To assess the impact of future climate change on geological hazard zoning in the Ili River Basin, Xinjiang, China, climate data from different scenarios of the Coupled Model Intercomparison Project Phase 6 (CMIP6) were selected to analyze climate change characteristics under various shared socioeconomic pathway (SSP) scenarios from 2021 to 2040. The information quantity-random forest model was employed to conduct the geological hazard assessment and generate a prediction map. The results indicate that: (1) High and extremely high hazard areas are primarily concentrated in northern Yining County, southern Nilka County, and northern Xinyuan County in the middle mountainous hilly regions; debris flow hazard areas are mainly located in southern Zhaosu County, the northern region of Keguqin Mountain in Huocheng County, Hejing County, and the middle-to-high mountainous areas in eastern Nilka County. (2) From 2021 to 2040, the Ili River Basin is projected to experience a general increase in temperature and precipitation, with a maximum annual average temperature rise of approximately 1.53 ℃ and a maximum precipitation increase of about 19.3 mm. (3) Under future SSP126, SSP245, SSP370, and SSP585 scenarios, high-hazard areas for landslides and rockfalls are expected to expand. The severity of landslides in southern Yining County, northern Xinyuan County, and southwestern Nilka County, as well as debris flows in northern Khorgas City and Yining County, is anticipated to worsen, with maximum increases of 17.31% and 8.77%, respectively. The findings of this study provide valuable insights for future disaster prevention and mitigation efforts in the Ili River Basin.

To investigate the characteristics of wind farm wake effects and their relationship with meteorological conditions, 33 wind turbines from a wind farm in central Inner Mongolia, China were selected for analysis. Wind resource assessment parameters, including average wind speed, wind direction, and wind frequency distribution, were statistically analyzed from 2021 to 2023. Using the Jensen wake model, wind speeds in the wake area were calculated for different wind directions, with a focus on the refined dominant wind direction. The correlation between wind speeds and meteorological factors, accounting for wake effects, was also explored. The findings are as follows: (1) From 2021 to 2023, the wind farm in central Inner Mongolia was predominantly influenced by southwest winds. High-frequency wind directions shifted from west to south throughout the year. Monthly wind directions were relatively stable, with concentrated wind directions and small wind speed variations. The average wind speed was highest under the dominant wind direction, and the wind speed frequency curve exhibited a positively skewed distribution. (2) Under average wind speeds for each direction, turbines most affected by the wake experienced wind speed losses exceeding 10%. More than half of the turbines were affected by wake effects under northwest and southeast winds, with the most significant losses occurring in the northeasterly downstream positions of the wind farm. Wind speed reductions were particularly pronounced under westerly winds. (3) The impact of barometric pressure, air temperature, and humidity on daily wind speed variation differed across wind directions. For southwest winds, the wake model performed best in the 4-5 m·s^-1 wind speed range, with the average absolute percentage error of wind speed negatively correlated with relative humidity. For northwest winds in the 9-10 m·s^-1 range, the wake model calculations closely matched measured wind speeds, with errors positively correlated with barometric pressure and temperature. In addition, the wake model performed well in the 9-10 m·s^-1 and 7-8 m·s^-1 ranges for southeast and northeast winds, respectively. These results provide valuable insights into the analysis of wind turbine wake effects and wind speed predictions for wind farms.

Using summer daily precipitation data from 102 meteorological stations from June to August during 1961—2022, the spatial and temporal characteristics of extreme and non-extreme precipitation were analyzed, and variations in these two precipitation types across different areas of the arid region of northwest China were compared. The results reveal the following: (1) Summer precipitation in the arid region of northwest China exhibited an increasing trend, particularly in the Ili River Valley and the western Tarim Basin, contributing an average of more than 40% to total annual precipitation. (2) Extreme precipitation in summer accounted for approximately 45% of total precipitation in the arid region, with an overall increasing trend, notably in the western Tarim Basin, Hexi-Alagxa, and northern Xinjiang. (3) Most meteorological stations in the region recorded increasing trends in extreme precipitation, extreme precipitation days, and extreme precipitation intensity. However, the number of non-extreme precipitation days showed significant decreases at most stations, while non-extreme precipitation intensity increased significantly. In the western Tarim Basin, the increase in summer precipitation was driven by both extreme and non-extreme precipitation, contributing 61% and 39% of the total increase, respectively. In other regions, the rise in summer precipitation was predominantly due to the increase in extreme precipitation. These findings enhance understanding of summer precipitation climate change in the arid region of northwest China.

Utilizing data from population censuses conducted in 2000, 2010, and 2020, this study employs the offset-sharing analysis, the random forest model and other methods to examine the spatio-temporal evolution and driving factors of population distribution in Lanzhou City, Gansu Province, China, from 2000 to 2020. The findings reveal that: (1) Population growth exhibits significant differences across periods and regions in Lanzhou City, with clear suburbanization trends characterized by a “jumping” diffusion from the central urban area to the far suburbs. The central urban area remains the most populous, although its growth rate has slowed, while suburban growth is accelerating. Population in the far suburbs initially declined but later increased rapidly. (2) The population offset growth pattern in Lanzhou City is uneven. Taking 2010 as a pivotal year, blocks with positive population deviation growth were primarily located in the central urban area before 2010 but shifted to the far suburbs afterward, particularly in national new districts and development zones, which demonstrate “enclave” population agglomeration. (3) Natural factors, economic conditions, social development levels, and historical evolution are the main drivers of population spatial changes. Meanwhile, the influence of policy interventions and environmental comfort is increasingly significant. The impact of these driving factors on population distribution is nonlinear. These findings provide valuable insights for optimizing population distribution policies in inland cities of northwest China.

Desertification is a critical global ecological and environmental challenge. The Mu Us Sandy Land is a pivotal region for desertification control in China. Over the past three decades, numerous studies have examined desertification dynamics in this area; however, a systematic analysis of these dynamics over the past 70 years has been lacking. This study conducted a Meta-analysis of desertification dynamics by integrating the findings of 39 case studies on the Mu Us Sandy Land since the 1950s and discussed the factors influencing desertification. The results indicate the following: (1) Over the past 70 years, the proportion of light desertification areas initially decreased and then increased, the proportion of moderate desertification areas exhibited a slight increasing trend, and the proportion of severely desertified areas first increased and then decreased, reflecting a notable reversal of desertification trends in the Mu Us Sandy Land. (2) A slight reversal trend of desertification was observed from 1980 to 1989, desertification expanded from 1990 to 1999, the expansion trend was reversed from 2000 to 2009, and desertification continued to steadily reverse from 2010 to 2019. The year 2000 marked a significant turning point in the reversal of desertification in the Mu Us Sandy Land. (3) Continuous drought has significantly promoted desertification expansion. The sustained reversal of desertification since 2000 is attributed to extensive ecological engineering efforts and the implementation of policies such as “prohibition against grazing, closed grazing, rotational grazing, and limiting grazing animal numbers based on pasture availability”. These findings provide valuable insights for understanding the development patterns of desertification in the Mu Us Sandy Land and formulating effective desertification control strategies.

The social network of urban ecological resilience and its influencing factors were analyzed to promote regional green synergistic development. Data from 2012 to 2021 for 63 prefecture-level cities in the Yellow River Basin, China, were used to construct a pressure-state-response model. The CRITIC-TOPSIS method, gravity model, and multi-scale geographic weighting model were applied to examine the ecological resilience of cities in the Yellow River Basin, the linkage relationships, and the influencing factors. The results reveal the following: (1) The ecological resilience of the Yellow River Basin fluctuates around 0.5, with the pattern “upstream>downstream>midstream”, and the average annual increase rates of each river reach was 0.41%, 0.30%, and 0.40%, respectively. (2) The Yellow River Basin is divided into seven major city networks (N₁-N₇). The degree of basin agglomeration and city association increases sequentially from the upper to the lower reaches. (3) Considering the influence of direct effect, regulatory effect and substitution effect, industrial structure upgrades significantly enhance the urban ecological resilience of city networks N₁-N₄, with impact coefficients of 0.4213, 0.4210, 0.5085, and 0.8883, respectively. In contrast, industrial structure rationalization more effectively enhances the ecological resilience of city networks N₅-N₇, with impact coefficients of 0.8483, 0.5669, and 0.8128.

This study analyzes the spatiotemporal changes in four durations of heavy precipitation (1 h, 3 h, 6 h, 12 h) for warm season (May-September) in Shaanxi Province, China during 1981—2020 based on 95 national meteorological observation stations and different statistical methods. The results show that: (1) The short duration heavy precipitation in Shaanxi mainly occurs during July-August. The regions with high frequency of heavy precipitation in four durations are located in the Qinling-Daba Mountains of southern Shaanxi, and with low frequency of that occur in the central Guanzhong Plain and the region along the Great Wall of northern Shaanxi. (2) The spatial differences of precipitation extremes in each duration are large, and the shorter duration with the stronger local distribution of the extreme precipitation. (3) In the past 40 years, the intensity and frequency of the each duration of heavy precipitation are increasing and enchancing, especially for the 3 h duration. (4) The spatial trends in each duration of heavy precipitation is non-uniformity with increased trends along the Yellow River in northern Shaanxi and south-central Shaanxi and decreased that in the south of northern Shaanxi and the central of Guanzhong Plain, and mostly, the shorter duration, the larger extent of increased trends. (5) The diurnal variation of heavy precipitation represent geographical differences in southern and northern Shaanxi. The shorter durations often accompany more obvious diurnal variation, especially for the northern Shaanxi, and the heavy precipitation usually occurs in the evening or at night, which would cause more serious disasters.

Desertification prevention and control is the main direction of implementation of China’s Three-North Shelter Forest Program (TNSFP) and is the key measure toward building a strong ecological shield in western China. To promote the research on the key techniques in desertification prevention and control, and support comprehensive prevention and control, and construct key ecological projects such as TNSFP, the existing findings in this field over 45 years since the implementation of TNSFP were reviewed by using Citespace and traditional literature induction. In addition, the overall characteristics, hotspot evolution, existing problems, and lacunae in the relevant literature were analyzed. Several conclusions were drawn. (1) The number of Chinese and English language papers on desertification prevention and control has been increasing continuously, and its international influence has been gradually enhanced. Such research is characterized by its multidisciplinary and cross-cutting nature. (2) During the last 45 years, such research has gone through three stages: 1978—2000, 2001—2012, and 2013—2023. In the first stage, the focus was on the causes of desertification and the dynamics of land degradation, and the technical model and countermeasures of desertification control. In the second stage, the focus was on ways to effectively implement desertification prevention and control via artificial sand fixation and afforestation. The third stage dealt with the sustainable development of desertification control, such as industrialized desertification control and natural restoration. (3) The research content mainly covers the driving factors and mechanism of desertification, control measures and models, policies and legal regulations, and changes in the ideas on the control of desertification. (4) Research on the causes of desertification has changed from qualitative analysis to qualitative and quantitative analysis. Monitoring of desertification has changed from a local to a cross-regional, cross-national, and cross-continental process and from a static to a dynamic and real-time process. The technical mode of desertification control changed from comparative research to comprehensive applied research. The focus has shifted from using a single technique to integrating multiple techniques and from only attending to wind-proof and sand-fixing benefits to noting their comprehensive effects. Ideas regarding desertification prevention and control are affected by economic, social, and ecological factors, and their evolutionary route encompasses replacing crops with trees or grass, working toward sustainable development, adopting a scientific approach to development, and applying Xi Jinping’s ecological thoughts. (5) Lastly, the current research deals with the rapid fixation of quicksand, new materials and techniques (models) for desertification control, biodiversity protection, realizing the value of ecological services, sand industry development and its policies, and so on. Future research should explore land use and its design, research and development of desertification control equipment and techniques, service function evaluation and exploration, policy improvement, and innovations in the key techniques for desertification control and promotion of these innovations.

The evaluation of landscape ecological risk is an emerging interdisciplinary field combining geography and ecology, with significant importance for regional environmental assessment and land resource planning. This study focuses on the Ebinur Lake Basin, Xinjiang, China using remote sensing data on land use from 1990, 2000, 2010, and 2020 to quantitatively analyze dynamic land use changes over three decades. The landscape ecological risk index and geostatistical methods were applied to assess the degree and spatiotemporal variation of ecological risks in the basin. Additionally, the PLUS model was used to simulate and predict the spatial distribution of land use and ecological risks under multiple 2030 scenarios. The results revealed the following: (1) Grassland and bare land dominate the basin, covering over 70% of the area, while shrubland and wetlands occupy smaller areas. Between 1990 and 2020, farmland and impervious surfaces expanded significantly, while grassland area shrank, representing the main land use changes. (2) From 1990 to 2020, the global landscape ecological risk, indicated by the Moran index (Moran’s I), showed a significant positive trend, with risks increasing and exhibiting a clustering effect, following a spatial distribution pattern of “low at the edges, high in the center”. (3) By 2030, land use changes in the basin are expected to stabilize, with grassland and bare land remaining dominant. (4) The spatial distribution of ecological risks in 2030 under different scenarios aligns with historical trends. Among these scenarios, the ecological protection scenario is most effective at mitigating risks while balancing socioeconomic development, making it ideal for achieving sustainable development.

The Loess Plateau of China has been experiencing an increase in extreme climate events due to global warming. Understanding the spatiotemporal characteristics of extreme precipitation events in this region is crucial for disaster prevention. This study analyzes daily precipitation data from 111 meteorological stations across the Loess Plateau, spanning the years 1960 to 2023. Using detrended fluctuation analysis (DFA), we established thresholds for extreme precipitation events and examined their spatiotemporal characteristics through the Mann-Kendall test and other methods. The findings reveal the following. (1) Extreme precipitation thresholds at meteorological stations vary between 27.4 mm and 89.1 mm, with 54% of the stations exceeding a threshold of 50 mm. The average threshold values across different ecological regions range from 35.0 mm to 59.6 mm, exhibiting a gradient that is lower in the northwest and higher in the southeast. (2) The amount and intensity of extreme precipitation events increase from 10.6 mm·a^-1 and 33.0 mm·d^-1 in the northwest to 71.5 mm·a^-1 and 133.0 mm·d^-1 in the southeast, respectively. The frequency of their occurrence increases from 0.3 d·a^-1 in the north to 0.8 d·a^-1 in the south. The number of extreme precipitation days closely aligns with heavy rain days, particularly in the loess hilly gully B2 sub-region. (3) The loess tableland gully, earth-rocky mountainous, and river valley plain regions are identified as high-risk areas for extreme precipitation events and should be prioritized for disaster prevention and control. (4) Over the past 64 years, extreme precipitation events have shown distinct interannual variability, with an overall increase observed, particularly in July and August. (5) In the last decade, the loess tableland gully and loess hilly gully regions have seen increased precipitation amounts and frequencies of extreme events. By contrast, the declining trend of extreme precipitation events in the sandy land and irrigated agricultural regions has slowed, whereas both the earth-rocky mountainous and river valley plain regions experienced a sudden spike in extreme precipitation events in 2020. This study serves as a reference for disaster prevention and mitigation regarding extreme precipitation events across the different ecological regions of the Loess Plateau.

Climate change and human activities are the primary factors influencing vegetation dynamics. The normalized difference vegetation index (NDVI) serves as an effective indicator for assessing vegetation changes, enabling evaluation of ecosystem dynamics and sustainability. Using SPOT/VEGETATION NDVI time series data, meteorological data, and land cover data, this study investigates the spatiotemporal evolution characteristics and driving mechanisms of NDVI in the Mu Us Sandy Land, China, from 1998 to 2019. Employing GIS spatial analysis, correlation analysis, and residual analysis, the study quantifies the relative contributions of climate change and human activities to NDVI variations. The findings reveal the following: (1) From 1998 to 2019, interannual NDVI in the Mu Us Sandy Land exhibited a significant upward trend, with a growth rate of 0.0067·a^-1. Spatially, NDVI displayed a gradual increase from northwest to southeast. However, the overall sustainability of NDVI growth was weak, indicating potential future fluctuations. (2) Both climate change and human activities jointly contributed to NDVI growth. NDVI changes were significantly positively correlated with precipitation, while correlations with temperature were weaker. Large-scale ecological projects and the interplay of climatic factors accounted for 86.30% of the observed vegetation improvement, aligning with existing studies on the impact of ecological projects. (3) Attribution analysis demonstrated that human activities contributed to 83.20% of NDVI growth, while precipitation accounted for 73.14%. The coupling effect of precipitation and human activities had a more pronounced influence on NDVI.

Exploring the spatial and temporal evolution characteristics and driving factors of cultivated land non-grain in grain production and marketing balance areas is crucial for providing references for differentiated control measures and long-term management strategies. This study employs spatial autocorrelation models, spatio-temporal geographically weighted regression models, K-means algorithms, and other methods to investigate the spatio-temporal evolution of cultivated land non-grain and its driving factors in Shaanxi Province, China, from 2000 to 2020. The results reveal the following: (1) The non-grain rate of cultivated land in Shaanxi Province increased from 16.11% in 2000 to 27.87% in 2020, representing a 73.00% rise. (2) The spatial distribution of non-grain in the province followed a pattern of “high in the north-south and low in the center.” The center of “high-high agglomeration” shifted gradually from the junction of the Guanzhong region and the northern region to the southern region of Shaanxi Province. Meanwhile, the “low-low agglomeration” was primarily concentrated in the Guanzhong region, exhibiting a diffusion trend from the center to surrounding areas. (3) The influence and scope of driving factors for cultivated land non-grain display significant spatio-temporal heterogeneity. The added value of the primary industry showed an increasing influence on cultivated land non-grain, while factors such as per capita cultivated land area, per capita mechanical labor force, average land slope, and annual precipitation demonstrated a decreasing influence. (4) The non-grain driving type of cultivated land in Shaanxi Province is mainly economic-driven, which is mainly distributed in Guanzhong region. Promoting the cost reduction and income increase of grain farmers and reducing the loss of rural population are the key points of control strategies. The types of production support are mainly distributed in the northern region, and the control strategies are mainly to improve the grain planting conditions and promote the development of the agricultural economy. The environmental restriction types are mainly distributed in the southern region of Shaanxi Province, and the combination measure of policy guidance and control strategies is the governance mode.

Investigating changes in vegetation cover, the driving mechanisms behind these changes, and the region’s ecological vulnerability in the Three-River Headwater Region (TRHR), Qinghai Province, China is essential for ensuring its ecological sustainability. Normalized difference vegetation index (NDVI) and kernel normalized difference vegetation index (kNDVI) were used, along with Theil-Sen Median trend analysis, Mann-Kendall significance test, and geographic detectors to explore the spatiotemporal changes in vegetation cover and driving forces. The sensitivity-resilience-pressure (SRP) model was used to assess ecological vulnerability. The results revealed the following trends: (1) From 2001 to 2020, both NDVI and kNDVI in the TRHR showed a fluctuating upward trend. Spatially, areas of improvement were mainly in the northeast and west, covering 73.70% and 79.79%, respectively, while areas of decline were primarily in the central and southern regions, covering 23.23% and 18.18%, respectively. (2) Precipitation, elevation, and temperature were the dominant factors influencing vegetation cover, with interactions among these factors led to bifactor or nonlinear enhancement effects. Precipitation between 573-675 mm and elevations of 3447-3850 m were most favorable for vegetation growth. (3) Ecological vulnerability increased from the southeast to the northwest, showing significant spatial variation. The region exhibited high ecological vulnerability, with areas of severe and extreme vulnerability, as indicated by NDVI and kNDVI, covering 35.38% and 36.85% of the total area, respectively.

The Qinling Mountains-Loess Plateau geological and geomorphological continuum features a fragile ecosystem where river ecological base flow and its thresholds are critical for ecosystem protection. This study examines the Qinling Mountains-Loess Plateau region in China, constructing a system of 22 ecological base flow influencing factors, including climate, vegetation, topography, soil structure, watershed morphology, and socio-economic variables. Using a self-organizing map (SOM) neural network and K-means clustering analysis, the region was divided into four sub-regions: the central Loess Plateau, southern Qinling, northern Qinling, and northwestern Loess Plateau. Partial least squares structural equation modeling (PLS-SEM) was applied to model and analyze ecological base flow influencing factors in three sub-regions. The results indicate that: (1) Ecological base flow is primarily influenced by precipitation concentration in the central Loess Plateau, by annual mean temperature in the southern Qinling, and by soil moisture content in the northern Qinling. (2) Significant regional differences were observed in ecological base flow thresholds, with values of 7.9% for the central Loess Plateau, 9.5% for the southern Qinling, 7.5% for the northern Qinling, and 4.1% for the northwestern Loess Plateau. (3) A linear regression model was developed to calculate and simulate ecological base flow, with determination coefficients exceeding 0.87, accounting for regional differences in environmental response. These findings provide a robust scientific basis for the quantitative estimation of ecological base flow, offer insights into river health maintenance and sustainable water resource utilization, and hold substantial theoretical and practical significance.

Utilizing detailed climate data collected from 105 national meteorological observation stations in Xinjiang, China during 1990—2020, this study systematically evaluates the tourism climate comfort and comfort period across fifteen prefectural and municipal cities in Xinjiang. Three key indicators [the temperature and humidity index (THI), wind-cold index (WCI), and index of clothing (ICL)] were employed for the analysis. The findings reveal as follows: (1) The months with the highest tourism climate comfort in Xinjiang are primarily May, June, and September. (2) According to the comprehensive tourism climate comfort index, Hami City, Altay Prefecture, Bortala Mongol Autonomous Prefecture, and counties and cities directly under Ili Kazakh Autonomous Prefecture and Kizilsu Kyrgyz Autonomous Prefecture exhibit an inverted U-shaped annual pattern. In contrast, Urumqi City, Karamay City, Shihezi City, Turpan City, Changji Hui Autonomous Prefecture, Bayingol Mongol Autonomous Prefecture, Tacheng Prefecture, Aksu Prefecture, Kashgar Prefecture, and Hotan Prefecture demonstrate an “M”-shaped pattern. (3) Analysis of the travel comfort period indicates that the southern border region enjoys the longest travel comfort period, followed by the northern border region, with the eastern border region having the shortest. Notably, Kashgar Prefecture and Hotan Prefecture have the longest comfort period, spanning March to October. However, the duration of the comfort period is not the sole determinant of tourist flow, as travel conditions may sometimes contradict comfort levels.

This study analyzes daily temperature data from 38 meteorological stations in Xizang, China, covering the period from 1981 to 2023. A new method for seasonal division in Xizang was employed to categorize the four seasons, and the regional climate changes, temporal shifts in the start dates of each season, and trends in seasonal changes were examined. The results show the following. (1) Areas with four distinct seasons in Xizang are primarily found along the Yarlung Zangbo River and in Nyingchi City, whereas regions with less distinct seasonal variations (including areas without a summer season) are mainly situated in the western and northern parts of Xizang as well as in the high-altitude regions of the Himalayas. (2) In Xizang, the beginning of spring and summer tends to occur earlier, whereas the onset of autumn and winter tends to be delayed. Notably, the start date of spring was significantly earlier in 2000, whereas the onset of autumn and winter was significantly delayed in 2003 and 1995, respectively. (3) Regarding the timing of seasonal starts, the first empirical orthogonal function (EOF1) for spring and autumn exhibited a pattern of “northwest low and southeast high in spring, and middle high and both sides low in autumn”. In spring, the second EOF (EOF2) presented a contrasting distribution pattern of “northwest positive and southeast negative”, whereas in autumn, EOF2 showed an opposite spatial distribution pattern characterized by “southwest positive and northeast negative”. The EOF1 in winter revealed a “high in the north and low in the southwest” pattern, whereas the EOF2 shared similarities with that of spring’s EOF2. (4) Looking ahead, we anticipate that the start dates of spring and summer will be delayed, whereas those for autumn and winter will be advanced.

Based on the manual observation of hail records in Inner Mongolia, China, from 1959 to 2021, the spatial and temporal characteristics of hail distribution are analyzed, and a hail recognition method is constructed based on machine learning algorithms. The results are as follows: (1) Regarding temporal distribution, the number of hail days and affected stations in Inner Mongolia shows a decreasing trend. In terms of spatial distribution, hail events are predominantly concentrated in the Yinshan Mountains and the Greater Hinggan Mountains, with hail-prone areas extending along these mountain ranges. (2) Hail exhibits distinct seasonal and diurnal characteristics. The peak hail months in Inner Mongolia are from May to September, accounting for 91.79% of the annual hail days. The most frequent period for hail occurrences is between 12:00 BST and 19:00 BST. (3) Four machine learning algorithms (random forest, LightGBM, K-proximity, and decision tree) are used to model and evaluate hail events in Inner Mongolia through data preprocessing, predictor selection, model training, and tuning. Verification results indicate that machine learning methods effectively identify hail events, with the threat score of each model exceeding 0.83 and hit rates surpassing 92%. Among these, the random forest algorithm demonstrates the best recognition performance on the test set. These findings provide useful references for hail forecasting and artificial hail prevention in Inner Mongolia.

Agriculture in arid regions plays a vital role in advancing local socio-economic development and ecological sustainability, given the unique resource and environmental constraints. This study examines Xinjiang, China, as a case study, utilizing the center of gravity transfer model, locational Gini coefficient, comparative advantage index, and global Moran’s I index at the county level to analyze the spatio-temporal evolution and influencing factors of the six major crops from 2000 to 2020. The results indicate that: (1) Xinjiang’s agricultural planting scale has been growing steadily from 2000 and 2020, and cotton, vegetables, and melons “advancing”, grain, oil, and sugar crops “restreating” constitute the basic competitive situation of the major crops. (2) The production centers for the six major crops are predominantly located in the central and western regions of Xinjiang. The concentration of cotton, vegetables, and melon production has steadily increased, with production becoming concentrated in a relatively small number of counties. (3) At the national level, all six major crops exhibit efficiency comparative advantages. Cotton, sugar, and melon production demonstrate both scale and comprehensive comparative advantages, with cotton showing a particularly pronounced scale advantage. At the regional level, most counties in Xinjiang lack comparative advantages in crop production. Counties with comparative advantages are primarily scale-dominated. (4) The evolution of Xinjiang’s crop production pattern has been influenced by several critical factors, including policy directives, technological advancements, and rising farmer incomes.

The performance evaluation of quantitative precipitation forecasts can provide a scientific basis for the application and improvement of such forecasts. In this study, hourly site precipitation observation data and the CMA-MESO model’s quantitative precipitation forecast data from May to September (the warm season) of 2022—2023. Using evaluation indicators such as the probability of correct rainfall, threat score (TS), false alarm ratio, and missed alarm ratio, precipitation forecast performance over the Ili River Valley was analyzed. The results revealed the following: (1) The CMA-MESO model can reasonably depict the 1 h and 24 h precipitation characteristics in the Ili River Valley during the warm season. As the precipitation intensity increases, both the forecast and observed frequencies of precipitation show a downward trend. (2) The TS for the CMA-MESO model forecast of precipitation of different intensities is closely related to the forecast bias of the accumulated precipitation probability. The 24 h precipitation forecast TS score for the range of 6.1-12.0 mm is the lowest, with the highest cumulative probability forecast bias, exceeding a mean of 2.0%. The 1 h forecast TS score significantly decreases with the enhancement of precipitation intensity, reaching a peak bias of 1.7% at 0.1 mm. (3) The frequency of the forecasted and observed precipitation shows an increasing trend with altitude. However, the 24 h forecast frequency exhibits a negative bias across all altitudes, while the 1 h forecast frequency shows a positive bias in the low-altitude areas and a negative bias in the sub-high-altitude areas. (4) In terms of diurnal variation, the CMA-MESO model did not accurately simulate the characteristic of low precipitation frequency during the day and higher frequency during the night in the Ili River Valley. Specifically, the model tends to have more false alarms for daytime precipitation and more missed alarms for nighttime precipitation. A comparison of the frequency of precipitation observations with forecasts shows that the pattern of the forecast trend from early morning to afternoon is completely opposite to the observed frequency; the most significant forecast biases occurs between 13:00—14:00 and 02:00—05:00.

The ecological environment of the Korla region, Xinjiang, China is highly sensitive and fragile, requiring meticulous attention and sustained efforts for its preservation. Understanding the variations in ecological environment quality in this area is crucial, forming the foundation for effective ecological protection and restoration policies by local authorities. This study employs the innovative concept of the remote sensing ecological index, adapted specifically temperature, land surface dryness, desertification degree, salinization degree, and evapotranspiration—a refined index termed the modified remote sensing ecological index (MRSEI) is developed through principal component analysis. This refined index is applied to conduct a comprehensive evaluation and analysis of the factors influencing the ecological environment quality in the Korla region from 1994 to 2021. The results demonstrate that the MRSEI effectively reflects the ecological environment quality of the Korla region. From 1994 to 2021, the MRSEI ranged from 0.253 to 0.346, showing an overall upward trend and indicating an improvement in ecological environment quality. However, the overall ecological environment quality is primarily categorized as “poor” and “relatively poor”, covering 70.96% of the area. The overall spatial distribution reveals a pattern of “relatively poor” in the western part and “relatively good” in the eastern part. Over the 27-year period, approximately 60.41% of the area exhibited minimal change in ecological environment quality, mainly in the western hilly areas and tablelands. Around 16.47% of the area experienced ecological degradation, particularly in the northern plains and some moderately and slightly undulating mountain areas, while 23.12% of the region showed improvement, primarily in the eastern plains and hilly regions. Climate and socioeconomic factors are closely linked to the ecological environment quality in the Korla region. Among climatic factors, evaporation exerts the most significant impact, while among socioeconomic factors, the year-end total population is the primary driver influencing ecological environment quality.