Whether millennial- to centennial-scale climate variations throughout the Holocene convey universal climate change is still widely debated. In this study, we aimed to obtain a set of high-resolution multi-proxy data (1343 particle size samples, 893 total organic carbon samples, and 711 pollen samples) from an alluvial-lacustrine-aeolian sequence based on an improved age-depth model in the northwestern margin of the East Asian monsoon region to explore the dynamics of climate changes over the past 30 ka. Results revealed that the sequence not only documented the major climate events that corresponded well with those reported from the North Atlantic regions but also revealed many marked and high-frequency oscillations at the millennial- and centennial-scale. Specifically, the late stage of the last glacial lasting from 30.1 to 18.1 cal. ka BP was a dry and cold period. The deglacial (18.1-11.5 cal. ka BP) was a wetting (probably also warming) period, and three cold and dry excursions were found in the wetting trend, i.e., the Oldest Dryas (18.1-15.8 cal. ka BP), the Older Dryas (14.6-13.7 cal. ka BP), and the Younger Dryas (12.5-11.5 cal. ka BP). The Holocene can be divided into three portions: the warmest and wettest early portion from 11.5 to 6.7 cal. ka BP, the dramatically cold and dry middle portion from 6.7 to 3.0 cal. ka BP, and the coldest and driest late portion since 3.0 cal. ka BP. Wavelet analysis results on the total pollen concentration revealed five substantially periodicities: c. 5500, 2200, 900, 380, and 210 a. With the exception of the c. 5500 a quasi-cycle that was causally associated with the Atlantic meridional overturning circulation, the other four quasi-cycles (i.e., c. 2200, 900, 380, and 210 a) were found to be indirectly causally associated with solar activities. This study provides considerable insight into the dynamic mechanism of the Asian climate on a long-time scale and future climatic change.

Exploring the current runoff characteristics after the large-scale implementation of the Grain for Green (GFG) project and investigating its sensitivities to potential drivers are crucial for water resource prediction and management. Based on the measured runoff data of 62 hydrological stations in the Weihe River Basin (WRB) from 2006 to 2018, we analyzed the temporal and spatial runoff characteristics in this study. Correlation analysis was used to investigate the relationships between different runoff indicators and climate-related factors. Additionally, an improved Budyko framework was applied to assess the sensitivities of annual runoff to precipitation, potential evaporation, and other factors. The results showed that the daily runoff flow duration curves (FDCs) of all selected hydrological stations fall in three narrow ranges, with the corresponding mean annual runoff spanning approximately 1.50 orders of magnitude, indicating that the runoff of different hydrological stations in the WRB varied greatly. The trend analysis of runoff under different exceedance frequencies showed that the runoff from the south bank of the Weihe River was more affluent and stable than that from the north bank. The runoff was unevenly distributed throughout the year, mainly in the flood season, accounting for more than 50.00% of the annual runoff. However, the trend of annual runoff change was not obvious in most areas. Correlation analysis showed that rare-frequency runoff events were more susceptible to climate factors. In this study, daily runoff under 10%-20% exceeding frequencies, consecutive maximum daily runoff, and low-runoff variability rate had strong correlations with precipitation, aridity index, and average runoff depth on rainy days. In comparison, daily runoff under 50%-99% exceeding frequencies, consecutive minimum daily runoff, and high-runoff variability rate had weak correlations with all selected impact factors. The sensitivity analysis results suggested that the sensitivity of annual runoff to precipitation was always higher than that to potential evaporation. The runoff about 87.10% of the selected hydrological stations were most sensitive to precipitation changes, and 12.90% were most sensitive to other factors. The spatial pattern of the sensitivity analysis indicated that in relatively humid southern areas, runoff was more sensitive to potential evaporation and other factors, and less sensitive to precipitation.

In the past few decades, meteorological datasets from remote sensing techniques in agricultural and water resources management have been used by various researchers and managers. Based on the literature, meteorological datasets are not more accurate than synoptic stations, but their various advantages, such as spatial coverage, time coverage, accessibility, and free use, have made these techniques superior, and sometimes we can use them instead of synoptic stations. In this study, we used four meteorological datasets, including Climatic Research Unit gridded Time Series (CRU TS), Global Precipitation Climatology Centre (GPCC), Agricultural National Aeronautics and Space Administration Modern-Era Retrospective Analysis for Research and Applications (AgMERRA), Agricultural Climate Forecast System Reanalysis (AgCFSR), to estimate climate variables, i.e., precipitation, maximum temperature, and minimum temperature, and crop variables, i.e., reference evapotranspiration, irrigation requirement, biomass, and yield of maize, in Qazvin Province of Iran during 1980-2009. At first, data were gathered from the four meteorological datasets and synoptic station in this province, and climate variables were calculated. Then, after using the AquaCrop model to calculate the crop variables, we compared the results of the synoptic station and meteorological datasets. All the four meteorological datasets showed strong performance for estimating climate variables. AgMERRA and AgCFSR had more accurate estimations for precipitation and maximum temperature. However, their normalized root mean square error was inferior to CRU for minimum temperature. Furthermore, they were all very efficient for estimating the biomass and yield of maize in this province. For reference evapotranspiration and irrigation requirement CRU TS and GPCC were the most efficient rather than AgMERRA and AgCFSR. But for the estimation of biomass and yield, all the four meteorological datasets were reliable. To sum up, GPCC and AgCFSR were the two best datasets in this study. This study suggests the use of meteorological datasets in water resource management and agricultural management to monitor past changes and estimate recent trends.

Vegetation growth status is an important indicator of ecological security. The Tarim River Basin is located in the inland arid region of Northwest China and has a highly fragile ecological environment. Assessing the vegetation net primary productivity (NPP) of the Tarim River Basin can provide insights into the vegetation growth variations in the region. Therefore, based on the Google Earth Engine (GEE) cloud platform, we studied the spatiotemporal variation of vegetation NPP in the Tarim River Basin (except for the eastern Gobi and Kumutag deserts) from 2001 to 2020 and analyzed the correlations between vegetation NPP and meteorological factors (air temperature and precipitation) using the Sen slope estimation method, coefficient of variation, and rescaled range analysis method. In terms of temporal characteristics, vegetation NPP in the Tarim River Basin showed an overall fluctuating upward trend from 2001 to 2020, with the smallest value of 118.99 g C/(m²?a) in 2001 and the largest value of 155.07 g C/(m²?a) in 2017. Regarding the spatial characteristics, vegetation NPP in the Tarim River Basin showed a downward trend from northwest to southeast along the outer edge of the study area. The annual average value of vegetation NPP was 133.35 g C/(m²?a), and the area with annual average vegetation NPP values greater than 100.00 g C/(m²?a) was 82,638.75 km², accounting for 57.76% of the basin. The future trend of vegetation NPP was dominated by anti-continuity characteristic; the percentage of the area with anti-continuity characteristic was 63.57%. The area with a significant positive correlation between vegetation NPP and air temperature accounted for 53.74% of the regions that passed the significance test, while the area with a significant positive correlation between vegetation NPP and precipitation occupied 98.68% of the regions that passed the significance test. Hence, the effect of precipitation on vegetation NPP was greater than that of air temperature. The results of this study improve the understanding on the spatiotemporal variation of vegetation NPP in the Tarim River Basin and the impact of meteorological factors on vegetation NPP.

Atmospheric particle pollution is one of the major factors leading to degradation of ancient wall paintings, particularly heritage sites in arid and semi-arid regions. However, current systematic research on the changes, sources, and influential factors of atmospheric particulate matter and its water-soluble ion concentrations is not sufficient. Thus, the major water-soluble ion concentrations, sources, and influential factors of atmospheric particles PM_2.5 and PM₁₀ (particulate matter with an aerodynamic equivalent diameter ≤2.5 and 10.0 μm, respectively, in ambient air) were collected from Cave 16 and its ambient exterior environment in the Dunhuang Mogao Grottoes, China, between April 2015 and March 2016. Results showed that the concentrations of PM_2.5 and PM₁₀ inside and outside the cave were the highest in March 2016 and the lowest in December 2015. The higher particle concentration from March to May was related to the frequent occurrence of sand and dust events, and the lower particle concentration from June to September was associated with good diffusion conditions, increased precipitation, and an established cave shelterbelt. The concentration of particulate matter inside the cave was affected by the concentration of particles in the air outside the cave. Ca²⁺, NH+ 4, Na⁺, Cl^-, and SO2- 4were the main components of the total ions of PM_2.5 and PM₁₀ both inside and outside the cave. The total ions inside the cave were frequently affected by the disturbance of tourists' activities during the peak tourist season from May to August. Under the influence of dust, the total concentrations of Cl^-, SO2- 4, Na⁺, NH+ 4, and Ca²⁺ in particles of different sizes inside and outside the cave increased, and the concentrations of Cl^-, SO2- 4, Na⁺, and Ca²⁺ decreased during precipitation period. Backward air mass trajectory analysis suggested that the pollutants were mainly from Xinjiang, China. The pollutant sources of air particulates are straw burning, secondary pollution sources, soil dust, dry spring rivers, and tourist activities.

Malus sieversii (wild apple tree), only distributed in the Tianshan Mountains in Central Asia, is a tertiary relic species and an ancestral species of cultivated apples. However, existing natural populations of wild apple trees have been declining. To date, spatiotemporal variations in the growth status of declining wild apple trees and influencing factors in the narrow valley areas in the Tianshan Mountains remain unclear. In this study, field investigation and sampling were carried out in three years (2016-2018) at four elevations (1300, 1400, 1500, and 1600 m) in the Qiaolakesai Valley (a typical longitudinal narrow valley in the Yili River Valley) of the western Tianshan Mountains in Xinyuan County, Xinjiang Uygur Autonomous Region, China. Projective coverage, dead branch percentage, and 18 twig traits (these 20 parameters were collectively referred to as plant traits) were determined to comprehensively reflect the growth status of declining wild apple trees. The values of dead branch percentage ranged from 36% to 59%, with a mean of 40%. Year generally showed higher impact on plant traits than elevation. In 2017 and 2018, projective coverage, leaf size, leaf nitrogen concentration, and nitrogen to phosphorous ratio were markedly higher than those in 2016. However, dead branch percentage and leaf and stem phosphorous concentrations showed the opposite trend. Most of the topological parameters of plant trait networks differed in the three years, but the strength of trait-trait association increased year by year. The mean difference between day and night temperatures (MDT), annual accumulative precipitation, soil electrical conductivity, and soil pH had the greatest impact on the plant trait matrix. The growth status of declining wild apple trees was directly and positively affected by MDT and leaf size. In conclusion, the growth of declining wild apple trees distributed in the narrow valley areas was more sensitive to interannual environmental changes than elevation changes. The results are of great significance for further revealing the decline mechanism of wild apple trees in the Tianshan Mountains.

In recent years, deep convolution neural network has exhibited excellent performance in computer vision and has a far-reaching impact. Traditional plant taxonomic identification requires high expertise, which is time-consuming. Most nature reserves have problems such as incomplete species surveys, inaccurate taxonomic identification, and untimely updating of status data. Simple and accurate recognition of plant images can be achieved by applying convolutional neural network technology to explore the best network model. Taking 24 typical desert plant species that are widely distributed in the nature reserves in Xinjiang Uygur Autonomous Region of China as the research objects, this study established an image database and select the optimal network model for the image recognition of desert plant species to provide decision support for fine management in the nature reserves in Xinjiang, such as species investigation and monitoring, by using deep learning. Since desert plant species were not included in the public dataset, the images used in this study were mainly obtained through field shooting and downloaded from the Plant Photo Bank of China (PPBC). After the sorting process and statistical analysis, a total of 2331 plant images were finally collected (2071 images from field collection and 260 images from the PPBC), including 24 plant species belonging to 14 families and 22 genera. A large number of numerical experiments were also carried out to compare a series of 37 convolutional neural network models with good performance, from different perspectives, to find the optimal network model that is most suitable for the image recognition of desert plant species in Xinjiang. The results revealed 24 models with a recognition Accuracy, of greater than 70.000%. Among which, Residual Network X_8GF (RegNetX_8GF) performs the best, with Accuracy, Precision, Recall, and F1 (which refers to the harmonic mean of the Precision and Recall values) values of 78.33%, 77.65%, 69.55%, and 71.26%, respectively. Considering the demand factors of hardware equipment and inference time, Mobile NetworkV2 achieves the best balance among the Accuracy, the number of parameters and the number of floating-point operations. The number of parameters for Mobile Network V2 (MobileNetV2) is 1/16 of RegNetX_8GF, and the number of floating-point operations is 1/24. Our findings can facilitate efficient decision-making for the management of species survey, cataloging, inspection, and monitoring in the nature reserves in Xinjiang, providing a scientific basis for the protection and utilization of natural plant resources.

Leaf traits can directly reflect the adaptation strategies of plants to the environment. However, there is limited knowledge on the adaptation strategies of heteromorphic leaves of male and female Populus euphratica Oliv. in response to individual developmental stages (i.e., diameter class) and canopy height changes. In this study, morphological and physiological properties of heteromorphic leaves of male and female P. euphratica were investigated. Results showed that both male and female P. euphratica exhibited increased leaf area (LA), leaf dry weight (LDW), leaf thickness (LT), net photosynthetic rate (P_n), transpiration rate (T_r), stomatal conductance (g_s), proline (Pro), and malondialdehyde (MDA) concentration, decreased leaf shape index (LI) and specific leaf area (SLA) with increasing diameter and canopy height. Leaf water potential (LWP) increased with increasing diameter, LWP decreased significantly with increasing canopy height in both sexes, and carbon isotope fraction (δ¹³C) increased significantly with canopy height in both sexes, all of which showed obvious resistance characteristics. However, males showed greater LA, LT, P_n, T_r, and Pro than females at the same canopy height, and males showed significantly higher LA, SLA, LT, P_n, T_r, g_s, and MDA, but lower LWP and δ¹³C than females at the same canopy height, suggesting that male P. euphratica have stronger photosynthetic and osmoregulatory abilities, and are sensitive to water deficiency. Moreover, difference between male and female P. euphratica is closely related to the increase in individual diameter class and canopy height. In summary, male plants showed higher stress tolerance than female plants, and differences in P_n, g_s, T_r, Pro, MDA, δ¹³C, and LWP between females and males were related to changes in leaf morphology, diameter class, and canopy height. The results of this study provide a theory for the differences in growth adaptation strategies during individual development of P. euphratica.