Spatiotemporal Evolution of Mountainous Ecosystem Services in an Arid Region and Its Influencing Factors: A Case Study of the Tianshan Mountains in Xinjiang

Abstract

Mountainous ecosystems provide humans with multiple ecosystem services (ESs), but global changes and anthropogenic activities threaten the supply of such services in arid regions. To maintain regional ecological security and achieve ecosystem sustainability, it is quite essential to understand the spatiotemporal characteristics of mountainous ESs in arid regions and clarify the main driving factors of different ESs. Using the integrated valuation of ecosystem services and tradeoffs (InVEST) and revised universal soil-loss equation (RUSLE) models, we evaluated the ESs provided by the Tianshan Mountains of Xinjiang from 2000 to 2020. The research showed that: (i) over the 20 years in question, habitat quality and carbon storage remained relatively stable, while soil retention and water yield fluctuated significantly. (ii) All ES pairs exhibited synergies. Spatial synergy areas were concentrated in the northwestern and southwestern areas; spatial trade-off areas alternated with spatial synergy areas. (iii) Hotspots with at least two ESs covered 73% of the study region. Middle- and high-altitude areas were the main supply areas of ES. (iv) Land-use types were the dominant driving factor of habitat quality and carbon storage, while mean annual precipitation had the strongest explanatory power for water yield. Soil retention was mainly affected by mean annual temperature and the normalized difference vegetation index. Our findings could provide guidance for policymakers when developing measures for ecosystem conservation and management.

A Refined Supply‐Demand Framework to Quantify Variability in Ecosystem Services Related to Surface Water in Support of Sustainable Development Goals

“…On one hand, this is attributed to the highest sensitivity of WY to precipitation. Xinjiang receives the highest precipitation under the SSP245 scenario (Figure 2b and Figure S2 in Supporting Information S1), leading to the largest WY in this scenario, which provides the possibility of high SDI under the 245_236 scenario (Lu et al., 2022; Redhead et al., 2016). On the other hand, IE directly affects the magnitude of irrigation water use.…”

Ecosystem Service Optimisation in the Central Plains Urban Agglomeration Based on Land Use Structure Adjustment

“…For example, in a quantitative evaluation study of water production, the temporal and spatial distribution characteristics of annual water production were closely related to the accuracy of the average annual temperature, actual annual evapotranspiration, and rainfall data in the study area [41]. The accuracy of land use and vegetation type distribution data has an important impact on the accuracy of the evaluation results for the spatiotemporal pattern of carbon storage in the study area [42]. The evaluation of ecosystem and water purification services is greatly restricted by the accuracy of terrain, soil, and other spatial data [43].…”

Differentiated factors drive the spatial heterogeneity of ecosystem services in Xinjiang Autonomous Region, China

“…Factors affecting ecosystem services mainly include two types: ecological factors and socio-economic factors (Hu et al, 2022;Huang et al, 2022;Lu et al, 2022;Zhuang et al, 2022). The ecological factors selected in this study include precipitation (PRE), temperature (TEM), fractional vegetation cover (FVC), slope (SL), and digital elevation model (DEM).…”

“…Li et al (2019) showed that the types and intensity of driving factors of ecosystem services in Xinjiang differed between southern and northern Xinjiang. Most studies on ecosystem services in Xinjiang have been undertaken at the grid scale (Wei et al, 2018;Yushanjiang et al, 2018;Lu et al, 2022). Therefore, given the high heterogeneity of Xinjiang's ecosystem, it is significant to study the ecosystem, and its driving factors at the county level for zoning management.…”