}$ is further defined as the average value of ${GSD}_t$, and $S_{GSD}$ is the standard deviation of ${GSD}_t$.

2.3.4. Evaluation of the Groundwater Sustainability

Sandoval-Solis [29] developed a sustainability index to guide sustainable water management. The conceptual basis for this index originates from a method by Loucks, D.P. [30] designed to quantify trends in groundwater systems sustainability. The Sustainability Index (SI), calculated as follows, is integrated with the GGDI to evaluate groundwater resource sustainability [31]:

$$SI=REL\times RES\times \left(1-VUL\right)$$

(6)

Among them, REL refers to the reliability of the groundwater system, RES refers to the resilience of the groundwater system, and VUL refers to the vulnerability of the groundwater system. Reliability is defined as the historical probability of aquifer storage being below normal conditions, i.e., the percentage of GGDI at reliable levels [29]:

$$REL={\displaystyle \frac{n_{GGDI>0}}{n}}$$

(7)

Restorative indicates the likelihood of the groundwater system recovering from a deficit state to a surplus state, i.e., the probability of the GGDI shifting from negative to positive values [29]:

$$RES={\displaystyle \frac{n_{GGDI<0\to GGDI>0}}{n}}$$

(8)

Vulnerability is defined as the probability of drought occurring in a groundwater system, i.e., the ratio of negative GGDI values [29]:

$$VUL={\displaystyle \frac{n_{GGDI<0}}{n}}$$

(9)

In the formula: $n_{GGDI>0}$ is the number of times GGDI is greater than 0, $n_{GGDI<0\to GGDI>0}$ is the number of times GGDI changes from less than 0 to greater than 0, and $n_{GGDI<0}$ is the number of times GGDI is less than 0.

The above indicators were classified and evaluated based on previous research [6,31], and the results are shown in

Table 1. Classification criteria for REL, RES, VUL, and SI.

Grade	Range
	REL	RES	VUL	SI
Extremely low	0–0.25	0–0.20	0–0.10	0–0.20
Low	0.25–0.40	0.20–0.30	0.10–0.40	0.20–0.30
Moderate	0.40–0.60	0.30–0.50	0.40–0.60	0.30–0.50
High	0.60–0.75	0.50–0.75	0.60–0.75	0.50–0.75
Extremely high	0.75–1	0.75–1	0.75–1	0.75–1

.

2.3.5. Analysis of Influencing Factors

In 2011, Reshef et al. proposed the Maximal Information Coefficient (MIC), which can be used to identify potential relationships between two variables and to measure linear and nonlinear associations between them [32].

The principle is that for a bivariate random variable (X, Y), its sample set is $D=\left\{(x_i,y_i)|i=\mathrm{1,2},\dots ,N\right\}$, and its sample capacity is N. By dividing the values of X and Y into m and n different intervals, respectively, the sample space can be discretized into an $m\times n$ grid G. Under the specified grid, the mutual information $I\left({D|}_G\right)$ can be further estimated [32]:

$$I\left({D|}_G\right)=\sum _{x\in X}\sum _{y\in Y}p\left(x,y\right){\mathit{log}}_2\left[{\displaystyle \frac{p\left(x,y\right)}{p\left(x\right)p\left(y\right)}}\right]$$

(10)

In the formula: The symbol ${D|}_G$ denotes the probability distribution that is introduced when the sample set is divided using grid G. The symbols $p\left(x\right)$ and $p\left(y\right)$ represent the empirical marginal densities of X and Y, respectively, and $p\left(x,y\right)$ denotes the empirical joint probability density of X and Y.

When discretizing the sample set D, there are multiple different value domain partitioning methods under the same $m\times n$ network. The maximum mutual information on all possible grids G is denoted as [33]:

$$I^{*}\left(D,m,n\right)={Max}_{G}I\left({D|}_G\right)$$

(11)

Further standardization of $I^{*}\left(D,m,n\right)$:

$${M\left(D\right)}_{m,n}={\displaystyle \frac{I^{*}\left(D,m,n\right)}{{log}_2\mathit{min}\left(m,n\right)}}$$

(12)

The MIC for random variables X and Y is given by the following expression [32]:

$$MIC\left(X\right)={Max}_{xy<B\left(N\right)}\left\{{M\left(X\right)}_{x,y}\right\}$$

(13)

In the formula, $B\left(N\right)$ is a function of the number of samples, typically set to 0.6. The value range of $MIC(X,Y)$ is [0, 1], with higher values indicating a stronger correlation.

3. Results

3.1. Spatiotemporal Anomalies in Groundwater Reserves

Figure 2 illustrates the monthly fluctuations in groundwater resources from 2002 to 2022. The highest recorded monthly average △GWS was 2.88 cm in June, while the lowest was −2.76 cm in September. The monthly average maximum △GWS of 6.67 cm was recorded in the northern part of Jilin City, while the minimum of −6.87 cm was observed in the southern part of Baishan City. In the central plain region, △GWS exhibited a seasonal pattern, with an increase from April to July and a subsequent decrease from August to the following March. A decline in △GWS throughout Jilin Province has been observed over the period from 2002 to 2022.

3.2. Grace Groundwater Drought Index (GGDI)

Figure 3 shows changes in the GGDI over time. From 2002 to September 2010, groundwater storge a state of surplus and relative stability, with the exception of August to November 2005, October and November 2007, and July 2008. From October 2010 to August 2018, the reserves fluctuated between surplus and deficit states. After September 2018, the GGDI entered a prolonged deficit phase and exhibited a sustained downward trajectory, with only a transient surplus in November 2022.

3.3. Groundwater Sustainability in the Jilin Province

This study examined the sustainability of the groundwater system in Jilin Province between April 2002 and December 2022. From a temporal perspective, the sustainability of groundwater in Jilin Province has exhibited a continuous downward trend. Sustainability was highest in 2002 (SI = 0.83) and remained high until 2004. After the first deficit in groundwater reserves in 2005, sustainability declined sharply to moderate levels, fell to low levels in 2006, and finally reached extremely low levels in the Jilin Province groundwater system after 2011.

Figure 4 shows the Reliability (REL), Restorative (RES), Vulnerability (VUL), and sustainability index (SI) of groundwater in Jilin Province over for the four periods of 2002–2007, 2008–2012, 2013–2017, and 2018–2022. From a spatial distribution perspective, the majority of regions within the plains and the majority of areas within Yanbian Korean Autonomous Prefecture exhibited extremely poor sustainability (SI < 0.2) between 2002 and 2007. Conversely, the southwestern part of Baicheng City, the northern part of Changchun City, and the northern part of Jilin City demonstrated extremely high sustainability (SI > 0.9). From 2008 to 2012, the overall situation underwent a marked deterioration. The sustainability index (SI) revealed an improvement in the northern plains, while the southern plains exhibited an extremely poor SI (SI < 0.1), particularly in Siping City, Liaoyuan City, southern Changchun City, and most of Jilin City. The area of extremely high sustainability in northern Jilin City has undergone expansion. From 2013 to 2017, the sustainability index (SI) rose over the period in the western part of the plains (SI > 0.3), while it experienced a decline in all other regions (SI < 0.2). From 2018 to 2022, sustainability levels exhibited a significant decline, reaching extremely low levels (SI < 0.2) across the entire province. However, a few areas in the southeast of Baishan City demonstrated slightly higher sustainability levels (SI > 0.2).

As illustrated in Figure 4 and Figure 5, there has been a significant decrease in the sustainability of the groundwater system since 2002. From 2002 to 2007, the proportion of regions exhibiting extremely low sustainability levels reached 60.4%. The province’s overall sustainability rating was determined to be moderate, with an average SI of 0.51. From 2008 to 2012, the proportion of extremely low sustainability increased to 78.93%, while the proportions of high and extremely high sustainability were less than 1%. The proportion of medium sustainability remained relatively constant, resulting in an overall decline in groundwater sustainability to a low level, with an average SI of 0.19. From 2013 to 2017, the proportion of extremely low sustainability remained relatively stable, while the proportions of medium and high sustainability increased. However, the overall sustainability exhibited a downward trend, with an average value of 0.12. From 2018 to 2022, the situation underwent a further deterioration, unveiling a distressing scenario. All regions exhibited extremely low or low sustainability levels, accounting for 96.96% and 3.04%, respectively, with an average SI of 0.08. This finding suggests that the groundwater system was in a precarious state.

3.4. Analysis of Factors Affecting Groundwater Sustainability

As illustrated in Figure 6, there is a discernible trend is evident in Precipitation (PRE), Potential Evapotranspiration (PET), Sown area (SA), and nighttime lights (NTL). The results of the MK trend test demonstrated a significant increase in PRE, PET, NTL, and SA. The present study did not exclude other potential influencing factors in order to examine the impact of a single factor on SI. The present study employed both the MIC analysis and Pearson correlation analysis to assess the relationship between the four indicators and SI. The MIC value and the absolute value of the R value for the sown area are the largest among the four indicators (

Table 2. Factor influencing groundwater in Jilin Province and SI correlation coefficient.

	SA	NTL	PRE	PET
MIC	0.698	0.374	0.615	0.474
R	−0.78	−0.708	−0.363	−0.417

), indicating that the sown area is the most significant factor affecting groundwater sustainability.

3.4.1. Relationship Between Precipitation and △GWS

Figure 7 demonstrates a certain correlation between GWS and precipitation in Jilin Province; however, this correlation is not significant (Table 2). A high degree of consistency has been observed between GWS and precipitation fluctuations, with both exhibiting cyclical fluctuations that are closely related. From 2010 to 2016, both precipitation and the GWS declined to a certain extent. Subsequent to 2016, precipitation levels remained relatively stable; however, the trend of declining GWS persisted.

3.4.2. Land Use Change

Figure 8 illustrates the land use changes in Jilin Province from 2002 to 2022. Among these, changes in cropland and forest areas were relatively minor, with cropland increasing by only 0.69% and forest decreasing by 3.02%. Grassland areas exhibited a pattern of first increasing and then decreasing, resulting in a total reduction of 13.81%. Water and impervious areas exhibited a marked increase, with a growth of 34.49% and 52.49%, respectively, over the 2002–2022 period. Conversely, barren and wetland areas demonstrated a decline, with a reduction of 43.63% and 46.43%, respectively, during the same interval.

3.4.3. The Impact of Agriculture on △GWS

Corn and rice collectively account for a significant proportion of the cultivated area in Jilin Province, with a combined area that exceeds 90%. A spatial analysis of the distribution of corn and rice elucidates the spatial impact of agricultural water use on groundwater. The calculation of the difference in the △GWS between the spring irrigation period and the corn silking and grain filling period—the two periods with the highest water demand—in 2002 and 2022 reveals a significant decrease in the groundwater level in the plains of Jilin Province in 2022 compared to 2002, as illustrated in Figure 9. The areas exhibiting groundwater level decline exhibited spatial consistency with the distribution of corn and rice. Furthermore, spatial autocorrelation analysis was conducted on the difference between the average annual irrigation volume and the decline in GWS. This analysis revealed a significant negative correlation between groundwater level decline and irrigation volume. This finding emphasizes the crucial part that agricultural water use plays in affecting groundwater levels in the plains of Jilin Province. This underscores the significant impact that agricultural water use has on the sustainability of the region’s groundwater resources.

4. Discussion

4.1. The Impact of Climate Factors on Groundwater Sustainability

Within the framework of regional water cycles, atmospheric precipitation serves as the foremost input to terrestrial water system. The principle of water mass conservation further reveals that variations in regional water reserves are governed by a multitude of hydrological processes, such as precipitation, runoff, evaporation, and groundwater infiltration, among others [34]. Evaporation has been demonstrated to exert an impact on the process of precipitation infiltration, which serves to replenish groundwater reserves [35,36,37]. On the other hand, evaporation leads to an augmentation in water demand, thereby prompting the extraction of groundwater for replenishment purposes [38,39,40]. Consequently, the present study has identified precipitation and potential evapotranspiration as the two factors to be examined in order to ascertain the impact of climate factors on groundwater.

It is noteworthy that (Figure 7), under typical conditions, precipitation exhibits a positive correlation with groundwater reserves and sustainability [41,42,43]. However, the R value indicates a negative correlation between precipitation and sustainability. From 2002 to 2022, precipitation and groundwater storage exhibited analogous fluctuations, i.e., groundwater storage increased with rainfall and decreased with a decrease in rainfall. However, in extensive time series, changes in groundwater storage exhibited no correlation with changes in annual precipitation. This finding suggests that factors other than precipitation are responsible for changes in groundwater storage. These factors are closely related to human activities [44,45].

4.2. The Impact of Human Activities on Groundwater Sustainability

As demonstrated in

Table 3. Land use changes in Jilin Province from 2002 to 2022.

Year	Land Use Types/km2
	Cropland	Forest	Grassland	Water	Barren	Impervious	Wetland
2002	89,206.69	84,744.14	5763.63	2370.56	2799.98	6018.73	18.37
2012	88,926.00	82,408.78	6589.94	2792.28	2334.88	7858.64	11.61
2022	89,817.99	82,181.95	4967.50	3188.23	1578.45	9178.15	9.84

and Figure 8, the impervious area, defined as the urban area, in Jilin Province has been increasing on a continuous basis. Compared with 2002, the impervious area increased by 30.57% and 52.49% in 2012 and 2022, respectively. Urban development has been demonstrated to exert an influence on groundwater, and concurrently. As cities grow, the demand for water increases. When surface water sources are insufficient, groundwater is extracted to meet urban expansion needs [46,47]. Nighttime lighting as an indicator of human activity and objectively reflects the production and living conditions of human society [48]. As the intensity of nighttime lighting increases, it indicates a rise in the demand for water for both living and production purposes [49,50], which in turn leads to increased groundwater extraction and impacts the sustainability of groundwater resources [51,52]. More water resources are used in four areas: municipal, industrial, residential, and artificial ecological environment replenishment.

Furthermore, as demonstrated in Figure 8 and Table 3, although the alteration in Cropland area between 2002 and 2022 was not substantial. However, according to data from the Jilin Province Statistical Yearbook, the province’s sown area exhibited an increase from 46,877 km² in 2002 to 62,264 km² in 2022, representing a 38.82% increase in sown area. The augmentation in the sown area resulted in a substantial escalation in grain production in Jilin Province, from 22.148 million tons in 2002 to 40.8078 million tons in 2022, signifying an increase of 84.25%. Although precipitation exhibited a marked upward trend from 2002 to 2022, as indicated by the MK test, which may have exerted a positive influence on agriculture, water reserves proved incapable of meeting the rapidly escalating agricultural demand, and crops frequently experienced water deficits [53]. Historically, the predominant irrigation method in Jilin Province was flood irrigation, a cost-effective approach that nevertheless led to substantial water resources expenditure. In recent years, with the popularization of drip irrigation and sprinkler irrigation and the construction of high-standard farmland, the effective water utilization coefficient for farmland irrigation has increased from 0.494 at the beginning to 0.611 in 2024, resulting in a slight improvement in the waste of irrigation water. To address the issue of inadequate effective precipitation for crop growth, large-scale groundwater extraction has emerged as a critical strategy for ensuring food security [54,55,56]. While this measure has led to an increase in food production, it has also contributed to the exacerbation of the decline in groundwater levels and sustainability [57].

4.3. Agricultural Policy-Driven Changes in Groundwater Sustainability

Since the 1980s, China’s grain production and per capita grain availability have been significantly below the world average. In order to address this issue, the Chinese government has formulated a series of policies with the aim of promoting agricultural development and increasing grain production (Figure 10) [58]. The implementation of the second round of the household contract responsibility system (1997–2027) and the abolition of agricultural taxes in 2005 greatly stimulated farmers’ enthusiasm, leading to consecutive years of growth in sown area and grain production. In 2009, the People’s Republic of China initiated a food security program with the objective of achieving an additional 50 million tons of grain production. Notwithstanding the occurrence of rare adverse effects, including low temperatures and heavy rainfall in the spring, as well as drought in the summer, Jilin Province’s grain production accounted for 4.63% of China’s total grain production in that same year. Under the influence of a series of policies, Jilin Province, a vital commercial grain production base in China, has consistently ranked first nationally for many consecutive years in terms of per capita grain availability, grain commercialization rate, grain outflow volume, and corn export volume. The accelerated augmentation of sown area and grain yield poses a grave threat to the sustainability of groundwater resources in Jilin Province.

It is widely accepted that food production represents the most significant potential driver of natural ecosystem decline and degradation. The present study observed a significant impact of agricultural water use on groundwater sustainability. Furthermore, population growth has slowed considerably in recent years, and grain yields per unit area have increased significantly compared to previous levels [59]. Currently, the primary method of increasing grain production is to expand the area dedicated to cultivation. It is imperative that we accelerate the comprehensive green transformation of agriculture. Specific measures should include constructing water diversion projects, developing irrigation technologies, and improving agricultural water use efficiency. These measures are crucial to reducing the impact of agricultural activities on groundwater systems [60].

4.4. Urbanization Policy-Driven Changes in Groundwater Sustainability

The social and economic development of the northeastern region has always been a key focus of both the central and local governments. The “Revitalization of the Northeast” policy, which was proposed in 2003, was a significant factor in the promotion of urban expansion and agricultural development in Jilin Province. During China’s 11th Five-Year Plan to the 13th Five-Year Plan (2005–2020), urbanization and agricultural development in Jilin Province received significant support [58].

Whilst pursuing the goal of encouraging urbanization, it is imperative to place equivalent priority on the imperative of improving the quality of that process. The ongoing urbanization process, accompanied by rising populations and economic development, has led to a significant increase in groundwater extraction. This has further exacerbated ecological concerns, manifesting in a persistent decline in groundwater levels, a deterioration in water quality, and the degradation of wetlands. These issues pose a considerable challenge to the sustainability of the groundwater system, and affect food security and ecological security [61]. Moreover, urbanization has imposed considerable strain on water resources and other ecosystem services, particularly in regions experiencing water scarcity [62]. Consequently, Jilin Province must achieve a judicious equilibrium between agricultural and social development and environmental sustainability. In the process of applying the concept of sustainable development, it is imperative that we incorporate the sustainability of groundwater systems. In China, the Jilin Province ecosystem has been affected by policy changes, and many other areas have also been significantly impacted [27,28,63,64,65].

5. Conclusions

This study applied the SSA interpolation method for gap-filling in the GRACE Mascon data. It also examined and evaluated groundwater sustainability drawing on diverse data sources, such as GRACE Mascon RL06 data, GLDAS hydrological data, precipitation data, and evaporation data. The following conclusions were reached:

• From 2002 to 2022, the ∆GWS in Jilin Province showed an overall downward trend, with the highest average groundwater level in June and the lowest in September.
• The GRACE Groundwater Drought Index (GGDI) for Jilin Province shows that groundwater reserves were in surplus most of the time before September 2010. From October 2010 to August 2018, the reserves fluctuated between surplus and deficit states. Since September 2018, the GGDI has shown a continuous downward trend and entered a state of long-term deficit.
• An assessment was conducted of the sustainability of groundwater in Jilin Province, with the Groundwater Drought Index (GGDI) serving as the primary analytical framework. Prior to 2005, the sustainability of groundwater in Jilin Province was classified as high or extremely high. However, following the first instance of a deficit in groundwater reserves in 2005, the situation experienced a precipitous decline, reaching low sustainability in 2006. By 2011, it had reached an extremely low level of sustainability, with ongoing deterioration.
• The present study employed both MIC analysis and Pearson study to determine the effect of four indicators on the sustainability of groundwater in Jilin Province. The findings suggest that policy-driven expansion of sown areas has the greatest influence on groundwater sustainability. Although increased precipitation positively impacts groundwater levels and sustainability, expansion of sown areas has created irrigation demands that cannot be met. This has led to a negative correlation between precipitation and groundwater sustainability.

In summary, from the perspective of groundwater system sustainability, it is recommended to halt or even reduce the expansion of cultivated areas, actively construct water diversion projects, and develop irrigation technologies. The augmentation of surface water irrigation volumes, in conjunction with the enhancement of the effective irrigation coefficient, has been demonstrated to effectively mitigate pressure on the groundwater system. These measures have the potential to promote the sustainable use of groundwater in the region.