Xiaohuang Liu

Ecosystems offer natural resources and habitats for humans, serving as the foundation for human social development. Taking the Tianshan Mountains as the study area, this study investigated the changing trends, hot spots, and driving factors of water yield (WY), soil conservation (SC), carbon storage (CS), and habitat quality (HQ), in the Tianshan region, from 1990 to 2020. To determine the trade-offs and synergies between the ESs, we employed the Spearman correlation coefficient, geographically weighted regression, the self-organizing map (SOM), and other methods. Five main results were obtained. (1) There were similar spatial distribution patterns for WY, HQ, CS, and SC, with high-value areas mainly concentrated in grassland zones, forest zones, river valleys, and the intermountain basins of the mountain range, while regions with low value were clustered in desert zones and snow/ice zones. (2) According to the hotspot analysis, areas with relatively strong ES provisioning for WY, HQ, CS, and SC, were primarily concentrated in the BoroHoro Ula Mountains and Yilianhabierga Mountains. In contrast, areas with relatively weak ES provisioning were mainly located in the Turpan Basin. (3) Precipitation was the primary explanatory factor for WY. Soil type, potential evapotranspiration (PET), and the normalized difference vegetation index (NDVI) were the primary explanatory factors for HQ. Soil type and NDVI were the primary explanatory factors for CS. PET was the primary explanatory factor for SC. (4) There were synergistic relationships between the WY, HQ, CS, and SC, with the strongest synergies found between CS–HQ, WY–HQ, and WY–SC. (5) Six ES bundles were identified through the SOM method, with their composition varying at different spatial scales, indicating the need for different ES management priorities in different regions. Our analysis of ESs, from various perspectives, offers insights to aid sustainable ecosystem management and conservation efforts in the Tianshan region and other major economic areas worldwide.

Elevational climatic heterogeneity, complex terrains, and varying subsurface properties affect the sensitivity of evapotranspiration (ET) in dryland mountain forests to hydrometeorological changes. However, the elevational distribution of ET sensitivity and its major influencing factors remain poorly understood. This study focused on the mid-altitude zone (1000–3500 m) forests in the Chinese Western Tianshan Mountains and assessed ET sensitivity to multiple climate variables, including precipitation (P) and potential evapotranspiration (PET), from 2000 to 2020. To evaluate the multi-year mean and trends in ET sensitivity, multi-source remote sensing data and regional survey data were analyzed using Spearman’s correlation coefficient, the sliding window method, and Kendall’s test. Furthermore, the relative importance of environmental variables (topography, geology, soil, and vegetation) was investigated. P and PET showed no significant trends, while ET exhibited a significant increasing trend (5.81 mm/yr, p < 0.01), particularly at elevations above 2000 m. Most forests (93.5%) showed a positive sensitivity of ET to P, and 70.0% showed a positive sensitivity of ET to PET, mainly at elevations of 1500–2500 m. Additionally, the trend in ET sensitivity to P decreased with an increasing elevation, with 64.5% showing a positive trend. Meanwhile, the trend in ET sensitivity to PET increased with elevation, with 88.1% showing a positive trend. Notably, 53.2% of the forests showed increasing ET sensitivity trends to both P and PET, primarily at elevations of 2000–3000 m with a mean normalized difference vegetation index (NDVI) of 0.56. Geological factors, particularly the hydrological properties of weathered bedrock, contributed the most (~47%) to mean sensitivity. However, geological and vegetative factors, including the NDVI and root zone water availability, were the main contributors (35% each) to the sensitivity. This study highlights the elevation-dependent sensitivity of dryland mountain forests to hydrothermal changes, with higher-elevation forests (>2000 m) being more sensitive to global warming.

The black soil region in Northeast China serves as a ballast for food security. However, the presence of scattered polluting enterprises poses a threat to the safety of the surrounding soil and agricultural products. In this study, the distribution patterns and agglomeration features of key industrial enterprises in Northeast China were elucidated through multi-source geographical big data and geographic information system (GIS) spatial analysis. Subsequently, the risk areas were extracted based on their potential impact on the soil environmental quality of the surrounding agricultural lands. The results revealed that pollution-related enterprises were widely distributed but locally clustered in the black soil area. The dominant industries were chemical manufacturing, petroleum processing, coking, and non-ferrous metal mining. The study found that the agricultural land area affected by polluting enterprises was 43,396.13 km², with the majority being at a low-risk level (83.42%). High-risk areas (1646.62 km²) were mostly aggregated west of Hulunbuir, east of Xilingol, and in most of Chifeng. These areas were primarily affected by the non-ferrous metal mining industry. Other high-risk hotspots were mainly influenced by the chemical manufacturing and metal processing industries. The emissions from industrial and mining enterprises are important heavy metals in the agricultural lands in this region. However, it is important to note that there are other sources of pollution as well. These results may contribute to future investigations on soil environmental quality and pollution source control in the black soil region in Northeast China.

Long-term tectonic movements have shaped the geomorphological features and hydrothermal conditions of mountains, influencing their vegetation growth patterns in both positive and negative ways. However, little is known about the effect of fault development on the spatio-temporal variation in vegetation along the elevation gradient in mountainous regions. To address this issue of montane tectonic ecology, this study selected the tectonically active mid-altitude zone (1000–3500 m) of the Chinese Western Tianshan Mountains. The role of tectonics is investigated by fault length density maps calculated from zonal statistics of region-scale fault survey data (1:250,000). The normalized difference vegetation index (NDVI) was chosen as an indicator to analyze the growth status of vegetation. The spatial distribution of fault length density, elevational, and interannual characteristics of the NDVIs from 2000 to 2020 and their relationships along the elevation gradient were investigated. The results show that the faulting zone accounts for 32.6% of the study area and the high faulting zone exhibits a unimodal distribution along the elevation gradient, with the maximum occurring at elevations of approximately 2000 m. The NDVIs of forests and high-coverage grassland show a unimodal distribution with elevation, with the maximum occurring at elevations of approximately 2000 m, coinciding with that of fault length density. In the elevation range of 1000–2500 m, the NDVI of the faulting zone is lower than that of the non-faulting zone, whereas that of the elevation range of 2500–3500 m is higher—a difference that is particularly evident in forests. This elevation-dependent contrasting effect of faults on vegetation growth could be attributed to more favorable hydrothermal conditions for vegetation in fault valleys and reduced landslide susceptibility with increasing elevation. This study highlights the need to consider fault distribution in understanding vegetation distribution and growth in tectonically active mountains.

Context: Analysing soil erosion has important research significance for the protection of the ecological environment and the prevention and control measures of soil erosion. Methods: This paper aims to discuss the soil erosion degree in the warm temperate grass farming subregion of the southern Shanxi and Guanzhong Basin, China, based on Universal Soil Loss Model, RUSLE. Aims: The soil erosion modulus from 1998 to 2020 of the study area was calculated and divided into five periods: 2000, 2005, 2010, 2015, and 2020. Key results: We highlight two key findings: (1) the average soil erosion modulus changed from 498.86 t km−2 a−1 in 2000 to 316.94 t km−2 a−1 in 2020, and the proportion of soil area with an unchanged erosion degree is above 85%; (2) the average annual erosion area is the largest when rainfall is greater than 550 mm and less than 620 mm. From 2000 to 2020, the area of cultivated land decreased by 3497.47 km2, and the area of grassland increased by 1364.96 km2. The degree of erosion of grassland is the most severe, with soil erosion is most intense when the Normalised Vegetation Index (NDVI) is greater than 0.55 and less than 0.75. Conclusions: The results show that the soil erosion in this area is slight on the whole and its degree has been decreasing. Implications: The analysis in this paper can elucidate the seriousness of the soil erosion problem so that the government can strengthen the key management of soil and water conservation and achieve the purpose of reducing soil erosion.

Due to climate change and human activities, the eco-environment quality (EEQ) of eco-fragile regions has undergone massive change, especially in the Tibet Plateau. The Qilian Mountains (QLM) region is an essential ecological function zone in the northeastern Tibet Plateau, which plays a vital role in northwestern China’s eco-environmental balance. However, EEQ changes in the QLM during the 21st century remain poorly understood. In this study, the spatiotemporal variations of the EEQ in the QLM were analyzed from 2000 to 2020 using a remote sensing ecological index (RSEI). The EEQ driving factors are identified by the geographic detector, and the spatial influence of critical factors is represented by a geographically weighted regression model. The results show low EEQ in the QLM. From 2000 to 2020, the EEQ initially slightly improved, then deteriorated, and finally gradually recovered. Spatially, the EEQ shows an increasing trend from northwest to southeast. Moran’s I of EEQ remains at around 0.95, representing high spatial aggregation. “High–High” and “Low–Low” clustering features dominate in the local spatial autocorrelation, indicating the EEQ of the QLM is polarized. Precipitation is the dominant positive factor in the EEQ, with a q statistics exceeding 0.644. Furthermore, the key factors (precipitation, distance to towns, distance to roads) affecting EEQ in different periods vary significantly in space. From results we can draw the conclusion that the natural factors mainly control the spatial patterns of EEQ, while the human factors mainly impact the temporal trend of EEQ, the EEQ in the QLM has been significantly improved since 2015. Our findings can provide theoretical support for future eco-environmental protection and restoration in the QLM.