Search Results (41)

Soil stoichiometric characteristics, as sensitive indicators of soil nutrient supply capacity and ecosystem stability, have emerged as a frontier research focus in biogeochemical cycling and ecological studies. However, the spatial variations of soil stoichiometric characteristics and driving factors in desert steppes remain unclear. Therefore, we investigated soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) contents and their ratios (C:N, C:P and N:P) in desert steppes in the Ili River basin, China. Results showed that: (1) in the Ili River basin, the SOC, TN, and TP contents were 30.27, 0.77, and 0.79 g·kg⁻¹, respectively, while the soil stoichiometry ratios of C:N, C:P, and N:P were 47.33, 35.48, and 1.13, respectively. All indicators demonstrated moderate variability, while soil C:P showed strong variability. (2) Significant seasonal variations were observed in SOC, TN, TP and stoichiometric ratios (p < 0.05), and soil stoichiometric characteristics were positively correlated with elevation. (3) According to Bayesian linear regression models and partial least squares-partial maximum likelihood (PLS-PM) models, climate was the principal driver of soil C, N, and their stoichiometric ratios, with mean annual temperature (MAT) and minimum temperature (Tmin) being the most influential determinants. These findings provide preliminary insights into the spatiotemporal variation patterns of soil chemical characteristics in desert steppe ecosystems of the Ili River basin. This study contributes to a deeper understanding of nutrient cycling processes within desert steppe ecosystems and offers a degree of scientific support. Full article

Locust outbreaks are major biological disturbances in grassland ecosystems of arid and semi-arid regions. Accurate identification of locust suitable habitats is important for regional monitoring and management. However, direct comparisons between multi-criteria analysis (MCA) and species distribution models (SDMs) under a unified framework remain limited. In this study, we compared these two approaches for dominant locust species in Xinjiang, China, including Calliptamus italicus, Gomphocerus sibiricus, and Locusta migratoria manilensis. We used the same environmental variables and occurrence records for all models. The MCA methods included the analytic hierarchy process (AHP), technique for order preference by similarity to ideal solution (TOPSIS), and ordered weighted averaging (OWA). The SDMs included the generalized linear model (GLM), maximum entropy model (MaxEnt), extreme gradient boosting (XGBoost), and an ensemble model. The results showed that SDMs had higher area under the receiver operating characteristic curve (AUC) and true skill statistic (TSS) values than MCA under the internal point-based evaluation framework, although both approaches effectively identified locust-suitable habitats. The two approaches also showed high spatial agreement in moderately and highly suitable habitats, with Jaccard indices of 0.88–0.92, and consistently identified the northern slopes of the Tianshan Mountains, the Ili River Valley, and the margins of the Junggar Basin as core suitable areas. These results indicate that the two approaches are complementary for locust monitoring and management. Full article

Morphological traits, as core components of functional traits, are fundamental in determining environmental adaptability. However, under climate warming, the adaptive morphological changes and associated ecological risks of locust populations migrating to higher altitudes remain poorly understood. Here, we investigated Calliptamus italicus, the dominant locust species in the desert steppes of the Ili River Basin, to explore the response patterns of its morphological functional traits along an altitudinal gradient and their relationships with environmental factors. Morphological measurements revealed that forewing area, width, and length, as well as hindwing width, exhibited highly significant positive correlations with altitude (p < 0.01); in contrast, body length, head width, head height, pronotum length, pronotum width, hind femur length, and hind tibia length displayed significant negative correlations with altitude (p < 0.05). All morphological indicators presented highly significant sexual dimorphism (p < 0.001). Ratio analysis showed that the pronotum width-to-head width ratio (M/C), pronotum height-to-head width ratio (H/C), and forewing length-to-hind tibia length ratio (E/F) were significantly positively correlated with the altitudinal gradient (p < 0.05), with all ratios exhibiting significant sexual differences (p < 0.05). Random Forest analysis showed that PC1 (75.5% of variation) reflected traits for feeding, jumping, and reproduction, whereas PC2 (5.6%) represented flight-related traits, with significant sexual dimorphism. This study demonstrates that trait variation in C. italicus along an altitudinal gradient is closely linked to environmental factors. Our findings provide critical data for predicting habitat adaptation responses in locust populations, thereby enhancing the precision and efficacy of locust plague management and contributing to the conservation and restoration of desert steppe ecosystems. Full article

The escalating scarcity of freshwater resources necessitates the utilization of alternative saline waters for sustainable aquaculture. Perca schrenkii, an endemic fish from the Ili River basin, demonstrates considerable potential for cultivation in chloride-type saline–alkaline waters: its 96 h acute salinity tolerance is higher than that of freshwater populations of its congeneric Perca fluviatilis. This study systematically investigated the histomorphological responses of its key osmoregulatory and metabolic organs—gill, kidney, intestine, and liver—under acute (12–14 ppt for 96 h) and chronic (3–7 ppt for 60 days) salinity stress. Acute exposure induced dose- and time-dependent structural damage, including lamellar fusion in gills, glomerular reduction in kidneys, mucosal atrophy in intestines, and hepatocellular swelling. In contrast, chronic acclimation revealed active remodeling, such as lamellar shortening, renal tubular dilation, intestinal muscularis thickening, and biphasic hepatocyte adjustments. A hierarchical framework of structural adaptation was proposed, delineating Safe (≤3 ppt), Acclimation (5 ppt), Tolerance (7 ppt), and Lethal (≥13 ppt) zones. These findings elucidate the structural basis of salinity tolerance in Perca schrenkii and provide practical morphological indicators for assessing fish health in saline aquaculture. Full article

Livestock represent a significant source of methane (CH₄) emissions, particularly in pastoral regions. However, in Xinjiang—a pivotal pastoral region of China—the spatiotemporal patterns of livestock CH₄ emissions remain poorly characterized, constraining regional mitigation actions. Here, a detailed CH₄ emissions inventory for livestock in Xinjiang spanning the period 2000–2020 is compiled. Eight livestock categories were covered, gridded livestock maps were developed, and the dynamic emission factors were built by using the IPCC 2019 Tier 2 approaches. Results indicate that the CH₄ emissions increased from ~0.7 Tg in 2000 to ~0.9 Tg in 2020, a 28.5% increase over the past twenty years. Beef cattle contributed the most to the emission increase (59.6% of total increase), followed by dairy cattle (35.7%), sheep (13.9%), and pigs (4.3%). High-emission hotspots were consistently located in the Ili River Valley, Bortala, and the northwestern margins of the Tarim Basin. Temporal trend analysis revealed increasing emission intensities in these regions, reflecting the influence of policy shifts, rangeland dynamics, and evolving livestock production systems. The high-resolution map of CH₄ emissions from livestock and their temporal trends provides key insights into CH₄ mitigation, with enteric fermentation showing greater potential for emission reduction. This study offers the first long-term, high-resolution CH₄ emission inventory for Xinjiang, providing essential spatial insights to inform targeted mitigation strategies and enhance sustainable livestock management in arid and semi-arid ecosystems. Full article

Wetlands function as crucial transitional zones between land and water ecosystems worldwide, contributing significantly to the stability of local ecosystems. However, there is limited research on landscape changes in Xinjiang’s arid interior regions and the factors driving these changes. This study uses data reanalysis techniques to examine the spatial and temporal evolution and landscape patterns of wetlands, as well as their driving forces, in Xinjiang between 1990 and 2023. The results show that over the past three decades, the wetland area in Xinjiang has grown from 18,427 km² in 1990 to 21,532 km² in 2023, with an annual increase of about 94 km². The greatest growth in wetlands, particularly lakes, marshes, and rivers, has occurred around the periphery of the Tarim Basin and the Ili River Basin, while mountainous areas have seen slight reductions. The distribution pattern shows higher wetland coverage in southern Xinjiang and less coverage in the north, with the largest proportion of wetlands found in the south. Additionally, wetland expansion has led to improvements in the number, density, aggregation, and connectivity of wetland patches, while the complexity of their shapes has decreased. The overall habitat quality of wetlands has also improved over time. Attribution analysis highlights that the rise in runoff due to temperature increases over the past 30 years is a major driver of wetland expansion, with warming accounting for the largest share of expansion in lakes (36%) and in rivers (47.9%). Furthermore, the implementation of large-scale engineering measures, such as ecological water diversion, water-saving irrigation, and reservoir management, has contributed significantly to wetland expansion and ecological restoration. These results provide useful insights for the long-term conservation and management of wetland resources in the arid areas of Xinjiang. Full article

Geological Disasters (Geo-disasters) are common in the Ili River Basin, with extreme precipitation being a major triggering factor. As the frequency and intensity of these events increase, the associated risks also rise. This study proposes a hazard assessment framework that integrates extreme precipitation recurrence periods with Geo-disaster susceptibility. Furthermore, based on a comprehensive risk assessment model encompassing hazard, exposure, vulnerability, and disaster mitigation capacity, the study evaluates Geo-disaster risk in the Ili River Basin under extreme precipitation conditions. Hazard levels are assessed by integrating geo-disaster susceptibility with recurrence periods of extreme precipitation, resulting in hazard and risk maps under various conditions. The susceptibility indicator system is refined using K-means clustering, the certainty factor (CF) model, and Pearson correlation to reduce redundancy. Key findings include: (a) Geo-disasters are influenced by a combination of factors. High-susceptibility areas are typically found in moderately sloped terrain (8.5–17.64°) at elevations between 1412 m and 2234 m, especially on east- and southeast-facing slopes. Lithology, soil, hydrology, fault proximity, and the topographic wetness index (TWI) are the primary influences, while high NDVI values reduce susceptibility. (b) The hazard pattern varies with the recurrence period of extreme precipitation. Shorter periods lead to broader high-hazard zones, while longer periods concentrate hazards, particularly in Yining City. (c) Exposure is higher in the east, vulnerability aligns with transportation networks, and disaster mitigation capacity is stronger in the north, particularly in Yining. (d) Low-risk areas are found in valleys and flat terrains, while medium to high-risk zones concentrate in southeastern Zhaosu, Tekes, and Gongliu counties. Some economically active regions require special attention due to their high exposure and vulnerability. Full article

The Ili River Basin is characterized by complex topography and diverse climatic zones with limited in situ observations. This study evaluates the performance of six widely used precipitation datasets, CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), ERA5_Land (European Centre for Medium-Range Weather Forecasts—ECMWF Reanalysis 5_Land), GPCC (Global Precipitation Climatology Centre), IMERG (Integrated Multi-satellite Retrievals for GPM), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and TerraClimate, against ground-based data from 2001 to 2023. The evaluation is conducted across multiple spatial scales and temporal resolutions. At the basin scale, most datasets exhibit strong correlations with in situ observations across all temporal scales (r > 0.7), except for PERSIANN, which demonstrates a relatively weaker performance during summer and winter (r < 0.6). All datasets except ERA5_ Land show low annual and monthly bias (<5%), although larger errors are observed during summer, particularly for IMERG and PERSIANN. Dataset performance generally declines with increasing elevation. Basin-wide gridded evaluations reveal distinct spatial variations across all elevation zones, with CHIRPS showing the strongest ability to capture orographic precipitation gradients throughout the basin. All datasets correctly identified 2008 as a drought year and 2016 as a wet year, even though the magnitude and spatial resolution of the anomalies varied among them. These findings highlight the importance of selecting precipitation datasets that are suited to the complex topographic and climatic characteristics of transboundary basins. Our study provides valuable insights for improving hydrological modeling and can be used for water sustainability and flood–drought mitigation support activities in the Ili River Basin. Full article

Snow cover variations significantly affect the stability of regional water supply and terrestrial ecosystems in arid northwest China. This study comprehensively evaluates snow resource changes since 2000 by integrating multisource remote sensing datasets and analyzing four key indicators: snow cover area (SCA), snow phenology (SP), snow depth (SD), and snow water equivalent (SWE). The results reveal a slight downtrend in SCA over the past two decades, with an annual decline rate of 7.13 × 10³ km². The maximum SCA (1.28 × 10⁶ km²) occurred in 2010, while the minimum (7.25 × 10⁵ km²) was recorded in 2014. Spatially, SCA peaked in December in the north and January in the south, with high-altitude subregions (Ili River Basin (IRB), Tarim River Region (TRR), North Kunlun Mountains (NKM), and Qaidam Basin (QDB)) maintaining stable summer snow cover due to low temperatures and high precipitation. Analysis of snow phenology indicates a significant shortening of snow cover duration (SCD), with 62.40% of the study area showing a declining trend, primarily driven by earlier snowmelt. Both SD and SWE exhibited widespread declines, affecting 75.09% and 84.85% of the study area, respectively. The most pronounced SD reductions occurred in TRR (94.44%), while SWE losses were particularly severe in North Tianshan Mountains (NTM, 94.61%). The total snow mass in northwest China was estimated at 108.95 million tons, with northern Xinjiang accounting for 66.24 million tons (60.8%), followed by southern Xinjiang (37.44 million tons) and the Hexi Inland Region (5.27 million tons). Consistency analysis revealed coherent declines across all indicators in 55.56% of the study area. Significant SD and SCD reductions occurred in TRR and Tuha Basin (THB), while SWE declines were widespread in NTM and IRB, driven by rising temperatures and decreased snowfall. The findings underscore the urgent need for adaptive strategies to address emerging challenges for water security and ecological stability in the region. Full article

The globose scale (GS) Sphaerolecanium prunastri (Boyer de Fonscolombe) (Hemiptera: Coccidae) is a serious pest affecting plants within the Rosaceae, notably wild apricot, Armeniaca vulgaris (Lamarck). Following its initial detection in 2019, more than 80% of valleys with wild apricots have become affected in the Ili River Basin of the Tianshan Mountains in Xinjiang, China. This study assessed GS population dynamics post invasion and its effects on the growth and reproductive traits of wild apricot trees from 2019 to 2024. Nymph densities have decreased but remain high, with densities per 20 cm of shoots of 986 (1^st-instar nymphs) and 120 (2^nd-instar nymphs) in 2024, respectively. Damage has declined, with high damage rankings decreasing from 24% to 11% of wild apricot trees. However, the mortality of trees was higher (25%) in infested than non-infested areas (13%). Interestingly, GS feeding stimulated the growth of spring shoots but significantly reduced the reproductive capacity of wild apricots. Heavily infested trees exhibited increased shoot length (2–3 times), decreased fruit yield (20-fold), lower flowering percentage (8-fold), and reduced flower bud density (2-fold) compared to non-infested trees. Overall, despite a decrease in damage severity, wild apricot forests remain threatened by GS. Implementing integrated pest management (IPM) strategies is essential for effective GS management and the recovery of wild apricot forests. Full article

Climate change is leading to an increase in the frequency and intensity of flooding, making it necessary to consider future changes in flood risk management. In regions where ground-based observations are significantly restricted, the implementation of conventional risk assessment methodologies is always challenging. This study proposes an integrated remote sensing and machine learning approach for flood risk assessment in data-scarce regions. We extracted the historical inundation frequency using Sentinel-1 SAR and Landsat imagery from 2001 to 2023 and predicted flood susceptibility and inundation frequency using XGBoost, Random Forest (RF), and LightGBM models. The risk assessment framework systematically integrates hazard components (flood susceptibility and inundation frequency) with vulnerability factors (population, GDP, and land use) in two SSP-RCP scenarios. The results indicate that in the SSP2-RCP4.5 and SSP5-RCP8.5 scenarios, combined high- and very-high-flood-risk areas in the Ili River Basin in China (IRBC) are projected to reach 29.1% and 29.7% of the basin by 2050, respectively. In the short term, the contribution of inundation frequency to risk is predominant, while vulnerability factors, particularly population, contribute increasingly in the long term. This study demonstrates that integrating open geospatial data with machine learning enables actionable flood risk assessment, quantitatively supporting climate-resilient planning. Full article

The aboveground biomass (AGB) of forests reflects the productivity and carbon-storage capacity of the forest ecosystem. Although AGB estimation techniques have become increasingly sophisticated, the relationships between AGB, spatial distribution, and growth stages still require further exploration. In this study, the Picea schrenkiana (Picea schrenkiana var. tianschanica) forest area in the Kashi River Basin of the Ili River Valley in the western Tianshan Mountains was selected as the research area. Based on forest resources inventory data, Gaofen-1 (GF-1), Gaofen-6 (GF-6), Gaofen-3 (GF-3) Polarimetric Synthetic Aperture Radar (PolSAR), and DEM data, we classified the Picea schrenkiana forests in the study area into three cases: the Whole Forest without vertical zonation and stand age, Vertical Zonality Classification without considering stand age, and Stand-Age Classification without considering vertical zonality. Then, for each case, we used eXtreme Gradient Boosting (XGBoost), Back Propagation Neural Network (BPNN), and Residual Networks (ResNet), respectively, to estimate the AGB of forests in the study area. The results show that: (1) The integration of multi-source remote-sensing data and the ResNet can effectively improve the remote-sensing estimation accuracy of the AGB of Picea schrenkiana. (2) Furthermore, classification by vertical zonality and stand ages can reduce the problems of low-value overestimation and high-value underestimation to a certain extent. Full article

The Ili River Transboundary Basin is an important area within the Belt and Road Initiative, and its ecological security impacts China–Kazakhstan diplomatic relations and the building of the Belt and Road Initiative. Using the copula method, this study quantifies the vulnerability of vegetation to drought in the Ili River Transboundary Basin based on the Normalized Difference Vegetation Index (NDVI) and the Standardized Precipitation Evapotranspiration Index (SPEI). The vulnerability of vegetation in the Ili River Transboundary Basin is highest in June, with the proportion of highly vulnerable areas reaching 63.29% under extreme drought conditions. As the drought severity increases, the probability of vegetation loss rises, with vegetation being affected the most in June. From May to June, drought-prone areas are mainly located in Almaty Oblast and East Kazakhstan. From July to September, drought-prone areas are mainly found in the Ili River Valley and southeastern Almaty Oblast. Rainfed croplands are most susceptible to drought, while, for irrigated croplands, higher drought severity enhances the mitigating effect of irrigation measures. Vegetation areas are most affected by drought in semi-arid regions, particularly in summer. These findings offer valuable scientific support for drought management and sustainable development in the region. Full article

The implementation of accurate water balance assessment in glacier basins is essential for the management and sustainable development of water resources in the basins. In this study, a hybrid modeling framework was constructed to enhance runoff prediction and water balance assessment in glacier basins. An improved physical hydrological model (SEGSWAT+) was combined with a machine learning model (ML) to capture the relationship between runoff residuals and water balance components through the Shapley additive explanations (SHAP) method. Based on the enhancement of the runoff fitting results of the existing model, the runoff residuals are decomposed and used to correct the hydrological process component values, thus improving the accuracy of the water balance results. We evaluated the performance and correction results of the method using various ML methods. We analyzed the results for two consecutive periods from 1959 to 2022 for the glacial sub-basins of three tributaries of the Upper Ili River Basin in central Asia. The results show that the hybrid framework based on extreme gradient boosting (XGBoost) with an average NSE value of 0.93 has the best performance, and the bias based on the evapotranspiration component and soil water content change component is reduced by 3.2–5%, proving the effectiveness of the water balance correction. This study advances the interpretation of ML models for hydrologic assessment of areas with complex hydrodynamic characteristics. Full article

Statistical characteristics of atmospheric circulation patterns (ACPs) and associated diurnal variation characteristics (DVCs) of precipitation in summer (June–August) from 2015 to 2019 over the complex terrain in northern Xinjiang (NX), northwestern arid region of China, were investigated based on NCEP FNL reanalysis data and Weather Research and Forecasting model simulation data from Nanjing University (WRF-NJU). The results show that six different ACPs (Type 1–6) were identified based on the Simulated ANealing and Diversified RAndomization (SANDRA), exhibiting significant differences in major-influencing synoptic systems and basic meteorological environments. Types 5, 3, and 2 were the most prevalent three patterns, accounting for 21.6%, 19.7%, and 17.7%, respectively. Type 5 mainly occurred in June and July, while Types 3 and 2 mainly occurred in August and July, respectively. From the perspective of DVCs, Type 1 reached its peak at midnight, while Type 5 was most frequent in the afternoon and morning. The overall DVCs of hourly precipitation intensity and frequency demonstrated a unimodal structure, with a peak occurring at around 16 Local Solar Time (LST). Basic meteorological elements in various terrain regions exhibit significant diurnal variation, with marked differences between mountainous and basin areas under different ACPs. In Types 3 and 6, meteorological elements significantly influence precipitation enhancement by promoting the convergence and uplift of low-level wind fields and maintaining high relative humidity (RH). The Altay Mountains region and Western Mountainous regions experience dominant westerly winds under these conditions, while the Junggar Basin and Ili River Valley regions benefit from counterclockwise water vapor transport associated with the Iranian Subtropical High in Type 6, which increases RH. Collectively, these factors facilitate the formation and development of precipitation. Full article