Precipitation is a key component of the hydrologic cycle, and accurate precipitation data are essential for regional water resources assessment. However, substantial discrepancies persist across precipitation products derived from different sources and methods for Xinjiang. Using observations from 105 national-level meteorological stations maintained by the National Meteorological Center, this study evaluated eight high-resolution gridded precipitation products and analyzed how different data sources and production methods affect their ability to capture the spatiotemporal variability of precipitation in Xinjiang over the 30 years between 1991 and 2020. The results show that all products capture broadly consistent spatial patterns and temporal fluctuations of precipitation, yet their precipitation totals diverge markedly: the difference between the products with the highest (135.73 mm) and lowest (59.73 mm) long-term average annual precipitation in the region is 76 mm. Spatially, the largest inter-product differences occur in the Turpan Basin and along the southern margin of the Kunlun Mountains. Among the data sources, satellite-based products exhibit considerable uncertainty in high-elevation regions, with pronounced wintertime overestimation. By contrast, precipitation data generated via multi-source data fusion demonstrate superior and more consistent performance in representing spatiotemporal variability. Moreover, incorporating machine-learning algorithms into multi-source data fusion can effectively mitigate systematic errors arising from complex topography, elevation differences, and biases associated with any single data source. These results offer a guidance on dataset selection for future investigations of climate change and drought and flood risk management in Xinjiang.
In the past few decades, a warming-wetting trend has been observed in the arid and semiarid Northwest China, especially in Xinjiang. According to the Clausius-Clapeyron (C-C) relationship, the atmospheric moisture-holding capacity increases with rising temperature, which may lead to an intensification of extreme precipitation. In this study, we examined the spatial distribution of scaling relationships between precipitation and dew point temperature in Xinjiang, using intra-annual binning scaling and inter-annual trend scaling based on station observations and ERA5. The results show that the predominant binning scaling in Xinjiang is sub C-C scaling (0%/°C-5%/°C), transitioning from northern Xinjiang to C-C like scaling (5%/°C-9%/°C) or super C-C scaling (>9%/°C) near the Tianshan Mountains and southern Xinjiang. The observed trend scaling exhibited a similar spatial pattern to that of binning scaling, but with more super C-C scaling. Furthermore, the reduction in vertical velocity at the southern foothills of the Tianshan Mountains may lead to a decrease in precipitation intensity. Overall, these findings provide a solid basis for enhancing our understanding of how climate change influences precipitation events in Xinjiang.
In the context of climate change, severe soil wind erosion has caused a series of negative impacts on the ecosystems in China's arid Northwest region. With the increasing frequency and intensity of extreme weather and climate events, the vulnerability of arid ecosystems has been further exacerbated. However, the mechanisms through which different extreme events affect soil wind erosion remain unclear. Thoroughly investigating the occurrence patterns of extreme events and revealing the response processes of soil wind erosion to these events hold significant importance for future climate risk mitigation initiatives. Based on this understanding, this study focused on China's northwestern arid region as the research area. Using the relative threshold method and the revised wind erosion equation model (RWEQ), we explored the spatiotemporal characteristics of extreme events—droughts, strong winds, and high temperatures—as well as soil wind erosion. The relationship between the intensity of extreme events and soil wind erosion during their occurrence was also analyzed. The main findings are as follows: 1) Areas with high drought intensity were primarily concentrated in the southwestern part of the study region, showing an initial decline followed by an increasing trend over time. Regions with high strong wind intensity were clustered in the central and northern areas, where strong wind intensity decreased significantly during the study period and stabilized after 1990. High-temperature intensity exhibited minimal spatial variation across the region and showed a significant upward trend over time. 2) Soil wind erosion across the study area remained at a relatively high level overall, with high-erosion zones mainly distributed in the Tarim Basin and the central part of the Hexi Corridor. Over the four decades, soil wind erosion displayed a pronounced decreasing trend throughout the region, particularly in areas near the Tianshan Mountains. 3) Soil wind erosion reached its peak during drought events, followed by high-temperature events. A positive correlation existed between the intensity of extreme events and soil wind erosion during such events. Additionally, extreme events incurred higher risks of soil wind erosion in barren lands and desert areas. This study provides a reference for understanding the impacts of extreme weather and climate events on soil wind erosion under global change and serves as a decision-making basis for regional desertification prevention and control.
The northern slope of the Tianshan Mountains is an important water source conservation area and a key grassland animal husbandry base in Northwest China. The meltwater from snow is crucial for maintaining ecosystems, agricultural irrigation, and urban water supply. In order to solve the problem of missing data caused by cloud interference in MODIS snow products, this study extended the MODIS data input and used existing snow data to identify pixels as either snow or non-snow as "true values". Machine learning algorithms such as random forest, support vector machine, and BP neural networks were applied to determine the best approach for snow identification. By combining various cloud removal methods with the hidden Markov random field (HMRF) algorithm, a comparative analysis of cloud removal effects was conducted, and the accuracy of the experimental results was verified using high-resolution Landsat data. The results show that: 1) The random forest model performed best in the binary snow classification task, with an accuracy of 90.15% and a precision of 91.95%. 2) Collaborative cloud removal using multiple data sources yielded good results, with a Kappa coefficient of 0.729, but combining the HMRF method achieved the best cloud removal effect, with an overall accuracy of 82.84%, producer accuracy of 88.46%, and Kappa coefficient of 0.795. 3) The trends of annual average snow days, snow-covered days in relation to altitude, monthly average snow coverage rate, and annual average snow-covered area show high consistency with existing data.
The northern slope area of the Tianshan Mountains, as an important energy base in China, is focusing on the development of renewable energy sources such as photovoltaic power. To support the industrial layout, there is an urgent need for high-quality, long-term historical radiation data. This study proposed a solar radiation data fusion method that combines subspace and boosting ensemble learning methods to construct a regression model, fully leveraging the spatiotemporal representativeness of the data sources. Based on this framework, three gridded radiation datasets—ERA5, FLDAS, and TERRA—were fused to generate the 0.1° solar radiation data for the northern slope area of the Tianshan Mountains from 1990 to 2020. Compared to the original datasets, the newly generated dataset shows significant improvements in validation metrics such as correlation coefficient, root mean square error (RMSE), and mean absolute error (MAE). Among the 12 seasonal indicators, the new dataset ranks the highest in 10, providing a more accurate reflection of radiation variations over the 30 years. The results indicate that the average radiation across the northern slope area of the Tianshan Mountains showed an increasing trend until 2008, after which it began to decrease. In the eastern region, radiation increased most significantly in the spring (with a rate of 3.6 W/(m2·10 a)), followed by the winter, while radiation in the western region showed a notable decrease in the autumn. Comparing the spatial distribution of solar radiation with existing photovoltaic power stations, the utilization of solar energy resources in the east of Urumqi and Hami can be further optimized. The research results can provide a reference for the high quality development of the regional photovoltaic industry.
A detailed analysis of the spatiotemporal dynamics of different land use types and their impacts on carbon storage in arid regions is of great significance for regional ecological protection and sustainable development. Taking the northern slope of the Tianshan Mountains in northwestern China's arid region as a case study, this study systematically analyzed the spatiotemporal change of land use and carbon storage, along with their driving mechanisms, from 2000 to 2030 based on the SD-PLUS-InVEST model and the Geodetector model. The results are summarized as follows: 1) From 2000 to 2020, land use transitions in the study area were frequent, with cultivated land and construction land expanding, while grassland and woodland declined. Under the 2030 land use projection scenarios (SSP119, SSP245, and SSP585), grassland and unused land are dominant land use types. Cultivated land, woodland, and construction land are projected to expand, while grassland and water bodies are expected to decline further. 2) Carbon stocks in the study area exhibited a general increasing trend over the past two decades, with a total increase of 4.70×107 t. The most significant increase in carbon stocks occurred in cultivated land, followed by construction land, whereas grassland consistently experienced decline in carbon storage but remained the region's primary carbon sink. Compared to 2020, carbon stocks are projected to increase across all scenarios by 2030, with increases of 5.90×107 t, 5.28×107 t, and 3.08×107 t respectively. 3) NDVI, soil type, and population density are the main driving factors of spatial variation in carbon storage in the study area, and their interactions show stronger explanatory power than individual factors, with the interaction between NDVI and soil type being the most significant. The results can provide a scientific basis for land spatial resource planning and carbon sink enhancement in the study area under the framework of the "dual carbon" goals.
Rural settlements play a key role in the process of rural transformation and development. Exploring the theory of rural settlement system evolution has practical significance for improving the energy efficiency of rural governance. Based on the analysis of the connotation and attribute characteristics of the evolution of the rural settlement system, this study conducted a theoretical exploration of the mechanism, stages, variables, and limits of the evolution of such system. The research found that: 1) The essence of the evolution of the rural settlement system is the process of leap and combination of elements such as the natural environment base, social economic systems, institutions and policies, and technology and culture from a low-level equilibrium to a high-level equilibrium. 2) The changes in the societal industrial structure caused by division of labor in society have led to "huge fluctuations", and the evolution of the rural settlement system can be divided into four stages: the early stage of traditional agriculture, the late stage of traditional agriculture, the early stage of industrialization, and the late stage of industrialization. 3) There are two types of variables influencing the evolution of rural settlement systems: fast variables and slow variables. Slow variables set the evolution boundaries of the rural settlement system, while the responses of fast variables to the external environment are the fluctuations that trigger the adaptive responses of the system. The different ratios of fast and slow variables result in the complexity of village evolution. 4) The limits of the evolution of the rural settlement system are the threshold manifestation of the adaptation of the human-environment relationships to multi-dimensional boundaries. The limit state of the evolution of the entire urban-rural regional system is the stage when the population flow between urban and rural areas tends to balance and the unit benefits of agriculture, industry, and services are similar. This research provides a reference for enhancing the theoretical understanding of the evolution of rural settlement systems and promoting the orderly implementation of the rural revitalization strategy.
Urban heat island (UHI) effect is an important factor affecting the spatiotemporal variations of energy consumption for building cooling and heating in urban areas. Understanding the impact of UHI effect on building energy consumption is crucial for urban energy conservation, carbon emission reduction, and high-quality development. This article reviewed the relevant literature on the impacts of UHI effect on building cooling and heating energy consumption. It described the basic framework for the assessment of UHI effect on building energy consumption, summarized problems with the current research, and suggested potential future research directions. The review of the research results and main findings both in China and internationally show that: 1) Generally, UHI effect increases building cooling energy consumption and reduces heating energy consumption in urban areas. Magnitude of the increase in cooling energy consumption and the decrease in heating energy consumption varies in different regions depending on their climate zones and urbanization stages. 2) There are significant spatial differences in the impacts of UHI effect on building cooling/heating energy consumption, with the strongest impacts in the central urban areas and the weakest in the suburban areas. 3) During extreme weather and climate events (for example, heat waves), the synergistic effect of the UHI and extreme events affects the occurrence time and level of peak energy consumption in urban buildings. There are still uncertainties in the study of the impacts of UHI effect on building cooling and heating energy consumption, as well as limitations in the studies of the effects of extreme weather and climate events, large-scale high spatiotemporal resolution assessment, and mechanisms of UHI effect on urban building energy consumption. Further investigations are needed in areas such as applying high spatiotemporal resolution data, innovating research methods, and incorporating extreme weather and climate impacts.
The development of new scientific knowledge in a region is imperative for the enhancement of regional independent innovation capacity. While extant studies have focused on the endogenous dynamics of regional scientific knowledge diversification, discussions on the role of external linkages are scarce and controversial. In this study, we incorporated external linkages into the relatedness-complexity framework and systematically investigated the impact of external linkages on the diversification of regional scientific knowledge and its heterogeneous characteristics. Based on the data of highly cited papers from CNKI, key research findings are as follows: 1) The scientific knowledge output of Chinese cities is characterized by the spatial feature of "large dispersion and small agglomeration", with municipalities and provincial capitals producing numerous highly cited papers. The entry of new scientific knowledge occurs more often in ordinary prefecture-level cities, and there is a spatial mismatch between the pattern of scientific knowledge introduction and production in cities. 2) The scientific knowledge base of most Chinese cities is characterized by "high-potential, high-quality" and "low-potential, low-quality" types. Cities with high potential for developing new scientific knowledge also possess scientific knowledge of higher complexity. The backbone network of China's urban scientific knowledge cooperation is characterized by a hierarchical structure. Only cities with high administrative levels, such as municipalities and provincial capitals, have numerous external knowledge linkages, while the external knowledge linkages of ordinary prefecture-level cities are characterized by preferential linkages or geographic proximity. 3) External linkages contribute significantly to regional scientific knowledge diversification, with the share of external linkages contributing to complexity diversification and hindering relatedness diversification, and the geographic diversity of external linkages contributing to relatedness diversification but hindering complexity diversification. 4) The impact of external linkages is characterized by heterogeneity in terms of network location and knowledge base dimensions. The scientific knowledge diversification in the network core region is positively influenced by an increase in geographic diversity of external linkages, while the network peripheral regions are positively influenced by an increase in the share of external linkages. The positive impact of geographic diversity of external linkages is more pronounced than the share of external linkages in the "high-potential, high-quality" regions, while the positive impact of geographic diversity of external linkages is less significant than the share of external linkages in the "low-potential, low-quality" regions. The findings provide policy implications for enhancing scientific knowledge innovation capacity in different regions of China by optimizing external linkages.
China's urban economic network is gradually transforming from a unipolar agglomeration to a polycentric and synergistic structure, driving dynamic adjustments in the interregional industrial division of labor. Revealing the spatiotemporal evolution and influencing mechanisms of advantageous industry overlap under this structural change holds significant theoretical and practical implications for promoting coordinated regional development. This study examined 275 prefecture-level and above cities in China for the years 2012, 2015, and 2017. Using intercity input-output data, it constructed the urban economic network and applied two novel indicators—directed alternative centrality (DAC) and directed alternative power (DAP)—to measure cities' positions within the network. Advantageous industry overlap was quantified through a supernetwork approach. The key findings are as follows: 1) Cities with high DAC and DAP were mainly concentrated in provincial capitals and municipalities with higher levels of economic development. Core cities had continuously strengthened their positions, while low-centrality cities had enhanced mutual connectivity, forming a differentiated network dominated by a few core cities but involving multi-tier linkages across the urban system. 2) The overlap of advantageous industries showed a gradually strengthening convergence effect over time, while spatially it presented a pattern of overall convergence with local divergence, characterized by persistent agglomeration and pronounced regional disparities. 3) Expanding disparities in cities' positions within the directed urban network, as measured by DAC and DAP, significantly reduced both the overlap of leading industries and overall industrial structure similarity, with heterogeneous effects across regions and between resource-based and non-resource-based cities. Further analysis revealed a U-shaped nonlinear relationship between network position disparities and advantageous industry overlap. This research examined the spatiotemporal change of urban network positions and advantageous industry overlap, while clarifying the mechanisms through which directed network status affects industrial convergence. The findings provide a theoretical support for optimizing regional industrial layouts and fostering polycentric and synergistic urban development.
China's excellent tourism cities are the core carriers of the country's tourism development, accounting for over 90% of the national total tourism economic output. They are not only concentrated areas of regional tourism resources and service capabilities but also important representatives of the overall regional image, playing a crucial exemplary and leading role in the development of the tourism industry. In recent years, against the backdrop of frequent global public health emergencies and complex economic fluctuations, tourism economic resilience has become a core topic in tourism research. This study collected relevant data on tourism development of 208 excellent tourism cities across China from 2003 to 2022, and further examined its influencing mechanism on the basis of measuring tourism economic resilience. The study found that the average value of tourism economic resilience of China's excellent tourism cities was generally greater than 0; LISA time path analysis indicated that some excellent tourism cities in the eastern region showed strong spatial dynamics of tourism economic resilience, while those in the central and western regions were relatively weak; the time path curvature was higher in economically developed areas, but lower in inland or late-developing areas. In the LISA spatiotemporal transition, 73.08% of the excellent tourism cities were in a state of self or neighborhood transition, which reflects that the tourism economic resilience of China's excellent tourism cities had significant dynamic change characteristics. Obstacle degree model analysis indicated that the number of students in institutions of higher education, employees in the tertiary industry, and the number of broadband users were the main obstacle factors that affect the tourism economic resilience of China's excellent tourism cities; there was also heterogeneity in the main influencing factors of tourism economic resilience among China's excellent tourism cities in different regions. Based on these conclusions, relevant recommendations for improving the tourism economic resilience of China's excellent tourism cities were put forward.
The industrial agglomeration of digital economy is an important support for promoting green technology innovation, which is of great strategic significance for realizing ecological protection and high-quality development in the Yellow River Basin. Based on the data of digital economy enterprises in 78 prefecture-level cities in the Yellow River Basin from 2011 to 2022, this study used the kernel density estimation and location entropy methods, reciprocal of Herfindahl-Hirschman index, and the two-way fixed effect model to explore the temporal dynamics, spatial patterns, agglomeration characteristics of specialized agglomeration and diversified agglomeration, and their impact on green technology innovation. The results show that: 1) From 2011 to 2022, the number of digital economy enterprises in the Yellow River Basin increased from 36400 to 402100, an increase of about 10.05 times. 2) The spatial distribution of kernel density changed from single-core or dual-core to multi-cores in individual province. The lower reaches formed a contiguous development trend, and the middle and upper reaches showed a scattered distribution of high-value agglomeration areas. The regional differences between specialized agglomeration and diversified agglomeration of digital economy industry were significant, and the overall distribution was high in the east and low in the west. 3) The influence of specialized agglomeration and diversified agglomeration of digital economy industry on green technology innovation showed obvious heterogeneity. Specialized agglomeration had a positive effect on green technology innovation, while diversified agglomeration inhibited green technology innovation in the basin to a certain extent, and there were significant differences in the upstream, midstream, and downstream industries. Based on these results, corresponding countermeasures and recommendations were put forward from the aspects of infrastructure development, industrial planning and layout, and regional cooperation, in order to promote the deep integration of digital economy industry and green technology innovation, and help the high-quality development of the Yellow River Basin.
In the context of global sustainable development and ecological civilization construction, the integration of ecological protection and people's well-being has gradually become a key focus of attention both in China and internationally. In particular, the Yellow River Basin in China, as an important ecological security barrier and economic zone, faces the dual pressure of balancing ecological protection and economic development. Ecological well-being performance is a comprehensive index to evaluate ecological protection and people's well-being. Improving ecological well-being performance is an objective requirement for ecological protection and high-quality development of the Yellow River Basin. Based on the reconstruction of an ecological well-being performance evaluation indicator system, this study used the Super-EBM model considering the undesirable outputs to quantitatively measure ecological well-being performance in the Yellow River Basin during 2005-2022, and used kernel density analysis and exploratory spatial data analysis methods to describe the characteristics of change of the spatial and temporal patterns of ecological well-being performance in the region. Finally, the fsQCA method was used to analyze the configuration mechanism of ecological well-being performance. The findings are as follows: 1) During the research period, the ecological well-being performance of the Yellow River Basin generally showed a continuous upward trend, but regional differences were significant, presenting a multi-level differentiation trend and complex evolution characteristics. 2) The ecological well-being performance in the Yellow River Basin presents a staged pattern of high in the east and low in the west, and high in the south and low in the north. The positive spatial autocorrelation has been enhanced, and the spatial disequilibrium and spatial agglomeration characteristics are significant. 3) In terms of configuration mechanism, three driving mechanisms can be identified: single dominance type, dual interaction type, and multiple cooperation type. Two limiting mechanisms exist: digital technology-science and technology deficiency type, and science and technology-environmental regulation deficiency type. The research results can provide a scientific reference for promoting regional ecological well-being performance.
Rural development is currently undergoing a pivotal shift from a resource-dependent model to one driven by innovation. The rural-urban reverse population flows have become a key factor in addressing the long-standing challenges of rural innovation and development. Using the input-support-output rural innovation efficiency framework, along with the moderating effect and multiscale geographically weighted regression (MGWR) methods, we analyzed the change of spatial pattern of rural-urban reverse population flows and rural innovation efficiency of 27 administrative villages in Luotuoao Town, Hubei Province—a typical rural-urban reverse population flow area—from 2018 to 2020. We also explored the impact of the rural-urban reverse population flows on rural innovation. The results show that: 1) The innovation level and the distribution of the rural-urban reverse population flow in each village of Luotuoao Town exhibited significant spatial imbalances, with some spatial correlation. 2) The counterflow population between urban and rural areas played a significant role in enhancing innovation in rural areas. It exerted the greatest influence on innovation input, with a spatial distribution characterized by a dual-core architecture and gradual reduction. 3) The impact of rural-urban reverse population flows on rural innovation is influenced by various factors and shows regional variation. Industrial structure, agricultural cooperatives, and nighttime light intensity all have positive effects. Additionally, low-altitude areas with dense road networks facilitate the flow and application of innovation resources, thereby boosting the innovation capacity of reverse migration populations. This study offers a theoretical support for building a sustainable mechanism to attract and retain rural talents.
Although ecological villages in the suburbs of metropolis can promote development by virtue of the spillover effect of the central urban area and the advantages of ecological landscape resources, they are faced with development bottlenecks such as hollowing out and limited transformation of ecological values due to the siphon effect and ecological controls. Taking Chayuan Village, an ecological village in Hubei Province in 2023, as an example, this study analyzed the characteristics and internal mechanisms of spatial transformation of ecological villages in metropolitan suburbs by using the embeddedness theory. The study found that: 1) Constructing a theoretical analysis framework from the four dimensions of structural embeddedness, relational embeddedness, institutional embeddedness, and cultural embeddedness can lay a foundation for analyzing the internal mechanisms of rural spatial transformation under the interaction of social system and agents. 2) The remarkable characteristics of rural spatial transformation are embodied in the orderly expansion of material space, the compound function of economic space, the pluralistic and open social space, and the richness and co-creation of cultural space. 3) With regard to the rural space transformation mechanisms, the position of the village in the social network, the connection strength of social relations, the intervention strength of the policy system, and the cultural and ideological situation of village residents deeply affect the process of rural space transformation. The modernization of village material space and the traditionalization of economic space and ideas coexist in the initial exploration stage under the weak relationship embedding. The upgrading of rural space in the slow development stage under the strong system embedding was limited, and the rural space transformation in the structural reorganization stage under the multiple embedding coordination has achieved some success. The research on the characteristics and mechanisms of ecological village spatial transformation in metropolitan suburbs is helpful for promoting their high-quality development.
As a typical social-ecological system, agricultural heritage sites are a spatial field with the participation and interaction of multiple subjects. Along with the process of urban-rural integration, their traditional production function space is undergoing a significant functional transformation. Promoting multi-subject participation and in-depth cooperation has become the key to driving the spatial renewal of agricultural heritage sites. By integrating the organizational embeddedness theory and social connection theory, this study constructed an analytical framework for the spatial renewal of agricultural heritage sites, and analyzed the processes and mechanisms of cooperation between internal and external subjects driven by the cooperative through the practice of the Xinglongwa Millet Ecological Cultivation Cooperative in Aohan County, Inner Mongolia. The study found that: 1) The cooperative first promoted the initial cooperation of subjects through the mobilization of goal matching, action synergy, and continuous input, and promoted the spatial function transformation of the heritage site. 2) Relying on the connection mechanism of politics, interests, emotions, and responsibilities, the cooperative connected multiple subjects for in-depth cooperation and strengthened the effectiveness of their participation in the protection of agricultural heritage and the governance of heritage sites. 3) The organizational embeddedness and social connection jointly promoted the gradual renewal of the physical, economic, social, and tourism space of the agricultural heritage site, and the locality of the space of the heritage site entered the stage of co-creation by multiple subjects. The results of the study can help transform the institutional advantages of collective economy such as cooperatives into practical effectiveness, and facilitate the high-quality development of agricultural heritage sites.
