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Article

Spatio-Temporal Evolution of the Ecological Environment in a Typical Semi-Arid Region of Northeast China

by
Achivir Stella Yawe
1,2,3,4,
Changlai Xiao
1,2,3,
Oluwafemi Adewole Adeyeye
4,5,
Mingjun Liu
1,2,3,
Xiaoya Feng
1,2,3 and
Xiujuan Liang
1,2,3,*
1
Key Laboratory of Groundwater Resources and Environment, Jilin University, Ministry of Education, Changchun 130021, China
2
National-Local Joint Engineering Laboratory of In-Situ Conversion, Drilling and Exploitation Technology for Oil Shale, Changchun 130021, China
3
College of New Energy and Environment, Jilin University, Changchun 130021, China
4
College of Resources and Environment, Southwest University, Chongqing 400700, China
5
Global Geosolutionz, Typesetters Biz Complex, Department of Geology, Ahmadu Bello University, Zaria 810107, Nigeria
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(1), 471; https://doi.org/10.3390/su15010471
Submission received: 17 November 2022 / Revised: 21 December 2022 / Accepted: 23 December 2022 / Published: 27 December 2022
Browse Figures
Versions Notes

Abstract

:
Increasing trends of groundwater and soil salinization, as well as desertification, is characteristic of many arid and semi-arid regions under climatic and anthropogenic influences. This has led to the implementation of management strategies to protect the ecological environment. Changling County in Northeast China is a typical semi-arid area that has experienced these changes. Thus, management strategies such as the “Three North Shelterbelt Project” which involves planting trees to reduce wind speed and halt desertification, and the Changling local alkaline land restoration project, from the year 2000, involving fencing of grasslands have been implemented in the area. Premised on the dynamic nature of the ecological environmental problems, this study was undertaken to assess the spatio-temporal evolution of the ecological environment using hydro-geochemical, spatial, remote sensing, and statistical techniques from the year 2001 to 2019. It was found that groundwater salinity was stable within the period due to groundwater exploitation that declined depth to groundwater table (DWT) thus reducing the impact of evaporation concentration of salts in groundwater. Salinized land area increased by about 6706 ha at a rate of 0.06%/year as a result of the reduction in the size of water bodies and swampland as the declining water table exposed shallow water to more evaporation. The effect of the conversion of water bodies and swamplands to salinized land is believed to overshadow the climatic influence of decreased evaporation-precipitation ratio that normally decreases soil salinization. Most of the study area was stable in terms of desertification (98.22%, 56,3497 ha) as significantly degraded lands covered only 0.03% (148 ha) of the area while 1.67% (9556 ha) had significantly increased vegetation, respectively. Precipitation had an insignificant relationship with desertification with irrigation believed to be the main driver of significant vegetation improvement. Water-saving irrigation practices and the growing of salt-tolerant or semi-tolerant crop species are recommended to maximize food production while stemming the environmental degradation trend due to declining DWT.

1. Introduction

Sustainable Development Goals (SDGs) of the United Nations such as SDG2 (Zero Hunger), SDG6 (clean water), SDG1 (sustainable cities and communities), and SDG15 (Life on Land) among others are dependent on proper management of the ecological environment. Maintenance of pristine groundwater quality is important for sustainable development [1]. Groundwater may evolve spatially and temporally due to climate change, deterioration of the ecological environment, groundwater overexploitation, or intense agricultural activities [2]. Groundwater salinization which refers to an increase in the overall mineralization of groundwater in comparison to background levels is one of the most important reasons for deteriorating groundwater quality [1,3,4,5]. It is a common challenge to groundwater resources in different world regions [6,7,8,9,10]. The causes of groundwater salinization are either natural or anthropogenic. Anthropogenic sources include large-scale irrigation, brine discharge, and over-exploitation of groundwater in coastal areas [11,12,13]. On the other hand, natural sources and inducers include intense evaporation, transpiration, mixing with deposited brine, and mineral dissolution [12,13].
Closely related to groundwater salinization in arid and semi-arid regions is soil salinization which often serves as a source of salt enrichment to groundwater depending mainly on precipitation, evapotranspiration, irrigation, and depth to water table [14,15]. The soil salinization problem is worsening globally including in China [16]. Salinized land accounts for 4.88% of China’s available land base appearing mainly in the arid Northwest to semi-arid North and Northeast [17]. The spatio-temporal variation of soil salinization indicates a decrease in some areas [18,19,20] and an increase in others [21]. On a hydrological basin scale, as observed by most studies, soil salinization increases downstream and vice-versa [22,23,24,25,26]. Thus, to manage soil salinity, remote sensing approaches to studying soil salinity are widely applied [20,27,28,29] together with classical sampling and analysis techniques.
Soil salinization is also a chemical soil degradation process that leads to desertification and is typical in China [22,30,31]. Desertification generally means the degradation of land in arid, semi-arid, and dry sub-humid areas primarily in response to anthropogenic and climatic factors [30,32]. Desertification is a type of land degradation in which a relatively dry area of land becomes a desert, losing its water bodies, vegetation and wildlife [33]. Desertification, which causes economic losses, increased health/safety hazards, decreased agricultural productivity and herbage yield, and deterioration of the ecological environment has been estimated to cause up to $3 billion worth of loss in China [34,35,36]. Due to the challenges caused by desertification, it is estimated that the Chinese government spends up to $2 billion annually on managing desertification [35].
Changling county, located within the Songnen Plain of North-eastern China is part of the most significant black soil regions globally and is known for agricultural grain cultivation and livestock farming [37]. It is affected by the salinization of groundwater and soil, as well as desertification [38,39]. For instance, Shi et al. using a cellular automata (CA) model found salinized land to increase within the area from 1986 to 2010 [18]. Similarly, Fang et al. found that sandy desertification in Changling had increased by approximately 7800 hectares from 1986 to 2008 [35]. Overgrazing associated with increased animal husbandry was a significant reason for the expansion of salinized land in the area from 1980 to 2000 [40]. Due to the importance of the area to irrigated agriculture, several national, regional, and local projects have been implemented to protect the groundwater ecological environment. The “Three North Shelterbelt Project” which involves planting trees to reduce wind speed and halt desertification and the Changling local alkaline land restoration project from the year 2000 involving fencing of grasslands to mitigate degradation are typical examples in this regard [41,42,43]. Though policy-driven grassland restoration programs after the year 2000 and increased demand for agriculture have led to reclamation and slowing down of land degradation including salinization [44,45], assessing the impact of these projects continually is important for modification or replication elsewhere due to the dynamic nature of the problem [46,47].
Consequently, this study was undertaken to assess the spatio-temporal evolution of the ecological environment from 2001 to 2019 (the late 2010s). The objectives of the study include: (1) Determination of groundwater and soil salinization variation; (2) Determination of desertification evolution; (3) Assessing anthropogenic and climatic factors influencing ecological environment evolution.

2. Methodology

2.1. Study Area

Changling county located in the west of Jilin province in China’s northeast covers an area of about 5732 km2 (573,200 ha). It is bounded between latitude 43°59′ and 44°42′ N and longitude 123°06′ to 124°45′ E. The natural climatic condition of the study area is arid and semi-arid belonging to the continental monsoon climate in the middle temperate zone. Changling has four (4) seasons comprising spring, summer, autumn, and winter, with an average annual temperature of 4.9 °C. There is severe cold in winter, no intense heat in summer, more drought and wind in spring, and frost in autumn. The sixty-three (63) year average annual precipitation from 1984 to 2018 was 442.9 mm with a minimum value of 258.9 mm in 2001, and a maximum of 716.2 mm in 1983. The elevation of the study area ranges from 126.8 to 272.6 m, with high altitude in the east and low altitude in the west (Figure 1). The depth to groundwater table (DWT) in the area shows a gradual increase from west to east and the water bodies in the study area include rivers, lakes, and canals.
The hydrogeology of the area consists of mainly phreatic water from Quaternary and Neogene aquifers as well as confined aquifers. At the base of the area are the Quantou, Qingshankou, and Nenjiang formations dominated by mudstone and fewer sandstone lithologies. These are overlain by upper cretaceous Sifangtai and Mingshui formations with grayish green, brownish red, mudstone, muddy-siltstone, and fine sandstone lithologies. The Neogene Da’an and Taikang formations, respectively, overlay the cretaceous strata with grayish-green mudstone, muddy-siltstone, gray, grayish black, grayish white muddy-siltstone, gravel rock, and gravel containing mudstone lithologies. Tertiary quaternary formations of Baitushan, Daqinggou, Huangshan, and Guxiangtun, respectively, overlay the cretaceous with recent Holocene sediments lying at the top.

2.2. Groundwater Sample Collection and Analysis

For this study, a total number of twenty-eight (28) groundwater samples were collected and analysed from shallow aquifers. Due to the absence of data from the year 2001 which is the beginning of this study, data from the year 2000 (nine samples) from Jilin Water Resources Bureau were compared with nineteen samples (19) obtained in 2017. The sample collection procedure followed the Chinese National Standard for sampling which involves collecting two water samples per location [48]. Samples for cation analyses were collected in rinsed plastic bottles and acidified with drops of HNO3 to lower the pH to about two. Samples for anionic analysis were taken to the laboratory directly. The sampled groundwater was analysed by Pony Testing International Group in Changchun. The methods of the National Standardization Administration of China were followed [49]. The pH was measured in situ using a calibrated EC/pH metre (HANNA, HI99131). Inductively coupled plasma atomic emission spectrometry (ICP-AES) was used to analyse the major cations (K+, Na+, Ca2+, Mg2+) while an ion chromatograph was used to measure the anions Cl−, NO3− and SO42−). The acid-based titration method was used to measure HCO3−. TDS was measured using an electric blast-drying oven along with an electronic analytical balance (Vapour-drying method).

2.3. Data Sources and Processing

The DWT data for at least nine years for the period 2001–2017 were obtained from the Jilin Water Resources Bureau. Evaporation, precipitation, and average annual temperature data were obtained online from the China Meteorological Date Service Centre (http://data.cma.cn/ (accessed on 10 October 2019)).
Cloud-free (<10%) Landsat Thematic Mapper (TM), Enhance Thematic Mapper (ETM), and Operational Land Imager (OLI) of path 119, row 029 were sourced online from the United States Geological Survey data (https://earthexplorer.usgs.gov/ (accessed on 8 September 2022)) (Table 1). ENVI image processing software and ArcGIS were used for data processing. The statistical analysis of data was undertaken using Microsoft Excel, SPSS, and Origin software, respectively.

2.4. Land Use/Land Cover (LULC) Analysis

The trend in areal coverage of salinized land was undertaken using LULC analysis. Cloud-free images obtained between April and early May for 2001, 2010, and 2019 were used for salinization analysis because images from the dry season are best suited for soil salinity analysis [50,51].
For TM/ETM/OLI, a conventional pseudo-colour composite was used, consisting of Near Infrared, Red, and Green Bands as RGB, respectively [40]. Topography, geomorphology, soil texture, groundwater depth, water chemistry, and vegetation all have an impact on how salinized the studied region is. Consequently, different soil types with differing salinization extents have distinct percentages of plant cover and features [21]. The maximum-likelihood supervised technique [52] and visual translation were used to produce the LULC maps for the study area. Since salinized land transformation was the main goal, settlements were masked throughout the study period using settlement (built-up area) polygons derived from the 10m resolution ESRI Sentinel global LULC map which is the most accurate global LULC map at the 10m resolution [53,54]. Masking of settlements can ease saline land delineation as there is difficulty in distinguishing between “salt” and “town” classes [51]. The accuracy of the settlement polygons in the study area in comparison to ground truth and Google earth points was found to be >95%. The study area was thus divided into grasslands, salinized land, dry cropland, swampland, water body, woodland, and paddy fields (paddy fields delineated in 2019 only). Subsequently, a transfer matrix was used to analyse the transformation between various LULC classes from 2001 to 2019.

2.5. Normalized Differential Vegetation Index (NDVI) Analysis

Desertification analysis was carried out using the highest vegetated cloud-free images obtained between July and September from 2001 to 2019 because that is the period when plants grow exuberantly [55]. Landsat images compared to lower resolution Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data are suitable for analysing small regions [35,56]. NDVI has been used as an index for assessing desertification as it is a good indicator of vegetation density in arid and semi-arid regions [57,58]. NDVI is based on Equation (1).
N D V I = ( N I R − R N I R + R )
where NIR means near-infrared and R means visible red. NDVI usually ranges from −1 to +1 with NDVI < 0 characteristic of water bodies, NDVI ≈ 0 meaning barren soil or rock (may infer desert), and NDVI ≫ 0 means various levels of vegetation [58,59].
The analysed NDVI data from 2001 to 2019 were analysed for trends using the univariate linear regression method (slope) as shown in Equation (2) [60,61].
S l o p e = n ∑ i = 1 n i   ×   N D V I i   − ∑ i = 1 n i ∑ i = 1 n N D V I i n ∑ i = 1 n i 2 − ( ∑ i = 1 n i ) 2
where slope means the trend of vegetation, n means the number of years in the study (19 years), i means the year, and NDVIi refers to the selected rainy season NDVI in the ith year. The slope was classified into degraded (<−0.01), stable (−0.01 ≤ slope ≤ 0.01), and increased vegetation trends, respectively. Significant vegetation change (p < 0.05) was classified into significant decrease (which signals desertification) and increase, respectively.
The Pearson correlation coefficient was used to analyse the factors influencing desertification using Equation (3). That is, NDVI was correlated with meteorological data, DWT, and regional groundwater exploitation data. The main characteristic used for desertification identification is a decline in vegetation which may be due to the degradation of soil, climatic alteration, and anthropogenic activities [58].
r = ∑ i = 1 n ( x i − x ¯ ) ( y i − y ¯ ) ∑ i = n n ( x i − x ¯ ) 2 ∑ i = n n ( y i − y ¯ ) 2
where xi means climatic factor, depth to the water table, or groundwater exploitation data, and yi means NDVI.

3. Results

3.1. Groundwater Chemical Evolution

Groundwater chemistry data results are shown in Table 2. In 2000, the mean concentration of Na+ is highest for the cations at 155.93 mg/L, though a high standard deviation (138.05 mg/L) indicated the contribution of an outlier to the mean Na+. Ca2+ was the dominant cation in most samples with an average value of 96.36 mg/L (ranging from 45.30 to 168.00 mg/L). HCO3− is the most dominant anion with an average concentration of 391.33 mg/L (range of 294.00–582.00 mg/L). The foregoing indicates a Ca-HCO3 dominant facies in most of the samples (66.67%) while Na-HCO3 facies formed 22.22% (Figure 2a). Mean groundwater salinity (TDS) was 906.56 mg/L (ranging from 546.00 to 1419.00 mg/L) while the mean NO3− concentration was 4.68 mg/L (ranging from 1.51 to 9.66 mg/L). The NO3− concentration implies the anthropogenic impact on groundwater was minimal as nitrate concentrations in all the analysed samples were below the Chinese Drinking Standard of 20 mg/L.
In 2017, Ca2+ was similarly the most dominant cation among the analysed groundwater samples with an average value of 128.63 mg/L (ranging from 44.30 to 368.00 mg/L). HCO3− was also the most dominant anion with a mean concentration of 459.74 mg/L (range of 164.00–1200.00 mg/L). Thus, as was the scenario in the year 2000, Ca-HCO3 facies dominated the analysed samples (84.21%); 10.52% of the samples were Na-HCO3 facies while Na+K - Cl - SO4 made up 5.26% of the samples (Figure 2b). Mean groundwater salinity (TDS) in 2017 was 835.26 mg/L (ranging from 295.00 to 2430.00 mg/L) while mean NO3− concentration was 26.87 mg/L (ranging from 0.01 to 113.00 mg/L) signalling anthropogenic impact on groundwater in 42.10% of the samples (NO3− > 20 mg/L).
A comparison of groundwater salinity between the years 2000 and 2017 indicates a slight reduction in mean salinity from 906.56 mg/L to 835.26 mg/L (Figure 2c). Additionally, the impact of surficial salts, which are characterized by a Na-HCO3 signature in the Songnen plain, is higher in the year 2000. However, the influence of NO3 is markedly higher in the year 2017 (42.10%) compared to 2000 with all samples within guideline limits (Figure 2d).

3.2. Soil Salinization Analysis

As earlier stated, settlements covering 6.41% (36,740 ha) were masked and kept constant in the respective years using polygons obtained from high-resolution Sentinel 10 m mapped by ESRI [53,54]. The result of the LULC as shown in Figure 3 and Table 3 indicates areal coverage of various LULC classes in the three analysed years (2001, 2010, and 2019) was in the order dry cropland > grassland > swampland > settlement> woodland > water body > salinized land.
In 2001, the areal coverage of the various LULC was, respectively, dry cropland, 63% (361,099 ha); grassland, 11.86% (67,998 ha); swampland, 10.18% (58,343 ha); settlement; woodland, 6.09% (34,918 ha); water body, 1.80% (10,319 ha); and salinized land, 0.67% (3829 ha). In 2010, the areal coverage of the various LULC classes showed a reduction in dry cropland of 1710 × 103 ha (at 0.30%/year); water body, 2062 ha (at 0.04%/year) and woodland, 15,716 ha (at 0.27%/year) (Table 3). All the other LULC classes showed increases from 2001 to 2010 with swampland having the highest increase of about 20,129 ha (0.35%/year). As of 2019, the order of area covered by the respective LULC classes was similar to previous years except that the area covered by salinized land had become greater than the water bodies. Additionally, paddy fields LULC classes which were not discernable in 2001 and 2010 were observed in 2019. The areal coverage of LULC classes in 2019 maintained dry cropland as the largest covering 59.32% (340,008 ha).
Other classes from largest to smallest include grassland (12.66%, 72,555 ha); swampland (7.81%, 44,757 ha); settlement; woodland (6.24%, 35,780 ha); salinized land (1.84%, 10,535 ha); and water body (0.65%, 3737 ha). The LULC change from 2010 to 2019 revealed a decrease in grasslands (8448 ha at a rate of 15%/year) and dry cropland (3961 ha at 0.07%/year).
Overall, from 2001 to 2019, salinization was observed to increase. Based on the conversion matrix constructed, major contributors to the increase in salinized land from 2001 to 2019 were swampland (3680 ha), water body (1975 ha), grassland (1899 ha), and dry cropland (1448 ha), (Table 4). Salinized land over the net period was also converted principally to swampland (1481 ha) and grassland (448 ha) (Table 4). Grasslands, which are often considered areas of mild salinization were also found to be overly increasing from 2001 to 2019 [62] (Table 4). Grasslands increased by 130,006 ha from 2001 to 2010 before reducing by 8448 ha from 2010 to 2019. Cumulatively from 2001 to 2019, grasslands increased by about 4558 ha (0.04%/year).

3.3. Desertification Analysis

The NDVI ranged from −0.47 to 0.54 in 2001 (Figure 4a), −0.16 to 0.48 in 2010 (Figure 4b), and −0.18 to 0.71 in 2019 (Figure 4c) with the low-lying areas in the western portion of the study area having the lower NDVI values. Based on the analysis of vegetation degradation using NDVI, the desertification trend in the study area from 2001 to 2010 showed that 1.82% (10,413 ha) was degraded, 97.86% (560,912 ha) was stable and 0.33% (1874 ha) was vegetated (Figure 4c, Table 5).
Within the degraded area, only 0.07% (398 ha) is significantly degraded at p < 0.05 indicating a desertification trend in affected areas (Figure 4d, Table 5). Most of the study area covering 99.9% (572,380 ha), was stable from 2001 to 2010 at p > 0.05. The significantly vegetated area similarly covered 0.07% (422 ha) of the study area at p < 0.05 (Figure 4g, Table 5).
From 2010 to 2019, an increased vegetated trend was observed in a large part of the study area covering 93.81% (5732 ha) of which 14.68% (84,174 ha) is significant at p < 0.05 (Figure 4e,h; Table 5). The vegetation degradation trend was negligible covering 0.02% (93 ha) of the study area with no significance at p < 0.05. The significantly stable area in the period from 2010 to 2019, covered 85.32% (489,026 ha) of the study area (Figure 4h, Table 5).
Overall, from 2001 to 2019, the NDVI trend showed that stable areas in the study were dominating, accounting for 96.65% (553,987 ha) of the total area. Vegetated areas covered 3.03% (17,395 ha) of the study area with only 1.67% (9556 ha) being significant (p < 0.05). Degraded areas that signify the desertification trend were minor, covering 0.32% (1819 ha) of the study area with a negligible portion of 0.03% (148 ha) being significant at p < 0.05 (Figure 4f,i; Table 5). Thus, within the overall period of study from 2001 to 2019, 98.31% (563,497 ha) of the study area was stable.

4. Discussion

4.1. Factors Influencing Groundwater Chemical Evolution

The groundwater facies within the study period were generally stable with most waters being Ca-HCO3, though a higher signature of Na-HCO3 was seen in earlier periods. Due to lower DWT at this time, the Na-HCO3 signature may be related to the influence of the surface soil, which is characterized by Na-HCO3 salinity in the Songnen plain [63,64]. TDS has severally been used as an index for assessing groundwater salinization in coastal, arid, and semi-arid regions [13,65,66]. The stability of groundwater salinity with only a slight decline in the mean TDS from 906.56 mg/L to 835.26 mg/L is averse to regional and proximal estimates which show a marginal increase [2,67]. However, the observed scenario may be due to a trend of increasing groundwater table depth which reduces the evaporation concentration of salts in shallow groundwater [68,69,70]. About 66.67% of observed wells in the study area show a significant decline of DWT with time at p < 0.05, 8.33% show a significant rise, and the remaining 25% with negligible rise (p > 0.05) (Figure 1 and Figure 5). The decline in the groundwater table in the area may be related to unsustainable exploitation of groundwater owing to limited surface water resources as groundwater provides about 98% of the total 150.91 million m3 of annual water use in the study area [71].
Additionally, evaporation was observed to overly decline over the study period implying a reduced influence on salt concentration in groundwater (Figure 6a). Though a significant increase was noticed in the period after 2010 (r = 0.82, p < 0.01) (Figure 6a Inset), most observation wells in the period after 2010 have DWT above 5m, which is the critical depth to the water table at which the effect of evaporation on groundwater is minimal in the area (Figure 5) [40,72].
Nitrate (NO3) has been used as an index to signal anthropogenic influence on the groundwater environment, especially due to the use of chemical fertilizers and sewage discharge [73,74,75]. As seen earlier (Figure 2d), the mean NO3 concentration in shallow groundwater in 2017 exceeded guideline limits with some samples with values up to 100 mg/L (500% higher than guideline limits). The elevated concentration is evidence for the anthropogenic influence on groundwater chemistry. Since the study area is part of the Songnen plain, which is a significant grain base for China, fertilizer usage has significantly increased over the years. The estimated grain yield and fertilizer application volume in western Jilin Province of the Songnen plain (including Changling) was estimated to increase significantly at a rate of 4.2 × 105 tonnes/year and 4.53 × 104 tonnes/year from 2001 to 2013 as established by earlier workers [2,76].

4.2. Factors Influencing Soil Salinization

The observed salinized land increase in the study area of about 1740 ha from 2001 to 2010 and 4966 ha from 2010 to 2019 (total of 6706 ha from 2001 to 2019) was similar to increases observed in other studies in Changling from 1954 to 2010 [18,40]. It is, however, opposed to other parts of the Songnen plain experiencing a decline in the salinized land area [67,77]. Soil salinization in the region is closely associated with meadow-forming processes, wetlands, and edges of water bodies [14,22]. As seen in the results, swampland and water body conversion are the highest contributors to salinized land from 2001 to 2019 (contributing 3680 ha and 1975 ha, respectively). Groundwater decline in response to over-exploitation has been fingered as one of the reasons for shrinking and disappearing wetlands including in the Songnen plain [78,79]. Some studies have observed a significant decrease in lake area and volume in response to a gradual increase in extraction [80]. Wetlands are at higher risk of secondary salinization due to their generally lower elevation making them susceptible to saline water inflows and shallow groundwater levels [81]. Thus, a reduction in water bodies (about 6582 ha) and swamplands (about 13,587 ha) in response to groundwater exploitation over the period of the study may explain the increase in salinized land as has been observed elsewhere [82,83].
High ratios of evaporation-precipitation in arid and semi-arid regions with shallow DWT foster soil salinization [83,84]. A reducing ratio displayed by a declining insignificant evaporation trend (r = −0.33, p > 0.05) and an increasing insignificant trend of precipitation (r = 0.39, p > 0.05) are expected to result in a decrease in salinized soil (Figure 6a,b). The persistent increase in the study period may be due to the stronger impact of declining water bodies and swamplands. The influence of temperature on salinization is also believed to be minimal as the trend of annual mean temperature was insignificant (Figure 6c). The 0.09%/year rate of salinization in the period 2010–2019, was higher than the 0.03%/year from 2001 to 2010 (Table 3). It may have been enhanced due to the significant temperature increase after 2010 (r = 0.63, p > 0.05) that resulted in a significant evaporation increase within the same period (Inset Figure 6a,c).

4.3. Factors Influencing Desertification

The overall desertification trend within the study area from 2001 to 2019 was insignificant as most of the study area was stable (98.31%) and other portions were vegetated (1.67%). The only portion that signalled vegetation degradation coincided with built-up areas and may be due to construction that claimed vegetation. The desertification trend (inferred from the NDVI trend) from 2001 to 2010 also had areas with significantly degraded vegetation up to 0.07% (398 ha). NDVI usually has a negative relationship with DWT implying shallower groundwater enhances the vegetation growth of plants [85,86,87]. Situations of weak NDVI vs. DWT correlation may also occur and imply the presence of alternative water sources or DWT below the ecological threshold [88,89]. Observation of the NDVI–DWT relationship in the study area shows a significant negative relationship in 2001 (−0.68, p < 0.05) and an insignificant relationship in 2019 (−0.35, p > 0.05) (Figure 7a,c). Though the correlation from 2010 shows a weak positive relationship, it was caused by an outlier that may have had an alternative water source. The exclusion of the outlier yielded a significant weak negative correlation of −0.46 at p < 0.05 between NDVI and DWT in 2010 (Inset Figure 6b). The weaker statistically insignificant relationship in 2019 may be due to increasing DWT under human impact that reduces the impact of groundwater on vegetation greenness.
The influence of precipitation on mean NDVI within the study period was assessed. Though precipitation increased slightly from 2001 to 2019 (r = 0.39. p > 0.05), the impact on NDVI was minimal with a negative correlation of (r = −0.11, p > 0.05). The negative correlation between NDVI and precipitation in August when many of the NDVI data were sourced (54%) has been observed in the Songnen plain and other arid regions [59,90]. Changling is part of the leading grain base in China where the peak irrigation period coincides with the rainy season when exploited groundwater is the main source of irrigation water. Thus, the weak impact of precipitation on NDVI is believed to be caused by water supplied through irrigation as seen in irrigated districts elsewhere [61]. A significant positive correlation of r = 0.58 (p < 0.05) between regional groundwater exploitation and mean annual NDVI further confirms the strong influence of irrigation on NDVI (Figure 7f).
The impact of mean temperature on mean NDVI was also assessed and found to be weakly positive with r = 0.23 (p > 0.05), indicating only a slight influence on vegetation (Figure 7e).

5. Conclusions

This study was undertaken to assess the spatio-temporal evolution of groundwater ecological environment in a typical semi-arid region under anthropogenic and climatic influences. Based on the use of hydro-geochemical, spatial, remote sensing, and statistical techniques, it was found that:
  • Stable groundwater salinity within the study period was engendered by groundwater exploitation that led to a decline in groundwater depth which superseded the impact of evaporation concentration of salts in groundwater. However, this was accompanied by increased agricultural production and fertilizer application, which led to increased nitrate concentrations in groundwater and deleterious levels in some places.
  • Salinized land area within the study period increased by about 6706 ha at a rate of 0.06%/year as a result of the size reduction in water bodies and swampland in response to the declining water table that exposed shallow water to more evaporation. Though the ratio of evaporation-precipitation over the study period favoured soil desalinization, it is believed that the conversion of water bodies and swamplands to salinized land under increasing groundwater exploitation overshadowed the climatic influence thus leading to an area increase in salinized land within the period.
  • Overall, from 2001 to 2019, most of the study area was stable in terms of desertification as significantly degraded lands covered only 0.03% (148 ha) of the area while stable land and significantly vegetated lands covered 98.32% (563,497 ha) and 1.67% (9556 ha), respectively. The role of precipitation in desertification was insignificant with irrigation believed to be the main driver of significant vegetation improvement.
From the foregoing, it is evident that management efforts at reducing the rate of desertification are succeeding with improvement in vegetated areas. However, additional efforts are required in mitigating groundwater and soil salinization. With the main factor for the evolution of these environmental challenges being the declining DWT in a quest for food security, water-saving irrigation practices that minimize evaporation, and thus groundwater extraction, are advised. Additionally, the growing of salt-tolerant plant species and the application of inorganic fertilizers are recommended to maximize food production while stemming the environmental degradation trend.

Author Contributions

Conceptualization, A.S.Y. and C.X.; methodology, A.S.Y. and C.X.; software, A.S.Y. and O.A.A.; formal analysis, A.S.Y.; data curation, C.X., X.L., O.A.A., M.L. and X.F.; writing—original draft preparation, A.S.Y. and O.A.A.; writing-review and editing, A.S.Y., C.X., O.A.A., M.L., X.F. and X.L.; Visualization, A.S.Y., C.X., O.A.A., M.L. and X.F.; Supervision, C.X.; Project administration, X.L.; Funding acquisition, C.X. and X.L. All authors have read and agreed to the published version of the manuscript.

Funding

This study is supported by the National Natural Science Foundation of China (41572216), the China Geological Survey Shenyang Centre (DD20190340-W09), and the Geological Survey Foundation of Jilin Province (2018-13).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Some of data that were used for this study have restricted access but can be made available upon reasonable request and with permission of the Jilin Water Resources Bureau.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Location map of the study area showing groundwater sampling points in 2000 and 2017, and shallow groundwater trend with time.
Figure 1. Location map of the study area showing groundwater sampling points in 2000 and 2017, and shallow groundwater trend with time.
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Figure 2. Piper plot of groundwater chemistry in 2000 (a) and 2017 (b) and comparison of mean 2000 and 2017 TDS (c) and NO3− (d).
Figure 2. Piper plot of groundwater chemistry in 2000 (a) and 2017 (b) and comparison of mean 2000 and 2017 TDS (c) and NO3− (d).
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Figure 3. Land Use/ Land cover map of Changling: (a) 2001, (b) 2010, and (c) 2019.
Figure 3. Land Use/ Land cover map of Changling: (a) 2001, (b) 2010, and (c) 2019.
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Figure 4. Desertification analysis (a) NDVI 2001, (b) NDVI 2010, (c) NDVI 2019, (d) NDVI Change 2001–2010, (e) NDVI Change 2010–2019, (f) NDVI Change 2001–2019, (g) Significant NDVI change 2001–2010, (h) Significant NDVI Change 2010–2019, (i) Significant NDVI Change 2001–2019.
Figure 4. Desertification analysis (a) NDVI 2001, (b) NDVI 2010, (c) NDVI 2019, (d) NDVI Change 2001–2010, (e) NDVI Change 2010–2019, (f) NDVI Change 2001–2019, (g) Significant NDVI change 2001–2010, (h) Significant NDVI Change 2010–2019, (i) Significant NDVI Change 2001–2019.
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Figure 5. Trend of DWT with time (2001–2017).
Figure 5. Trend of DWT with time (2001–2017).
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Figure 6. Trend of climatic factors with time: (a) evaporation; (b) precipitation; and (c) annual mean temperature within the study area from 2001 to 2018 (Inset: 2001–2010 and 2010–2018).
Figure 6. Trend of climatic factors with time: (a) evaporation; (b) precipitation; and (c) annual mean temperature within the study area from 2001 to 2018 (Inset: 2001–2010 and 2010–2018).
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Figure 7. Correlation between: (a) NDVI and Depth to the water table in 2001, (b) NDVI and Depth to the water table in 2010, and (c) NDVI and Depth to the water table in 2019; (d) Annual mean NDVI and precipitation; (e) Annual mean NDVI and temperature; (f) Annual mean NDVI and regional groundwater exploitation.
Figure 7. Correlation between: (a) NDVI and Depth to the water table in 2001, (b) NDVI and Depth to the water table in 2010, and (c) NDVI and Depth to the water table in 2019; (d) Annual mean NDVI and precipitation; (e) Annual mean NDVI and temperature; (f) Annual mean NDVI and regional groundwater exploitation.
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Table
Table 1. Data type and date of remote sensing image (All samples used were used to analyse desertification (NDVI) except those with * that were used for salinization.
Table 1. Data type and date of remote sensing image (All samples used were used to analyse desertification (NDVI) except those with * that were used for salinization.
Image TypeDateImage TypeDate
Landsat-5 *13 April 2001Landsat-513 September 2010
Landsat-530 August 2002Landsat-817 June 2013
Landsat-525 August 2003Landsat-89 July 2015
Landsat-526 July 2004Landsat-828 August 2016
Landsat-517 August 2006Landsat-831 August 2017
Landsat-519 July 2007Landsat-82 August 2018
Landsat-56 August 2008Landsat-8 *1 May 2019
Landsat-5 *8 May 2010Landsat-822 September 2019
Table
Table 2. Summary of groundwater physical and chemical properties in 2000 and 2017 (all measured in mg/L except pH).
Table 2. Summary of groundwater physical and chemical properties in 2000 and 2017 (all measured in mg/L except pH).
pHTDSCa2+Mg2+K+Na+Cl−SO42−CO32−HCO3−NO3−
2000 (n = 9)
Mean7.69906.5696.3642.292.42155.93119.4757.881.05391.334.68
Minimum7.40546.0045.3016.600.8020.9011.1011.600.00294.001.51
Maximum8.301419.00168.0081.106.80400.00291.0097.009.44582.009.66
Stdev *0.28317.4647.8523.091.79138.05101.4627.683.1591.843.25
2017 (n = 19)
Mean7.69835.26128.6340.331.0496.42109.3752.45–459.7426.87
Minimum7.40295.0044.3011.800.3214.406.201.17–164.000.01
Maximum8.302430.00368.00176.003.56503.00445.00336.00–1200.00113.00
Stdev *0.28622.0188.5739.880.85137.48123.9883.84–236.9435.80
* means standard deviation.
Table
Table 3. LULC change in Changling from 2001 to 2019.
Table 3. LULC change in Changling from 2001 to 2019.
LULC ClassArea (%)Area (ha)Change 2001–2010Change 2010–2019Change 2001–2019
200120102019200120102019HaRate (%/Year)HaRate (%/Year)HaRate (%/Year)
Dry cropland63.0060.0159.32361,099343,969340,008−17,129−0.30−3961−0.07−21,091−0.19
Grassland11.8614.1312.6667,99881,00472,55513,0060.23−8448−0.1545580.04
Paddy0.000.005.07 029,07700.0029,0770.5129,0770.27
Salinized land0.670.971.843829556910,53517400.0349660.0967060.06
Swampland10.1813.697.8158,34378,47344,75720,1290.35−33,716−0.59−13,587−0.12
Waterbody1.801.440.6510,31982583737−2062−0.04−4520−0.08−6582−0.06
Woodland6.093.356.2434,91819,20235,780−15,716−0.2716,5780.298620.01
Table
Table 4. Areal coverage of LULC classes in Changling in 2001, 2010 and 2019.
Table 4. Areal coverage of LULC classes in Changling in 2001, 2010 and 2019.
Land Use TypeLULC 2019
Dry CroplandGrasslandPaddySalinized LandSwamplandWaterbodyWoodlandGrand Total
LULC 2001Dry cropland296,49423,443850214486412024,438361,099
Grassland17,82329,4486534189912,0112419967,998
Salinized land73448739148190518303829
Swampland653115,8978513368023,17829822258,343
Waterbody7792427451975135532331010,319
Woodland18,7742323201347883010,85334,918
Grand Total340,00872,55529,07710,53544,757373735,780536,506
Net Change−21,091455829,0776706−13,587−6582862
Table
Table 5. Desertification Trend in Changling based on NDVI Analysis.
Table 5. Desertification Trend in Changling based on NDVI Analysis.
PeriodClassNDVI ChangeArea %Area (ha)
2001–2010Slope < −0.01Degradation1.8210,413
−0.01 < Slope < 0.01Stable97.86560,912
Slope > 0.01Vegetated0.331874
Sum100.00573,199
Significance
p < 0.05Degradation0.07398
p > 0.05Stable99.86572,379
p < 0.05Vegetated0.07422
2010–2019Slope < −0.01Degradation0.0293
−0.01 < Slope < 0.01Stable6.1835,398
Slope > 0.01Vegetated93.81537,710
Significance
p > 0.05Stable85.32489,026
p < 0.05Vegetated14.6884,174
2010–2019Slope < −0.01Degradation0.321819
−0.01 < Slope < 0.01Stable96.65553,987
Slope > 0.01Vegetated3.0317,395
Significance
p < 0.05Degradation0.03148
p > 0.05Stable98.31563,497
p < 0.05Vegetated1.679556
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Yawe, A.S.; Xiao, C.; Adeyeye, O.A.; Liu, M.; Feng, X.; Liang, X. Spatio-Temporal Evolution of the Ecological Environment in a Typical Semi-Arid Region of Northeast China. Sustainability 2023, 15, 471. https://doi.org/10.3390/su15010471

AMA Style

Yawe AS, Xiao C, Adeyeye OA, Liu M, Feng X, Liang X. Spatio-Temporal Evolution of the Ecological Environment in a Typical Semi-Arid Region of Northeast China. Sustainability. 2023; 15(1):471. https://doi.org/10.3390/su15010471

Chicago/Turabian Style

Yawe, Achivir Stella, Changlai Xiao, Oluwafemi Adewole Adeyeye, Mingjun Liu, Xiaoya Feng, and Xiujuan Liang. 2023. "Spatio-Temporal Evolution of the Ecological Environment in a Typical Semi-Arid Region of Northeast China" Sustainability 15, no. 1: 471. https://doi.org/10.3390/su15010471

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