Vulnerability assessment of water resources in a karst mountainous area based on GIS/RS technology: a case study of Guiyang, Southwest China

Rapid development of the social economy has led to the remarkable expansion of urban boundaries and population, as well as deterioration of the natural environment, resulting in a series of problems such as a shortage of regional water resources and a decline in aquatic ecosystem quality (Zhang et al. 2009; Anandhi & Kannan 2018). Factors such as changes in the eco-environment and human activities have intensified the vulnerability of regional water resources, resulting in challenges to sustainable regional development (Li & Li 2012; Boruff et al. 2018). Thus, groundwater and water resource vulnerability has attracted considerable attention and become a key issue since Albinet and Margat first raised concerns regarding it in 1970 (Albinet & Margat 1970).

Since the inception of the ‘International Conference on Soil and Groundwater Vulnerability’ in 1987, scholars around the world have analyzed the vulnerability of water resources. For example, Brouwer and Falkenmark selected indicators to evaluate water resource vulnerability from the perspective of the regional water supply and demand balance (Brouwer & Falkenmark 1989). Muhammetoğlu et al. (2002) selected nine monitoring station indicators to evaluate the groundwater vulnerability of plains as a result of agricultural activities (Muhammetoğlu et al. 2002). Moreover, Rahman (2008) used GIS technology and a DRASTIC (Depth to water, Recharge, Aquifer media, Soil media, Topography, Impact of the vadose zone, Hydraulic conductivity) model to evaluate the vulnerability of regional water resources on the city scale. The results showed that more than 80% of water resources in the region considered were at a medium level or above (Rahman 2008).

Research on water resource vulnerability in China began in the 1990s (Qin et al. 2020). For example, Tang et al. (2000) discussed the concept, content, and technical methods implemented for water resource vulnerability assessment from the perspective of climate change. Ma & Gao (2003) selected related stress factors to evaluate the vulnerability of water resources in the southern margin of the Tarim Basin, revealing that most regions are in a vulnerable state. Furthermore, Zou et al. (2014) used the Hengyang Basin as an example, and combined GIS technology and the influence of human activities to analyze the vulnerability of regional water resource systems from a joint perspective.

Most of the above studies were conducted on watersheds, plains, basins, and their surroundings, which exhibited various degrees of limitations (Sun et al. 2016; Gao et al. 2019; Gui et al. 2021). There are relatively few studies on mountainous areas with complex climate and topography. As such, it is difficult to apply the current methods and indexes of assessment to karst areas. Karst regions exhibit peak-cluster depressions, peak-forest basins, with different shapes and sizes on the surface, and various types of dissolution gaps with underground karst caves, which form super-large natural reservoirs (Jones et al. 2019). This provides a large area for the stagnation and leakage of surface water resources, resulting in extreme difficulty in storing water on the surface. Conventional assessment methods and models do not consider factors such as surface water leakage, terrain fragmentation, shallow soil, and water intake. Moreover, most studies incorporate administrative divisions as the minimum assessment unit, and each administrative division unit consists of only one vulnerability result; this provides approximate results that cannot accurately reflect the spatial distribution of water resource vulnerability (Yao et al. 2020).

Therefore, grid-scale water resource vulnerability assessments are required to provide a more effective spatial analysis for regional water resource development and use. Although it is easy to obtain measured and statistical data in micro-regional water resource surveys and evaluations, the factors influencing water resource vulnerability in karst areas are complex and diverse. Moreover, there are currently no statistical or observational data on the vulnerability of water resources in karst areas. Furthermore, the traditional data analysis used to establish the assessment model cannot accurately reflect the spatial distribution and intrinsic nonlinear properties and laws of vulnerability (Zhou et al. 2007). Therefore, a new method based on geographical information system (GIS) and remote sensing (RS) technology is proposed in this study.

This study focuses on Guiyang City as an example to quantitatively evaluate grid-scale water resource vulnerability, with the help of GIS and RS technology. The aim of this assessment is to enrich research on urban water resources in karst mountainous cities and provide a decision-making reference for regional water resource system management, as well as the sustainable and rational use of regional water resources in similar regions.

Guiyang City is located in the centre of Guizhou Province, spanning the watershed zone between the Yangtze River Basin and the Pearl River Basin in China. Its geographical position lies between 106° 07′–107° 17′ N and 26° 11′–26° 55′ E. The dominant landform is inland karst mountains, with rugged and broken surfaces and significant vertical differences as well as an average altitude of approximately 1,100 m. It has a subtropical humid monsoon climate, with an average annual temperature of 14–16 °C and annual average precipitation of 1,129 mm.

Data sources include the following: (1) The normalized difference vegetation index (NDVI). The data were calculated using Landsat 8-OLI satellite images, with a spatial resolution of 30 m. (2) Meteorological data, including annual average rainfall, average temperature, and hours of sunshine. (3) DEM (SRTM) data, with a spatial resolution of 30 m. (4) Soil type data derived from the 1:100,0000 soil database in China. (5) Spatial distribution data of rocky desertification in the study area from 2010. (6) Night light data from Luojia No.1 (LJ1-01) in 2019, with a spatial resolution of 130 m. (7) Land-use data with six categories: forest land, cultivated land, construction land, grassland, water areas, industrial and mining residential land, with 25 secondary classifications. The entire data were clipped using the vector boundary of the study area, and uniformly converted into a WGS84/Albers Equal Area Conic projection for subsequent spatial calculation.

In this study, GIS technology in combination with RS technology and the analytic hierarchy process (AHP) were used to study the vulnerability of regional water resources. AHP can effectively analyze and evaluate the non-sequential relationship between the target factors at different levels (Zhuang et al. 2003), which can generate practical and operable criteria for assessing the vulnerability of karst water resources. RS technology quickly and accurately acquires large-scale spatial environment data. Moreover, GIS technology achieves powerful spatial analysis and processing, and can deal with non-uniformity of the assessment index in space and time. Thus, this method has a great advantage in data processing.

Based on the actual situation of the study area, the characteristics of GIS technology, and the availability of data, 10 indicators were selected from the three aspects of natural vulnerability, burdening vulnerability, and human vulnerability of water resources (Nguyen et al. 2020). These indicators were used to construct a vulnerability assessment indicator system of water resources in Guiyang City, according to the principles of comprehensiveness, scientific validity, and feasibility. The assessment indicators include positive and negative indicators; positive indicators have a positive correlation with the vulnerability of water resources, whereas negative indicators have a negative correlation with the vulnerability of water resources. A comprehensive assessment of water resource vulnerability in Guiyang City was conducted by combining qualitative and quantitative methods.

Precipitation collects on the surface through confluence. Under the action of dissolution, erosion, and scouring of surface water, fissures and pores in the rocks become larger and more extensive; thus, the permeability of the rocks increases (Fang et al. 1993). The more significant the karst action, the greater the groundwater circulation. For example, in underground rivers and karst caves, serious infiltration of surface water (which is difficult to store) reduces the availability of surface water resources. However, the development and utilization of groundwater is challenging, resulting in serious water shortages in the region along with high vulnerability of carbonatite (a type of igneous rock which has a mineralogical composition of greater than 50% carbonate) mineral distribution areas. Thus, water resource vulnerability is highest in carbonate areas, moderate in mixed carbonate and non-carbonate areas, and relatively lower in non-carbonate areas. The karst distribution in the study area is shown in Figure 1.

Soil is the direct carrier of surface water and the main facilitator of water storage. A change in soil moisture is affected by many factors. In addition to being affected by precipitation, temperature, and cultivated crops, the water storage capacity of soils is also influenced by their physical and chemical properties, such as texture and organic matter content. In addition, the spatial difference of soil water storage capacity is large in karst mountain areas due to factors such as steep slopes and poor soil quality. The soil water storage capacity affects the potential storage capacity of water resources. Based on the 1:1 million soil database in China, this study compiled the soil records and research results of Zhou et al. (2007) and He et al. (2001), assigned values according to the water storage capacity of different soil types, and finally obtained the spatial distribution of soil water storage capacity in the study area, as shown in Figure 2(a).

Theoretically, water resources located closer to the surface are easier to extract. Considering factors such as high topographic relief, steep slopes, broken terrain, and other factors, the lakes, rivers, and reservoirs have been incorporated from the current land-use data with a 500 m buffer area. Thus, the shorter the straight-line distance from the buffer area, the higher the water resource availability indicator. Conversely, the farther the distance from the 500 m buffer zone, the more difficult it is to access the water and the lower the water resource availability indicator. Using the ArcGIS spatial analysis function, the linear distance between each raster point outside the buffer zone and the water source in the buffer zone of 500 m was calculated after converting the data into raster format. The data space was standardized to construct a spatial distribution map of the water resource availability indicator for the study area (Figure 2(d)).

An increase in rocky desertification will lead to fewer water resources and greater vulnerability of the water resources. Based on the investigation of rocky desertification data, its intensity was determined according to the degree of its dependence on water resources in the non-karst area. Thus, non-rocky, potentially rocky, lightly rocky, moderately rocky, severely rocky, and intensely rocky desertification areas were assigned the values of 0.1, 0.2, 0.3, 0.5, 0.7, 0.9, and 1, respectively. The higher the score, the higher the vulnerability of water resources. The spatial distribution of the rocky desertification vulnerability indicator in the study area is shown in Figure 2(e).

The change in land-use structure is an important factor affecting the regional eco-system, the circulation of material and energy, and the vulnerability of water resources. It is also the main driver of short-term regional hydrological changes. To a certain extent, it destroys the natural pattern and evolution process of regional primitive water resource circulation, causing a change in the spatial distribution of regional water resource quantity and water quality. Spatial change is one of the main factors influencing water resource vulnerability and also exerts serious pressure on freshwater systems (Li et al. 2002; Abera et al. 2019). Due to the increased demand of different land-use types for water resources, as well as the characteristics of land-use types in the study area (Figure 2(f)), the water resource vulnerability weight of different land-use types was determined in combination with expert advice. The weight values are listed in Table 1.

Human interference with water resources has always been a key research topic in many disciplines, such as hydrology, geography, and ecology (Wang et al. 2006; Lin et al. 2009). The higher the population density, the stronger the intensity of human activity and the greater the demand and vulnerability associated with water resources. In terms of spatial distribution, population density is the most intuitive manifestation of social and economic activities and urban expansion of humans. A previous study found that night-time light data are closely related to human activities and has been widely used in various fields of study related to human activities (Pan & Li 2016; Wang et al. 2019; Zhong & Liu 2019). In this study, the night-time light data of the Luojia-1 satellite (LJ1-01) from 2019 were used as the data source to simulate the intensity of human activities (Figure 5).

To facilitate the use of each indicator in the calculation of results, 10 assessment factors were initially rasterized by the GIS spatial analysis function. Each raster unit forms a basic spatial calculation unit, and each indicator data standard was spatially normalized. In the normalization process, the forward indicator is normalized in the forward direction, and the reverse indicator is normalized in the reverse direction. The specific standardization formula is as follows:

According to Tables 3 and 4, and Figure 6, the study area exhibits characteristics of good water resources in the northern and middle areas, and poor water resources in the southern area. Thus, there is a clear spatial variation in water resource vulnerability. Within the study area, mildly vulnerable areas account for the largest proportion, followed by moderately vulnerable areas, non-vulnerable areas, and severely vulnerable areas. However, extremely vulnerable areas represent the smallest proportion. These areas are described in more detail below.

Non-vulnerable areas: These areas have a water resource vulnerability indicator value between 0.12 and 0.22 and account for an area of 1,474.48 km². This region has the lowest vulnerability to water resources and is mainly distributed in the southern Huaxi District and the surrounding areas of Hongfeng Lake and Baihua Lake in Qingzhen City, with the rest distributed in Wudang District and Kaiyang County. The areas of Huaxi District and Wudang District, which are not vulnerable, account for 52 and 33% of the total land area of their respective districts and counties. Among each district and county, Huaxi District and Qingzhen City have the largest proportion of non-vulnerable areas, accounting for over 300 km² and 55.91% of the total non-vulnerable area in the study area. These non-vulnerable areas are rich in water resources, with relatively flat terrain, dense vegetation coverage, strong water conservation capacity, rich precipitation, and low water demand. The major component of water is used for agricultural irrigation, with strong water resources guaranteeing adequate capacity and sufficient water resources per capita.

Mildly vulnerable areas: Areas with a water resource vulnerability indicator value between 0.22 and 0.26 represent 2,880.49 km² and are predominantly distributed in the Wudang District, Xiuwen County, Kaiyang County, and Qingzhen City (three counties and one city), where the area of mildly vulnerable areas is above 300 km². The total area of mildly vulnerable areas in this region accounts for 73.15% of the total area of mildly vulnerable locations in the entire study area and 55.75% of the total land area of the three counties and one city. Kaiyang County has the largest area, comprising 790.10 km², and Yunyan District and Nanming District exhibit the smallest areas of 20.79 km² and 69.23 km² respectively, with other districts and counties being above 100 km².

Moderately vulnerable areas: These areas have a water resource vulnerability indicator value between 0.26 and 0.31 and represent an area of 2,329.45 km². They are mainly distributed in three counties and one city, in the study area (Xifeng County, Kaiyang County, Xiuwen County, and Qingzhen City). The moderately vulnerable areas in these locations represent 758.08 km², 434.97 km², 371.01 km², and 358.84 km², respectively, accounting for 37.44, 41.99, 34.62, and 26.00% of the district and county area, respectively. Less than 50 km² each of Yunyan District and Nanming District are moderately vulnerable, i.e., 15.06 km² and 34.58 km², respectively. The spatial distribution of this category is relatively scattered, with moderately vulnerable areas distributed in the areas surrounding cities and towns. The water demand is predominantly for agricultural and industrial use.

Severely vulnerable areas: These areas have a water resource vulnerability indicator value between 0.31 and 0.37 and comprise 1,092.87 km² of the study area. They are mainly distributed in Liuchang Miao Township in the west of Qingzhen City, Liuguang, Dashi, and Liutong Township in the northwest of Xiuwen County, Jiuzhuang Town, Xiaozhaiba Town, and Wenquan Town in Xifeng County, and Fengsan Town, Machang Town, and Zhaiji Township in Kaiyang County. The severely vulnerable parts of these areas account for 84.98% of the total severely vulnerable area throughout the study area. Xifeng County and Kaiyang County have the largest severely vulnerable areas, reaching 351.48 km² and 265.33 km², respectively, accounting for 32.16 and 24.28% of the severely vulnerable areas in Guiyang City, whereas Yunyan District and Wudang District have the smallest areas, at only 18.33 km² and 18.79 km², respectively. As the regional surface fluctuates greatly, karst action is strong, water resources are scarce, the guaranteed capacity is poor, the consumption capacity is large, and the storage capacity is poor.

Extremely vulnerable areas: These areas have a water resource vulnerability indicator value between 0.37 and 0.59, and an area of 256.71 km². They are mainly distributed in the Nanming District, Yunyan District, Guanshanhu District, Baiyun District, and Yanglongsi Town of Xifeng County in Guiyang City. Among these districts and towns, the total extremely vulnerable area, in the four districts of the main city (Nanming District, Yunyan District, Guanshanhu District, and Baiyun District), is 119.51 km², accounting for 46.55% of the total extremely vulnerable area. Thus, the pressure on water resources in urban areas is significantly higher than the average level of the study area. Secondary and tertiary industries in the main urban area and its surroundings are relatively developed with highly intensive human activities. In 2018, the total GDP in the four districts of the main urban area accounted for 53.92% of the total GDP in the whole city. The total population accounted for 47.18% of the total population in Guiyang City, and the population density was 2,896.97 people/km², far higher than the average level of 606.95 people/km² for Guiyang City. A large amount of productive and domestic water resulted in huge water consumption, resulting in extremely vulnerable water resources.

The empirical study shows that it is feasible to assess the vulnerability of water resources on a grid scale. Moreover, this study achieved an effective expression of the spatial distribution of regional water resource vulnerability. The main characteristics of the spatial variability of regional water resource vulnerability in the study area are as follows:

• (1)

  The high vulnerability of water resources in areas with intensive human activities is mainly due to the influx of a large number of people from districts, counties, villages, and rural areas since the 21st century, with an obvious acceleration of urbanization and a highly concentrated urban population. Population agglomeration forces large areas of newly constructed land to expand into areas surrounding the city and changes the original land-use/cover structure of the region. With the development of the social economy, both water consumption by tertiary industries and domestic use increases rapidly, whereas the total amount of regional water resources remains relatively constant. This leads to a very severe situation for regional water resources.

• (2)

  Under constant rainfall conditions, rocks are continuously weathered under the action of rainwater dissolution. Geomorphic features such as karst caves, underground rivers, and dissolved crevices are formed, and the soil in rocky desertification areas turn barren. The soil types are mainly mountain red soil, yellow soil, and lime soil. Loose and porous sandy loam evaporates vigorously, resulting in a poor regional water holding capacity, high precipitation infiltration intensity with infiltration rates greater than 95%. Most of the precipitation is infiltrated underground. Groundwater is difficult to extract where slopes exceed angles of 25° in mountainous areas. Under the same precipitation conditions, slope runoff is a direct source of water flow. The higher the slope, the greater the concentration of rainfall, the faster the down flow speed, and shorter the time of confluence on the slope. In addition to the small amounts of water stored in soil and vegetation, a large amount of running water flows out through gravitational potential energy, formed by the slope, and flows into rivers and lakes, resulting in a large total amount of regional water resources. This results in a small amount of available water resources per capita, and complex water utilization, which are the main factors for large spatial differences in water resource vulnerability in the study area.

Most traditional vulnerability assessment methods are based on administrative divisions, regardless of their size, generating single assessment results. Therefore, the results do not reflect the spatial difference in regional water resource vulnerability. By contrast, this study uses GIS spatial analysis to quantitatively study the vulnerability of water resources in Guiyang City on a grid scale from three aspects: natural, environmental, and man-made. Therefore, the assessment results are more detailed and accurate than traditional assessment results based on administrative regions.

The assessment results reflect better characteristics and spatial differences in water resource vulnerability than those obtained using traditional assessment methods, thereby providing a theoretical basis and decision support for the rational utilization and scientific planning of regional water resources. The key findings are as follows:

• (1)

  The water resource vulnerability index of Guiyang ranged from 0.10 to 0.59, and predominantly belonged to the mildly vulnerable category, which accounted for 35.85% of the study area, followed by moderately vulnerable (28.99%), non-vulnerable (18.35%), severely vulnerable (3.60%), and extremely vulnerable (3.20%). Among the districts and counties, four areas (Yunyan District, Xiuwen County, Kaiyang County, and Xifeng County) exhibited moderate vulnerability or above (moderate + severe + extreme) in more than 50% of the total land area.

• (2)

  The spatial distribution of water resources and water resource vulnerability in Guiyang is highly uneven; there is a substantial difference between the northern and the southern areas. The major vulnerable urban areas are the north of Kaiyang County, the north of Xifeng County, the north of Xiuwen County, the west of Qingzhen City, and areas surrounding these main urban areas. The distribution of productive forces and population does not match the distribution of water resources. Therefore, water supply and demand are extremely unbalanced, and the influence of human activities is the major factor leading to high vulnerability of water resources. Other highly vulnerable areas are mainly caused by natural factors such as a large number of slopes, difficulty of water intake and conservation, and other harsh natural conditions. The south and centre of Wudang districts in Kaiyang County, the north of Baiyun District, the east of Qingzhen City, and the south of Huaxi District possess low water resource vulnerability, low population density, and superior natural environments, with water resources being predominantly used for agriculture.

• (3)

  The influencing factors of water resource vulnerability in different regions are variable. The main factors affecting the water resource vulnerability in the central part of the study area are ranked as follows: the human activity intensity indicator (population density or water demand) has the strongest impact, followed by the water resource availability index, followed by the land use type index, and the other factors index has the weakest influence. In the north of the study area such as Xifeng and Kaiyang County, the main influencing factors are ranked as follows: the first is water resource availability, the second is the soil water storage capacity indicator, the third is the average annual precipitation, and the last is the other factors. In the south of the study area such as Huaxi area, water resource availability dominates. It is followed by average annual precipitation and human activity intensity. The other factors have the weakest impact. In brief, human activity intensity (population density) and water resource availability have a greater influence on regional water resource vulnerability.

Compared with traditional methods, the results of this study are more detailed and accurate. However, due to the limited availability of data, some relevant assessment indicators have been omitted. Moreover, in terms of the spatial distribution characteristics of water resource vulnerability, the present study did not involve an analysis of the spatial-temporal evolution or a quantitative analysis of the factors influencing the evolution of water resource vulnerability in the study area. Therefore, future research should attempt to obtain high-precision data, optimize the determination of assessment indicators and weight methods, and verify the results of this study.

This research was funded by the Basic Research Program of Guizhou Province (Qiankehe Foundation [2018]1418), the Guizhou Provincial Science and Technology Projects (Qiankehe support [2018]2806) analysis and interpretation of data and the Youth Fund of the Guizhou Academy of Sciences (Grant number [J] [2021]25).

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