Search Results (39)

Reconstructing historical grazing intensity is essential for understanding long-term human–environment interactions in arid and semi-arid regions. However, historical documents often lack continuous, site-specific information on land use and grazing pressure. We present a high-resolution reconstruction of pastoral activity and hydrological evolution since 1560 AD using fungal spore assemblages from a 92 cm lacustrine-peat sequence from the Sichanghu (SCH) peatland on the northern slope of the Tianshan Mountains, Central Asia. Quantitative analysis of coprophilous fungal spores and principal component analysis (PCA) of spore influxes identify three distinct phases of pastoral intensity: gradual intensification from 1560 to 1730 AD, a sharp decline from 1730 to 1770 AD, and rapid intensification from 1770 AD to the present. These transitions are consistent with historical records of land use and human migration in Xinjiang. Additionally, fungal assemblages reveal a long-term drying trend at Sichanghu, broadly consistent with regional aridification in northwestern China. However, centennial-scale discrepancies in humidity between local and regional records—particularly during the late Little Ice Age—indicate that local hydrological responses were strongly influenced by anthropogenic disturbances. This study highlights the value of fungal spores, particularly influx-based interpretations, as robust indicators of both human activities and hydroclimatic variability. It also underscores the importance of integrating local and regional signals when reconstructing past environmental changes in sensitive dryland ecosystems. Full article

Against the background of global warming, forests across environmental gradients show distinct responses to climate change, necessitating research on tree growth patterns under specific conditions. Climate and competition are critical factors affecting tree growth, yet their combined effects across altitudinal gradients remain unclear, especially in arid regions such as Central Asia. This study investigated how climate and competition influence radial growth of Picea schrenkiana Fisch. et C.A.Mey. across altitudinal gradients (1500–2670 m) in the Middle Tianshan Mountains. Using dendroclimatology, competition indices, multivariate statistical analyses, and nonlinear models across 12 plots, we examined spatial variability in growth responses. Results revealed significant altitudinal differences in growth responses to climate and competition across altitudes. At low elevations, growth is primarily limited by water availability; drought indices and spring precipitation exert positive effects, while high temperatures inhibit growth. At mid-elevations, climate becomes the dominant driver, particularly spring temperature and precipitation playing key roles, while competition has no significant effect. At high elevations, temperature becomes the primary driver of growth; however, the overall sensitivity to climate is reduced compared to lower elevations. Multiple regression analyses confirm that water-related factors drive growth at lower and middle elevations, whereas temperature is the primary driver at higher elevations. Further model comparison indicates that while nonlinear models performed slightly better at mid-elevations, linear approaches similarly provided interpretable climate–growth relationships. This study demonstrates significant spatial variation in growth determinants, with water-driven controls dominating at lower elevations and competition effects ranging from significant to non-significant as altitude increases. Future warming may further intensify drought stress at lower elevations, and whether or not the weak positive responses currently observed at higher elevations will persist remains uncertain. These findings provide a scientific basis for sustainable management of arid mountain forests under climate change. Full article

The southern slope of the Tianshan Mountains features complex terrain and an arid climate, yet paradoxically experiences frequent extreme precipitation events (EPEs), which pose significant challenges for weather forecasting. This study investigates an EPE that occurred from 20 to 21 August 2019 using multi-source data to examine circulation patterns, mesoscale characteristics, moisture dynamics, and energy-instability mechanisms. The results reveal distinct spatiotemporal variability in precipitation, prompting a two-stage analytical framework: stage 1 (western plains), dominated by localized convective cells, and stage 2 (northeastern mountains), characterized by orographically enhanced precipitation clusters. The event was associated with a “two ridges and one trough” circulation pattern at 500 hPa and a dual-core structure of the South Asian high at 200 hPa. Dynamic forcing stemmed from cyclonic convergence, vertical wind shear, low-level convergence lines, water vapor (WV) transport, and jet-induced upper-level divergence. A stronger vorticity, divergence, and vertical velocity in stage 1 resulted in more intense precipitation. The thermodynamic analysis showed enhanced low-level cold advection in the plains before the event. Sounding data revealed increases in precipitable water and convective available potential energy (CAPE) in both stages. WV tracing showed vertical differences in moisture sources: at 3000 m, ~70% originated from Central Asia via the Caspian and Black Seas; at 5000 m, source and path differences emerged between stages. In stage 1, specific humidity along each vapor track was higher than in stage 2 during the EPE, with a 12 h pre-event enhancement. Both stages featured rapid convective cloud growth, with decreases in total black body temperature (TBB) associated with precipitation intensification. During stage 1, the EPE center aligned with a large TBB gradient at the edge of a cold cloud zone, where vigorous convection occurred. In contrast to typical northern events, which are linked to colder cloud tops and vigorous convection, the afternoon EPE in stage 2 formed near cloud edges with lesser negative TBB values. These findings advance the understanding of multi-scale extreme precipitation mechanisms in arid mountains, aiding improved forecasting in complex terrains. Full article

Central Asia, as a chronically water-stressed region marked by extreme aridity, faces significant environmental challenges from intensifying desertification and deteriorating ecological stability. The region’s vulnerability to shifting precipitation regimes and extreme hydrometeorological events has been magnified under anthropogenic climate forcing. Although global climate models (GCMs) remain essential tools for climate projections, their utility in Central Asia’s complex terrain is constrained by inherent limitations: coarse spatial resolution (~100–250 km) and imperfect parameterization of orographic precipitation mechanisms. This investigation advances precipitation modeling through deep learning-enhanced statistical downscaling, employing convolutional neural networks (CNNs) to generate high-resolution precipitation data at approximately 10 km resolution. Our results show that the deep learning models successfully simulate the high center of precipitation and extreme precipitation near the Tianshan Mountains, exhibiting high spatial applicability. Under intermediate (SSP-245) and high-emission (SSP-585) future scenarios, the increase in extreme precipitation over the next century is significantly more pronounced compared to mean precipitation. By the end of the 21st century, the interannual variability of mean precipitation and extreme precipitation will become even larger under SSP-585, indicating an increased risk of extreme droughts/floods in Central Asia under high greenhouse gas emissions. Our findings provide technical support for climate change impact assessments in the region and highlight the potential of CNN-based downscaling for future climate change studies. Full article

The Tianshan Mountains are located in the hinterland of the Eurasian continent, spanning east to west across China, Kazakhstan, Kyrgyzstan, and Uzbekistan. As the primary water source for Central Asia’s arid regions, the Tianshan mountain system is pivotal for regional water security and is highly sensitive to the nuances of climate change. Utilizing ERA5 precipitation datasets alongside 24 atmospheric circulation indices, this study delves into the variances in Tianshan’s precipitation patterns and their correlation with large-scale atmospheric circulation within the timeframe of 1981 to 2020. We observe a seasonally driven dichotomy, with the mountains exhibiting increasing moisture during the spring, summer, and autumn months, contrasted by drier conditions in winter. There is a pronounced spatial variability; the western and northern reaches exhibit more pronounced increases in precipitation compared to their eastern and southern counterparts. Influences on Tianshan’s precipitation patterns are multifaceted, with significant factors including the North Pacific Pattern (NP), Trans-Niño Index (TNI), Tropical Northern Atlantic Index (TNA*), Extreme Eastern Tropical Pacific SST (Niño 1+2*), North Tropical Atlantic SST Index (NTA), Central Tropical Pacific SST (Niño 4*), Tripole Index for the Interdecadal Pacific Oscillation [TPI(IPO)], and the Western Hemisphere Warm Pool (WHWP*). Notably, NP and TNI emerge as the predominant factors driving the upsurge in precipitation. The study further reveals a lagged response of precipitation to atmospheric circulatory patterns, underpinning complex correlations and resonance cycles of varying magnitudes. Our findings offer valuable insights for forecasting precipitation trends in mountainous terrains amidst the ongoing shifts in global climate conditions. Full article

The Tianshan Mountains are known as the “Water Tower of Central Asia” and are of significant strategic importance for Xinjiang as well as the Central Asian region. Accurately monitoring the spatiotemporal distribution of precipitation in the Tianshan Mountains is crucial for understanding global water cycles and climate change. Raindrop Size Distribution (DSD) parameters play an important role in improving quantitative precipitation estimation with radar and understanding microphysical precipitation processes. In this study, DSD parameters in the Tianshan Mountains were evaluated on the basis of Global Precipitation Measurement mission (GPM) dual-frequency radar data (DPR) and ground-based laser disdrometer observations from 2019 to 2024. With the disdrometer observations as the true values, we performed spatiotemporal matching between the satellite radar and laser disdrometer data. The droplet spectrum parameters retrieved with the GPM dual-frequency radar system were compared with those calculated from the laser disdrometer observations. The reflectivity observations from the GPM DPR in both the Ku and Ka bands (ZKu and ZKa) were greater than the actual observations, with ZKa displaying a greater degree of overestimation than ZKu. In the applied single-frequency retrieval algorithm (SFA), the rainfall parameters retrieved from the Ka band outperformed those retrieved from the Ku band, indicating that the Ka band has stronger detection capability in the Tianshan Mountains area, where light rain predominates. The dual-frequency ratio (DFR), i.e., the differences in the reflectivity of the raindrop spectra obtained from both the Ku and Ka bands, fluctuated more greatly than those of the GPM DPR. DFR is a monotonically increasing function of the mass-weighted mean drop diameter (Dm). Rainfall rate (R) and Dm exhibited a strong positive correlation, and the fitted curve followed a power function distribution. Full article

Artificial water bodies in Central Asia offer unique environments in which to study plankton diversity influenced by topographic barriers. However, the complexity of these ecosystems and limited comprehensive studies in the region challenge our understanding. In this study, we systematically investigated the water environment parameters and phytoplankton community structure by surveying 14 artificial waters on the southern side of the Altai Mountains and the northern and southern sides of the Tianshan Mountains in the Xinjiang region. The survey covered physical and nutrient indicators, and the results showed noticeable spatial differences between waters in different regions. The temperature, dissolved oxygen, total nitrogen, and total phosphorus of artificial water in the southern Altai Mountains vary greatly. In contrast, the waters in the northern Tianshan Mountains have more consistent physical indicators. The results of phytoplankton identification showed that the phytoplankton communities in different regions are somewhat different, with diatom species being the dominant taxon. The cluster analysis and the non-metric multidimensional scaling (NMDS) results also confirmed the variability of the phytoplankton communities in the areas. The variance partitioning analysis (VPA) results showed that climatic and environmental factors can explain some of the variability of the observed data. Nevertheless, the residual values indicated the presence of other unmeasured factors or the influence of stochasticity. This study provides a scientific basis for regional water resource management and environmental protection. Full article

Central Asia (CA) is one of the major sources of global dust aerosols. They pose a serious threat to regional climate change and environmental health and also make a significant contribution to the global dust load. However, there is still a gap in our understanding of dust transport in this region. Therefore, this study utilizes Cloud–Aerosol LiDAR with Orthogonal Polarization (CALIOP) data from 2007 to 2022 to depict the three-dimensional spatiotemporal distribution of dust aerosols over CA and to analyze their transport processes. In addition, the Tropospheric Monitoring Instrument (TROPOMI) was employed to assist in monitoring the movement of typical dust events, and the trajectory model was utilized to simulate the forward and backward trajectories of a dust incident. Additionally, a random forest (RF) model was employed to rank the contributions of various environmental factors. The findings demonstrate that high extinction values (0.6 km⁻¹) are mostly concentrated within the Tarim Basin of Xinjiang, China, maintaining high values up to 2 km in altitude, with a noticeable decrease as the altitude increases. The frequency of dust occurrences is especially pronounced in the spring and summer seasons, with dust frequencies in the Tarim Basin and the Karakum and Kyzylkum deserts exceeding 80%, indicating significant seasonal and regional differences. The high values of dust optical depth (DOD) in CA are primarily concentrated in the summer, concurrent with the presence of a stable aerosol layer of dust in the atmosphere with a thickness of 0.62 km. Furthermore, dust from CA can traverse the Tianshan mountains via the westerlies, transporting it eastward. Additionally, skin temperature can mitigate regional air pollution. Our results contribute to a deeper understanding of the dynamic processes of dust in CA and provide scientific support for the development of regional climate regulation strategies. Full article

The Tianshan Mountains, the largest arid mountain range in Central Asia, feature diverse terrains and significant landscape heterogeneity. The grasslands within the Xinjiang Tianshan region are particularly sensitive to climate change and human activities. However, until recently, the patterns and mechanisms underlying grassland changes in this region have been unclear. In this study, we analyzed spatial and temporal changes in grassland fractional vegetation cover (FVC) from 2001 to 2020, analyzed spatial and temporal changes in grassland, and predicted future trends using Global Land Surface Satellite (GLASS) FVC data, trend analysis, and the Hurst index method. We also explored the driving mechanisms behind these changes through the structural equation model (SEM). The results showed that from 2001 to 2020, the grassland FVC in the Tianshan region of Xinjiang was higher in the central and western regions and lower in the northern and southern regions, showing an overall fluctuating growth trend, with a change in the growth rate of 0. 0017/a (p < 0.05), and that this change was spatially heterogeneous, with the sum of significant improvement (20.6%) and slight improvement (29.9%) being much larger than the sum of significant degradation (0.6%) and slight degradation (9.5%). However, the Hurst index (H = 0.47) suggests that this trend may not continue, and there is a risk of degradation. Our study uncovers the complex interactions between the Tianshan barrier effect and grassland ecosystems, highlighting regional differences in driving mechanisms. Although the impacts of climatic conditions in grasslands vary over time in different regions, the topography and its resulting hydrothermal conditions are still dominant, and the extent of the impact is susceptible to fluctuations of varying degrees due to extreme climatic events. Additionally, the number of livestock changes significantly affects the grasslands on the southern slopes of the Tianshan Mountains, while the effects of nighttime light are minimal. By focusing on the topographical barrier effect, this study enhances our understanding of grassland vegetation dynamics in the Tianshan Mountains of Xinjiang, contributing to improved ecosystem management strategies under climate change. Full article

Arid central Asia (ACA) is dominated by mid-latitude westerlies and characterized by a climate optimum (a relatively humid climate that has supported the development of human culture) in clear contrast with the climate of monsoonal Asia during the Holocene. Significantly, whether the onset of the Holocene Climate Optimum (HCO) had an impact on cultural exchanges along the ancient Silk Road remains unknown. In this study, we compared the onset of the HCO in different parts of the vast ACA region by referring to a variety of previously established paleo-moisture/precipitation records. Intriguingly, we found significant differences in the onset of the HCO between the western and eastern parts of ACA. The onset of the HCO in the western part of ACA (i.e., to the west of the Tianshan Mountains) mainly occurred at ~8 ka BP (1 ka = 1000 cal yr BP). In contrast, the onset of the HCO occurred at ~6 ka in northern Xinjiang and even as late as ~5 ka in southern Xinjiang; this is a delay of 2–3 thousand years compared with the western part of ACA. These results likely indicate that the onset of the HCO occurred in a time-transgressive manner in ACA, namely, ‘early in the west but late in the east’. On the other hand, we found that the onset of the HCO in the western part of ACA may have resulted in the inception of wheat planting and the development of agricultural civilization and that the onset of the HCO in northern Xinjiang may have prompted the southward migration of Afanasievo culture after ~5 ka. Additionally, the initiation of the HCO in southern Xinjiang could provide an environmental basis for the spread and planting of wheat and millet in this area after ~4.5 ka. We speculate that the spatial differences in the onset of the HCO in ACA are mainly related to temporal changes in the intensity and position of the mid-latitude westerly jet. Although the increase in insolation and reduction in the global ice volume would have led to an increase in the water vapor feeding the western part of ACA around 8 ka, the climate in the eastern part of ACA (namely, the Xinjiang region) could have only become humid after 6 ka when the westerlies were intensified and became positioned in the south. Moreover, the delayed HCO in southern Xinjiang probably benefited from the stronger westerly winds that appeared around 5 ka, which could have overcome the influence of the tall topography of the Tianshan Mountains. Therefore, in addition to external forcing (i.e., insolation), the ocean–atmospheric teleconnection, the regional topography, and their connection to the climate system are important in determining the spatial differences in the time-transgressive onset of the HCO in ACA. Our findings contribute to understanding the spatio-temporal characteristics of the hydroclimate in regions with complex eco-environmental systems and a diverse history of human activity. Full article

With global warming, the frequency, intensity, and period of extreme climates in more areas will probably increase in the twenty first century. However, the impact of climate extremes on forest vulnerability and the mechanisms by which forests adapt to climate extremes are not clear. The eastern Tianshan Mountains, set within the arid and dry region of Central Asia, is very sensitive to climate change. In this paper, the response of Picea schrenkiana and Larix sibirica to climate fluctuations and their stability were analyzed by Pearson’s correlation based on the observation of interannual change rates of climate indexes in different periods. Additionally, their ecological adaptability to future climate change was explored by regression analysis of climate factors and a selection of master control factors using the Lasso model. We found that the climate has undergone significant changes, especially the temperature, from 1958 to 2012. Around 1985, various extreme climate indexes had obvious abrupt changes. The research results suggested that: (1) the responses of the two tree species to extreme climate changed significantly after the change in temperature; (2) Schrenk spruce was more sensitive than Siberian larch to extreme climate change; and (3) the resistance of Siberian larch was higher than that of Schrenk spruce when faced with climate disturbance events. These results indicate that extreme climate changes will significantly interfere with the trees radial growth. At the same time, scientific management and maintenance measures are taken for different extreme weather events and different tree species. Full article

As a distinct species in the Tianshan Mountains (TS) of Central Asia (CA), Picea schrenkiana plays a significant role in water purification, soil and water conservation, and climate regulation. In the context of climate change, rapidly and accurately obtaining its spatial distribution has critical decision-making significance for maintaining ecological security in the arid area of CA and the sustainable development of the “Silk Road Economic Belt”. However, conventional methods are extremely challenging to accomplish the high-resolution mapping of Picea schrenkiana in the TS, which is characterized by a wide range (9.97 × 10⁵ km²) and complex terrain. The approach of geo-big data and cloud computing provides new opportunities to address this issue. Therefore, the purpose of this study is to propose an automatic extraction procedure for the spatial distribution of Picea schrenkiana based on Google Earth Engine and the Jeffries–Matusita (JM) distance, which considered three aspects: sample points, remote-sensing images, and classification features. The results showed that (1) after removing abnormal samples and selecting the summer image, the producer accuracy (PA) of Picea schrenkiana was improved by 2.95% and 0.24%–2.10%, respectively. (2) Both the separation obtained by the JM distance and the analysis results of eight schemes showed that spectral features and texture features played a key role in the mapping of Picea schrenkiana. (3) The JM distance can seize the classification features that are most conducive to the mapping of Picea schrenkiana, and effectively improve the classification accuracy. The PA and user accuracy of Picea schrenkiana were 96.74% and 96.96%, respectively. The overall accuracy was 91.93%, while the Kappa coefficient was 0.89. (4) The results show that Picea schrenkiana is concentrated in the middle TS and scattered in the remaining areas. In total, 85.7%, 66.4%, and 85.9% of Picea schrenkiana were distributed in the range of 1500–2700 m, 20–40°, and on shady slope and semi-shady slope, respectively. The automatic procedure adopted in this study provides a basis for the rapid and accurate mapping of the spatial distribution of coniferous forests in the complex terrain. Full article

Snow is an important source of freshwater in the Tianshan Mountains of Central Asia. This study established 18 tree ring width chronologies and compound chronologies and analyzed the effects of snow depth, measured both by remote sensing and at meteorological stations, on the radial growth of spruce trees. The results showed that the established standard chronology of tree ring width is suitable for the analysis of tree ring climatology. The correlation coefficient of the ring width index (RWI) and the remote sensing snow depth was greater than that of the meteorological station snow depth. For the remote sensing snow depth, the correlation coefficients were greater in the winter and spring months compare to other periods, while the correlation coefficients of the meteorological stations were greater only in the winter. The nonlinear method (BRNN) showed good fitting in the reconstruction of the historical snow depth. The reconstructed snow depth exhibited a decreasing trend in the Bharakonu Mountains (BM), Narathi Mountains (NM), and Halke mountains (KM) sub-regions in the overall reconstructed period; however, the trends were inconsistent in both the historical and the observed periods, indicating the importance of reconstructing snow depth in the Tianshan Mountains. Full article

Simulated historical extreme precipitation is evaluated for Coupled Model Intercomparison Project Phase 6 (CMIP6) models using precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The indices of 33 Global Circulation Models (GCMs) are evaluated against corresponding indices with observations from the Global Climate Center Precipitation Dataset (GPCC V2020) over five sub-regions across Arid Central Asia (ACA), using the Taylor diagram, interannual variability skill score (IVS) and comprehensive rating index (MR). Moreover, we compare four multi-model ensemble approaches: arithmetic average multi-model ensemble (AMME), median multi-model ensemble (MME), pattern performance-based multi-model ensemble (MM-PERF) and independence weighted mean (IWM). The results show that CMIP6 models have a certain ability to simulate the spatial distribution of extreme precipitation in ACA and the best ability to simulate simple daily intensity (SDII), but it is difficult to capture the spatial bias of consecutive wet days (CWD). Almost all models represent different degrees of wet bias in the southern Xinjiang (SX). Most GCMs are generally able to capture extreme precipitation trends, but to reproduce the performance of interannual variability for heavy precipitation days (R10mm), SDII and CWD need to be improved. The four multi-model ensemble methods can reduce the internal system bias and variability within individual models and outperform individual models in capturing the spatial and temporal variability of extreme precipitation. However, significant uncertainties remain in the simulation of extreme precipitation indices in SX and Tianshan Mountain (TM). Comparatively, IWM simulations of extreme precipitation in the ACA and its sub-regions are more reliable. The results of this study can provide a reference for the application of GCMs in ACA and sub-regions and can also reduce the uncertainty and increase the reliability of future climate change projections through the optimal multi-model ensemble method. Full article

Karez systems are ancient hydraulic works that use underground waterways to divert water by gravity and have historically been popular in arid regions across Central Asia. Karez systems have undergone thousands of years of development and have been used for irrigation in 40 countries and regions worldwide. Although there are different opinions about the origin of karezes, the role and significance of karezes are similar. The Turpan Basin is a relatively closed inland basin in China, far from the ocean, with a very dry climate and high evaporation rates. However, due to the ice and snow meltwater of the Tianshan Mountains, the groundwater resources in the basin are abundant. Karezes are an important support for Turpan’s farming civilization and tourism culture and represent a great masterpiece of how people in arid areas have used the natural environment. This study used historical CORONA images to visually interpret the karez system in the 1970s and compared it with the karez system in 2020 to analyze the spatial distribution variation characteristics of the karezes. The impact of land use/land cover change on the karezes was also analyzed. The results showed that from 1970 to 2020, as the population grew, there was an increase in arable land and built-up areas while the water area decreased. In general, the increase in arable land and built-up areas, the decrease in water area, and the increase in the number of electromechanical wells have combined to reduce the number of karez systems. Based on the CORONA image from 1970, it is possible to visualize the shaft area that existed in 1970 but did not exist in 2020. Some karez shafts that existed in bare terrain areas in 1970 were truncated when the land use/land cover type changed to arable land. The area where the disappeared karez shafts were located is approximately 87.77 square kilometers. Through the study of the changes in the spatial distribution of karezes and the impact of land use/land cover change on karezes, this research provides a valuable reference for the construction of karez conservation areas or urban planning. The investigation of the distribution of historical karezes is of great significance for studying the changes in karezes and excavating the historical and cultural value of karezes. Full article