Next Article in Journal
Meteorological Droughts in the Paraopeba River Basin: Current Scenarios and Future Projections
Previous Article in Journal
Developing Indicators for the Valuation of River Ecosystem Services
Previous Article in Special Issue
A Review of Biomass Estimation Methods for Forest Ecosystems in Kenya: Techniques, Challenges, and Future Perspectives
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Land
Volume 14
Issue 10
10.3390/land14102092
land-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Ecological Risk Assessment of the Aksu River Basin Based on Ecological Service Value

by
Guozhu Xia
,
Guanghui Lv
* and
Jianjun Yang
College of Resources and Environmental Sciences, Xinjiang University, Urumqi 830000, China
*
Author to whom correspondence should be addressed.
Land 2025, 14(10), 2092; https://doi.org/10.3390/land14102092
Submission received: 27 August 2025 / Revised: 6 October 2025 / Accepted: 17 October 2025 / Published: 21 October 2025
(This article belongs to the Special Issue Forest Soil-Water Resilience to Climate Change: Mechanisms and Management)
Browse Figures
Versions Notes

Abstract

Understanding spatiotemporal dynamics and drivers of ecosystem service value (ESV) is critical for informing ecological restoration and sustainable land management, particularly in arid inland river basins. Analyzing the spatiotemporal dynamics of ESV in arid river basins and identifying key ecological and environmental drivers enable more precise diagnosis of ecological problems and provide a scientific basis for effective governance. This study evaluated the changes in ESV in the Aksu River Basin from 1990 to 2020 using the InVEST model, based on land use data, meteorological records, and biophysical parameters. A comprehensive assessment of seven key ecosystem services—including food production, water conservation, and biodiversity protection—was conducted. SHAP (SHapley Additive exPlanations) values were applied to interpret the contribution of ecological and environmental variables to ESV changes. The results showed that total ESV increased from CNY 189.05 billion in 1990 to a peak of CNY 22.326 billion in 2010, followed by a slight decline to CNY 20.805 billion in 2020. Spatially, Wensu, Xinyuan, and Bachu counties exhibited substantial ESV gains, while Atushi, Akto, and Awat counties experienced significant losses. SHAP analysis identified forest quality, soil erosion, and grassland condition as the dominant drivers of ESV variation, surpassing the influence of land area alone. By combining biophysical modeling with interpretable machine learning, this study highlights the critical role of ecosystem quality rather than land area alone, offering a transferable approach for diagnosing ecological risk assessment in arid regions.

1. Introduction

Ecosystem services (ESs) refer to the various benefits that ecosystems provide to human well-being through their structures, functions, and processes [1,2,3]. These services constitute the material foundation and environmental support for human society. However, with the continuous intensification of human activities, large-scale land use changes have led to severe degradation of terrestrial environments, placing approximately two-thirds of the world’s natural resources at risk of depletion and significantly weakening ecosystem service functions [4,5,6,7]. This issue is particularly prominent in arid and semi-arid regions, where the uneven spatial and temporal distribution of water resources imposes tremendous pressure on oasis ecosystems, threatening regional ecological security and constraining sustainable socio-economic development [8]. Under these circumstances, the assessment of ecosystem service value (ESV) can, on the one hand, provide scientific evidence for the establishment of ecological conservation and compensation mechanisms, thereby facilitating a dynamic balance between resource exploitation and ecological protection [9]. On the other hand, quantifying the spatiotemporal evolution of ESV and its driving mechanisms can help diagnose ecological problems in arid regions, optimize ecological security patterns, and support regional decision-making for sustainable development [10]. In particular, under the dual pressures of climate change and intensified human activities, an in-depth understanding of the formation and maintenance mechanisms of ecosystem services in arid river basins has become a frontier topic in ecological research and an urgent regional management need [11,12,13,14].
The Tarim River Basin, China’s largest inland river basin, mainly relies on runoff contributions from its headwater rivers, including the Aksu, Yarkand, Hotan, and Kaidu–Kongque Rivers, to sustain its hydrological processes and ecological functions. Among them, the Aksu River contributes more than 70% of the Tarim’s mainstream runoff, playing a decisive role in maintaining hydrological processes and ecological stability within the basin [15,16,17,18]. However, in recent years, high-intensity human activities have increasingly intensified ecological degradation and land desertification in certain areas of the basin. Large-scale agricultural irrigation and industrial water use have caused continuous declines in groundwater levels and significant shrinkage of natural wetlands. Simultaneously, urban expansion and fragmented land use have altered the original landscape pattern, undermining the regulatory and supporting functions of the ecosystem [19,20,21,22]. These changes have further triggered soil erosion, water area reduction, and land quality degradation, severely threatening the sustainable supply of ecosystem services within the basin. To safeguard the ecological security of the basin, the local government has recently implemented the “Integrated protection and restoration project of mountains, rivers, forests, farmlands, lakes, grasslands, and deserts in the Aksu River Basin”. While this initiative represents a comprehensive effort to enhance ecological resilience and restore degraded ecosystems, the challenge remains as to how ecological problems in the region can be addressed with greater precision and how the effectiveness of restoration measures can be further improved. Therefore, scientifically assessing the ESV of the Aksu River Basin and systematically analyzing its key ecological issues are of great theoretical and practical significance for developing regional ecological restoration strategies, optimizing water resource management, and promoting sustainable development [23,24,25].
ESV is a key indicator for measuring the structural integrity and functional health of ecosystems [26,27,28,29,30]. Its spatiotemporal dynamics can directly reflect the ecological status of a region and the extent of human impacts. The dynamic changes in ESV across regions serve as an important basis for diagnosing ecological problems. By quantitatively assessing the evolution of ESV and its driving factors, the main causes of ecological degradation and their spatial heterogeneity can be identified, providing scientific support for ecological restoration and sustainable management. At present, ESV assessments mainly rely on land use/land cover change (LUCC) data and ecosystem process models (e.g., InVEST, SolVES) [31,32,33,34]. By constructing frameworks that link “land use–ESV–driving mechanisms,” researchers aim to reveal the combined impacts of human activities and natural factors on ecosystem services. In arid river basins, related studies have primarily focused on quantifying key services such as water supply, soil conservation, and biodiversity maintenance, and exploring the synergistic effects of climate change and human interventions (e.g., agricultural expansion and urbanization) on ESV [34]. However, limited research has specifically examined the impacts of ecological problems on ESV changes and corresponding ecological restoration strategies [35]. Therefore, beyond the calculation of ecosystem service value (ESV), it is crucial to conduct an in-depth analysis of the key drivers influencing its temporal and spatial dynamics, and to identify the major ecological and environmental risks.
Conceptually, ESV assessment treats ecosystems as assets that generate flows of goods and services for human well-being. Previous studies have primarily emphasized land use changes as proxies for ESV dynamics, but this reductionist perspective overlooks the role of ecological quality (e.g., vegetation condition, soil stability) in shaping service provision. By integrating biophysical modeling (InVEST) with interpretable machine learning (SHAP), this study develops a conceptual framework linking ecosystem structure, ecological processes, and service value, thereby offering a more holistic understanding of ecological risks and restoration needs in arid river basins.
This study takes the Aksu River Basin as a case study to assess the spatiotemporal dynamics of ecosystem service value from 1990 to 2020, analyze the main factors driving ESV changes, diagnose the key ecological issues within the basin, and propose targeted ecological restoration measures. The specific objectives are as follows: (1) to analyze land use changes in the Aksu River Basin from 1990 to 2020; (2) to estimate the ESV during this period using the InVEST model; and (3) to apply the SHAP analysis method to reveal the impacts of human activities and climate change on ESV dynamics and identify the key ecological issues in the basin. The findings of this study will provide a comprehensive diagnosis of ecological problems and scientific evidence for sustainable water resource management and ecological restoration strategies in inland river basins of arid regions.

2. Materials and Methods

2.1. Study Area

The Aksu River Basin extends across China’s Xinjiang region, Kyrgyzstan, and Kazakhstan; however, this study primarily focuses on the portion located within China (Figure 1). The basin is situated in the southwestern part of Xinjiang, specifically in the northwestern area of Aksu Prefecture. It lies on the southern slopes of the mid-section of the Tianshan Mountains near Tomur Peak and along the northern margin of the Tarim Basin, forming an important headwater region of the Tarim River. Geographically, the basin is located between 40°9′~42°5′ N latitude and 78°23′~81°58′ E longitude.
Positioned on the northern edge of the Tarim Basin in the heart of the Eurasian continent, the basin is far from any oceanic influence and is surrounded by high mountain ranges, resulting in a typical continental arid climate. The main climatic characteristics include large annual and diurnal temperature variations, low air humidity, long sunshine duration, strong evaporation, and limited precipitation. The elevation ranges from 1000 m to 2500 m, with extensive irrigated agricultural areas distributed across the river valley plains. The mean annual temperature is approximately 9.8 °C, with recorded extremes reaching up to 40.7 °C and as low as −27.6 °C. The long-term mean annual precipitation is about 62 mm, while the mean annual evaporation is approximately 1890 mm.

2.2. Data Sources

The research data used in this study include information on river network distribution, basin boundaries, digital elevation models, land use and vegetation cover, meteorological records, and basic soil properties. The details, data sources, and related information are summarized in Table 1 [36,37,38,39,40,41,42,43]. The temporal coverage of the dataset spans the years 1990, 2000, 2010, and 2020. After acquisition, all spatial datasets were uniformly resampled to a spatial resolution of 30 m × 30 m using bilinear interpolation ArcGIS (version 10.6) and reprojected to the GCS_WGS_1984 geographic coordinate system to ensure consistency for subsequent analyses.

2.3. Model Method

(1) Ecosystem service value (ESV)
In this study, the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model (version 3.10.2) was employed to estimate the value of ecosystem services in the Aksu River Basin [44]. The InVEST model is a suite of open-source software tools developed by The Nature Conservancy in collaboration with institutions such as Stanford University. It is designed to assess and predict changes in ecosystem services and their impacts on human well-being. The model integrates multiple ecosystem process modules and links ecosystems with human society through a “Supply–Service–Value” framework. By utilizing land use/land cover data, biophysical environmental factors, and socio-economic parameters, the model quantifies the functions and economic values of various ecosystem services. InVEST is capable of simulating multiple services, including carbon storage, water yield, and biodiversity conservation, and can explicitly display the spatial distribution and temporal trends of these services.
Using the InVEST model, we quantified the economic values of seven key ecosystem services in the Aksu River Basin from 1990 to 2020: food production, raw material production, climate regulation, water conservation, soil retention, biodiversity protection, and cultural and recreational services. The spatiotemporal variation characteristics of these services over the past three decades were then analyzed to provide a scientific basis for diagnosing and identifying ecological problems in the Aksu River Basin.
The InVEST model requires land use/cover data as the primary input and assumes a direct correspondence between land use type and ecosystem service coefficients. The biophysical parameters were adopted from published literature [45]. While InVEST provides spatially explicit estimates of multiple ecosystem services, it does not explicitly account for feedbacks between services, groundwater processes, or policy-driven land management dynamics. Therefore, the results should be interpreted as indicative approximations rather than absolute values.
(2) Shap interpretable machine learning model
To identify the ecological and environmental factors that influenced changes in the value of ecosystem services in the Aksu River Basin from 1990 to 2020, this study applied the SHAP interpretability method to open the “black box” of traditional machine learning models, thereby revealing the internal operating mechanisms of the model and identifying key environmental variables from a data-driven perspective. This approach enabled an exploration of how meteorological conditions, vegetation cover, land use, and topographic factors have affected changes in ecosystem service value over the past 30 years in the Aksu River Basin [46].
Before applying the SHAP interpretability analysis, a Random Forest (RF) regression model was trained to fit the relationship between ecological, environmental, and socioeconomic factors and the estimated ecosystem service values (ESV). The fitted model demonstrated a very high predictive accuracy, with a coefficient of determination (R2) of 0.99, indicating that the selected predictors effectively captured the variability in ESV across the study area. This high level of accuracy provided a reliable basis for subsequent SHAP analysis, which was employed to interpret the contribution and influence of individual variables on the model predictions.
SHAP is an approach used for interpretable machine learning analysis, which quantifies both the magnitude and direction of the influence of input variables on model outputs. For a trained machine learning model M and input variables X = x 1 , x 2 , x 3 , … … , x q , SHAP determines the contribution of each input variable E to the predictions of model M by interpreting the impact of E on the output:
E = ϕ 0 + ∑ i = 1 q ϕ i t i
In Equation (1), q is the number of input variables of model, and t is a simplified version of the input variables, ϕ ∈ R indicating the contribution of each input variable to model M . The function ϕ can be expressed as:
ϕ i ( M , x ) = ∑ t ⊆ x t ! q − t − 1 ! q ! [ M t − M t \ i ]
In Equation (2), \ is the complement operator in set operations. SHAP analyses in this study were implemented in Python (version 3.0) using the open-source shap package.
(3) RUSLE model
The RUSLE (Revised Universal Soil Loss Equation) model was employed to analyze the spatiotemporal variation characteristics of soil erosion intensity in the Aksu River Basin [47]. RUSLE is an empirical model widely used in fields such as agriculture and forestry to estimate soil erosion rates. The model quantifies soil loss by integrating multiple factors, including rainfall erosivity, soil erodibility, slope length and steepness, vegetation cover, and conservation or management practices. The basic calculation formula of the RUSLE model is as follows.
S E a = R × K × L × S × C × P
In Equation (3), S E a is the annual average soil loss; R is the rainfall erosibility factor; K is the soil erosion factor; L is the slope length factor; S is the slope factor; C is the vegetation coverage and management factor, and P is the soil and water conservation practice factor. The RUSLE model provides a quantitative tool for evaluating the effectiveness of different land management and protection measures in reducing soil erosion, which is helpful for guiding the rational utilization and protection of land resources.

3. Results

3.1. Changes in Land Use Types in the Aksu River Basin

Between 1990 and 2020, spatiotemporal changes in land use occurred in the Aksu River Basin, as illustrated by the area statistics in Table 2 and the transition flows depicted in Figure 2. Cultivated land expanded substantially from 5876.42 km2 in 1990 to 9167.24 km2 in 2020, representing an increase of over 56%. This expansion is primarily attributed to the conversion of grassland and bare land into agricultural use. Figure 2 clearly shows that a considerable portion of grassland and bare land transitioned into cultivated land during the study period, highlighting the intensification of agricultural activities in response to regional population growth and economic development.
Meanwhile, construction land increased dramatically from 9.79 km2 in 1990 to 464.91 km2 in 2020, indicating rapid urbanization and infrastructure development. This expansion occurred mainly at the expense of adjacent cultivated land and, to a lesser extent, grassland areas. Conversely, grassland area slightly declined overall, shrinking from 18,911.69 km2 to 17,816.74 km2, although Figure 2 indicates that part of the grassland was also replaced by forest land and construction land, reflecting complex interactions among land use types.
Bare land area decreased by nearly 10% from 30,172.10 km2 to 27,160.48 km2, suggesting that parts of previously unused or sparsely vegetated regions have been reclaimed for cultivation and urban expansion. Changes in water bodies, glaciers, and forest land were relatively minor in terms of absolute area but still demonstrate localized ecological shifts that may affect ecosystem service functions.
Figure 3 illustrates the spatial distribution of major land use types in the Aksu River Basin in 1990 and 2020. Over this 30-year period, spatial transformations have occurred, particularly the expansion of cultivated land and construction land in the central and southern parts of the basin. Areas originally characterized by grassland and bare land have been increasingly converted into agricultural zones, reflecting intensified land reclamation and irrigation development. In addition, the noticeable increase in construction land along river corridors and urban fringes highlights accelerated urbanization and infrastructure expansion. Conversely, grassland coverage has declined in the peripheries, indicating possible ecological stress from land conversion. The persistence of glacier and snow-covered areas in the northern highlands and the relatively stable distribution of water bodies underscore the role of topography and hydrological conditions in shaping land use patterns.

3.2. The Changes in the Ecological Service Value of the Aksu River Basin

Table 3 provides a detailed overview of the changes in ecosystem service values (ESV) for the Aksu River Basin from 1990 to 2020, covering seven key service categories: food production, raw material production, climate regulation, water conservation, soil retention, biodiversity conservation, and cultural and recreational services.
During the 30-year period, the total ESV showed an overall increase from CNY 189.05 billion in 1990 to a peak of CNY 223.26 billion in 2010, before slightly declining to CNY 208.05 billion by 2020. Provisioning services, represented by food and raw material production, exhibited steady growth throughout the study period. Food production value increased by approximately 24.6%, from 10.36 billion to CNY 12.91 billion, largely reflecting the expansion of cultivated land observed in Figure 3 and Figure 4. Raw material production increased by 25%, indicating more intensive exploitation of local natural resources to support economic development.
Climate regulation value rose by 22% between 1990 and 2010 but decreased by about 9% by 2020, likely due to the loss of grassland and changes in vegetation cover associated with urbanization and agricultural intensification. Similarly, water conservation services grew significantly to CNY 68.13 billion in 2010 but declined to CNY 60.32 billion by 2020, reflecting both improvements from water resource management and increasing pressure from irrigation and land conversion.
Figure 4 illustrates the spatial changes in total ecosystem service value (ESV) in the Aksu River Basin between 1990 and 2020. Panel (a) reveals clear spatial heterogeneity, with widespread ESV increases (pink areas) occurring in most counties across the central and eastern parts of the basin, particularly in regions undergoing vegetation restoration or improved land management. In contrast, localized declines in ESV (gray areas) are observed primarily around urban centers and intensive agricultural zones, reflecting land use conversion, construction land expansion, and ecological degradation.
Panel (b) quantifies the net change in ESV at the county level. Wensu County, Xinhe County, and Bachu County experienced the most increases, each with gains exceeding CNY 5 hundred million. These improvements are likely associated with enhanced ecosystem functions, afforestation, or conservation initiatives. Notably, Aksu City also recorded a positive net ESV gain, reflecting balanced urban development with ecological protection.In contrast, the largest ESV losses were concentrated in Artux City, and Awat County, with net declines of up to CNY 6–8 hundred million. These areas may have undergone extensive land use conversion or overexploitation of natural resources.

3.3. Identification of Ecological Problems in the Aksu River Basin

Figure 5 illustrates the contribution and influence of various ecological and environmental factors on total ecosystem service value (ESV) in the Aksu River Basin, as determined using SHAP (SHapley Additive exPlanations) values derived from the machine learning model. Each point represents a SHAP value for a single observation and variable, where the horizontal position indicates the magnitude and direction of the variable’s effect on the model output. Variables with larger mean absolute SHAP values (longer spread along the x-axis) exert stronger influence on ESV predictions. Red and blue colors denote higher and lower feature values, respectively. This visualization allows the interpretation of complex machine learning models by quantifying how each input factor increases or decreases ESV.
Figure 5a presents the SHAP summary plot, where each dot represents a SHAP value for a given input variable across the dataset. The spread of values shows not only the strength of influence but also the direction (positive or negative) for each predictor. Forest quality, soil erosion, and grassland quality were found to have the most substantial effects on ESV, with higher forest and grassland quality contributing positively, while soil erosion had a strong negative influence. Figure 5b ranks the environmental variables by their mean absolute SHAP values, quantifying their relative importance. Forest quality emerged as the most influential factor, followed by soil erosion extent, grassland quality, annual precipitation, and cultivated land soil content. Climatic variables such as average temperature and precipitation also played important roles but to a lesser extent.
Land use area variables (e.g., forest area, cultivated land area, wetland area) had comparatively smaller SHAP values, indicating that the quality of ecological elements (e.g., vegetation condition, soil integrity) had a more pronounced impact on ESV than quantity alone. These findings highlight the importance of maintaining ecological integrity over merely expanding land area types in promoting ecosystem services in arid inland river basins.

4. Discussion

4.1. Identification of Key Ecological Restoration Zones Based on ESV

Human-induced land use changes have considerably altered the spatial structure of the basin’s landscapes (Figure 3). The spatial and temporal analysis of ecosystem service value (ESV) from 1990 to 2020 (Figure 2, Figure 3 and Figure 4; Table 3) reveals heterogeneity in ecological function dynamics across the Aksu River Basin. Regions such as Wensu County, Xinhe County, and Bachu County exhibited notable increases in total ESV, while Artux City, Jiashi County, and Awat County showed pronounced declines. These spatial discrepancies can be attributed to differences in land use intensity, vegetation conditions, and urban expansion patterns. Comparable findings are also reported in the Manas River Basin, another typical arid inland river system in Xinjiang, where ecosystem service values exhibited pronounced changes under the combined influence of land use transformation and water management policies. Previous studies demonstrated that intensive agricultural expansion and irrigation practices significantly altered land cover patterns and reshaped the provision of ecosystem services in the Manas River Basin [45]. These conclusions are somewhat different from those observed in the Aksu River Basin. Here, the principal ecological and environmental risks causing a decline or alteration in ecosystem service value (ESV) are linked to the degradation of forest and grassland quality and the intensification of soil erosion. Such risks highlight the joint dominance of human activities—such as land conversion and overgrazing—and climate change in shaping ecosystem service dynamics. This finding emphasizes the need to consider both anthropogenic and climatic drivers when diagnosing ecological risks, and provides additional evidence that restoration efforts should prioritize improving ecosystem quality rather than simply expanding ecological land area.
By overlaying spatial ESV change data (Figure 5a) with county-level ESV variation (Figure 5b), we identify several high-priority ecological restoration zones: (1) southwestern counties (e.g., Akto, Atushi), where sharp ESV losses correlate with increased soil erosion and vegetation degradation [48]; (2) central irrigation areas, where agricultural expansion may have triggered declines in regulating services such as water conservation and climate regulation [49]; and (3) urban peripheries near Awat and Aksu City, facing ecosystem fragmentation and service imbalances. These areas represent critical ecological restoration zones where targeted interventions could yield substantial ecological benefits and service recovery [50].

4.2. Strategies to Enhance ESV in Key Restoration Zones

Enhancing ESV in identified critical zones requires tailored, region-specific strategies that address the most influential ecological drivers. SHAP analysis results (Figure 4) indicate that forest quality, soil erosion, and grassland quality are the top three determinants of ESV, far outweighing the contribution of land use area alone. This suggests that improving ecosystem quality—not just expanding ecological land area—should be central to restoration planning [51,52,53].
In forest-fringe counties such as Akto and Atushi, restoration strategies should focus on afforestation with native drought-resistant species, forest structure optimization, and undergrowth management to enhance forest health and carbon storage capacity. In grassland-degraded areas, rotational grazing, reseeding, and fencing for natural regeneration can improve biodiversity and soil stabilization. In regions where cultivated land has encroached on natural ecosystems, especially along rivers and wetlands, ecological buffer zones should be established to mitigate disturbance and restore hydrological functions. These strategies should be supported by long-term monitoring and incentive-based policy mechanisms to ensure sustainability.

4.3. Ecological Governance Measures and Restoration Planning

Effective ecological restoration in the Aksu River Basin must be embedded within a broader framework of ecological governance that integrates land management, water resource regulation, and socio-economic development. The observed decline in regulating and supporting services (e.g., water conservation, climate regulation, biodiversity conservation) in some counties underscores the need for a paradigm shift from productivity-focused land use to multifunctional landscape management.
Governance strategies should include (1) zoning-based ecological redlining to restrict development in ecologically sensitive areas [54,55,56,57,58]; (2) cross-sector coordination to align agricultural, urban, and environmental policies [59,60,61,62]; and (3) implementation of market-based instruments such as ecological compensation and water rights trading. Furthermore, regional-scale ecological planning should prioritize connectivity of habitat patches, restoration of degraded riparian corridors, and integration of nature-based solutions into urban infrastructure design [63,64]. Such measures can collectively enhance ecological resilience and ensure the sustainable provision of ecosystem services in the face of ongoing climate change and human pressures.
It should be clear trade-offs between provisioning services and regulating services. Expansion of cultivated land enhanced food and raw material production, yet this occurred at the expense of water conservation and climate regulation. Such trade-offs reflect the challenge of balancing agricultural development with ecological sustainability in arid inland basins. From a conceptual perspective, the identification of key ecological restoration zones aligns with the resilience framework of social-ecological systems, where interventions target the most vulnerable nodes to enhance overall system stability. Moreover, restoration strategies should not be viewed as isolated technical measures but as part of a broader governance framework that mediates trade-offs and synergies among multiple ecosystem services. For instance, afforestation improves climate regulation and biodiversity, but may compete with water yield in arid basins; therefore, restoration planning requires a multifunctional landscape perspective that integrates ecological, hydrological, and socio-economic dimensions.

5. Conclusions

This study revealed substantial spatial and temporal variation in ecosystem service value (ESV) in the Aksu River Basin over the past three decades. The total ESV increased from CNY 189.05 billion in 1990 to a peak of CNY 223.26 billion in 2010, followed by a decline to CNY 208.05 billion in 2020. Spatially, Wensu, Xinyuan, and Bachu counties recorded substantial gains, while Atushi, Akto, and Awat counties showed significant losses. SHAP analysis further revealed that ecosystem quality indicators—such as forest condition, soil erosion, and grassland quality—were more decisive for ESV than land use area alone. While provisioning services improved due to land development, declines in regulating and supporting services signal growing ecological pressure in urban and agricultural areas. Through SHAP-driven interpretation of machine learning models, forest quality, soil erosion, and grassland condition were identified as the most influential factors affecting ESV. The methodology in this study demonstrates broad applicability to arid inland river basins worldwide. By combining biophysical modeling with interpretable machine learning, the study highlights the importance of ecosystem quality rather than area alone, offering a transferable approach for diagnosing ecological problems and guiding restoration in similar fragile environments. The findings provide strong technical support for the formulation of region-specific restoration strategies and land use governance mechanisms, offering practical insights into ecosystem-oriented management in arid regions under the dual pressures of human activity and climate change.

Author Contributions

G.X., formal analysis, validation, writing—original draft and methodology. G.L. and J.Y. funding acquisition, writing—review and editing, conceptualization investigation. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the National Natural Science Foundation of China (42361144792), the Tianshan Talent Training Program (2023TSYCLJ0049).

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Peng, J.; Biswas, A.; Jiang, Q.; Zhao, R.; Hu, J.; Hu, B.; Shi, Z. Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province, China. Geoderma 2019, 337, 1309–1319. [Google Scholar] [CrossRef]
  2. Tang, X.; Xu, L.-P.; Zhang, Z.-Y.; Lv, X. Effects of glacier melting on socioeconomic development in the Manas River basin, China. Nat. Hazards 2013, 66, 533–544. [Google Scholar] [CrossRef]
  3. Xu, Z.; Fan, W.; Wei, H.; Zhang, P.; Ren, J.; Gao, Z.; Ulgiati, S.; Kong, W.; Dong, X. Evaluation and simulation of the impact of land use change on ecosystem services based on a carbon flow model: A case study of the Manas River Basin of Xinjiang, China. Sci. Total Environ. 2019, 652, 117–133. [Google Scholar] [CrossRef]
  4. Li, X.; Cheng, G.; Ge, Y.; Li, H.; Han, F.; Hu, X.; Tian, W.; Tian, Y.; Pan, X.; Nian, Y.; et al. Hydrological Cycle in the Heihe River Basin and Its Implication for Water Resource Management in Endorheic Basins. J. Geophys. Res. Atmospheres 2018, 123, 890–914. [Google Scholar] [CrossRef]
  5. Huang, F.; Chunyu, X.; Zhang, D.; Chen, X.; Ochoa, C.G. A framework to assess the impact of ecological water conveyance on groundwater-dependent terrestrial ecosystems in arid inland river basins. Sci. Total Environ. 2020, 709, 136155. [Google Scholar] [CrossRef]
  6. Chen, Y.; Li, Z.; Fang, G.; Li, W. Large Hydrological Processes Changes in the Transboundary Rivers of Central Asia. J. Geophys. Res. Atmos. 2018, 123, 5059–5069. [Google Scholar] [CrossRef]
  7. Huang, F.; Ochoa, C.G.; Chen, X.; Cheng, Q.; Zhang, D. An entropy-based investigation into the impact of ecological water diversion on land cover complexity of restored oasis in arid inland river basins. Ecol. Eng. 2020, 151, 105865. [Google Scholar] [CrossRef]
  8. Shafroth, P.B.; Schlatter, K.J.; Gomez-Sapiens, M.; Lundgren, E.; Grabau, M.R.; Ramírez-Hernández, J.; Rodríguez-Burgueño, J.E.; Flessa, K.W. A large-scale environmental flow experiment for riparian restoration in the Colorado River Delta. Ecol. Eng. 2017, 106, 645–660. [Google Scholar] [CrossRef]
  9. Peng, S.; Ding, Y.; Liu, W.; Li, Z. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth Syst. Sci. Data 2019, 11, 1931–1946. [Google Scholar] [CrossRef]
  10. Wang, H.; Gui, D.; Liu, Q.; Feng, X.; Qu, J.; Zhao, J.; Wang, G.; Wei, G. Vegetation coverage precisely extracting and driving factors analysis in drylands. Ecol. Inform. 2024, 79, 102409. [Google Scholar] [CrossRef]
  11. González, E.; González-Sanchis, M.; Cabezas, Á.; Comín, F.A.; Muller, E. Recent Changes in the Riparian Forest of a Large Regulated Mediterranean River: Implications for Management. Environ. Manag. 2010, 45, 669–681. [Google Scholar] [CrossRef]
  12. Natural Capital Project. InVEST 3.14.2 User’s Guide. Stanford University, University of Minnesota, Chinese Academy of Sciences, The Nature Conservancy, World Wildlife Fund, and Stockholm Resilience Centre. [WWW Document]. 2022. Available online: https://storage.googleapis.com/releases.naturalcapitalproject.org/invest-userguide/latest/zh/index.html (accessed on 8 April 2024).
  13. Cao, T.; Yi, Y.; Liu, H.; Yang, Z. Integrated ecosystem services-based calculation of ecological water demand for a macrophyte-dominated shallow lake. Glob. Ecol. Conserv. 2020, 21, e00858. [Google Scholar] [CrossRef]
  14. Chen, L.; Wei, W. Spatiotemporal Changes in Land Use and Habitat Quality in A Typical Dryland Watershed of Northwest China. Ecol. Environ. Sci. 2022, 31, 1909–1918. [Google Scholar] [CrossRef]
  15. Vörösmarty, C.J.; McIntyre, P.B.; Gessner, M.O.; Dudgeon, D.; Prusevich, A.; Green, P.; Glidden, S.; Bunn, S.E.; Sullivan, C.A.; Liermann, C.R.; et al. Global threats to human water security and river biodiversity. Nature 2010, 467, 555–561. [Google Scholar] [CrossRef]
  16. Millennium Ecosystem Assessment; World Resources Institute (Eds.) Ecosystems and Human Well-Being: Opportunities and Challenges for Business and Industry; World Resources Institute: Washington, DC, USA, 2005. [Google Scholar]
  17. Hu, S.; Ma, R.; Sun, Z.; Ge, M.; Zeng, L.; Huang, F.; Bu, J.; Wang, Z. Determination of the optimal ecological water conveyance volume for vegetation restoration in an arid inland river basin, northwestern China. Sci. Total Environ. 2021, 788, 147775. [Google Scholar] [CrossRef] [PubMed]
  18. Ling, H.; Xu, H.; Shi, W.; Zhang, Q. Regional climate change and its effects on the runoff of Manas River, Xinjiang, China. Environ. Earth Sci. 2011, 64, 2203–2213. [Google Scholar] [CrossRef]
  19. Tao, H.; Gemmer, M.; Song, Y.; Jiang, T. Ecohydrological responses on water diversion in the lower reaches of the Tarim River, China. Water Resour. Res. 2008, 44, W08422. [Google Scholar] [CrossRef]
  20. Hengl, T.; Mendes De Jesus, J.; Heuvelink, G.B.M.; Ruiperez Gonzalez, M.; Kilibarda, M.; Blagotić, A.; Shangguan, W.; Wright, M.N.; Geng, X.; Bauer-Marschallinger, B.; et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 2017, 12, e0169748. [Google Scholar] [CrossRef] [PubMed]
  21. Su, Y.; Li, X.; Feng, M.; Nian, Y.; Huang, L.; Xie, T.; Zhang, K.; Chen, F.; Huang, W.; Chen, J.; et al. High agricultural water consumption led to the continued shrinkage of the Aral Sea during 1992–2015. Sci. Total Environ. 2021, 777, 145993. [Google Scholar] [CrossRef]
  22. Liu, Q.; Dai, H.; Gui, D.; Hu, B.X.; Ye, M.; Wei, G.; Qin, J.; Zhang, J. Evaluation and optimization of the water diversion system of ecohydrological restoration megaproject of Tarim River, China, through wavelet analysis and a neural network. J. Hydrol. 2022, 608, 127586. [Google Scholar] [CrossRef]
  23. Stone, R. Saving Iran’s great salt lake. Science 2015, 349, 1044–1047. [Google Scholar] [CrossRef]
  24. Wang, S.; Fu, B.; Bodin, Ö.; Liu, J.; Zhang, M.; Li, X. Alignment of social and ecological structures increased the ability of river management. Sci. Bull. 2019, 64, 1318–1324. [Google Scholar] [CrossRef]
  25. Wang, Z.; Guo, J.; Ling, H.; Han, F.; Kong, Z.; Wang, W. Function zoning based on spatial and temporal changes in quantity and quality of ecosystem services under enhanced management of water resources in arid basins. Ecol. Indic. 2022, 137, 108725. [Google Scholar] [CrossRef]
  26. Huang, Y.; Li, Y.P.; Chen, X.; Bao, A.M.; Zhou, M. Simulation-based optimization method for water resources management in Tarim River Basin, China. Procedia Environ. Sci. 2010, 2, 1451–1460. [Google Scholar] [CrossRef]
  27. Li, M.; Liang, D.; Xia, J.; Song, J.; Cheng, D.; Wu, J.; Cao, Y.; Sun, H.; Li, Q. Evaluation of water conservation function of Danjiang River Basin in Qinling Mountains, China based on InVEST model. J. Environ. Manag. 2021, 286, 112212. [Google Scholar] [CrossRef]
  28. Ling, H.; Guo, B.; Yan, J.; Deng, X.; Xu, H.; Zhang, G. Enhancing the positive effects of ecological water conservancy engineering on desert riparian forest growth in an arid basin. Ecol. Indic. 2020, 118, 106797. [Google Scholar] [CrossRef]
  29. Liu, Q.; Wang, G.; Gui, D.; Liu, Y.; Kotb Abd-Elmabod, S.; Wei, G. Precise ecological restoration under water diversions-groundwater-ecosystem interactions in drylands. J. Hydrol. 2024, 628, 130601. [Google Scholar] [CrossRef]
  30. Liu, R.; Dong, X.; Wang, X.; Zhang, P.; Liu, M.; Zhang, Y. Study on the relationship among the urbanization process, ecosystem services and human well-being in an arid region in the context of carbon flow: Taking the Manas river basin as an example. Ecol. Indic. 2021, 132, 108248. [Google Scholar] [CrossRef]
  31. Peng, S.; Ding, Y.; Wen, Z.; Chen, Y.; Cao, Y.; Ren, J. Spatiotemporal change and trend analysis of potential evapotranspiration over the Loess Plateau of China during 2011–2100. Agric. For. Meteorol. 2017, 233, 183–194. [Google Scholar] [CrossRef]
  32. Li, Y.; Xu, B.; Li, Y.; Wan, Y. Impact of LULC in Coastal Cities on Terrestrial Carbon Storage and Ecosystem Service Value: A Case Study of Liaoning Province. Sustainability 2025, 17, 2889. [Google Scholar] [CrossRef]
  33. Duan, L.; Yang, S.; Xiang, M.; Li, W.; Li, J. Spatiotemporal evolution and driving factors of ecosystem service value in the Upper Minjiang River of China. Sci. Rep. 2024, 14, 23398. [Google Scholar] [CrossRef]
  34. Li, Y.; Wei, M.; Zhou, R.; Jin, H.; Chen, Y.; Hong, C.; Duan, W.; Li, Q. Identifying priority restoration areas based on ecological security pattern: Implications for ecological restoration planning. Ecol. Indic. 2025, 174, 113486. [Google Scholar] [CrossRef]
  35. Liu, S.; Zhang, J.; Zhang, J.; Wang, K.; Wu, X.; Xu, L. Simulated glacier responses to temperature and precipitation trends in a snowmelt-dominated river basin. Ecol. Indic. 2019, 106, 105492. [Google Scholar] [CrossRef]
  36. Werden, L.K.; Alvarado, J.P.; Zarges, S.; Calderón, M.E.; Schilling, E.M.; Gutiérrez, L.M.; Powers, J.S. Using soil amendments and plant functional traits to select native tropical dry forest species for the restoration of degraded Vertisols. J. Appl. Ecol. 2018, 55, 1019–1028. [Google Scholar] [CrossRef]
  37. Pan, N.; Guan, Q.; Wang, Q.; Sun, Y.; Li, H.; Ma, Y. Spatial Differentiation and Driving Mechanisms in Ecosystem Service Value of Arid Region: A case study in the middle and lower reaches of Shule River Basin, NW China. J. Clean. Prod. 2021, 319, 128718. [Google Scholar] [CrossRef]
  38. Rastogi, M.; Kolur, S.M.; Burud, A.; Sadineni, T.; Sekhar, M.; Kumar, R.; Rajput, A. Advancing Water Conservation Techniques in Agriculture for Sustainable Resource Management: A review. J. Geogr. Environ. Earth Sci. Int. 2024, 28, 41–53. [Google Scholar] [CrossRef]
  39. Wang, W.; Zhang, Z.; Duan, L.; Wang, Z.; Zhao, Y.; Zhang, Q.; Dai, M.; Liu, H.; Zheng, X.; Sun, Y. Response of the groundwater system in the Guanzhong Basin (central China) to climate change and human activities. Hydrogeol. J. 2018, 26, 1429–1441. [Google Scholar] [CrossRef]
  40. Liu, Q.; Gui, D.; Zhang, L.; Niu, J.; Dai, H.; Wei, G.; Hu, B.X. Simulation of regional groundwater levels in arid regions using interpretable machine learning models. Sci. Total Environ. 2022, 831, 154902. [Google Scholar] [CrossRef]
  41. Han, S.; Hu, Q.; Yang, Y.; Wang, J.; Wang, P.; Wang, Q. Characteristics and Driving Factors of Drainage Water in Irrigation Districts in Arid Areas. Water Resour. Manag. 2015, 29, 5323–5337. [Google Scholar] [CrossRef]
  42. Han, Q.; Xue, L.; Liu, Y.; Yang, M.; Chu, X.; Liu, S. Developing a multi-objective simulation-optimization model for ecological water conveyance in arid inland river basins. J. Hydrol. Reg. Stud. 2023, 50, 101551. [Google Scholar] [CrossRef]
  43. Hao, Y.; Wu, J.; Sun, Q.; Zhu, Y.; Liu, Y.; Li, Z.; Yeh, T.J. Simulating effect of anthropogenic activities and climate variation on Liulin Springs discharge depletion by using the ARIMAX model. Hydrol. Process. 2013, 27, 2605–2613. [Google Scholar] [CrossRef]
  44. Shang, H.; Wang, W.; Dai, Z.; Duan, L.; Zhao, Y.; Zhang, J. An ecology-oriented exploitation mode of groundwater resources in the northern Tianshan Mountains, China. J. Hydrol. 2016, 543, 386–394. [Google Scholar] [CrossRef]
  45. Zhang, H.; Ahmed, Z.; Han, W.; Sun, G. Ecosystem service changes and water management in the manas river basin. Water 2024, 16, 3585. [Google Scholar] [CrossRef]
  46. Halik, Ü.; Aishan, T.; Betz, F.; Kurban, A.; Rouzi, A. Effectiveness and challenges of ecological engineering for desert riparian forest restoration along China’s largest inland river. Ecol. Eng. 2019, 127, 11–22. [Google Scholar] [CrossRef]
  47. Moody, A.; Hickey, R. Comparing RUSLE LS Calculation Methods Across Varying DEM Resolutions. Modern Geografia. 2025, 20, 97–123. [Google Scholar] [CrossRef]
  48. Wang, X.; Luo, Y.; Sun, L.; Zhang, Y. Assessing the effects of precipitation and temperature changes on hydrological processes in a glacier-dominated catchment. Hydrol. Process. 2015, 29, 4830–4845. [Google Scholar] [CrossRef]
  49. Liao, S.; Xue, L.; Dong, Z.; Zhu, B.; Zhang, K.; Wei, Q.; Fu, F.; Wei, G. Cumulative ecohydrological response to hydrological processes in arid basins. Ecol. Indic. 2020, 111, 106005. [Google Scholar] [CrossRef]
  50. Wang, Y.; Feng, Q.; Chen, L.; Yu, T. Significance and Effect of Ecological Rehabilitation Project in Inland River Basins in Northwest China. Environ. Manag. 2013, 52, 209–220. [Google Scholar] [CrossRef]
  51. Jiao, A.; Wang, W.; Ling, H.; Deng, X.; Yan, J.; Chen, F. Effect evaluation of ecological water conveyance in Tarim River Basin, China. Front. Environ. Sci. 2022, 10, 1019695. [Google Scholar] [CrossRef]
  52. Sun, X.; Li, F. Spatiotemporal assessment and trade-offs of multiple ecosystem services based on land use changes in Zengcheng, China. Sci. Total Environ. 2017, 609, 1569–1581. [Google Scholar] [CrossRef]
  53. Bowen, G.J.; Cai, Z.; Fiorella, R.P.; Putman, A.L. Isotopes in the Water Cycle: Regional- to Global-Scale Patterns and Applications. Annu. Rev. Earth Planet. Sci. 2019, 47, 453–479. [Google Scholar] [CrossRef]
  54. Chan, K.K.Y.; Ren, Z.H.; Liu, Y.F.; Song, H.; Bai, Y.Q.; Xu, B. Land Cover Change and Fragmentation Within China’s Ramsar Sites. Remote Sens. 2025, 17, 869. [Google Scholar] [CrossRef]
  55. Shumilova, O.; Tockner, K.; Thieme, M.; Koska, A.; Zarfl, C. Global Water Transfer Megaprojects: A Potential Solution for the Water-Food-Energy Nexus? Front. Environ. Sci. 2018, 6, 150. [Google Scholar] [CrossRef]
  56. Lu, F.; Hu, H.; Sun, W.; Zhu, J.; Liu, G.; Zhou, W.; Zhang, Q.; Shi, P.; Liu, X.; Wu, X.; et al. Effects of national ecological restoration projects on carbon sequestration in China from 2001 to 2010. Proc. Natl. Acad. Sci. USA 2018, 115, 4039–4044. [Google Scholar] [CrossRef]
  57. Cheng, G.; Li, X.; Zhao, W.; Xu, Z.; Feng, Q.; Xiao, S.; Xiao, H. Integrated study of the water–ecosystem–economy in the Heihe River Basin. Natl. Sci. Rev. 2014, 1, 413–428. [Google Scholar] [CrossRef]
  58. Czuba, J.A. A Lagrangian framework for exploring complexities of mixed-size sediment transport in gravel-bedded river networks. Geomorphology 2018, 321, 146–152. [Google Scholar] [CrossRef]
  59. Hamel, P.; Bryant, B.P. Uncertainty assessment in ecosystem services analyses: Seven challenges and practical responses. Ecosyst. Serv. 2017, 24, 1–15. [Google Scholar] [CrossRef]
  60. Liu, Y.; Jin, M.; Wang, J. Insights into groundwater salinization from hydrogeochemical and isotopic evidence in an arid inland basin. Hydrol. Process. 2018, 32, 3108–3127. [Google Scholar] [CrossRef]
  61. Xie, G.; Zhang, C.; Zhang, L.; Chen, W.; Li, S. Improvement of the Evaluation Method for Ecosystem Service Value Based on Per Unit Area. J. Nat. Resour. 2015, 30, 1243–1254. [Google Scholar]
  62. Franklin, J.F.; Lindenmayer, D.B. Importance of matrix habitats in maintaining biological diversity. Proc. Natl. Acad. Sci. USA 2009, 106, 349–350. [Google Scholar] [CrossRef] [PubMed]
  63. Everard, M.; Sharma, O.P.; Vishwakarma, V.K.; Khandal, D.; Sahu, Y.K.; Bhatnagar, R.; Singh, J.K.; Kumar, R.; Nawab, A.; Kumar, A.; et al. Assessing the feasibility of integrating ecosystem-based with engineered water resource governance and management for water security in semi-arid landscapes: A case study in the Banas catchment, Rajasthan, India. Sci. Total Environ. 2018, 612, 1249–1265. [Google Scholar] [CrossRef] [PubMed]
  64. Wang, C.; Liu, X.; Wu, J.; Qiao, W.; Liu, Y. Planning a water-constrained ecological restoration pattern to enhance sustainable landscape management in drylands. J. Environ. Manag. 2023, 335, 117514. [Google Scholar] [CrossRef] [PubMed]
Land 14 02092 g001
Figure 1. Location of the study area.
Figure 1. Location of the study area.
Land 14 02092 g001
Land 14 02092 g002
Figure 2. Sankey diagram of land use transfer in the Aksu River Basin from 1990 to 2020.
Figure 2. Sankey diagram of land use transfer in the Aksu River Basin from 1990 to 2020.
Land 14 02092 g002
Land 14 02092 g003
Figure 3. The spatial distribution of land use types in the Aksu River Basin for 1990 and 2020.
Figure 3. The spatial distribution of land use types in the Aksu River Basin for 1990 and 2020.
Land 14 02092 g003
Land 14 02092 g004
Figure 4. Spatial changes in the total value of ecosystem services in the Aksu River Basin from 1990 to 2020, (a) the spatial variation trend of the ESV; (b) the ESV change of the counties in the Aksu River Basin.
Figure 4. Spatial changes in the total value of ecosystem services in the Aksu River Basin from 1990 to 2020, (a) the spatial variation trend of the ESV; (b) the ESV change of the counties in the Aksu River Basin.
Land 14 02092 g004
Land 14 02092 g005
Figure 5. The contribution and influence of various ecological and environmental factors on total ecosystem service value (ESV) in the Aksu River Basin, (a) the SHAP values of the influence of each ecological environment factor on the ESV; (b) the ranking of the influence of various ecological environment factors on the ESV.
Figure 5. The contribution and influence of various ecological and environmental factors on total ecosystem service value (ESV) in the Aksu River Basin, (a) the SHAP values of the influence of each ecological environment factor on the ESV; (b) the ranking of the influence of various ecological environment factors on the ESV.
Land 14 02092 g005
Table 1. Data Information.
Table 1. Data Information.
Serial NumberData NameTime RangeSpatial AccuracySource
1Basin boundary vector20241 mGlobal Change Research Data Publishing&Repository (www.geodoi.ac.cn, accessed on 24 February 2025)
2Distribution of river systems20241 mGlobal Change Research Data Publishing&Repository (www.geodoi.ac.cn, accessed on 24 February 2025)
3Digital Elevation Model (DEM)202030 mGeospatial Data Cloud (www.gscloud.cn, accessed on 5 March 2025)
4Administrative division2020-Geospatial Data Cloud (www.gscloud.cn, accessed on 5 March 2025)
5Land use type1990, 2000, 2010, 202030 mNational Cryosphere Desert Data Center (www.ncdc.ac.cn, accessed on 5 March 2025)
6Average annual surface temperature1990, 2000, 2010, 202030 mNational Earth System Science Data Center (www.geodata.cn, accessed on 5 March 2025)
7Annual surface rainfall1990, 2000, 2010, 202030 mNational Earth System Science Data Center (www.geodata.cn, accessed on 5 March 2025)
8NDVI Index1990, 2000, 2010, 202030 mGoogle Earth Engine (https://code.earthengine.google.com, accessed on 15 March 2025)
9Physical and chemical properties of soil20201 kmHarmonized World Soils Database version 2.0 (HWSD v2.0)
10Soil salinity index1990, 2000, 2010, 20201 kmHarmonized World Soils Database version 2.0 (HWSD v2.0)
12Slope factor202030 mGeospatial Data Cloud (www.gscloud.cn, accessed on 7 March 2025)
13Slope length factor202030 mGeospatial Data Cloud (www.gscloud.cn, accessed on 7 March 2025)
16Rainfall erosion factor1990, 2000, 2010, 202030 mNational Earth System Science Data Center (www.geodata.cn, accessed on 7 March 2025)
17Net primary productivity (NPP)1990, 2000, 2010, 202030 mGoogle Earth Engine (https://code.earthengine.google.com, accessed on 7 March 2025)
Table 2. The area changes in different land use types in the Aksu River Basin from 1990 to 2020.
Table 2. The area changes in different land use types in the Aksu River Basin from 1990 to 2020.
Area in 1990 (km2)Area in 2000 (km2)Area in 2010 (km2)Area in 2020 (km2)
Cultivated land5876.426211.507890.979167.24
Forest27.8041.8049.2680.87
Grassland18,911.6918,921.1118,934.8417,816.74
Water bodies249.59258.32315.90269.17
Glacier and snow cover2061.582151.952858.622349.41
Bare land30,172.1029,679.1826,984.9127,160.48
Construction land9.7945.08274.45464.91
Wetland0.000.000.010.14
Table 3. Ecosystem service values (ESV) in the Aksu River Basin from 1990 to 2020 (Unit: CNY 100 million).
Table 3. Ecosystem service values (ESV) in the Aksu River Basin from 1990 to 2020 (Unit: CNY 100 million).
Ecosystem Service1990200020102020
Food production10.3610.6512.1312.91
Raw material production1.461.521.711.83
Climate regulation36.6337.6844.7640.75
Water conservation53.2455.3268.1360.32
Soil retention41.3941.9444.6144.04
Biodiversity conservation35.1535.4837.3936.07
Cultural and recreational services10.8111.2514.5012.14
Total ESV189.05193.84223.26208.05
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Xia, G.; Lv, G.; Yang, J. Ecological Risk Assessment of the Aksu River Basin Based on Ecological Service Value. Land 2025, 14, 2092. https://doi.org/10.3390/land14102092

AMA Style

Xia G, Lv G, Yang J. Ecological Risk Assessment of the Aksu River Basin Based on Ecological Service Value. Land. 2025; 14(10):2092. https://doi.org/10.3390/land14102092

Chicago/Turabian Style

Xia, Guozhu, Guanghui Lv, and Jianjun Yang. 2025. "Ecological Risk Assessment of the Aksu River Basin Based on Ecological Service Value" Land 14, no. 10: 2092. https://doi.org/10.3390/land14102092

APA Style

Xia, G., Lv, G., & Yang, J. (2025). Ecological Risk Assessment of the Aksu River Basin Based on Ecological Service Value. Land, 14(10), 2092. https://doi.org/10.3390/land14102092

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop