The mass elevation effect (MEE) is a thermal effect, in which heating produced by long wave radiation on a mountain surface generates atmospheric uplift, which has a profound impact on the hydrothermal conditions and natural geographical processes in mountainous areas. Based on multi-source remote sensing data and field observations, a spatial downscaling inversion of temperature in the Tianshan Mountains in China was conducted, and the MEE was estimated and a spatio-temporal analysis was conducted. The GeoDetector model (GDM) and a geographically weighted regression (GWR) model were applied to explore the spatial and temporal heterogeneity of the study area. Four key results can be obtained. (1) The temperature pattern is complex and diverse, and the overall temperature presented a pattern of high in the south and east, but low in the north and west. There were clear zonal features of temperature that were negatively correlated with altitude, and the temperature difference between the internal and external areas of the mountains. (2) The warming effect of mountains was prominent, and the temperature at the same altitude increased in steps from west to east and north to south. Geomorphological units, such as large valleys and intermontane basins, weakened the latitudinal zonality and altitudinal dependence of temperature at the same altitude, with the warming effect of mountains in the southern Tianshan Mountains. (3) The dominant factors affecting the overall pattern of the MEE were topography and location, among which the difference between the internal and external areas of the mountains, and the absolute elevation played a prominent role. The interaction between factors had a greater influence on the spatial differentiation of mountain effects than single factors, and there was a strong interaction between terrain and climate, precipitation, the normalized difference vegetation index (NDVI), and other factors. (4) There was a spatial heterogeneity in the direction and intensity of the spatial variation of the MEE. Absolute elevation was significantly positively correlated with the change of MEE, while precipitation and the NDVI were dominated by negative feedback. In general, topography had the largest effect on the macroscopic control of MEE, and coupled with precipitation, the underlying surface, and other factors to form a unique mountain circulation system and climate characteristics, which in turn enhanced the spatial and temporal heterogeneity of the MEE. The results of this study will be useful in the further analysis of the causes of MEE and its ecological effects.

Exploring the coupling coordinated level of rural population-land-industry (PLI) and its underlying driving mechanism contributes to the scientific decision-making on rural sustainable development. This study assessed the coupling coordinated level of PLI based on an improved evaluation index system and then revealed the regional differentiation and driving mechanism in China’s rural areas in 2020. The results showed that the rural PLI coupling coordinated degree was 0.4694, and thus was in the stage of approximate incoordination. In addition, China’s rural PLI coupling coordinated degree formed a spatially heterogeneous pattern with high levels in the northeast, eastern and central regions, and the intragroup difference contributed more than 80% to the total difference. The rural PLI coupling coordinated level was influenced by the combined effects of rural kernel and peripheral systems, but the rural kernel system mostly determined the differentiation. In the future, rural areas should first exploit population quality improvement projects, land consolidation projects and industrial integration development strategies to promote benign mutual feedback of PLI. Second, driving factors should be comprehensively regulated by implementing a “one village, one product” strategy, breaking the urban-rural dual system, improving agricultural machinery subsidies policy, and promoting urban-rural integrated development.

Situated in the hinterland of Eurasia, Central Asia is characterized by an arid climate and sparse rainfall. The uneven spatial distribution of water and land resources across the region has pressured economic and social development. An accurate understanding of Central Asia's water resources carrying capacity (WRCC) is vital for enhancing the sustainability of water resources utilization and guiding regional economic and social activities. This study aims to facilitate the sustainability of water resources utilization by evaluating the region's WRCC from the viewpoints of economic and technological conditions and social welfare. A concise yet effective model with relatively fewer parameters was established by adopting water resources data from the Food and Agriculture Organization (FAO) and socioeconomic data from the World Bank. The results indicated that the WRCC of all five Central Asian countries showed an increasing trend with improved water use efficiency from 1995 to 2020. Kazakhstan's WRCC was significantly higher than the other four countries, reaching 54.03 million people in 2020. The water resources carrying index (WRCI) of the five Central Asian countries varied considerably, with the actual population sizes of Turkmenistan and Uzbekistan highly overloaded. Although there has been a decrease in Central Asian countries’ WRCI between 1995 and 2020, water resources utilization problems in the region remain prominent. Based on the water resources carrying capacity evaluation system, to increase available water resources and improve production water use efficiency are key to address these issues. In light of this, this study offers practical and feasible solutions at the policy level: (1) The implementation of signed multilateral agreements on transboundary water resources allocation must proceed through joint governmental efforts. (2) Investments in advancing science and technology need to be increased to improve water use efficiency in irrigation systems. (3) The output of water-intensive crops should be reduced. (4) The industrial structure could be further optimized so that non-agricultural uses are the primary drivers of gross domestic product (GDP) growth.

Karst depressions are common negative topographic landforms formed by the intense dissolution of soluble rocks and are widely developed in Guizhou province. In this work, an inventory of karst depressions in Guizhou was established, and a total of approximately 256,400 karst depressions were extracted and found to be spatially clustered based on multidistance spatial cluster analysis with Ripley's K function. The kernel density (KD) can transform the position data of the depressions into a smooth trend surface, and five different depression concentration areas were established based on the KD values. The results indicated that the karst depressions are clustered and developed in the south and west of Guizhou, while some areas in the southeast, east and north have poorly developed or no clustering. Additionally, the random forest (RF) model was used to rank the importance of factors affecting the distribution of karst depressions, and the results showed that the influence of lithology on the spatial distribution of karst depressions is absolutely dominant, followed by that of fault tectonics and hydrological conditions. The research results will contribute to the resource investigation of karst depressions and provide theoretical support for resource evaluation and sustainable utilization.

Transpiration (Tc) is a critical component of the global water cycle. Soil moisture (SM) and vapor pressure deficit (VPD) are key regulators of Tc, and exploring their contributions to changes in Tc can deepen our understanding of the mechanisms of water cycling in terrestrial ecosystems. However, the driving roles of VPD and SM in Tc changes remain debated because of the coupling of SM and VPD through land-atmosphere interactions which restrict the quantification of the independent effects of SM and VPD on Tc. By decoupling the correlations between SM and VPD using a novel binning approach, this study analyzed the dominant drivers of vegetation transpiration in subtropical China from 2003 to 2018 based on multi-source data, including meteorological reanalysis, remotely sensed soil moisture, transpiration, and land cover data. The results show that Tc first increased and then remained stable with an increase in SM across the study area but changed slightly with increasing VPD. Overall, the relative contribution of SM to the change in Tc was approximately five times that of VPD. The sensitivities of Tc to SM and VPD differed among vegetation types. Although the sensitivity of Tc to SM was greater than that of VPD for all four vegetation types, the thresholds of Tc in response to SM were different, with the lowest threshold (approximately 35%) for the other forests and the highest threshold (approximately 55% ) for short wood vegetation. We infer that this is associated with the differences in ecological strategies. To verify the reliability of our conclusions, we used solar- induced chlorophyll fluorescence (SIF) data as a proxy for Tc based on the tight coupling between photosynthesis and transpiration. Consistent results were obtained by repeating the analyses. The results of this study, in which the impacts of SM and VPD on Tc were decoupled, are beneficial for further understanding the critical processes involved in water cycling in terrestrial ecosystems in response to climate change.

Effective soil particle size composition can more realistically reflect the particle size sorting process of erosion. To reveal the individual contributions of rainfall intensity and slope to splash erosion, and to distinguish the enrichment ratio of each size and the critical size in splash, loessial soil collected on the Loess Plateau in May 2019 was tested under different rainfall intensities (60, 84, 108, 132, 156 mm h^-1) and slopes (0°, 5°, 10°, 15°, 20°). The results demonstrated that 99% of splash mass was concentrated in 0-0.4 m. Rainfall intensity was the major factor for splash according to the raindrop generation mode by rainfall simulator nozzles. The contributions of rainfall intensity to splash erosion were 82.72% and 93.24%, respectively in upslope and downslope direction. The mass percentages of effective clay and effective silt were positively correlated with rainfall intensity, while the mass percentages of effective very fine sand and effective fine sand were negatively correlated with rainfall intensity. Opposite to effective very fine sand, the mass percentages of effective clay significantly decreased with increasing distance. Rainfall intensity had significant effects on enrichment ratios, positively for effective clay and effective silt and negatively for effective very fine sand and effective fine sand. The critical effective particle size in splash for loessial soil was 50 μm.

Rural decline is a global issue accompanied by the regional imbalanced development and dysfunction in rural areas. Coordinated interaction among production, living, and ecological functions is essential for the sustainability of rural regional systems. Based on the framework of “element-structure-function”, an indicator system was constructed to explore the evolution characteristics and driving factors of rural regional functions in the farming-pastoral ecotone of northern China (FPENC) using the models of entropy-based TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), revised vertical and horizontal comparison, and GeoDetector. The results indicated a gradual synergy of rural production, living, and ecological functions during the period 2000-2020. Improvements were observed in production and living functions, and higher ecological function was found in Hebei, Inner Mongolia, Liaoning, and Shaanxi. However, conflicts between ecological function and production and living functions were evident in Shanxi, Gansu, and Ningxia. The spatial structure played a dominant role in determining rural production, living, and ecological functions, with ratios of 38%, 56%, and 84%, respectively. Land and industry emerged as the main driving factors influencing the evolution of rural regional functions. Notably, combined interactions of rural permanent population and primary industry output (0.73), grassland area and tertiary industry output (0.58), and forest area and tertiary industry output (0.72) were responsible for the changes observed in rural production, living, and ecological functions, respectively. The findings suggest that achieving coordinated development of rural regional functions can be accomplished by establishing differentiated rural sustainable development strategies that consider the coupling of population, land, and industry in FPENC.

There is a great uncertainty in generation and formation of non-point source (NPS) pollutants, which leads to difficulties in the investigation of monitoring and control. However, accurate calculation of these pollutant loads is closely correlated to control NPS pollutants in agriculture. In addition, the relationships between pollutant load and human activity and physiographic factor remain elusive. In this study, a modified model with the whole process of agricultural NPS pollutant migration was established by introducing factors including rainfall driving, terrain impact, runoff index, leaching index and landscape intercept index for the load calculation. Partial least squares path modeling was applied to explore the interactions between these factors. The simulation results indicated that the average total nitrogen (TN) load intensity was 0.57 t km^-2 and the average total phosphorus (TP) load intensity was 0.01 t km^-2 in Chengdu Plain. The critical effects identified in this study could provide useful guidance to NPS pollution control. These findings further our understanding of the NPS pollution control in agriculture and the formulation of sustainable preventive measures.

Construction land is the leading carrier of human activities such as production and living. Evaluating the construction land suitability (CLS) on the Qinghai-Tibet Plateau (QTP) holds significant implications for harmonizing the relationship between ecological protection and human activity and promoting population and industry layout optimization. However, no relevant studies provide a complete CLS assessment of the QTP. In this study, we developed a model-based CLS evaluation framework coupling of pattern and process to calculate the global CLS on the QTP based on a previously developed CLS evaluation model. Then, using the land-use data of 1990, 2000, 2010, and 2020, we examined the adaptability of existing construction land (ECL) to the CLS assessment result through the adaptability index and vertical gradient index and further analyzed the limitations of maladaptive construction land. Finally, we calculated the potential area of reserve suitable construction land. This article includes four conclusions: (1) The highly suitable, suitable, moderately suitable, marginally suitable, and unsuitable CLS classes cover areas of 0.33×10⁴ km², 10.42×10⁴ km², 18.06× 10⁴ km², 24.12×10⁴ km², and 205.29×10⁴ km², respectively. Only approximately 11% of the study area on the QTP is suitable for large-scale permanent construction land, and approximately 79.50% of the area is unsuitable under current economic and technological conditions. (2) The ECL adaptability index is 85.16%, 85.93%, 85.18%, and 78.01% during 1990-2020, respectively, with an average adaptability index exceeding 80% on the QTP. The ECL distribution generally conforms to construction land suitable space characteristics but with a significant spatial difference. (3) From 1990 to 2020, the maladaptive ECL was dominated by rural settlement land, transport land, and special land, with a rapidly increasing proportion of urban and other construction land. The maladaptive ECL is constrained by both elevation and slope in the southern Qinghai Plateau, the Hengduan Mountains, and the Qilian Mountains. In contrast, elevation is significantly more limiting than slope in the northern Tibet Plateau, the Gangdis Mountains, and the Himalayan Mountains. (4) The potential area of reserve suitable construction land is 12.41×10⁴ km², accounting for 4.81% of the total land area of the QTP, and the per capita area is 9928 m². Regions of Qaidam Basin, Gonghe Basin, and Lhasa-Shannan Valley have the richest and most concentrated land resource of reserve suitable construction land. The research results provide spatial decision support for urban and rural settlement planning and ecological migration on the QTP.

The increasing frequency of recent droughts has an adverse effect on the ecosystem of the Mongolian Plateau. The growth condition of NPP is considered an indicator of the ecological function. Therefore, identifying the relationship between NPP and drought can assist in the prevention of drought-associated disasters and the conservation of the ecological environment of the Mongolian Plateau. This study used the Carnegie-Ames-Stanford Approach (CASA) model to simulate the NPP capacity of the Mongolian Plateau between 1982 and 2015, as well as drought indicators (drought probability, vulnerability, and risk) to explore the drought risk of NPP. The findings pointed to an overall increase in NPP with regional variances; however, the NPP rate in Inner Mongolia was considerably higher than that in Mongolia. The standardized precipitation evapotranspiration index (SPEI) showed an overall downward trend, with Inner Mongolia experiencing a substantially lower rate of decline than Mongolia. The areas most likely to experience drought were primarily in the center and north while the areas with the highest drought vulnerability were primarily in the northeast, center, and southeast. Mongolia showed a higher probability of drought compared to Inner Mongolia. Drought-prone regions of the Mongolian Plateau increased during the 21st century while drought-vulnerable areas increased and shifted from north to south. Alpine grasslands and coniferous forests were least vulnerable to drought, while other vegetation types experienced temporal variation. In the 21st century, the primary determinants of drought risk shifted from precipitation and the normalized difference vegetation index (NDVI) to temperature and relative humidity.

Land subsidence is a geohazard phenomenon caused by the lowering of land elevation due to the compression of the sinking land soil body, thus creating an excessive constraint on the safe construction and sustainable development of cities. The use of accurate and efficient means for land subsidence prediction is of remarkable importance for preventing land subsidence and ensuring urban safety. Although the current time-series prediction method can accomplish relatively high accuracy, the predicted settlement points are independent of each other, and the existence of spatial dependence in the data itself is lost. In order to unlock this problem, a spatial convolutional long short-term memory neural network (ConvLSTM) based on the spatio-temporal prediction method for land subsidence is constructed. To this end, a cloud platform is employed to obtain a long time series deformation dataset from May 2017 to November 2021 in the understudied area. A convolutional structure to extract spatial features is utilized in the proposed model, and an LSTM structure is linked to the model for time-series prediction to achieve unified modeling of temporal and spatial correlation, thereby rationally predicting the land subsidence progress trend and distribution. The experimental results reveal that the prediction results of the ConvLSTM model are more accurate than those of the LSTM in about 62% of the understudied area, and the overall mean absolute error (MAE) is reduced by about 7%. The achieved results exhibit better prediction in the subsidence center region, and the spatial distribution characteristics of the subsidence data are effectively captured. The present prediction results are more consistent with the distribution of real subsidence and could provide more accurate and reasonable scientific references for subsidence prevention and control in the Beijing-Tianjin-Hebei region.

The variation of land surface temperature (LST) has a vital impact on the energy balance of the land surface process and the ecosystem stability. Based on MDO11C3, we mainly used regression analysis, GIS spatial analysis, correlation analysis, and center-of -gravity model, to analyze the LST variation and its spatiotemporal differentiation in China from 2001 to 2020. Furthermore, we employed the Geodetector to identify the dominant factors contributing to LST variation in 38 eco-geographic zones of China and investigate the underlying causes of its pattern. The results indicate the following: (1) From 2001 to 2020, the LST climate average in China is 9.6℃, with a general pattern of higher temperatures in the southeast and northwest regions, lower temperatures in the northeast and Qinghai-Tibet Plateau, and higher temperatures in plains compared to lower temperatures in mountainous areas. Generally, LST has a significant negative correlation with elevation, with a correlation coefficient of -0.66. China’s First Ladder has the most pronounced negative correlation, with a correlation coefficient of -0.76 and the lapse rate of LST is 0.57℃/100 m. (2) The change rate of LST in China during the study is 0.21℃/10 a, and the warming area accounts for 78%, demonstrating the overall spatial pattern a “multi-core warming and axial cooling”. (3) LST’s variation exhibits prominent seasonal characteristics in the whole country. The spatial distribution of average value in winter and summer differs significantly from other seasons and shows more noticeable fluctuations. The centroid trajectory of the seasonal warming/cooling area is close to a loop shape and displays corresponding seasonal reverse movement. Cooling areas exhibit more substantial centroid movement, indicating greater regional variation and seasonal variability. (4) China’s LST variation is driven by both natural influences and human activities, of which natural factors contribute more, with sunshine duration and altitude being key factors. The boundary trend between the two dominant type areas is highly consistent with the “Heihe-Tengchong Line”. The eastern region is mostly dominated by human activity in conjunction with terrain factors, while the western region is predominantly influenced by natural factors, which enhance/weaken the change range of LST through mutual coupling with climate, terrain, vegetation, and other factors. This study offers valuable scientific references for addressing climate change, analyzing surface environmental patterns, and protecting the ecological environment.

The transboundary influence of environmental change is a critical issue in the Lancang-Mekong region. As the largest river-connected lake in the lower Mekong, the ecological change and influence of Tonle Sap Lake have received widespread attention and discussion, especially after 2008, when the hydrological regime of the Lancang-Mekong River mainstream underwent distinct changes. However, the linkage and coupling mechanism between the lake riparian environment and mainstream water level change are still unclear. In this study, the interannual spatiotemporal changes in land cover in the Tonle Sap Lake riparian zone (TSLRZ) and their relationship with mainstream water levels were analysed. The results showed that the expansion of farmland was the most notable change in 1988-2020. After 2008, the land cover changes intensified, manifested as accelerated farmland expansion, intensified woodland fragmentation and significant water body shrinkage. Furthermore, the responses of the water body, degraded land, wasteland and grassland areas to the mainstream water levels weakened after 2008. Evidently, the land cover changes in the TSLRZ in the last 30 years were less related to the mainstream water level change than to local reclamation and logging. These results can offer a new scientific basis for the transboundary influence analysis of hydrological change.

Megacities serve as global centers for economic, cultural, and high-tech industries. The structural features and population agglomerations are typical traits of urbanization, yet little is known about the morphological features and expansion patterns of megacities worldwide. Here we examined the spatiotemporal variations of urban land in megalopolises from 2000 to 2020 using the Urban Expansion Intensity Differentiation Index. The fractal features and expansion patterns of megacities were analyzed using the Area-Radius Multidimensional Scaling Model. Urban land use efficiency was then evaluated based on the linear relationship between urban land area and population. We found that Southeast Asia and China were the hotspots of urban expansion in megacities from 2000 to 2020, with urban land areas expanding by 3148.32 km² and 5996.26 km², respectively. The morphological features and expansion patterns of megacities exhibited a growing trend towards intensification and compactness, with the average radial dimension increasing from 1.54 to 1.56. The annual decrease in fractal dimensions indicated the integration of inner urban areas. North America and Europe megacities showed a low urban land use efficiency, with a ratio of urban land area to population ranging from 0.89 to 4.11 in 2020. Conversely, South Asia and Africa megacities exhibited a high urban land use efficiency, with the ratios between 0.23 and 0.87. Our results provide information for promoting efficient urban land utilization and sustainable cities. It is proposed to control the scale of urban expansion and to promote balanced development between inner and outer urban areas for achieving resilient and sustainable urban development.

Identification of the spatial mismatch between land use functions (LUFs) and land use efficiencies (LUEs) is essential to regional land use policies. However, previous studies about LUF-LUE mismatch and its driving factors have been insufficient. In this study, we explored the spatiotemporal mismatch of LUFs and LUEs and their influencing factors from 2000 to 2018 in the Middle Reaches of the Yangtze River (MRYR). Specifically, we used Spearman correlation analysis to reveal the trade-off relationship between LUFs and LUEs and determine the direction of the influencing factors on the LUF-LUE mismatch, adopted spatial mismatch analysis to measure the imbalance between LUFs and LUEs, and used the geographical detector model to analyze the factors influencing this spatial mismatch. The results showed that production function (PDF), living function (LVF), ecological function (ELF), agricultural production efficiency (APE), urban construction efficiency (UCE), and ecological services efficiency (ESE) all displayed significant spatial heterogeneity. The high trade-off areas were widely distributed and long-lasting in agricultural space and urban space, while gradually decreasing in ecological space. Wuhan and Changsha showed high spatial mismatch coefficients in urban space, but low spatial mismatch coefficients in agricultural space. Hunan generally presented high spatial mismatch coefficients in ecological space. Furthermore, the interaction of the proportion of cultivated area and transportation accessibility exacerbated the mismatch in agricultural space. The interaction effects of capital investment and technology innovation with other factors have the most intense impact on the mismatch in urban space. The internal factor for cultivated area interacts with other external factors to drastically affect ecological spatial mismatch.

In this study, the interplay between ecosystem services and human well-being in Seni district, which is a pastoral region of Nagqu city on the Qinghai-Tibet Plateau, is investigated. Employing the improved InVEST model, CASA model, coupling coordination model, and hierarchical clustering method, we analyze the spatiotemporal patterns of ecosystem services, the levels of resident well-being levels, and the interrelationships between these factors over the period from 2000 to 2018. Our findings reveal significant changes in six ecosystem services, with water production decreasing by 7.1% and carbon sequestration and soil conservation services increasing by approximately 6.3% and 14.6%, respectively. Both the habitat quality and landscape recreation services remained stable. Spatially, the towns in the eastern and southern areas exhibited higher water production and soil conservation services, while those in the central area exhibited greater carbon sequestration services. The coupling and coordination relationship between ecosystem services and human well-being improved significantly over the study period, evolving from low-level coupling to coordinated coupling. Hierarchical clustering was used to classify the 12 town-level units into five categories. Low subjective well-being townships had lower livestock breeding services, while high subjective well-being townships had higher supply, regulation, and support ecosystem services. Good transportation conditions were associated with higher subjective well-being in townships with low supply services. We recommend addressing the identified transportation disparities and enhancing key regulatory and livestock breeding services to promote regional sustainability and improve the quality of life for Seni district residents, thus catering to the diverse needs of both herdsmen and citizens.

Global extreme hydrological events pose considerable challenges to the sustainable development of human society and river ecology. Land use/cover change (LUCC) is a visible manifestation of human activity and has caused substantial alterations in extreme hydrological regimes across rivers worldwide. The Jinsha River lies upstream of the Yangtze River and its hydrological variability has had profound socioeconomic and environmental effects. In this study, we developed Hydrological Simulation Program-FORTRAN (HSPF) and land-use simulation models of the entire watershed to simulate the effects of LUCC on hydrological extremes and quantify the inter-relationships among them. The main land-use changes between 1995 and 2015 were those associated with cropland, forest land, and grassland. Between 2015 and 2030, it is estimated that the coverage of forest land, grassland, construction land, and unused land will increase by 0.64%, 0.18%, 69.38%, and 45.08%, respectively, whereas that of cropland, water bodies, and snow- and ice-covered areas will decline by 8.02%, 2.63%, and 0.89%, respectively. LUCC has had irregular effects on different hydrological regimes and has most severely altered stream flows. The responses of hydrological extremes to historical land-use change were characterized by spatial variation. Extreme low flows increased by 0.54%-0.59% whereas extreme high flows increased by 0%-0.08% at the lowest outlet. Responses to future land-use change will be amplified by a 0.72%-0.90% reduction in extreme low flows and a 0.08%-0.12% increase in extreme high flows. The hedging effect caused by irregular changes in tributary stream flow was found to alleviate the observed flow in mainstream rivers caused by land-use change. The extreme hydrological regimes were affected mainly by the net swap area transferred from ice and snow area to forest (NSAIF) and thereafter to cultivated land (NSAIC). Extreme low flows were found to be positively correlated with NSAIF and NSAIC, whereas extreme high flows were positively correlated with NSAIC and negatively correlated with NSAIF.

The taiga vegetation in Western Siberia has been seriously threatened by climate warming in recent decades. However, how vegetation in different growing states and climate conditions responds to climate changes differently is still unclear. Here we explore the vegetation activity trends in Western Siberia taiga forests using the annual rate of change in leaf area index (LAI) during 1982-2018 so as to answer two questions: (1) how did climate warming affect taiga vegetation activity in the recent last decades? (2) Did the growing state of taiga forest affect its response to climate warming? Our results revealed that climate warming promoted taiga vegetation activity in Western Siberia before 2000. However, continuous warming caused excessive evapotranspiration and led to decreased vegetation activity after 2000. Moreover, the intensity of vegetation growth response to warming was positively related to canopy height and LAI, indicating that both the positive and negative effects of warming were more significant in taiga forests in better growing state. Since these forests generally have higher productivity and play more important roles in ecosystem functioning (e.g., carbon sink and biodiversity conservation), our results highlight their vulnerability to future climate change that need more research attention.

Urban shrinkage has attracted the attention of many geographers and urban planners in recent years. However, there are fewer studies on vacant housing in shrinking cities. Therefore, this study combines multi-source remote sensing images and urban building data to assess the spatiotemporal variation patterns of housing vacancy in a typical shrinking city in China. The following points were obtained: (1) We developed an evaluation model to identify vacant residential buildings in shrinking cities by removing the contribution of nighttime lights from roads and non-residential buildings; (2) The residential building vacancy rate in Fushun city significantly increased from 2013 to 2020, resulting in a significant high-value clustering effect. The impact of urban shrinkage on vacant residential buildings was higher than that on vacant non-residential buildings; (3) The WorldPop population data demonstrated consistent spatial distribution and trend of population change in Fushun with the residential building vacancy rate results, suggesting good reliability of the constructed evaluation model in this study. Identifying housing vacancies can help the local government to raise awareness of the housing vacancy problem in shrinking cities and to propose reasonable renewal strategies.

Significant changes to the world’s climate over the past few decades have had an impact on the development of plants. Vegetation in high latitude regions, where the ecosystems are fragile, is susceptible to climate change. It is possible to better understand vegetation’s phenological response to climate change by examining these areas. Traditional studies have mainly investigated how a single meteorological factor affects changes in vegetation phenology through linear correlation analysis, which is insufficient for quantitatively revealing the effects of various climate factor interactions on changes in vegetation phenology. We used the asymmetric Gaussian method to fit the normalized difference vegetation index (NDVI) curve and then used the dynamic threshold method to extract the phenological parameters, including the start of the season (SOS), end of the season (EOS), and length of the season (LOS), of the vegetation in this study area in the Tundra-Tagar transitional zone in eastern and western Siberia from 2000 to 2017. The monthly temperature and precipitation data used in this study were obtained from the climate research unit (CRU) meteorological dataset. The degrees to which the changes in temperature and precipitation in the various months and their interactions affected the changes in the three phenological parameters were determined using the GeoDetector, and the results were explicable. The findings demonstrate that the EOS was more susceptible to climate change than the SOS. The vegetation phenology shift was best explained by the climate in March, April, and September, and the combined effect of the temperature and precipitation had a greater impact on the change in the vegetation phenology compared with the effects of the individual climate conditions. The results quantitatively show the degree of interaction between the variations in temperature and precipitation and their effects on the changes in the different phenological parameters in the various months. Understanding how various climatic variations effect phenology changes in plants at different times may be more intuitive. This research provides as a foundation for research on how global climate change affects ecosystems and the global carbon cycle.

In the context of climate change and human activities, flood disasters in arid mountainous areas have become increasingly frequent, and seriously threatened the safety of people’s lives and property. Rapid and accurate flash flood inundation modelling is an essential foundational research area, which can aid in the reduction of casualties and the minimization of disaster losses; however, this modelling is also very difficult, and models need to be urgently developed to address flash flood forecasting and warnings. The objective of this study is to construct a numerical modelling method for flash floods in drylands. Based on a 2D high-resolution flood numerical model (FloodMap-HydroInundation2D), we hindcasted the dynamic process of flash flooding and show the spatio-temporal characteristics of flash flood inundation for the “8·18” flash flood disaster that occurred in Datong county, Qinghai province. The results showed that the model output effectively agreed with the observed inundation after the event in terms of both spatial extent and temporal process. Extensive flooding mainly occurred between 00:00 and 01:00 on August 18, 2022. Qingshan, Hejiazhuang and Longwo villages were affected most heavily. We further conducted model sensitivity analysis and found that the model was highly sensitive to both roughness and hydraulic conductivity in drylands, and the effect of hydraulic conductivity was more pronounced. Our study confirmed the good performance of our model for the simulation of flash flooding in arid areas and provides a potential method for flash flood assessment and management in arid areas.

Current ecosystem models used to simulate global terrestrial carbon balance generally suggest that terrestrial landscapes are stable and mature, but terrestrial net primary productivity (NPP) data estimated without accounting for disturbances in species composition, environment, structure, and ecological characteristics will reduce the accuracy of the global carbon budget. Therefore, the steady-state assumption and neglect of elevation-related changes in forest NPP is a concern. The Qilian Mountains are located in continental climate zone, and vegetation is highly sensitive to climate change. We quantified aboveground biomass (AGB) and aboveground net primary productivity (ANPP) sequences at three elevations using field-collected tree rings of Picea crassifolia in Qilian Mountains of Northwest China. The results showed that (1) There were significant differences between AGB and ANPP at the three elevations, and the growth rate of AGB was the highest at the low elevation (55.99 t ha^-1 10a^-1). (2) There are differences in the response relationship between the ANPP and climate factors at the three elevations, and drought stress is the main climate signal affecting the change of ANPP. (3) Under the future climate scenario, drought stress intensifies, and the predicted decline trend of ANPP at the three elevations from mid-century to the end of this century is -0.025 t ha^-1 10a^-1, respectively; -0.022 t ha^-1 10a^-1; At -0.246 t ha^-1 10a^-1, the level of forest productivity was significantly degraded. The results reveal the elevation gradient differences in forest productivity levels and provide key information for studying the carbon sink potential of boreal forests.

Global warming is causing melting of glaciers, which is affecting socioeconomic development. It is essential to study the combined influence of changes in structures of glaciers on human well-being and socioeconomic systems. Herein, we considered Qilian Mountains as an example, quantified the regional socioeconomic benefits of glaciers and human well-being, and attempted to draw a correlation between glacier service value and human well-being. The findings of our study reveal that the value of glacier services in Qilian Mountains decreased from 1.84 × 10¹⁰ yuan in 1998 to 1.72 × 10¹⁰ yuan in 2018, with a spatial trend of circling down from the central region to the western and eastern regions. The distribution of human well-being showed an increasing trend, and a phenomenon of “low value central and western clustering, high value eastern sporadic distribution.” There is an increasing degree of coordination between human well-being and glacier services value; the spatial distribution shows a decreasing trend from the west to the east, with differences in the nature of coordinated development in different regions at the same coordination stage being obvious. We analyzed the changes in glacier services value and their relationship with human well-being from both micro and macro perspectives to provide theoretical support for formulating management strategies for glacier resource conservation and improving the interface between glacier service evaluation results and government decision-making.

Urban-rural integration is an advanced form resulting from the future evolution of urban-rural relationships. Nevertheless, little research has explored whether urban and rural areas can move from dual segmentation to integrated development from a theoretical or empirical perspective. Based on the research framework of welfare economics, which offers an appealing paradigm to frame the underlying game between cities and villages, this study clarifies the ideal state of urban-rural integration. It then proposes a series of basic assumptions, and constructs a corresponding objective function and its constraints. Moreover, it assesses the possibility of seeing the transmutation from division to integration between urban and rural areas with continuous socio-economic development. The authors argue that the ideal state of urban-rural integration should be a Pareto-driven optimal allocation of urban-rural resources and outputs, and the maximization of social welfare in the entire region. Based on a systematic demonstration using mathematical models, the study proposes that urban and rural areas can enter this ideal integrated development pattern when certain parameter conditions are met. In general, this study demonstrates the theoretical logic and scientific foundations of urban-rural integration, enriches theoretical studies about urban-rural relationships, and provides basic theoretical support for large developing countries to build a coordinated and orderly urban-rural community with a shared future.

This study uses green patent data from 264 cities in China between 2006 and 2020 to examine the evolution of spatial patterns in urban green technology innovation (GTI) across the country and identify the underlying driving factors. Moran’s I index, Getis-Ord G_i^* index, standard deviation ellipse, and geographical detector were used for the analysis. The findings indicate an increase in the overall level of GTI within Chinese cities. Provincial capitals, cities along the eastern coast, and planned cities emerge as the prominent “highlands” of GTI, whereas the “lowlands” of GTI predominantly lie in the western and northeastern regions, forming the spatial pattern of “hot in the east and center of the country, cold in the northwest and the northeast.” The distribution center of gravity of GTI is toward the southwest of China. The distribution pattern is in the “northeast-southwest” direction, which is characterized by “diffusion,” followed by “agglomeration.” Differences in economic development have the highest determining power on the spatial differentiation of GTI in Chinese cities, whereas differences in environmental regulation and industrial structure have the lowest degree of relative influence. The interaction between any two factors contributes to an amplified explanatory power in understanding the differences in GTI.

Since the Bonn 2011 conference, the “water-energy-food” (WEF) nexus has aroused global concern to promote sustainable development. The WEF nexus is a complex, dynamic, and open system containing interrelated and interdependent elements. However, the nexus studies have mainly focused on natural elements based on massive earth observation data. Human elements (e.g., society, economy, politics, culture) are described insufficiently, because traditional earth observation technologies cannot effectively perceive socioeconomic characteristics, especially human feelings, emotions, and experiences. Thus, it is difficult to simulate the complex WEF nexus. With the development of earth observation sensor technologies and human activity perception methods, geographical big data covering both human activities and natural elements offers a new opportunity for in-depth WEF nexus analysis. This study proposes a five-step framework by leveraging geographical big data mining to dig for the hidden value in the data of various natural and human elements. This framework can enable a thorough and comprehensive analysis of the WEF nexus. Some application examples of the framework, major challenges, and possible solutions are discussed. Geographical big data mining is a promising approach to enhance the analysis of the WEF nexus, strengthen the coordinated management of resources and sectors, and facilitate the progress toward sustainable development.

Settlements are excellent spatiotemporal indicators for studying historical human activities and environmental change. This paper discusses the spatial and temporal changes of sites on the Ordos Plateau in China since the Neolithic Age, based on an analysis of spatiotemporal distribution characteristics of the human settlements. The frequency of human settlements on the Ordos Plateau presented a phased fluctuation process, and the sizes were mainly small and medium. The spatial distribution of human settlements was fractal, and the D value of the aggregation dimension was generally small, indicating that the spatial distribution of the sites was agglomerated. Affected by the desert, the sites were mainly distributed in the south and east of the Ordos Plateau. The spatiotemporal distribution pattern of human settlements in the Ordos Plateau resulted from the combined action of natural and human factors, especially climate change. Moreover, local livelihood patterns significantly affected the frequency of human settlements. The number of human settlements in the farming period was large, and the distribution was concentrated. In contrast, the number of sites in the nomadic period was small and scattered. The central government’s policies and the conflicts between farming and nomadic groups further affected the spatiotemporal distribution of human settlements. This study may contribute to a better understanding of historical environmental change and human-land relationships in the Ordos Plateau.

Quantitative assessments of the impacts of climate change and anthropogenic activities on runoff help us to better understand the mechanisms of hydrological processes. This study analyzed the dynamics of mountainous runoff in the upper reaches of the Shiyang River Basin (USRB) and its sub-catchments, and quantified the impacts of climate change and human activities on runoff using the improved double mass curve (IDMC) method, which comprehensively considers the effects of precipitation and evapotranspiration on runoff, instead of only considering precipitation as before. The results indicated that the annual runoff depth in the USRB showed a slightly increased trend from 1961 to 2018, and sub-catchments were increased in the west and decreased in the east. The seasonal distribution pattern of runoff depth in the USRB and its eight sub-catchments all showed the largest in summer, followed by autumn and spring, and the smallest in winter with an increasing trend. Quantitative assessment results using the IDMC method showed that the runoff change in the USRB is more significantly affected by climate change, however, considerable differences are evident in sub-catchments. This study further developed and improved the method of runoff attribution analysis conducted at watershed scale, and these results will contribute to the ecological protection and sustainable utilization of water resources in the USRB and similar regions.

Traditional music is an important component of cultural heritage. However, studies have scarcely explored the tourism development potential and the obstacle factors of traditional music. This study takes Xiangxi as the research site, constructs an evaluation index system, and utilizes survey and geographical methods. The major research results are as follows: First, the average potential of resource endowment, tourism industry development, and socio-economic conditions are 0.28, 0.36, and 0.24, respectively. The potential of resource endowment is higher in the west and lower in the east, that of tourism industry development is higher in the north and south and lower in the center, and that of socio-economic conditions is higher in the south and lower in the north. Second, the comprehensive potential is 0.29, which is higher in the northwest and southwest regions. Coordination in most administrative units is at a medium level, and most regions have obvious advantages in resource endowment. Third, the primary obstacle factor is socio-economic conditions (39.21%), followed by resource endowment (30.73%) and tourism industry development (30.06%). The administrative units can be classified into four groups: socio-economic condition obstacle, resource endowment and tourism industry development obstacles, resource endowment and socio-economic condition obstacles, and tourism industry development and socio-economic condition obstacles. The highest priority goals for Xiangxi involve economic development and the integration of modern technologies to stimulate passenger flow. For regions with limited traditional music items (eastern area of Xiangxi), tourism development should be restrained and other resources with stronger inherent advantages should be exploited. This study explored the quantification of traditional music tourism potential, representing a novel breakthrough in this field of research. The indicator system and research methods used in this study can provide guidance and methodological references for cultural heritage research. The suggestions proposed in this article contribute to the rational development and effective protection of cultural heritage resources and the healthy development of the tourism industry.

Cities are the key areas for human beings to achieve sustainable development goals in the future. Estuarine cities are a special type of coastal city in urgent need of a clear definition. This paper proposed that estuarine cities are cities developed on the coast where rivers and oceans meet and defined four connotations, including the intersection of rivers and marine water systems, the coordinated development of land and oceans, the location advantages of connecting rivers and seas, and the important fragility of the ecological environment. We used HydroSHEDS, OSM, GPW, and urban socioeconomic statistics and selected 50 estuarine cities with large rivers as representatives to summarize the main geographical basis and socioeconomic characteristics. Cities are primarily found in low-altitude, flat regions with average annual temperatures that mainly vary from 10°C to 25°C, relatively abundant precipitation, and extensive biological resources. There are substantial variations in the socioeconomic features of estuarine cities. We proposed eight development patterns, including open and inclusive city spirit, high-quality livable cities, high-quality development driven by innovation, integration of internal and external communication with ports and cities, construction of an international financial center, ecological environment protection and restoration, active promotion of cultural tourism, and positive international exchanges.