Exploring Spatio-Temporal Variations in Water and Land Resources and Their Driving Mechanism Based on the Coupling Coordination Model: A Case Study in Western Jilin Province, China

Abstract

Water and land resources (WLR) are the most important basic resources for social and economic development. The effective alignment of WLR is crucial for maximizing resource utilization and promoting sustainable regional development. This study focuses on Western Jilin Province (WJP), China, employing the degree of coupling coordination model, spatial autocorrelation, and the center of gravity transfer model to assess and characterize the spatio-temporal differentiation patterns of water and land resource matching from 2006 to 2020. Five indicators—annual average temperature (AAT), urbanization rate (UR), population density (PD), reclamation rate (RR), and water resource utilization rate (WRUR)—were selected as influencing factors. A Tobit model was constructed to elucidate the driving mechanisms behind the evolution of the WLR coupling coordination degree (CCD) in WJP. The results indicate the following: (1) From a temporal perspective, the coupling coordination degree of WLR in WJP has shown a year-on-year increase from 2006 to 2020, transitioning from a moderate imbalance to intermediate coordination, reflecting a trend of continuous improvement. (2) Regarding spatial distribution, the overall center of gravity of water and land resource coupling coordination remained relatively stable between 2006 and 2020; however, the direction of distribution gradually shifted from the northeast to the southwest and then from the northwest to the southeast. (3) The AAT, PD, and RR from 2006 to 2020 were all statistically significant at p < 0.01. Notably, the RR positively influences the CCD of WLR, whereas the AAT and PD exert a negative impact. In contrast, the UR and WRUR do not significantly affect the CCD of WLR.

1. Introduction

Water and land resources (WLR) are vital to human survival and social development, significantly contributing to regional economic growth and the overall health of the ecological environment. In recent decades, rapid population growth and the continuous expansion of socio-economic activities have presented serious ecological and environmental challenges to humanity. Particularly evident challenges include water contamination, the overutilization of water resources, and the ongoing loss of cultivated land and forest areas, all of which contribute to an imbalance and mismatch in WLR. Water resources significantly influence agricultural land use. Variations in the quantity of available water can lead to changes in land use patterns. Conversely, the unreasonable use of WLR can hinder the development of the regional economy and raises concerns regarding food production safety. Consequently, investigating the matching relationship between agricultural WLR has emerged as a prominent topic in water and land resource management [1,2,3].

Current research on the matching of WLR employs several common methodologies, including the Gini coefficient method [4], PSR model [5], GIS spatial evaluation analysis [6], and the Entropy Weight–TOPSIS method combined with coupling coordination degree (CCD) [7], etc. The primary focus of this research encompasses the matching relationship, carrying capacity, and evaluation of WLR [8,9,10]. Previous studies have predominantly concentrated on the internal dynamics of water or land resource systems, primarily examining their patterns of change [11,12]. By utilizing the Gini coefficient and the method of assessing water resources per unit area, researchers have evaluated the degree of matching between farmland and water resources to understand their impact on agriculture [13,14]. Some studies analyzed the matching relationship of regional agricultural WLR by exploring the quantitative relationship between these two resource types [15]. Liu et al. established a measurement model for the matching index of generalized agricultural WLR, which accounts for the combined impacts of blue and green water, and estimated the matching degree of agricultural WLR in relevant regions [16]. Separate studies on water resource systems and land resource systems, while effectively illustrating each system’s internal dynamics, fall short of addressing the current research needs. This limitation arises from the unavoidable influence of surrounding systems and factors on any single system. Consequently, prior analyses of WLR systems that were conducted separately do not adequately address the current research requirements.

However, as society and the economy develop and natural resource consumption increases, many studies have begun to view the two subsystems of WLR as an interactive and interdependent whole [17,18]. For instance, studies that employ optimization simulation methods to determine the allocation ratio of WLR facilitate the management of agricultural WLR, thereby enhancing the matching degree of agricultural WLR [19]. A coupled coordination system refers to the relationship and the degree of coordination among more systems, or among various elements within a system, in terms of their mutual influence and interaction [20,21]. In recent years, research on the integration of WLR systems with other systems—such as food production [22], economic development [23], ecological environments [24], and energy [25]—has intensified. Cao et al. employed a regression model to analyze the relations among water, land, and crops, discussing the impact of these resources on food production from 2005 to 2020 with the aim of ensuring future food security [26]. Meng et al. studied the interrelationship among energy, water, and land in four megacities in China: Beijing, Shanghai, Guangzhou, and Shenzhen. They analyzed these relationships across multiple scales and levels by constructing a coupling relationship diagram of urban energy, water, and land. The study explores urban sustainable development within a multi-level economic context [27]. The aforementioned research spans diverse domains, including agriculture and economics, leveraging various analytical models and underscoring the importance of multi-system coupling coordination, thereby amplifying these studies’ influence. Nevertheless, in-depth analysis and a discussion of universal cases are often lacking, necessitating continuous improvements in future research endeavors.

As an agricultural powerhouse, China’s agricultural and grain security are intrinsically linked to WLR. Jilin Province, recognized as a principal grain production base in China, benefits significantly from the rational allocation and efficient utilization of WLR, which will enhance grain production. However, coupling analyses of WLR in various regions of Jilin Province are limited. Consequently, this study analyzes WLR as a cohesive system, employing multiple models to process data and integrating analyses from other systems, including economic, ecological, and climatic factors. This paper takes the Western Jilin Province (WJP) as the research object, based on extensive field research and a comprehensive analysis of water and land resource data from 2006 to 2020, aiming to provide evaluation support and improvement strategies for improving the matching degree of WLR. The objectives are as follows: (1) to construct a CCDM that evaluates the matching status of WLR in WJP; (2) to analyze the spatial–temporal distribution feature of the coupled coordination degree of WLR; (3) to explore the driving factors influencing the coupled coordination degree of WLR in WJP. Additionally, GIS technology is utilized to visually represent the coupled coordination data of WLR in the study area, thereby facilitating a comprehensive analysis of the matching patterns.

2. Research Area and Data Sources

2.1. Study Area

WJP, illustrated in Figure 1, encompasses Baicheng City, Songyuan City, and Shuangliao City, situated in WJP. This area is recognized as one of the three major soda saline regions worldwide and is also among the most ecologically vulnerable areas within Jilin Province. WJP is located inland in northeast China and falls within the temperate continental monsoon climate zone. This zone is characterized by marked climatic variations and significant temperature fluctuations throughout the year, with an average annual temperature ranging from 4 °C to 6 °C. Rainfall is primarily concentrated in July and August, with average annual precipitation ranging from 200 to 500 mm, showing a declining trend from the southeast to the northwest. The average annual evaporation in the study area varies between 1500 and 1900 mm. In recent decades, population growth, combined with the poor management of WLR and an increase human activities, has resulted in the degradation of the WLR system in WJP, negatively impacting the ecological environment.

2.2. Data Sources

The data are primarily divided into two sections (

Table 1. Data sources and descriptions.

Input Data	Resolution/Data Level	Source
Precipitation resource quantity	District and County level	National Tibetan Plateau Science Data Center (https://data.tpdc.ac.cn) accessed on 29 October 2024;
Surface-water resource quantity		Water Resources Bulletin (http://slj.jlbc.gov.cn/) accessed on 10 April 2023; Municipal and county statistical departments (http://tjj.jl.gov.cn/tjsj/) accessed on 12 June 2023;
Groundwater resource quantity
The total water resources
Per capita water resources
Total amount of Water resources per unit area
The agricultural water consumption
The industrial water consumption
The domestic water consumption
The ecological water consumption
Water resource utilization rate
The total grain output		China County Statistical Yearbook
Population density
Per capita cultivated land area	30 m	Resource and Environment Science and Data Center (www.resdc.cn) accessed on 15 May 2024;
Cultivated
Forest
Grassland
Water
Barren
Impervious
Annual temperature	1 km	National Tibetan Plateau Science Data Center (https://data.tpdc.ac.cn) accessed on 29 October 2024
Urbanization rate		‘Developing improved time-series DMSP-OLS-like data (1992–2019) in China by integrating DMSP-OLS and SNPP-VIIRS’
Reclamation rate	30 m	Resource and Environment Science and Data Center (www.resdc.cn) accessed on 15 May 2024

). The first section includes essential information for analyzing water resources across various districts and counties of WJP. The second section includes essential information for analyzing land resources across various districts and counties of WJP. Data regarding the area of different land use types were obtained from China’s annual 30 m land cover grid dataset, spanning from 1990 to 2022 [28]. Additionally, population figures and total grain production data were derived from the respective editions of the ‘China County Statistical Yearbook’ for the corresponding years.

3. Methodology

3.1. Research Framework

A detailed research framework is shown in Figure 2.

3.2. Index System and Weight

Establishing a comprehensive indicator system is fundamental for objectively evaluating the degree of coupling between WLR. Guided by the principles of scientific rigor, rationality and comprehensiveness, the indicators were established and selected considering the features of WLR utilization in the study area in conjunction with field research findings and specific regional attributes. Utilizing data collected from 2006 to 2020 regarding WLR in WJP, 18 indicators were compiled to create the coupling indicator system for WLR. This study used a comprehensive weighting approach to address the challenges posed by excessive subjectivity in calculating indicators through subjective weighting methods and the difficulties in capturing the varying degrees of emphasis that decision-makers place on different indicators using objective weighting methods. This approach integrates subjective (Analytic Hierarchy Process) and objective (Entropy Weighting Method) weighting techniques. By averaging the results derived from these two methods, the final weight values for each indicator within the WLR system were established (

Table 2. Index system and weights of water and land resources.

Subsystem	Indicator Name	Indicator Unit	Indicator Weight	Indicator Attribute
The water resources system	Precipitation resource quantity	mm	0.031	+
	Surface-water resource quantity	108 m3	0.084	+
	Groundwater resource quantity		0.079	+
	The total water resources		0.085	+
	Per capita water resources	108 m3/104 person	0.037	+
	Total amount of Water resources per unit area	104 m3/km2	0.034	-
	The agricultural water consumption	104 m3	0.048	-
	The industrial water consumption		0.078	-
	The domestic water consumption		0.034	-
	The ecological water consumption		0.037	-
The land resources system	Cultivated	km2	0.044	+
	Forest		0.055	+
	Grassland		0.040	+
	Water		0.041	-
	Barren		0.029	-
	Impervious		0.010	-
	Per capita cultivated land area	km2/104 person	0.188	+
	The total grain output	t	0.047	+

Note: + represents a positive indicator; - represents a negative indicator.

) [29,30]. Except for the proportion of agricultural, industrial, domestic, and ecological water use in the water resource indicators, and the bare land area and impermeable ground area in the land resource indicators, which belong to negative indicators, all other indicators were positive indicators.

3.3. Construction of Coupling Coordination Model

3.3.1. Data Standardization

To ensure comparability among indicators with varying dimensions and units, this paper utilized extreme difference methods to standardize the original indicator data, converting all indicator values into the range [0, 1] [31,32].

$$x_{ij}^{\prime }=\left\{\begin{array}{c}{\displaystyle \frac{x_{ij}-min\left(x_j\right)}{max\left(x_j\right)-min\left(x_j\right)}} \mathrm{Positive} \mathrm{indicators}\\ {\displaystyle \frac{max\left(x_j\right)-x_{ij}}{max\left(x_j\right)-min\left(x_j\right)}} \mathrm{Negative} \mathrm{indicators}\end{array}\right.$$

(1)

where $x_{ij}$ represents the original data for the j indicator in region i for a certain year, where $max\left(x_j\right)$ and $min\left(x_j\right)$ are the max and min values of the original data for the j indicator, respectively, and $x_{ij}^{’}$ is the value after standardization.

3.3.2. Coupling Coordination Model

The coupling degree refers to the extent of the interaction among two or more systems; in this context, it specifically pertains to the interaction and influence between water resource systems and land resource systems [33]. A higher coupling degree indicates a greater interconnection among these systems.

$$C=n{\left\{{\displaystyle \frac{{\theta }_1{\times \theta }_2{\times \cdot \cdot \cdot \times \theta }_n}{{{(\theta }_1{+\theta }_2{+\cdot \cdot \cdot +\theta }_n)}^n}}\right\}}^{{\displaystyle \frac{1}{n}}}$$

(2)

$$\theta =\sum _{j=1}^m{w}_j\left(x_{ij}-Min\left(x_j\right)\right)$$

(3)

where ${\theta }_n$ represents the comprehensive evaluation index of the subsystem, ${\mathrm{w}}_{\mathrm{j}}$ represents the indicator weight (Table 2), n represents the number of subsystems, and C is the coupling degree, with a value range of [0, 1]. Since there are only two subsystems, water resources and land resources, n equals 2; ${\theta }_1$ and ${\theta }_2$ represent the comprehensive evaluation indices of the water resources and land resources systems, respectively.

$$D=\sqrt{C\times T}$$

(4)

where D represents the CCD of the water and land resources system, with a value range of [0, 1]. T stands for the comprehensive coordination index of WLR, reflecting the overall matching effect of the regional water and land resources system. $T=\alpha {\theta }_1+\beta {\theta }_2$, $\alpha$ and $\beta$ are the undetermined coefficients for the contribution of the WLR system, respectively. Due to the mutual influence and interaction between the water and land resources systems, the two subsystems have the same effect; therefore, they were set equal values of $\alpha$= $\beta$= 0.5 [34,35,36].

3.3.3. Coupling Coordination Degree Classification System

To accurately represent the degree of coupling and coordination between the water resources system and the land resources system, the coupling coordination degree of the WLR system was classified into several categories. In WJP, the CCD of the WLR system was categorized into ten levels (

Table 3. Classification criteria for coupling coordination level.

Coupling Coordination	Level of Coordination	Coupling Coordination Degree	Classification of Coupling Coordination Degree
[0.0~0.1)	1	Extreme Imbalance	Low-level Coordination
[0.1~0.2)	2	Serious Imbalance
[0.2~0.3)	3	Intermediate Imbalance	Grudging Coordination
[0.3~0.4)	4	Mild Imbalance
[0.4~0.5)	5	Bordering on Imbalance	Primary Coordination
[0.5~0.6)	6	Barely Coordinating
[0.6~0.7)	7	Primary Coordination	Intermediate Coordination
[0.7~0.8)	8	Intermediate Coordination
[0.8~0.9)	9	Good Coordination	High-level Coordination
[0.9~1.0]	10	Excellent Coordination

) [37]. Based on the existing research and the specific circumstances of this study, the CCD of WLR was categorized into five distinct types [38,39,40,41].

3.4. Spatial Autocorrelation

Spatial autocorrelation analysis involves examining whether a specific attribute within a spatial unit is correlated with the same attribute in neighboring spatial units. This relationship can be quantified using Moran’s I [42]. Spatial autocorrelation analysis can be categorized into global spatial autocorrelation and local spatial autocorrelation. Global spatial autocorrelation assesses the overall degree of spatial dependence across a broader spatial range, while the local spatial autocorrelation analysis concentrates on investigating the local spatial features within the specific study area [43,44].

$$\begin{gathered} Moran’s I={\displaystyle \frac{{\Sigma }_{i=1}^n{\Sigma }_{j=1}^n{W}_{ij}(x_i-\overline{x})(x_j-\overline{x})}{S^2{\Sigma }_{i=1}^n{\Sigma }_{j=1}^n{W}_{ij}}} \\ Local Moran’s I={\displaystyle \frac{\mathrm{n}({\mathrm{x}}_{\mathrm{i}}-\overline{\mathrm{x}}){\mathsf{\Sigma }}_{\mathrm{j}=1}^{\mathrm{m}}{\mathrm{W}}_{\mathrm{i}\mathrm{j}}({\mathrm{x}}_{\mathrm{j}}-\overline{\mathrm{x}})}{{\mathsf{\Sigma }}_{\mathrm{i}=1}^{\mathrm{n}}{({\mathrm{x}}_{\mathrm{i}}-\overline{\mathrm{x}})}^2}} \end{gathered}$$

(5)

where n represents the number of spatial units in the study area and m denotes the number of regions geographically adjacent to region j. $x_i$ and $x_j$ represent the CCD of WLR in region i and region j, respectively, $W_{ij}$ represents the spatial weight matrix of regions i and j, and $\overline{x}$ represents the average CCD of water and land resources.

3.5. Center of Gravity Transfer Model

The regional centroid is the point in space where the average values of specific geographical or climatic elements achieve equilibrium. This concept describes these elements’ development trends and changes and analyzes the density, orientation, quantity, and dynamic variations in space [45,46]. Utilizing the Center of Gravity Transfer Model, this study calculated and analyzed the coupled coordination degree of the study area from 2006 to 2020.

$$\begin{gathered} P_x={\displaystyle \frac{{\sum }_{i=1}^n{X}_i{P}_i}{{\sum }_{i=1}^n{P}_i}} \\ {P}_y={\displaystyle \frac{{\sum }_{i=1}^n{Y}_i{P}_i}{{\sum }_{i=1}^n{P}_i}} \end{gathered}$$

(6)

where ($P_x$, $P_y$) represents the longitude and latitude of the centroid of the CCD for a certain year. $X_i$ and $Y_i$ represent the longitude and latitude coordinates of the ith region, $P_i$ represents the CCD of WLR for the corresponding ith region, and n represents the total number of regions within the study area.

3.6. Standard Deviational Ellipse

The standard deviation ellipse (SDE) tool in ArcGIS was employed to illustrate the spatial distribution trend of the centroid relative to the CCD. The SDE is a geostatistical method that effectively represents the spatial distribution characteristics of various geographic elements [47]. The main parameters of the SDE include the center of gravity, the orientation angle, and the long and short axes. The gravity center of the circle signifies the mean center of the data, indicating the relative location of the distribution of the data in space. The long and short axes represent the distribution direction and range, respectively. Additionally, the difference between the long axis and short axis reflects the degree of directionality of the dispersed points. The orientation angle denotes the rotational angle of the major axis, measured clockwise from the north direction [48,49].

(1) The coordinates (X, Y) of the ellipse center can be calculated as follows:

$$\begin{gathered} X=\sqrt{{\displaystyle \frac{{\sum }_{i=1}^n{\left(X_i-\overline{X}\right)}^2}{n}}} , Y=\sqrt{{\displaystyle \frac{{\sum }_{i=1}^n{\left(Y_i-\overline{Y}\right)}^2}{n}}} \\ \overline{X}={\displaystyle \frac{{\sum }_{i=1}^n{X}_i}{n}} , \overline{Y}={\displaystyle \frac{{\sum }_{i=1}^n{Y}_i}{n}} \end{gathered}$$

(7)

(2) The orientation angle θ of the ellipse can be calculated as follows:

$$\mathit{tan}\theta ={\displaystyle \frac{a+b}{c}}$$

(8)

where

$a={\sum }_{i=1}^n{\left(X_i-\overline{X}\right)}^2-{\sum }_{i=1}^n{\left(Y_i-\overline{Y}\right)}^2$;

$b=\sqrt{{\left({\sum }_{i=1}^n{\left(X_i-\overline{X}\right)}^2-{\sum }_{i=1}^n{\left(Y_i-\overline{Y}\right)}^2\right)}^2+4{\left({\sum }_{i=1}^n\left(X_i-\overline{X}\right){\sum }_{i=1}^n\left(Y_i-\overline{Y}\right)\right)}^2}$;

$c=2{\sum }_{i=1}^n\left(X_i-\overline{X}\right){\sum }_{i=1}^n\left(Y_i-\overline{Y}\right)$;

(3) The major axis ${\gamma }_1$ and minor axis ${\gamma }_2$ of the ellipse can be calculated as follows:

$$\begin{gathered} {\gamma }_1=\sqrt{2}\sqrt{{\displaystyle \frac{{\sum }_{i=1}^n{\left(\left(X_i-\overline{X}\right)\mathit{cos}\theta -\left(Y_i-\overline{Y}\right)\mathit{sin}\theta \right)}^2}{n}}} , \\ {\gamma }_2=\sqrt{2}\sqrt{{\displaystyle \frac{{\sum }_{i=1}^n{\left(\left(X_i-\overline{X}\right)\mathit{sin}\theta -\left(Y_i-\overline{Y}\right)\mathit{cos}\theta \right)}^2}{n}}} \end{gathered}$$

(9)

where $X_i$ and $Y_i$ are the spatial coordinates of each element, X and Y represent the center of the standard deviation ellipse, $\overline{X}$ and $\overline{Y}$ are the average centers of each element, and n is the total number of elements.

3.7. Tobit Model

The Tobit model is a regression analysis method that is commonly employed to address censored dependent variables, effectively managing lower and upper bound issues. In contrast with linear regression models, the Tobit model offers specific advantages when dealing with censored data and bounded dependent variables [50,51]. Based on the theoretical basis of Equation (10), this study uses Stata software (14.0)to analyze the driving factors. The model is as follows:

$$Y_{it}={\alpha }_i+\sum _{j=1}^n{\beta }_j{X}_{ijt}+{\upsilon }_{it}$$

(10)

where $Y_{it}$ represents the CCD value of region i in year t, ${\alpha }_i$ is the constant term, $X_{ijt}$ represents the variable for the j influencing factor, ${\beta }_j$ is the coefficient for the j influencing factor variable, and ${\mathsf{\upsilon }}_{\mathrm{i}\mathrm{t}}$ represents the residual term, which has a normal distribution.

4. Results

4.1. Spatio-Temporal Variation Characteristics of WLR in WJP

4.1.1. Analysis of Spatio-Temporal Variation Characteristics of the WLR Subsystem

The comprehensive indices of WLR for 2006, 2010, 2015, and 2020 were selected to analyze the temporal variation characteristics of the water resource system and the land resource system. As illustrated in Figure 3, the overall trend of the comprehensive water resource index (U1) in WJP shows a gradual upward trajectory, increasing from 0.539 in 2006 to 0.664 in 2020. Over time, except for Shuangliao City, Zhenlai County, Qian’an County, and Changling County, the U1 of other districts and counties in the study area showed an increasing trend. This observation suggests that these regions may not have prioritized the sustainable utilization of water resources. Notably, the U1 of Taonan City consistently remained at a higher level, rising from 0.812 in 2006 to 0.912 in 2020.

In contrast, although the U1 of Ningjiang District increased from 0.165 in 2006 to 0.289 in 2020, it remained at a lower level throughout this period. Regarding the variation in the comprehensive land resource index (U2), it is evident that, except for Ningjiang District, the U2 of other districts and counties in WJP exhibits an upward trend. The decline in the U2 of Ningjiang District may be attributed to the local climate. This district is situated in a continental monsoon climate zone, characterized by hot and rainy summers and cold, dry winters. Furthermore, its location downstream of the Songhua River results in elevated river levels and increased land salinity, which impede the more rational utilization of local land.

As illustrated in Figure 4, the CCD of WLR in the study area from 2006 to 2020 demonstrates a fluctuating upward trend, transitioning from a moderate imbalance to intermediate coordination. This trend suggests an increasingly better matching of WLR in WJP. Among the four selected periods, the CCD in Ningjiang District remains constant at 0.1, reflecting conditions of severe imbalance. In contrast, the CCD in Qian’an County and Changling County exhibits a decline, while the variations in the CCD of Zhenlai County, Fuyu City, and Qianguo County are relatively minor. Conversely, the CCD in Da’an City, Taobei District, Taonan City, and Tongyu County show an increase, indicating that the matching of WLR in these four regions is improving.

4.1.2. Characteristics of Spatio-Temporal Changes in the WLR

Results Analysis of CCD

Based on the characteristics of the CCD in WJP, this paper categorized the degree of coordination into five major classes: low-level coordination, grudging coordination, primary coordination, intermediate coordination, and high-level coordination. As shown in Figure 5, spatially, the overall CCD in WJP from 2006 to 2020 was relatively high. Regarding the number of administrative regions at each coupling coordination level, the most significant proportion was found in regions with moderate coordination. Over the period from 2006 to 2020, there was a slight decline in the number of administrative regions classified as having high-level coordination. As illustrated in Figure 5, Fuyu City consistently maintained a high-level coordination status. Fuyu City benefits from fertile soil and abundant water resources in the alluvial plain of the Nen River and the Songhua River. Furthermore, the implementation of effective ecological and environmental protection measures ensured that the CCD of WLR in Fuyu City remained high.

In contrast, Ningjiang District was consistently in a low-level coordination state. Despite the presence of major rivers such as the Songhua River and the Nen River, the severe drought in 2002 and the uneven spatial–temporal distribution of precipitation over the years led to a decrease in the water resources of the rivers flowing through the region [52]. As the economic center of Songyuan City, Ningjiang District hosts diverse industries, particularly in the oil sector, which contributes to the heightened water consumption. This increased demand exacerbates the scarcity of local water resources, leading to a persistently low CCD between water and land resources.

Spatial Autocorrelation Analysis

The spatial autocorrelation model was constructed to analyze the CCD of WLR in WJP from 2006 to 2020. The results, illustrated in Figure 6, reveal that the global spatial autocorrelation Moran’s I for the study area was −0.484 in 2006, −0.489 in 2010, −0.436 in 2015, and −0.132 in 2020. Each value falls within the range of (−1, 0), indicating that the CCD in WJP displayed a global spatial negative correlation during these periods. Most data points are situated in the second and fourth quadrants, which represent areas with ‘low-level’ coupling coordination surrounded by ‘high-level’ areas and areas with ’high-level’ coupling coordination surrounded by ‘low-level’ areas, respectively. This distribution suggests that the CCD of these regions was negatively correlated.

Conversely, a limited number of points were found in the first and third quadrants, which denote areas with ‘high-level’ coupling coordination surrounded by other ‘high-level’ areas and areas with ‘low-level’ coupling coordination surrounded by other ‘low-level’ areas, respectively. This indicates a positive correlation in the coupling coordination of these regions. Notably, from 2006 to 2020, the Moran’s Index in the study area showed an increasing trend, suggesting that the spatial autocorrelation of the CCD of WLR was strengthened over the last 15 years.

Using ArcGIS software (10.8.1), a local spatial autocorrelation analysis was conducted to assess the CCD of WLR in WJP. The analysis revealed that high–high-level coupling coordination areas and low–low-level coupling coordination areas exhibit a positive spatial correlation, while high–low-level coupling coordination areas and low–high-level coupling coordination areas demonstrate a negative spatial correlation. As illustrated in Figure 7, in 2006, Zhenlai County, Qianguo County, and Fuyu City were identified as high–high-level coupling coordination clusters, whereas Taobei District, Da’an City, Changling County, and Shuangliao City were categorized as low–high-level coupling coordination clusters. Furthermore, Taonan City, Tongyu County, Qian’an County, and Ningjiang District were classified as high–low-level coupling coordination clusters. In 2010, Fuyu City and Taobei District emerged as high–high-level coupling coordination clusters, while Da’an City, Qianguo County, Changling County, and Shuangliao City were noted as low–high-level coupling coordination clusters. Zhenlai County, Taonan City, Tongyu County, Qian’an County, and Ningjiang District were identified as high–low-level coupling coordination clusters. In 2015, Zhelai County and Qianguo County continued to be recognized as high–high-level coupling coordination clusters, while Da’an City, Fuyu City, Changling County, and Shuangliao City were classified as low–high-level coupling coordination clusters. Notably, Taobei District was identified as a low–low-level coupling coordination cluster, and Taonan City, Tongyu County, Qian’an County, and Ningjiang District were categorized as high–low-level coupling coordination clusters. In 2020, Zhenlai County, Taonan City, Qian’an County, and Changling County were classified as high–high-level coupling coordination clusters. Conversely, Da’an City, Qianguo County, and Shuangliao City were identified as low–high-level coupling coordination clusters. Fuyu City and Taobei District were low–low-level coupling coordination clusters, whereas Tongyu County and Ningjiang District were categorized as high–low-level coupling coordination clusters.

A comparison of the local spatial autocorrelation indices across the four periods reveals a significant increase in the number of high–high-level coupling coordination clusters. In contrast, the quantities of low–low-, high–low-, and low–high-level coupling coordination clusters gradually declined. Overall, the CCD of all districts and counties within the study area was influenced by the characteristics of their surrounding regions.

Center of Gravity Transfer Analysis

A transference of the center of gravity can spatially reflect the CCD’s temporal and spatial evolution characteristics within the study area [53]. By employing the center of gravity migration model and the formula for calculating the standard deviation ellipse, the CCDs of each district and county in WJP from 2006 to 2020 were utilized as weights to determine the longitude and latitude coordinates of the center of gravity for each study area’s CCD during this period. Subsequently, an interannual variation map was created depicting the longitude and latitude coordinates of the center of gravity for the CCD (Figure 8). Furthermore, an ArcGIS-generated map was utilized to illustrate the location of the center of gravity for the CCD in WJP and its trajectory of movement (Figure 9).

The interannual variation in the longitude and latitude coordinates of the center of gravity for the CCD in the study area reveals that the latitude generally exhibited an increasing trend, while the longitude showed a decreasing trend. Consequently, the center of gravity coordinates demonstrated a gradual shift towards the northwest. The average latitude of the center of gravity was 45.0483204° N, with a maximum value of 45.1497° N in 2020 and a minimum of 45.003176° N. The average longitude of the center of gravity was 123.7722819° E, with a maximum value of 123.84556° E in 2020 and a minimum value of 123.697472° E. These data indicate a major change in the position of the gravity center for the CCD in 2020 compared to 2019.

The center of gravity of the CCD within the study area generally shifted between Da’an City and Qian’an County (Figure 9). In 2014, this center of gravity distinctly moved towards the northwest. By 2019, it had shifted towards the southeast. In 2020, there was a significant movement in the center of gravity towards the northwest. In contrast, during the other years, the center of gravity of the CCD was relatively concentrated.

The construction of the SDE model reveals that the SDE of the CCD in the study area exhibited an orientation angle of 136.48° from 2006 to 2020, indicating that its distribution was primarily in the northwest direction. This suggests that the center of gravity of the CCD during this period is predominantly aligned along the northwest to southeast axis. By segmenting the period from 2006 to 2020 into three consecutive time intervals for analysis, it can be observed that the orientation angle of the standard deviation ellipse for the years 2006–2010 was 30.67°. This finding indicates that, during this interval, the center of gravity of the CCD was oriented along the northeast to southwest direction. Conversely, for the periods 2011–2015 and 2016–2020, the orientation angles of the standard deviation ellipses were 135.29° and 137.77°, respectively. These results indicate that the centers of gravity of the CCD in these periods were aligned along the northwest to southeast direction, consistent with the overall distribution of the CCD’s centers of gravity in WJP from 2006 to 2020. Based on the calculated results of the standard deviation ellipse’s major and minor axes, it is evident that, from 2006 to 2020, the major axis of the SDE was oriented from the northwest to southeast.

Furthermore, the flattening rate of the SDE was 2.66, indicating that the degree of dispersion of the center of gravity of the CCD was greater in the northwest–southeast direction than in the northeast–southwest direction. The flattening rates of the SDE for the CCD during the periods 2006–2010, 2011–2015, and 2016–2020 are 1.78, 3.74, and 3.13, respectively. This suggests that the distribution of the CCD exhibited a stronger directional characteristic during the 2011–2015 period, while it was more concentrated and less dispersed during the 2006–2010 period. Consequently, it can be concluded that, from 2006 to 2020, the center of gravity of the CCD gradually shifted from the northeast–southwest direction to the northwest–southeast direction. Additionally, the matching of WLR within the study area gradually became more balanced.

4.2. Analysis of Driving Factors for the CCD of WLR

To conduct a comprehensive analysis of the factors influencing the matching of WLR in WJP, this study considered the annual average temperature (AAT), urbanization rate (UR), population density (PD), reclamation rate (RR), and water resource utilization rate (WRUR) as independent variables, with the CCD serving as the dependent variable. A Tobit model was then established for the regression analysis. The results (

Table 4. Index system for factors influencing the matching of water and land resources in the research area.

Influence Factor	Regression Coefficient	Standard Error	z-Value	p-Value
Annual Temperature	−0.070 ***	0.012	−5.666	0.000
Urbanization Rate	0.000 *	0.000	0.514	0.608
Population Density	−0.002 ***	0.000	−12.829	0.000
Reclamation Rate	0.891 ***	0.138	6.462	0.000
Water Resource Utilization Rate	0.030	0.019	1.596	0.110

Note: *** and * indicate that the parameter estimates are significant at the p < 0.01 and p < 0.1 levels, respectively.

), presented in the accompanying table, indicate a significant relationship among AAT, UR, PD, RR, and CCD. The regression coefficients reveal a positive correlation among RR and CCD, while AAT and PD demonstrate a negative correlation with the CCD. Additionally, no linear relationship was observed between UR, WRUR, and the CCD.

The AAT and PD demonstrate a noticeable negative correlation with the CCD. The study area is characterized by high temperatures and considerable evaporation, with precipitation primarily concentrated in July and August. Overall, precipitation is low and unevenly distributed throughout the year. Moreover, higher temperatures increase the water requirements for crops, subsequently affecting the CCD of the WLR in the area. In WJP, the rising PD and lower levels of economic development have led to an increased reliance on agriculture. Furthermore, the irrational exploitation of WLR has resulted in a poor matching between these resources. Consequently, PD adversely impacts the CCD of WLR in WJP [54].

The RR demonstrates a positive correlation with the CCD, evidenced by a regression coefficient of 0.891, which is statistically significant at the p < 0.01 level. In WJP, land use has been optimized and planting structures were adjusted to convert specific dry fields into paddy fields. This transformation could potentially enhance the efficient utilization of WLR within the region [55,56].

5. Discussion

5.1. Analysis of the Coupled and Coordinated Development Pattern of Water and Land Resources in WJP

WLR are interdependent and mutually influential in terms of human survival and social development. Various factors influence the degree of their matching. This paper examines the CCD between water and land resources in WJP, identifying the factors that influence this coordination.

The results of the U1 indicate a significant decline in the index for the Ningjiang District. Previous research identified the predominant land type in this area as saline–alkali. The impacts of climate change, coupled with the accelerated urbanization process in recent years, have contributed to a decrease in the productivity of cultivated land, while the areas designated for forestland, grassland, and other land use types have been continuously reduced. The expansion of urban land areas further exacerbated the irrational utilization of local land resources. Consequently, Ningjiang District must optimize its climate change warning mechanism, utilize various land types judiciously, and emulate the comprehensive land consolidation efforts of Qianguo County to activate its resource potential and promote the sustainable development of regional land resources.

Furthermore, based on the results of the comprehensive water resource index, among the four districts and counties that experienced a decline in this index, Shuangliao City and Changling County are situated in semi-arid regions, while Qian’an County and Zhenlai County are located in arid and semi-arid regions. These areas face challenges such as scarce rainfall, insufficient surface vegetation coverage, and high levels of land desertification, all of which contribute to a shortage of water resources [57]. In Shuangliao City, Zhenlai County, Qian’an County, and Changling County, the scarcity of rainfall necessitated a reliance on groundwater extraction. However, this dependence has led to over-extraction in many areas, exacerbating water waste and increasing consumption, resulting in a steady decline in groundwater levels. In recent years, urbanization has contributed to the discharge of untreated wastewater from small and medium-sized enterprises, which is further compounded by the irrational use of pesticides and fertilizers, leading to groundwater contamination [58]. Therefore, under the current climatic conditions of Shuangliao City, Zhenlai County, Qian’an County, and Changling County, it is essential to vigorously develop water-saving irrigation technologies, construct water conservancy projects, and enhance water supply levels to address the uneven spatial–temporal distribution of water resources. Promoting a green economy, implementing stringent pollution control measures, and establishing effective wastewater treatment systems can convert polluted water into a valuable resource. This approach will facilitate the rational utilization of water resources and ecological restoration, improve the regional recycled water circulation system, and ultimately achieve healthy and sustainable water resource management.

Based on the results of the CCD between water and land resources, the study area demonstrates an overall trend of fluctuating increases. However, recent research has highlighted that the functional alterations in water and soil resources, driven by climate change and urbanization, have resulted in the uneven development of their coupling coordination, presenting significant challenges to the high-quality development of WJP [59]. Climate change and urbanization have led to an imbalance in the coupling coordination development of WLR, presenting challenges in the high-quality development of WJP. In districts and counties with high levels of coupling coordination, such as Fuyu County, WLR have attained a favorable degree of coordination. Nevertheless, issues such as engineered water scarcity and an insufficient balance between land occupation and compensation necessitate a focus on enhancing the exploitation and utilization of transboundary and inter-basin water resources in future development. Additionally, a scientific land use layout should be re-established to mitigate the discrepancies in land supply and demand. Conversely, in districts and counties with low levels of coupling coordination, such as Ningjiang District, the long-term influences of climate, topography, and landforms have resulted in significant water and land losses. The accelerated urbanization process in recent years further exacerbated water resource wastage and reduced utilization efficiency, increasing the prevalence of industrial and underutilized or abandoned land types. Therefore, WJP should form a strict land management system, optimize the planting structure, determine optimal areas for crop planting, and improve the land utilization rate so as to realize the comprehensive utilization of land based on agricultural development, consistent with the existing research [60]. Furthermore, controlling pollution emissions and promoting water-saving and anti-pollution measures are essential for achieving the coordinated development of WLR.

The spatial analysis of the CCD reveals that WJP demonstrates a high degree of the spatial aggregation of WLR. Notably, Qian’an County significantly influences the coupling coordination of these resources in the surrounding areas. Furthermore, from 2006 to 2020, the center of gravity for the CCD of WLR was consistently located in Qian’an County for 13 years. Consequently, efforts to enhance the CCD of WLR should prioritize planning and management in Qian’an County. By fully capitalizing on its radiative effect, this approach can promote the rational utilization of WLR in adjacent areas, ultimately leading to the optimized allocation of these resources throughout WJP.

5.2. The Impact of Water and Land Resources on Sustainable Development in WJP

WJP faces low precipitation, high evaporation rates, and severe desertification and salinization, which heavily reduce the productive capacity of cultivated land. Furthermore, the region’s ecological fragility complicates efforts to restore the ecological environment. These factors pose significant challenges when aiming to achieve sustainable economic and ecological environment development [61]. Consequently, the degree of matching between WLR is of paramount importance for WJP.

The WLR relationship in WJP has undergone significant changes in recent years. From 2006 to 2020, the CCD between these resources exhibited a year-on-year increase, although the overall level of CCD remained relatively low. Abundant cultivated land resources characterize the region; however, it faces severe water shortages, resulting in an uneven spatial and temporal distribution of these resources. Climate change and economic development have significantly influenced the balance between water and land resources in this area [62]. Studies have shown that, in terms of economic development, population changes, one of the driving factors behind the CCD of water and soil resources, has an important impact on the sustainable development of regional water and soil resources. The smaller the population, the more efficient the use of water resources, which may be related to people’s awareness of water conservation and a high level of technical skills. This is consistent with the results of this study [63]. In the past 50 years, with global warming, evaporation has increased with the increase in temperature, resulting in an imbalance in water and soil resources. Many studies have shown that climate change has changed the spatio-temporal distribution of WLR, resulting in prominent ecological and environmental problems and a crisis regarding the sustainable development of the region [64,65]. WLR and the ecological environment are facing dual risks. Overextraction is also a primary cause of the reduction in water resources within the region. Furthermore, the scarcity and uneven distribution of water resources have contributed to the deterioration of the local ecological environment, directly impacting the sustainable development of the economy and society. Therefore, WJP should prioritize the efficient and intensive use of water resources while balancing the occupation of and compensation for land. It is essential to invest in policies and financial support, actively transform the development model, and fully leverage local energy advantages. To enhance the alignment of WLR in WJP, measures such as soil improvement and fertilization, water-saving irrigation, and the restoration of saline–alkali land should be implemented. These initiatives aim to enhance the quality of cultivated land and the efficiency of irrigation. Comprehensive land improvement projects should be executed throughout the region to optimize the utilization structure of land resources. Strategies must be tailored to local conditions to establish a ’water diversion from the east to the west’ pattern. This approach will help mitigate the water scarcity issues in the central and western regions, which have been exacerbated by economic development and population growth [66]. Ultimately, the efficient allocation of WLR in WJP can be achieved [67].

5.3. Limitations and Prospects of Research

The CCD model emphasizes the interactivity of system elements, the laws governing their actions, the outcomes of these actions, and the comprehensiveness of the results for different systems. This model quantitatively measures the CCD between systems, providing a basis for decision-makers to manage and optimize systems holistically through evaluations and analyses based on specific criteria [68,69]. This method has been extensively applied in various research fields, including the social economy, healthcare, tourism, and construction [70,71,72,73]. However, it has been less frequently utilized in the field of WLR research. The components of the WLR system interact with and constrain one another, evolving in a coordinated manner both temporally and spatially. With the advancement of society and the economy, mismatches and contradictions between water and land resources have become increasingly prominent. Optimizing the allocation of these resources is an effective strategy for resolving these contradictions in the development WLR. However, evaluating the trends in the coupled and coordinated development of WLR is a prerequisite for achieving this goal. The WLR system is a nonlinear, multi-layered, and complex holistic system. The CCD model can categorize it into two distinct systems: water resources and land resources. This framework allows for an investigation of the temporal and spatial variation characteristics between these resources. Furthermore, employing the calculated results of the CCD, the Tobit model can be utilized to analyze the factors influencing WLR in WJP. The findings of this research provide significant support for decision-making when aiming to enhance the compatibility of WLR and optimize their allocation in WJP. During the evaluation process, the calculation of the weights of the evaluation indicators is enhanced through the integration of subjective and objective methods, improving the standardization of these weights. However, the selection of indicators and their associated weights may differ significantly in research conducted by different scholars. This subjectivity in indicator selection can further influence the final coupling results, resulting in a lack of comparability among different research studies.

Additionally, the management of WLR is influenced by larger-scale factors, particularly in water resource management, where basin-scale factors exert a notable impact. Therefore, developing and promoting research methodologies that operate on a larger scale is essential. This approach will enable a more comprehensive assessment and the more comprehensive management of WLR, fostering their coupled and coordinated development, and ultimately achieving the sustainable development of these resources. The overarching goal is to optimize the allocation of WLR in WJP while improving the ecological environment.

6. Conclusions

This study selected 18 indicators (10 water resource indicators and 8 land resource indicators) and 5 variable indicators to analyze the spatio-temporal characteristics of WLR matching and the correlation between influencing factors and the matching degree of WLR in WJP from 2006 to 2020. The main conclusions are as follows:

(1)

In terms of temporal distribution characteristics, from 2006 to 2020, the comprehensive water and land resource index and CCD in WJP showed an upward trend. The specific analysis in this paper suggests that WJP ought to prioritize drought mitigation, ensure the rational distribution of water resources, and unlock the latent potential of its soil resources.

(2)

In terms of spatial distribution characteristics, the global spatial autocorrelation analysis indicated that the CCD from 2006 to 2020 exhibited a strong spatial correlation. The local spatial autocorrelation analysis indicated that the number of high–high-level coupling coordination areas increased. The center of gravity migration analysis indicated that from 2006 to 2020, the center of gravity of the CCD gradually shifted from the northeast–southwest direction to the northwest–southeast direction. Consequently, based on the center of gravity model, enhancing the rational planning of WLR in WJP is imperative.

(3)

From 2006 to 2020, the AAT, PD, and RR were all significant at the p < 0.01 level. The RR showed a significant positive correlation with the CCD, while the AAT and PD showed negative correlations. There was no linear correlation between the UR, WRUR, and the CCD. The reclamation rate is a key factor affecting the coordination of WLR in WJP. Therefore, it is essential to optimize land use and resource allocation in WJP in the future [60].