Construction and Application of a Groundwater Overload Evaluation System Based on the PSR Model

Abstract

Groundwater assessment has several technical deficiencies, especially because the decision-making platform for basic data collection, analysis, and evaluation has not been established. Based on the computer architecture theory and business processing logic, we designed a set of groundwater resource overload evaluation systems using the ArcGIS platform and Python language based on the comprehensive analysis of many research schemes. According to the pressure–state–response (PSR) theoretical programming model, the system pre-sets the evaluation index from three aspects: overexploitation, bearing capacity, and overload. The entropy weight method was used to determine the factor weight. The system was used to evaluate and analyze the groundwater overload in Xi’an, and the performance and accuracy of the system were also verified. The results showed that the serious overload area of groundwater in Xi’an was located in the alluvial fan area of Yanliang District, Gaoling District, and Huyi District, and the alluvial fan area of Lintong District, City Six Districts, and Zhouzhi County. The general overload area was located in the alluvial plain area of City Six Districts, Chang’an District, the flood plain and the first terrace of Huyi District, the loess tableland area of Zhouzhi County, and the alluvial fan area of Lantian County. The remaining areas were not overloaded. The evaluation results matched the study, which confirmed that the evaluation system was stable and that the results were reliable.

1. Introduction

In 2020, the Ministry of Water Resources of the People’s Republic of China released the list of the Yellow River water resource overload areas for the first time and proposed the concept of groundwater overload. Any overexploited water source can experience overload [1]. Groundwater overload indicates that a region’s water intake and water consumption have exceeded the carrying capacity of groundwater resources. Due to over-exploitation, groundwater cannot be replenished on time, the balance of extraction and replenishment cannot be achieved, and the groundwater level continues to decline [2]. After more than 10 years of rapid development, the research and evaluation of water resources have advanced further, which might provide stronger support for the improvement of national strength, environmental governance, improvement of the living standards of people, and economic progress in the new era [3,4,5,6,7,8].

Since the concept of groundwater overload was recently proposed in China, studies on the theory of groundwater resource overload are limited. Moreover, the evaluation of groundwater resources, which started in the 1990s, is mostly classified into sustainable development theory [9,10,11]. From the perspective of the study area, the evaluation of groundwater resources is concentrated in the main agricultural production areas, such as north China, northeast China, and northwest China. Agricultural production in these areas mainly depends on groundwater resources, and groundwater problems are relatively prominent [12,13,14,15]. The research methods mainly include the comprehensive index method, principal component analysis, analytic hierarchy process, fuzzy comprehensive method, gray correlation analysis method, matter element analysis method, TOPSIS method, projection pursuit clustering method, artificial neural network method, DRASTIC standard model, GIS spatial analysis, etc. [16,17,18,19,20,21]. However, the secondary development of computer software is less intense. The research mainly includes groundwater evolution and driving mechanism research, groundwater pollution, groundwater overexploitation evaluation, groundwater carrying capacity evaluation, groundwater vulnerability evaluation, etc. [22,23,24,25,26]. However, research on groundwater resource overload is scarce.

The limitation of groundwater resources determines the importance of evaluating groundwater resource overload. Many researchers have studied groundwater resources, but a complete computer evaluation system is missing. System design is a major step in the innovation and advancement of information technology management, which proposes practical requirements for designing and implementing the groundwater resource overload evaluation system [27]. Therefore, based on computer architecture and business processing logic technology, we designed a groundwater resource overload evaluation system using information technology. The system used ArcGIS and the Python language, based on the pressure–state–response (PSR) theoretical programming model and the pre-set evaluation indicators from the aspects of overexploitation, bearing capacity, and overload. The entropy weight method was used to determine the factor weight. The groundwater carrying capacity of the study area was calculated, and the overload level was divided. The system was used to evaluate and analyze the groundwater overload in Xi’an, and the performance and accuracy of the system were verified.

The design of the groundwater resource overload evaluation system based on ArcGIS not only evaluates the overload of groundwater resources but also takes a step forward for utilizing groundwater sustainably. It incorporates computer-based digital operations in the evaluation of groundwater resources, thus making the evaluation process smoother and more convenient. It improves users’ work efficiency, guarantees the quality of evaluation results, forms a complete information management platform, improves the management level of groundwater resources in the study area, and provides users with intelligent decision-making services. From a theoretical perspective, groundwater resource overload is a key issue of sustainable development. In this study, we examined the concepts and evaluation methods of groundwater resource overload, which is conducive to sustainable development. Along with multidisciplinary data, multi-level index factors were established for evaluating the complex and comprehensive system to determine the bearing capacity and the maximum bearing capacity of groundwater for human production and life, solve many problems in development, and thus, achieve sustainable development. From the perspective of discipline, system development can realize data integration, write the original data to the fixed storage location of the system, and call it directly for system use. This ensures the integrity of the data for system users and avoids abnormal data in the process of repeated storage. The development of the system becomes easy to manage. The design and implementation of a groundwater resource overload evaluation system can enrich groundwater evaluation using computer programs.

2. PSR Model Construction and Evaluation Method

2.1. PSR Model Construction Process

The pressure–state–response (PSR) model was established by the United Nations Economic Cooperation and Development Agency. It is often used to evaluate resource utilization and is a conceptual framework for reflecting sustainable development. The PSR model can be used to simplify and conceptualize the elements to elucidate the relationship between elements [28], as shown in Figure 1. Among them, pressure is used to reflect the direct effect of groundwater overexploitation on the target; status is used to reflect the current status or future trend of the target and its environment; response is used to reflect the response to pressure in the current situation.

After collecting and analyzing the data on the geological structure and groundwater recharge, runoff, and discharge conditions in the study area, the horizon, thickness, lithological characteristics, regional distribution, and groundwater type of the groundwater aquifer group were evaluated, and the groundwater recharge, discharge, extraction, and recoverability were determined. The results of the analysis revealed the dynamic characteristics of the groundwater level during the period of groundwater development and utilization. Based on the PSR model, the overexploitation of groundwater was used to represent the pressure (P) in the groundwater overload evaluation model. The groundwater in the study area was analyzed from three aspects of water level, water volume, and water balance based on the multi-year average decline rate of water level, the overexploitation coefficient, and the water balance method. Then, the pressure overexploitation of groundwater was classified into severe overexploitation, general overexploitation, and no exploitation using the ArcGIS spatial analysis method. Taking the groundwater carrying capacity state as the state (S) in the groundwater overload evaluation model, according to the main influencing factors of the groundwater state in the study area, the evaluation indicators of the groundwater carrying capacity were determined as follows: precipitation, burial depth of the phreatic water level, the permeability coefficient of the phreatic aquifer, the thickness of the submerged aquifer, the decline of the submerged water level, the permeability coefficient of the confined aquifer, the thickness of the confined aquifer, and the buried depth of the confined water level; thus, eight indicators were used to establish an evaluation index system for the carrying capacity of groundwater. The entropy weight method is a factor weight determination method. Based on the bearing capacity index, the bearing capacity of the study area was divided into three grades: high, medium, and low. The ArcGIS spatial analysis method evaluated the response (R) overload in the groundwater overload evaluation system by spatial superposition analysis of pressure overexploitation and the state-bearing capacity.

2.2. Evaluation Method

According to the “Guidelines for the Evaluation of Groundwater Overexploitation Areas (SL 286–2003)”, among the shallow groundwater overexploitation areas, fissure water overexploitation areas, and karst water overexploitation areas at all levels, if one of the following conditions is met, the area is considered to be a serious overexploitation area: ① the average annual groundwater overexploitation coefficient is greater than 0.3; ② the annual average groundwater level of pore water decreases at a rate greater than 1.0 m, and that of fissure water or karst water decreases at a rate greater than 1.5 m. The areas in the groundwater overexploitation zones not matching the above conditions shall be determined as general overexploitation zones [29].

(1) The continuous decline rate of the annual average groundwater level in the groundwater overexploitation area can be calculated according to the equation:

$$\mathrm{v}=\frac{{\mathrm{H}}_1-{\mathrm{H}}_2}{\mathrm{T}}$$

In the equation, v indicates the rate of continuous decline of the annual average groundwater level (m/a);

H₁ indicates the groundwater level at the beginning of the groundwater development and utilization period (m);

H₂ indicates the groundwater level at the end of the groundwater development and utilization period (m);

T indicates the number of years in the period of groundwater development and utilization (a).

(2) The annual average groundwater overexploitation coefficient in the groundwater overexploitation area can be calculated as follows:

$$\mathrm{k}=\frac{{\mathrm{Q}}_{\mathrm{e}}-{\mathrm{Q}}_{\mathrm{a}}}{{\mathrm{Q}}_{\mathrm{a}}}$$

In the equation, k indicates the annual average groundwater overexploitation coefficient;

${\mathrm{Q}}_{\mathrm{e}}$ indicates the annual average groundwater extraction volume during the period of groundwater development and utilization (10,000 m³);

${\mathrm{Q}}_{\mathrm{a}}$ indicates the annual average groundwater exploitable volume (10,000 m³) during the period of groundwater development and utilization.

(3) Water equilibrium method:

∆W = Q_recharge − Q_discharge

In the equation, ΔW indicates the difference in groundwater equilibrium;

Q_recharge indicates groundwater recharge;

Q_discharge indicates groundwater discharge.

(4) Entropy weight method: The entropy weight method is a classical algorithm for calculating the weight of an indicator. It refers to a mathematical method used to judge the degree of dispersion of an indicator. The greater the degree of dispersion, i.e., the greater the amount of information, the smaller the uncertainty and the smaller the entropy; similarly, the smaller the amount of information, the greater the uncertainty and the greater the entropy. According to entropy characteristics, we can judge the randomness and disorder degree of an event by calculating the entropy value. The entropy value can also be used to judge the discrete degree of an index. The greater the degree of dispersion of the index, the more significant the impact of the index on the comprehensive evaluation. The implementation steps are as follows:

Assuming that the dataset has n rows of records and m variables, the data can be represented by an n × m matrix A (n rows and m columns, that is, the number of n rows of records, m feature columns).

$$\mathrm{A}=\left[\begin{array}{ccc}{x}_1& \cdots & x_m\\ ⋮& \ddots & ⋮\\ x_n& \cdots & \end{array}\right]$$

b. Data normalization

$$x_{ij}=\frac{x_{ij}-min\left(x_j\right)}{max\left(x_j\right)-min\left(x_j\right)}$$

In the equation, $x_{ij}$ indicates the ith row and the jth column elements of matrix A;

$min\left(x_j\right)$ indicates the smallest element in the jth column of matrix A;

$max\left(x_j\right)$ indicates the smallest element in the jth column of matrix A.

c. Calculating the proportion of the ith record under the jth indicator

$$P_{ij} =\frac{x_{ij}}{{{\displaystyle \sum }}_1^n{x}_{ij}}(j=1,2,3,\dots ,m)$$

In the equation, $P_{ij}$ indicates the proportion of the ith record under indicator j;

$x_{ij}$ indicates the ith row and jth column element of matrix A.

d. Calculating the entropy value of the jth index

$$e_j=-[1/\ln \left(n\right)]\times {{\displaystyle \sum }}_1^n{P}_{ij}\times \log \left(P_{ij}\right)$$

In the equation, $e_j$ indicates the entropy value of the jth index;

$n$ indicates the number of rows of matrix A;

$P_{ij}$ indicates the proportion of the ith record under indicator j.

e. Calculating the difference coefficient of the jth index

$$g_j=1-e_j$$

In the equation, $g_j$ indicates the difference coefficient of index j;

$e_j$ indicates the entropy value of the jth index.

f. Calculating the weight of the jth indicator

$$W_j=\frac{g_j}{{{\displaystyle \sum }}_1^m{g}_j}$$

In the equation, $W_j$ indicates the weight of the jth indicator;

$g_j$ indicates the difference coefficient of index j;

$m$ indicates the number of columns of matrix A.

(5) ArcGIS spatial analysis method

After evaluating the pressure overexploitation and state-carrying capacity divisions, the divisions were uniformly classified and converted into raster data. Using the ArcGIS spatial analysis tools, the grid-weighted superposition operation was performed, and the results of the analysis of groundwater overload in Xi’an were obtained.

3. Overview of the Study Area

Xi’an City is located in the hinterland of the Guanzhong Plain, on both sides of the Wei River, in the middle of Qinchuan. It extends from 33°39′ N–34°45′ N and 107°40′ E–109°49′ E. Xi’an is 204 km in the east–west direction, and it has a maximum width of about 116 km in the north–south direction; it covers an area of 10,108 km². In a stepped structure, the terrain is high in the south and low in the north. From south to north, the geographical features that are present include the Qinling Mountains, loess hills and gully, loess plateau, alluvial–proluvial fan at the foot of the mountain, and the Weihe River and its tributaries that run through the plains (Figure 2). Xi’an has a warm, semi-arid, and semi-humid continental monsoon climate. The multi-year average temperature of the city is 13.8 °C, the multi-year average water surface evaporation is 1287.9 mm, and the annual average precipitation is 573.4 mm. The distribution of precipitation is uneven between years. The precipitation from May to September accounts for more than 60% of the annual precipitation. The precipitation difference between wet and dry years can be up to three times. Xi’an City is located in the middle of the Weihe fault depression basin, which has very thick Cenozoic loose continental strata that provide favorable conditions for the formation and storage of groundwater. Quaternary strata in the study area are widely distributed, thick, and water-rich. According to the geomorphology, groundwater depth, and hydraulic conditions, along with groundwater development and utilization, the water-bearing rock groups below the city’s surface can be divided into two water-bearing rock groups: phreatic water and phreatic confined water.

4. Results

4.1. Division of the Evaluation Areas

The plain area of Xi’an is divided into 29 sub-districts according to the administrative divisions and geomorphological features, which are used as the basic unit for evaluating groundwater overload. A representative point was selected in each area for evaluation, and the partitions are shown in Figure 3.

The name, number, and landform of each zone are shown in

Table 1. Groundwater overload assessment zoning in the study area.

Regions	Sub-Regions	Evaluation Area Number	Geomorphology of the Evaluation Area
Yanliang District	First terrace	1	First terrace
Gaoling District	First terrace	2	First terrace
Lintong District	Floodplain and first terrace	3	Floodplain and first terrace
	Second terrace	4	Second terrace
	Ma’e plateau	5	Loess plain
City Six Districts	Weibin water resource	6	Floodplain
	Floodplain	7	Floodplain
	Bahe water resource	8	First terrace
	Northern suburb	9	First terrace
	Near the western wall	10	Second terrace
	Southern suburb	11	Third terrace
	Shaoling plateau	12	Loess plain
	Madu Wang	13	First terrace
Liantian County	Third terrace	14	Third terrace
	Bailu plateau	15	Loess plain
Chang’an District	Shaoling plateau	16	Loess plain
	Diluvial fan	17	Diluvial fan
	Shenhe plateau	18	Loess plain
	Second or third terrace	19	Second or third terrace
	First terrace	20	First terrace
	Fengzaohe water resource	21	Floodplain
Huyi District	Floodplain	22	Floodplain
	First terrace	23	First terrace
	Second terrace	24	Second terrace
	Diluvial fan	25	Diluvial fan
Zhouzhi County	Floodplain	26	Floodplain
	First terrace	27	First terrace
	Lanmei plateau	28	Loess plain
	Diluvial fan	29	Diluvial fan

.

4.2. Groundwater Overload Evaluation System

(1)

Data loading

The diversity of data formats requires the system to have different methods to load the data for design and implementation. For groundwater overload assessment, the groundwater data must be in vector format, and the vector layers must be loaded for query, analysis, and other operations. The data file can be opened using the load shapefile function in the Data Load sub-function (Figure 4).

(2)

Selection and calculation of evaluation factors

The evaluation result and evaluation standard were directly related. Thus, the selection of evaluation factors directly affected the evaluation result during the evaluation. According to the PSR model, some indices were selected by the researchers based on their judgment in the groundwater overload evaluation system. To realize the intuitive expression of the data, we assigned values to the indices of each zone after selecting the indices. Then, the system provided the map mapping method for pressure overexploitation analysis, the entropy weight method for state-bearing capacity analysis, and the ArcGIS superposition analysis method for response overload analysis, as shown in Figure 5.

(3)

Map cartography settings

The cartography module included functions such as categorical symbolization, quantitative symbolization, map features, map grids, and map output. After mapping, it is necessary to set the scale, compass, legend, and other mapping elements. The system provided printing and output functions. Users could view page settings, print preview, paper settings, etc. The maps could also be exported to other formats. After comprehensive evaluation using this system, the evaluation results were presented as a map (Figure 6).

4.3. Pressure Overproduction Analysis

(1) Groundwater level: According to the “Guidelines for the Evaluation of Groundwater Overexploitation Areas (SL 286–2003)”, in this study, we divided the study area into three areas based on the three-year average water level decline rate. The average annual groundwater level continuous decline rate greater than 1.0 m indicated a seriously overexploited area. The average annual groundwater level continuous decline rate of less than 1.0 m but greater than 0 indicated a general overexploitation area. The annual average groundwater level continuous decline rate of less than 0 indicated a non-overexploited area. The results are shown in Figure 7.

As shown in Figure 7, the serious overexploitation area caused by the decline of water level only appeared near the monitoring hole 306 in Gaoling District, accounting for only 0.026% of the area evaluated. These results might be due to the influence of human factors (pumping activity) near the hole, resulting in abnormal monitoring values of the hole. Therefore, the evaluation results of the serious overexploitation area near the hole were ignored. The general overexploitation areas were mainly distributed in Yanliang District, Gaoling District, Lintong District, and most of the City Six Districts, accounting for 52.08% of the study area. The rest of the areas were not overexploited.

(2) Groundwater volume: According to the “Guidelines of the Evaluation of Groundwater Overexploitation Areas (SL 286–2003)”, the study area was divided into severe overexploitation areas (k ≥ 0.3), general overexploitation areas (0 ≤ k < 0.3), and no overexploitation areas (k < 0). The results are shown in Figure 8.

As shown in Figure 8, the areas with severe water overexploitation were mainly distributed in Yanliang District, Gaoling District, City Six Districts, Lintong District, and the Loess Plateau in Zhouzhi County. The general overexploitation areas were distributed in the alluvial–proluvial plain area of Chang’an District, and the remaining areas were not overexploited.

(3) Groundwater balance: The groundwater balance difference (ΔW) was used to represent the balance of groundwater recharge and discharge, where ΔW < 0 indicated a negative equilibrium area, and ΔW ≥ 0 indicated a positive equilibrium area. The zoning based on the groundwater equilibrium is shown in Figure 9.

The figure shows that the negative equilibrium area was distributed in the alluvial–proluvial plain areas of Yanliang District, Gaoling District, Huyi District, City Six Districts and Chang’an District. The rest of the regions were positive equilibrium areas.

(4) Comprehensive analysis of groundwater overexploitation

The ArcGIS spatial overlay analysis method comprehensively analyzed the groundwater level, water volume, and water balance division, and the groundwater in Xi’an was divided into severe overexploitation areas, general overexploitation areas, and non-overexploited areas. The final result of pressure overexploitation zoning is shown in Figure 10.

As shown in the figure, the areas with severe overexploitation of groundwater in Xi’an included Yanliang District, Gaoling District, City Six Districts, and the Loess Plateau in Lintong District. General overexploitation areas included Huyi District, Chang’an District, the alluvial plain area, and the loess plateau in Lantian County and Zhouzhi County. The remaining areas were not overexploited.

4.4. Evaluation of the State-Bearing Capacity

4.4.1. Selection of Indicators

The lithology of the stratum was analyzed, and the results indicated the hydrogeological conditions, recharge, runoff, and discharge conditions of the water-bearing rock group in the study area. The evaluation indicators of the groundwater carrying capacity were determined using eight indices, which included precipitation, the buried depth of the phreatic water level, the permeability coefficient of the phreatic aquifer, the thickness of the phreatic aquifer, the drop in the phreatic water level, the permeability coefficient of the confined aquifer, the thickness of the confined aquifer, and the buried depth of the confined water level.

The relationship between each index and groundwater carrying capacity was analyzed. Generally, the greater the amount of precipitation, the greater the recharge of diving and confined water, and the greater the carrying capacity of groundwater. The greater the permeability coefficient of the phreatic aquifer, the faster the water moves in the aquifer. The greater the thickness of the phreatic aquifer, the greater the water storage capacity of the aquifer. The smaller the drop in the phreatic water level, the greater the water volume of the existing phreatic aquifer. The larger the permeability coefficient of the confined aquifer, the faster the water moves in the aquifer. The greater the thickness of the confined aquifer, the greater the amount of water that can be stored in the confined aquifer. The lower the overload index, the greater the water storage.

4.4.2. Calculating Weights

After selecting the indicators, the weight of each indicator in the evaluation was calculated. The entropy weight method is a weight calculation method that considers the mathematical characteristics through mathematical formulae. The system used this method to calculate the weight of each indicator selected by the user. Calculations were performed to provide users with each indicator’s entropy weight and entropy value and a basis for the scientific evaluation of indicators.

4.4.3. Evaluation Results

Based on the classification, the groundwater overload evaluation system was used to draw a map, and a hierarchical display map of the groundwater carrying capacity of Xi’an was obtained (Figure 11).

As shown in the figure, the overall trend of groundwater in the plain area of Xi’an extends from the north to the south. The groundwater decreased from the floodplain, first terrace, second terrace, third terrace, and alluvial fan to the loess platform source. The abundance of water decreased, and the water supply became more challenging.

We found that 11 areas had high bearing capacity, which corresponded to the representative points 3, 6, 7, 8, 9, 10, 14, 20, 21, 22, and 26; all points were located in the floodplains or first terraces. These areas with abundant groundwater in Xi‘an have good groundwater recharge conditions and strong water abundance. Eight areas with medium bearing capacity were located in Huyi Qu, Chang’an District, City Six Districts, Lintong District, Yanliang District, and Gaoling District. The representative points were 1, 2, 4, 11, 17, 19, 23, and 27. Various evaluation indicators, such as water richness, exploitation volume, etc., were in the middle level in these evaluation areas. There were 10 areas with low bearing capacity located in Zhouzhi County, Huyi District, Chang’an District, Lintong District, City Six Districts, and the south plain district of Lantian County. The representative points were 5, 12, 13, 15, 16, 18, 24, 25, 28, and 29. The submerged water level in these areas was deep, the thickness of the confined aquifer was small, and the permeability coefficient of the submerged aquifer was smaller than that of the confined aquifer.

4.5. Response Overload Evaluation Results

Based on the groundwater pressure and state evaluation results, the dominant factor analysis and the ArcGIS spatial overlay analysis were performed to assess the groundwater overload state. The spatial analysis function of ArcGIS was applied to calculate the grids between layers according to the corresponding weights of these partition maps. The groundwater overloading partition map of the study area was obtained, as shown in Figure 12.

The analysis showed that the groundwater overload evaluation results matched the actual exploitation situation. Due to the lack of data on individual sub-regions, the representative points of sub-regions were poorly represented, resulting in differences in the evaluated results, which might be improved by selecting more representative points. According to the evaluation results, the severely overloaded groundwater areas in Xi’an were located in the pluvial sector of Yanliang District, Gaoling District, and Huyi District, and the pluvial sector of Lintong District, City Six Districts, and Zhouzhi County. The representative points were 2, 5, 11, 12, 13, 24, 25, and 28. Except for Yanliang District and Gaoling District, which were located in the first terrace areas, the rest were located in the alluvial fan and loess plateau areas. Due to the large buried depth of the phreatic water level, rapid economic development, and population agglomeration, pressure overexploitation indices suggested serious overexploitation, and the indices of state-bearing capacity were in the low-to-medium level in the evaluated area. These findings suggested that restrictions should be placed on groundwater exploitation in these areas.

The general overload areas were located in the alluvial–proluvial plain area of City Six Districts and Chang’an District, the floodplain and first terraces of Huyi District, the Loess Plateau of Zhouzhi County, and the alluvial sector of Lantian County. The representative points were 6, 7, 8, 9, 10, 15, 16, 18, 19, 20, 21, 22, 23, and 29. Although the area was located in the floodplain and the first and second-class terraces, due to the rapid economic development, population concentration, and poor supply conditions, the indicators of pressure overexploitation suggested general-to-serious overexploitation, and the indicators of state-bearing capacity were at a medium–high level in the evaluated area. Thus, better planning is required for utilizing groundwater in these areas.

5. Discussion

With an increase in water shortage, ways to strengthen the sustainable utilization of water sources, especially groundwater, and the techniques to evaluate and restore groundwater overload are the key problems that need to be solved urgently [30]. With the rapid development of computer software technology, the original data processing methods cannot meet the actual needs. Object-oriented data processing and service functions based on computer programming might be used as a new efficient working mode [31]. In this study, we developed an independent computer groundwater evaluation system and integrated complex workflow and tedious work steps into a desktop system, which could solve the problem of insufficient knowledge for non-professionals and contribute to the improvement of the government’s work efficiency and relevant departments.

Xi’an is one of China’s 40 serious water shortage cities; specifically, it is a resource-based water shortage city [32]. The system constructed in this study was used to evaluate and analyze the groundwater in Xi’an. The evaluation results showed that the seriously overloaded area of groundwater in Xi’an was located in the pluvial fan area of Yanliang District, Gaoling District, Huyi District, Lintong District, City Six District, and Zhouzhi County. These areas received groundwater from the Weibin Groundwater Source, Northwest Suburb Groundwater Source, Bahe Groundwater Source, and Fengzao River Groundwater Source. These areas are well-developed with substantial human activity. Rapid economic development, population agglomeration, and other reasons have significantly increased groundwater exploitation. The overexploitation of groundwater has caused a rapid decline in the groundwater level; i.e., the water level is deep, and groundwater recharge is insufficient, which has led to severe groundwater overload in these areas. We found that the general overload area was located in the alluvial plain area of City Six Districts, Chang’an District, the flood plain, the first terrace of Huyi District, the loess tableland area of Zhouzhi County, and the alluvial fan area of Lantian County. Most of these areas are the distribution area of agricultural irrigation production wells, and a small number of self-provided wells by enterprises and institutions are also present. Due to the high mining intensity of shallow confined water, the groundwater head is decreasing yearly, forming a general overload area.

The evaluation results of this study were similar to the actual situation, which confirmed the performance and accuracy of the system. The development of the system can enrich the functions of computer software and also promote the application of the system in the field of resource exploitation. The proper utilization of each module function and programming design can provide functional design ideas to software programmers in related fields, clarify business logic for computer software programmers, and provide users with the use principle. It is also the key support for the design and implementation of this software. Our study might contribute to the comprehensive evaluation of large-scale resource exploitation.

6. Conclusions

Based on ArcGIS analysis, we designed and implemented a set of evaluation systems for groundwater overload, including designing a functional system and constructing a database. The PSR model was constructed using this system, and the analysis and evaluation of groundwater pressure overexploitation, state-bearing capacity, and response overload were conducted in Xi’an.

Regarding research methods, a complete groundwater overload evaluation system with sub-module operation and systematic integration was designed. All data and systems were built on the ArcGIS platform; the Python language was used to construct the system. GIS methods were used to mine the data. The GIS spatial analysis functions were integrated into the system, and an evaluation system was developed that integrated analysis and visualization of spatial data.

By verifying the system function, we analyzed the groundwater overload in Xi’an. The results showed that the serious overload area of groundwater in Xi’an was located in the alluvial fan area of Yanliang District, Gaoling District, and Huyi District, and the alluvial fan area of Lintong District, City Six District, and Zhouzhi County. The general overload area was located in the alluvial plain area of City Six Districts, Chang’an District, the flood plain, the first terrace of Huyi District, the loess tableland area of Zhouzhi County, and the alluvial fan area of Lantian County. The remaining areas were not overloaded. Based on these findings, we suggested groundwater exploitation strategies for groundwater zoning.