The imbalance in global streamflow gauge distribution and regional data scarcity, especially in large transboundary basins, challenge regional water resource management. Effectively utilizing these limited data to construct reliable models is of crucial practical importance. This study employs a transfer learning (TL) framework to simulate daily streamflow in the Dulong-Irrawaddy River Basin (DIRB), a less-studied transboundary basin shared by Myanmar, China, and India. Our results show that TL significantly improves streamflow predictions: the optimal TL model achieves an average Nash-Sutcliffe efficiency of 0.872, showing a marked improvement in the Hkamti sub-basin. Despite data scarcity, TL achieves a mean NSE of 0.817, surpassing the 0.655 of the process-based model MIKE SHE. Additionally, our study reveals the importance of source model selection in TL, as different parts of the flow are affected by the diversity and similarity of data in the source model. Deep learning models, particularly TL, exhibit complex sensitivities to meteorological inputs, more accurately capturing non-linear relationships among multiple variables than the process-based model. Integrated gradients (IG) analysis further illustrates TL’s ability to capture spatial heterogeneity in upstream and downstream sub-basins and its adeptness in characterizing different flow regimes. This study underscores the potential of TL in enhancing the understanding of hydrological processes in large-scale catchments and highlights its value for water resource management in transboundary basins under data scarcity.

Social upgrading does not inherently follow economic upgrading; rather, they present a complex interplay. This paper, focusing on China, utilizes the coupling coordination degree and a panel regression model to shed light on the intricate interaction between social upgrading and economic upgrading. It is found that the coupling coordination degree of social and economic upgrading in China has improved from 0.33 to 0.49 since the mid-1990s, undergoing a shift from a stage of slight imbalance to low-level coordination. However, significant regional disparities are present in terms of economic upgrading, social upgrading, and their coupling coordination degree. Developed areas exhibit a higher degree of coupling coordination compared to less developed regions, indicating a connection between the coupling coordination degree and the level of economic growth. Economic globalization, public governance, and the legal environment positively impact the coupling coordination between social and economic upgrading, while economic privatization and corporate violations of law exert negative effects. The paper concludes with policy discussions for enhancing the coupling coordination between social and economic upgrading.

To understand the spatio-temporal variability of precipitation (P) in the Third Pole region (centered on the Tibetan Plateau-TP), it is necessary to quantify the interannual periodicity of P and its relationship with large-scale circulations. In this study, Morlet wavelet transform was used to detect significant (p<0.05) periodic characteristics in P data from meteorological stations in four climate domains in the Third Pole, and to reveal the major large-scale circulations that triggered the variability of periodic P, in addition to bringing large amounts of water vapour. The wavelet transform results were as follows. (1) Significant quasi- periodicity varied from 2 to 11 years. The high-frequency variability mode (2 to 6 years quasi-periods) was universal, and the low-frequency variability mode (7 to 11 years quasi-periods) was rare, occurring mainly in the westerlies and Indian monsoon domains. (2) The majority of periods were base periods (53%), followed by two-base periods. Almost all stations in the Third Pole (95%) showed one or two periods. (3) Periodicity was widely detected in the majority of years (84%). (4) The power spectra of P in the four domains were dominated by statistically significant high-frequency oscillations (i.e., with short periodicity). (5) Large-scale circulations directly and indirectly influenced the periodic P variability in the different domains. The mode of P variability in the different domains was influenced by interactions between large-scale circulation features and not only by the dominant circulation and its control of water vapour transport. The results of this study will contribute to better understanding of the causal mechanisms associated with P variability, which is important for hydrological science and water resource management.

The joint study of agriculture and rural areas is of great significance for safeguarding agricultural development, revitalizing rural areas, and enhancing farmers’ well-being. This paper aims to assess the spatiotemporal evolution characteristics of the coupling and coordination degree of agricultural resilience and rural land use efficiency and their dynamic transfer law and driving mechanisms, based on panel data of 31 provinces (municipalities and autonomous regions) in China from 2010 to 2020. The results showed: (1) Good coupling and coordination of agricultural resilience and rural land use efficiency, with reduced temporal differentiation degrees between regions; (2) Significant spatial autocorrelation between the overall coupling and coordination degrees of agricultural resilience and rural land use efficiency, forming cold spot and hot spot spatial patterns in the western and eastern parts, respectively, with a central transition area; (3) A spillover effect of the dynamic transfer process, with a manifested specific law as “club convergence”, “Matthew effect”, and progressive development characteristics; (4) The key roles of the natural, social, economic, and policy indicators in the coupling and coordination development process of agricultural resilience and rural land use efficiency. However, the selected indicators showed substantial spatial differences in their influences on the coupling and coordination process between provinces.

In the context of accelerated globalization, intercity factor flows are becoming increasingly dependent on a reasonable and orderly spatial structure. Therefore, an in-depth study of the optimization and adjustment of spatial structure is essential for coordinated development. This study quantitatively evaluated urban development levels and introduced network analysis methods to analyse the spatial structure and robustness of development. The results indicated the following: (1) The urban development level in the Beijing-Tianjin- Hebei (BTH) region increased in all dimensions, and the transmission efficiency significantly improved. (2) The spatial structure of the BTH region has been relatively stable, as illustrated by the main pattern of the spatial distribution of central cities, with a trend towards contiguous development. (3) The ranking of network robustness is environment>society>economy, and the core network and key nodes are primarily located within the radiation of the three central cities of Beijing, Tianjin, and Shijiazhuang. (4) The coordinated development of the BTH region is effective but still needs to be optimized and adjusted, and the strategic significance of edge cities has not been completely exploited. This study aims to provide an emerging analytical perspective for optimizing regional spatial structure and promoting regional coordinated development.

Urbanization interacts with land use through resource consumption and space encroachment. Clarifying the spatial correlations of the interactive relationship between urbanization and land use, along with their spatiotemporal dynamics, is of vital importance for addressing the complex interplay between urban development and land resources and identifying regional differences. However, previous studies have not sufficiently explored these issues. Herein, we introduce a coupling coordination degree (CCD) model and present the results of exploratory spatiotemporal analyses involving in-depth investigation of the CCD between urbanization quality and land-use intensity in 290 Chinese cities. The results demonstrate that the CCD for most cities was at the transition-period or basic-coordination stage. The dynamics of the spatial correlation of the CCD was found to increase from the east to the central and western regions, but this was found to decline overall. The movement direction and spatial dependence of the local spatial structure of the CCD exerted a dominant synergistic effect. The transition of the spatial correlation was mainly Type I (stable local and neighboring morphology), showing strong transfer inertia, path dependence, and locking features. Dynamic transitions occurred more in central and eastern cities. The results suggest that more cross-city cooperation could contribute to moderate land-resource exploitation for high-quality urbanization.

Changes in surface temperature extremes have become a global concern. Based on the daily lowest temperature (TN) and daily highest temperature (TX) data from 2138 weather stations in China from 1961 to 2020, we calculated 14 extreme temperature indices to analyze the characteristics of extreme temperature events. The widespread changes observed in all extreme temperature indices suggest that China experienced significant warming during this period. Specifically, the cold extreme indices, such as cold nights, cold days, frost days, icing days, and the cold spell duration index, decreased significantly by −6.64, −2.67, −2.96, −0.97, and −1.01 days/decade, respectively. In contrast, we observed significant increases in warm extreme indices. The number of warm nights, warm days, summer days, tropical nights, and warm spell duration index increased by 8.44, 5.18, 2.81, 2.50, and 1.66 d/decade, respectively. In addition, the lowest TN, highest TN, lowest TX, and highest TX over the entire period rose by 0.47, 0.22, 0.26, and 0.16°C/decade, respectively. Furthermore, using Pearson’s correlation and wavelet coherence analyses, this study identified a strong association between extreme temperature indices and atmospheric circulation factors, with varying correlation strengths and resonance periods across different time-frequency domains.

In the context of global warming, escalating water cycles have led to a surge in drought frequency and severity. Yet, multidecadal fluctuations in drought and their multifaceted influencing factors remain inadequately understood. This study examined the multidecadal changes in drought characteristics (frequency, duration, and severity) and their geographical focal points within China’s north-south transitional zone, the Qinling-Daba Mountains (QDM), from 1960 to 2017 using the Standardized Precipitation Evapotranspiration Index (SPEI). In addition, a suite of eight scenarios, correlation analysis, and wavelet coherence were used to identify the meteorological and circulation factors that influenced drought characteristics. The results indicate the following: (1) From 1960 to 2017, the QDM experienced significant interdecadal variations in drought frequency, duration, and severity, the climate was relatively humid before the 1990s, but drought intensified thereafter. Specifically, the 1990s marked the period of the longest drought duration and greatest severity, while the years spanning 2010 to 2017 experienced the highest frequency of drought events. (2) Spatially, the Qinling Mountains, particularly the western Qinling Mountain, exhibited higher drought frequency, duration, and severity than the Daba Mountains. This disparity can be attributed to higher rates of temperature increase and precipitation decrease in the western Qinling Mountain. (3) Interdecadal variations in droughts in the QDM were directly influenced by the synergistic effects of interdecadal fluctuations in air temperature and precipitation. Circulation factors modulate temperature and precipitation through phase transitions, thereby affecting drought dynamics in the QDM. The Atlantic Multidecadal Oscillation emerges as the primary circulation factors influencing temperature changes, with a mid-1990s shift to a positive phase favoring warming. The East Asian Summer Monsoon and El Niño-Southern Oscillation are the main circulation factors affecting precipitation changes, with positive phase transitions associated with reduced precipitation, and vice versa for increased precipitation.

Rural vitality is the life force expressed by a combination of endogenous dynamics and external influences. Exploring the complex relationship between rural functions, elements and flows could achieve sustainable rural development. This study constructed a theoretical framework guided by the four functions of production, living, ecology and culture with the support of mobile big data. Furthermore, the network centrality of villages was estimated to reflect the intensity of urban-rural social mobility ties. The results indicated marked spatial disparities in rural vitality, and the coupling of ecological-cultural vitality has a high degree of coherence. Four rural vitality grades were identified: high level (38, 14.08%), medium-high level (66, 24.44%), medium-low level (110, 40.74%) and low level (56, 20.74%), covering 270 administrative village units. The flow intensity of social linkage elements is consistent with rural vitality and the socioeconomic spillover effect of urban centers on neighboring villages was noticeable. Topographic complexity negatively affected the living function, mainly in the T1 and T2 terrain gradients; the rural ecological function was not fully correlated with urban adjacency, and proximity could lead to adverse effects such as urban sprawl and resource destruction. The application of this study is to explore the importance of “flow” by utilizing mobile big data to refine the evaluation unit to the village scale. Accelerating the construction of network coverage and information interconnection and promoting the elemental flow of people, transportation and information between urban and rural areas are important ways to enhance rural vitality.

Resilience studies have long been a focal point in the fields of geography, social science, urban studies, and psychology. Recently, resilience studies from multiple disciplines have scrutinized resilience at an individual scale. As one important behavior in the daily life of human beings, travel behavior is characterized by spatial dependence, spatiotemporal dynamics, and group heterogeneity. Moreover, how to understand the interaction between travel behavior (or demand) and transportation supply and their dynamics is a fundamental question in transportation studies when transportation systems encounter unexpected disturbances. This paper refines the definition of travel behavior resilience based on fundamental theories from multiple disciplines, including ecology, transportation engineering, and psychology. Additionally, this paper proposes a conceptual theoretical framework of travel behavior resilience based on the dynamic equilibrium characteristics of transportation supply and demand. In general, travel behavior has three stages of variation, namely, dramatic reduction, rapid growth, and fluctuation recovery, which have helped capture the travel behavior resilience triangle. Then, we construct a corresponding evaluation methodology that is suitable for multiscale and multidimensional perspectives. We emphasize that the evaluation of travel behavior resilience should be process-oriented with temporal continuity or capture the inflection points of travel behavior. Using multisource big data such as mobile phone signaling data and smart card data, this paper reviews empirical studies on travel behavior resilience, exploring its spatial heterogeneity and group differences. With location-based analysis, we confirm that people show greater travel behavior resilience in places where people engage in various socioeconomic activities. The group-based analysis shows that age and socioeconomic attributes of mobility groups significantly affect travel behavior resilience. Travel behavior resilience can be one pillar, offering geographic perspectives in resilience studies. In the future, the study of travel behavior resilience at multiple scales and from multiple perspectives can explore the spatial heterogeneity of transportation re-equilibrium and travel modal differences, contributing to urban spatial structure studies. Studying travel behavior resilience can provide scientific and technological support for urban management and resilient city construction.

Improving the supply-demand balance of ecosystem services (SDBES) from the perspective of land use is essential for managing regional ecosystem and realizing sustainable development. By combining land use with the supply and demand of ecosystem services (SDES), a technical framework for defining land use threshold and optimizing its structure to improve the SDBES state was constructed and applied to a practical case. The spatial pattern of supply and demand of each ES in Lancang county was distinctly heterogeneous, with significant differences in SDES across different land use types. Strong spatial heterogeneity existed in the ESDR of each ES at the grid scale, and the areas of deficit were ranked as carbon sequestration > water conservation > habitat quality > food production. The structure of dry land, paddy field, tea, evergreen broad-leaved forest, grassland, urban construction land, and industrial and mining construction land were the focus of land use optimization. Based on the land use area thresholds under the SDBES, the optimal land use structure for maximizing comprehensive benefits contributed to a balanced relationship between SDES and promoted sustainable regional development. The study provides a new perspective and method for improving the SDBES state, alleviating land conflicts, and managing ecological environment.

Construction land expansion is a key driver of urbanization and industrialization, yet it poses the risk of losing farmland and cascading impacts on food supply. The spatial characteristics of farmland occupied by construction land and its association with grain yield in China were unclear. We analyzed the characteristics of farmland converted into construction land, and its relationship with grain yield in China for 2000-2020. Construction land increased in area in central and western regions of China, and farmland decreased in area in southeastern China. The expansion of construction land in the North China Plain, Northeast China Plain, and the Loess Plateau, occurred at the expense of farmland. Except the southeast coast of China, grain yield increase was only weakly dependent on farmland area. Patterns in which farmland was converted into construction land and grain-yield change were highly coupled in southeastern coastal China, Sichuan Basin, Shandong Peninsula, and the Hu Huanyong Line. It should be noted that expansion in construction land area does have some influence on grain production; ultimately it is greatly affected by yield per unit area.

Flood susceptibility modeling is crucial for rapid flood forecasting, disaster reduction strategies, evacuation planning, and decision-making. Machine learning (ML) models have proven to be effective tools for assessing flood susceptibility. However, most previous studies have focused on individual models or comparative performance, underscoring the unique strengths and weaknesses of each model. In this study, we propose a stacking ensemble learning algorithm that harnesses the strengths of a diverse range of machine learning models. The findings reveal the following: (1) The stacking ensemble learning, using RF-XGB- CB-LR model, significantly enhances flood susceptibility simulation. (2) In addition to rainfall, key flood drivers in the study area include NDVI, and impervious surfaces. Over 40% of the study area, primarily in the northeast and southeast, exhibits high flood susceptibility, with higher risks for populations compared to cropland. (3) In the northeast of the study area, heavy precipitation, low terrain, and NDVI values are key indicators contributing to high flood susceptibility, while long-duration precipitation, mountainous topography, and upper reach vegetation are the main drivers in the southeast. This study underscores the effectiveness of ML, particularly ensemble learning, in flood modeling. It identifies vulnerable areas and contributes to improved flood risk management.

Glaciers are considered to be ‘climate-sensitive indicators’ and ‘solid reservoirs’, and their changes significantly impact regional water security. The mass balance (MB) from 2011 to 2020 of the Qiyi Glacier in the northeast Tibetan Plateau is presented based on field observations. The glacier showed a persistent negative balance over 9 years of in-situ observations, with a mean MB of −0.51 m w.e. yr⁻¹. The distributed energy-mass balance model was used for glacier MB reconstruction from 1980 to 2020. The daily meteorological data used in the model were from HAR v2 reanalysis data, with automatic weather stations located in the middle and upper parts of the glacier used for deviation correction. The average MB over the past 40 years of the Qiyi Glacier was −0.36 m w.e. yr⁻¹ with the mass losses since the beginning of the 21st century, being greater than those in the past. The glacier runoff shows a significant increasing trend, contributing ~81% of the downstream river runoff. The albedo disparity indicates that the net shortwave radiation is much higher in the ablation zone than in the accumulation zone, accelerating ablation-area expansion and glacier mass depletion. The MB of the Qiyi Glacier is more sensitive to temperature and incoming shortwave radiation variation than precipitation. The MB presented a non-linear reaction to the temperature and incoming shortwave radiation. Under future climate warming, the Qiyi Glacier will be increasingly likely to deviate from the equilibrium state, thereby exacerbating regional water balance risks. It is found that the mass losses of eastern glaciers are higher than those of western glaciers, indicating significant spatial heterogeneity that may be attributable to the lower altitude and smaller area distribution of the eastern glaciers.

Polder is a type of irrigation field unique to the lower Yangtze River of China. It is a product of long-term and ingenuous human modifications of wetland landscapes. In the pre-Qin Period, 3000 years ago, the poldered area of eastern Wuhu city was once a large lake called the ancient Danyang wetland. The introduction of agricultural civilization and polder technology to the area during the Wu and Yue Kingdoms period gradually transformed it into an agricultural area. With an accelerating rate of land reclamation under a changing late-Holocene regional climate, the ancient Danyang wetland became an aquatic system strongly influenced by intensifying anthropogenic activities. In this study, based on field survey data, historical documents, and remote-sensing and archaeological data, we reconstructed the spatial distribution of the polder landscape over the last 3000 years and identified their structural diversity. We found that polder landscapes began to emerge in the Spring and Autumn Period, land reclamation intensified in the Three Kingdoms and developed rapidly in the Song Dynasty before eventually reaching the peak from the Ming and Qing Dynasties. The relocation of historical sites to low-altitude areas also marked the expansion of poldered fields from the center of the wetland to the southeast and northwest. The development and evolution of the polder landscape are related to regional climate conditions, changing social and economic statuses, and the development of agricultural technology in the Song Dynasty and succeeding periods.

Since China’s reform and opening-up in 1978, rapid urbanization has coincided with a surge in carbon emissions. Statistical, geospatial, and time-series analysis methods were utilized to examine the dynamic relationship between urbanization and carbon emissions over the past 43 years; elucidate the mechanisms through which dimensions of urbanization, such as population, land, economy, and green development, impact carbon emissions at various stages; and further explore the heterogeneity among cities of different scales. The analysis reveals that 2001 and 2011 represent significant turning points in China’s carbon emission growth “S” curve. The phase of rapid carbon emissions growth is associated with an increase in the urbanization rate from 40% to 50%, a shift in industrial structure from being dominated by secondary industry to tertiary industry, and a decrease in urban population density from 19,600 to 16,000 people per square kilometer of built-up area. Regions northeast of the “Bayannur-Ningde Line” have experienced rapid increases in carbon emissions, with large and medium-sized cities being the primary contributors nationwide. The TVP-VAR results indicate that higher urbanization rates have short-term carbon and mid- to long-term carbon-reducing effects. Population concentration in large cities facilitates short- to mid-term carbon reduction, whereas intensive urban development, industrial upgrading, and the promotion of clean energy use have sustained carbon-reducing effects. Carbon emissions exhibit path dependence. Increased urbanization rates in mega-cities and super-cities result in carbon-increasing effects, whereas the optimization of industrial structures exerts an inhibitory effect on carbon emissions in medium-sized and large cities. The changes in impulse response values of various variables are influenced by the developmental trajectory of Chinese cities from “small to large and then to agglomerations.” These recommendations indicate the necessity for differentiated emission reduction strategies contingent on the specific regions and types of cities in question.

Urban sprawl has been a prevailing phenomenon in developing countries like China, potentially resulting in significant carbon dioxide (CO₂) emissions from the transport sector. However, the impact of urban sprawl on transport CO₂ emissions (TCEs) is still not fully understood and remains somewhat rudimentary. To systematically investigate how urban sprawl influences TCEs, we employ panel regression and panel threshold regression for 274 Chinese cities (2005-2020), and obtain some new findings. Our results affirm that the degree of urban sprawl is positively associated with TCEs, and this holds true in different groups of city size and geographical region, while significant heterogeneity is observed in terms of such impact. Interestingly, we find urban sprawl nonlinearly impacts TCEs—with an equal increase in urban sprawl degree, TCEs are even lower in cities with larger population size and better economic condition, particularly in East China. Furthermore, the low-carbon city pilot policy shows potential in mitigating sprawl’s impact on TCEs. Drawing on our findings, we argue that to achieve the target of TCEs reduction in China by curbing urban sprawl, more priority should be placed on relatively small, less developed, and geographically inferior cities for cost-efficiency reasons when formulating future urban development strategies.

Reference crop evapotranspiration (ET₀) is essential for determining crop water requirements and developing irrigation strategies. In this study, ET₀ was calculated via the FAO-56 Penman‒Monteith model, and the spatiotemporal variations in ET₀ over China from 1960 to 2019 were analyzed. We then quantified the contributions of five driving factors (air temperature, wind speed, relative humidity, sunshine hours, and CO₂ concentration) to the ET₀ trends via a detrending experiment. The results revealed that nationwide ET₀ showed no significant (p>0.05) decreasing trend from 1960 to 2019, with a trend of -8.56×10^-2 mm a^-2. The average temperature and wind speed were identified as the dominant factors affecting ET₀ trends at the national scale. The contributions of the driving factors to the ET₀ trends were ranked in the following order: average temperature (21.3%) > wind speed (-15.63%) > sunshine hours (-11.99%) > CO₂ concentration (6.36%) > relative humidity (3.58%). Spatially, the dominant factors influencing the ET₀ trends varied widely. In the southeastern region, average temperature and sunshine hours dominated the trends of ET₀, whereas wind speed and average temperature were the dominant factors in the northwestern region. The findings provide valuable insights into the dominant factors affecting ET₀ trends in China and highlight the importance of considering different driving factors in calculating crop water requirements.

Reducing carbon emissions from the transport sector is essential for realizing the carbon neutrality goal in China. Despite substantial studies on the influence of urban form on transport CO₂ emissions, most of them have treated the effects as a linear process, and few have studied their nonlinear relationships. This research focused on 274 Chinese cities in 2019 and applied the gradient-boosting decision tree (GBDT) model to investigate the nonlinear effects of four aspects of urban form, including compactness, complexity, scale, and fragmentation, on urban transport CO₂ emissions. It was found that urban form contributed 20.48% to per capita transport CO₂ emissions (PTCEs), which is less than the contribution of socioeconomic development but more than that of transport infrastructure. The contribution of urban form to total transport CO₂ emissions (TCEs) was the lowest, at 14.3%. In particular, the effect of compactness on TCEs was negative within a threshold, while its effect on PTCEs showed an inverted U-shaped relationship. The effect of complexity on PTCEs was positive, and its effect on TCEs was nonlinear. The effect of scale on TCEs and PTCEs was positive within a threshold and negative beyond that threshold. The effect of fragmentation on TCEs was also nonlinear, while its effect on PTCEs was positively linear. These results show the complex effects of the urban form on transport CO₂ emissions. Thus, strategies for optimizing urban form and reducing urban transport carbon emissions are recommended for the future.

Phenological changes play a central role in regulating seasonal variation in the ecological processes, exerting significant impacts on hydrologic and nutrient cycles, and ultimately influencing ecosystem functioning such as carbon uptake. However, the potential impact mechanisms of phenological events on seasonal carbon dynamics in subtropical regions are under-investigated. These knowledge gaps hinder from accurately linking photosynthetic phenology and carbon sequestration capacity. Using chlorophyll fluorescence remote sensing and productivity data from 2000 to 2019, we found that an advancement in spring phenology increased spring gross (GPP) and net primary productivity (NPP) in subtropical vegetation of China by 2.1 gC m⁻² yr⁻¹ and 1.4 gC m⁻² yr⁻¹, respectively. A delay in autumn phenology increased the autumnal GPP and NPP by 0.4 gC m⁻² yr⁻¹ and 0.2 gC m⁻² yr⁻¹, respectively. Temporally, the contribution of the spring phenology to spring carbon uptake increased significantly during the study period, while this positive contribution showed a nonsignificant trend in summer. In comparison, the later autumn phenology could significantly contribute to the increase in autumnal carbon uptake; however, this contributing effect was weakened. Path analysis indicated that these phenomena have been caused by the increased leaf area and enhanced photosynthesis due to earlier spring and later autumn phenology, respectively. Our results demonstrate the diverse impacts of vegetation phenology on the seasonal carbon sequestration ability and it is imperative to consider such asymmetric effects when modeling ecosystem processes parameterized under future climate change.

Gully erosion, an integrated result of various social and environmental factors, is a severe problem for sustainable development and ecology security in southern China. Currently, the dominant driving forces on gully distribution are shown to vary at different spatial scales. However, few systematic studies have been performed on spatial scaling effects in identifying driving forces for gully erosion. In this study, we quantitatively identified the determinants of gully distribution and their relative importance at four different spatial scales (southern China, Jiangxi province, Ganxian county, and Tiancun township, respectively) based on the Boruta algorithm. The optimal performance of gully susceptibility mapping was investigated by comparing the performance of the multinomial logistic regression (MLR), logistic model tree (LMT), and random forest (RF). Across the four spatial scales, the total contributions of gully determinants were classified as lithology and soil (32.65%) > topography (22.40%) > human activities (22.31%) > climate (11.32%) > vegetation (11.31%). Among these factors, precipitation (7.82%), land use and land cover (6.16%), rainfall erosivity (10.15%), and elevation (11.59%) were shown to be the predominant factors for gully erosion at the individual scale of southern China, province, county, and township, respectively. In addition, contrary to climatic factors, the relative importance of soil properties and vegetation increased with the decrease of spatial scale. Moreover, the RF model outperformed MLR and LMT at all the investigated spatial scales. This study provided a reference for factor selection in gully susceptibility modeling and facilitated the development of gully erosion management strategies suitable for different spatial scales.

The Qinba Mountains are climatically and ecologically recognized as the north- south transitional zone of China. Analysis of its phenology is critical for comprehending the response of vegetation to climatic change. We retrieved the start of spring phenology (SOS) of eight forest communities from the MODIS products and adopted it as an indicator for spring phenology. Trend analysis, partial correlation analysis, and GeoDetector were employed to reveal the spatio-temporal patterns and climatic drivers of SOS. The results indicated that the SOS presented an advance trend from 2001 to 2020, with a mean rate of -0.473 d yr^-1. The SOS of most forests correlated negatively with air temperature (TEMP) and positively with precipitation (PRE), suggesting that rising TEMP and increasing PRE in spring would forward and delay SOS, respectively. The dominant factors influencing the sensitivity of SOS to climatic variables were altitude, forest type, and latitude, while the effects of slope and aspect were relatively minor. The response of SOS to climatic factors varied significantly in space and among forest communities, partly due to the influence of altitude, slope, and aspect.

Railways are a crucial part of the African transport network and have a significant impact on the socio-economy and urban development. Previous studies have mainly considered the impacts of railways in Africa from the perspective of economy, politics, security, and natural environment with few attempts to consider land use. Based on Landsat remote sensing data for the 10 km buffer zone along the Ethiopian section of the Addis Ababa-Djibouti Railway (ADR) in 2013, 2017, and 2021, we studied the land use change (LUC) in the area and explored its influencing factors using the ordinary least square model (OLS) and geographical weighted regression model (GWR). There were six key results. (1) Farmland, forest, grassland, and others (including sandy land and bare land) were the primary types of land use, but from 2013 to 2021, the area of built-up land and farmland increased, whereas the area of forest, grassland, and other land decreased. (2) There was a noticeable pattern in the degree of change in the area of built-up land, farmland, and forest as the buffer distance increased along the railway. This pattern indicated a gradual shift in land use and LUC gradients. (3) The land use structure and its changes in the areas surrounding different stations displayed obvious differences. (4) The construction and operation of the ADR is one of the direct factors affecting landscape change along the railway. (5) The distance from the train station, whether the station provides a passenger service, the population size, and the distance from the central city had a positive effect on the expansion of built-up land surrounding the station. The factor of whether the station provides a freight service had a negative correlation with the expansion of built-up land. Socio-economic factors have gradually replaced railway factors as the main driving force of the expansion of built-up land around the stations. (6) The effect strength of different factors on the expansion of built-up land varied in the areas surrounding different stations.

The metropolitan area is a crucial spatial element in promoting new urbanization in China. It possesses theoretical and empirical value in the determination of the evolutionary patterns and development trends necessary for regional integration and high-quality development. This study focused on Nanjing Metropolitan Area, the first national metropolitan area in China, and established three development scenarios by combining ecological-economic spatial conflict (EESC) zones and national key ecological functional areas. These scenarios simulate the spatial distribution of future land use and land cover change (LUCC) using the development zone planning function of the patch generation land use simulation (PLUS) model. The results show that: (1) Between 2000 and 2020, the most prominent characteristics of land use change were largely the massive expansion of built-up land and the significant decline of farmland, while changes in the area of ecological land were less evident. (2) EESC areas experienced significant changes over the past 20 years, but the overall level of conflict was low. Ecological land was the main land use type in the lowest-conflict area, while built-up land was the main land use type in the highest-conflict area. (3) From 2030 to 2050, further expansion of built-up areas is expected, with continued decrease of farmland. The regulation of these land use changes can be achieved through different development zone plans. The economic development scenario had the largest built-up land area, while the ecological protection scenario had the largest farmland area. This study simulates the spatial pattern changes in the metropolitan area based on spatial conflict patterns and national main functional area planning process, providing a scientific reference for future spatial planning and management.

Land-use and land-cover change (LUCC) simulations are powerful tools for evaluating and predicting future landscape dynamics amid rapid human‒nature interactions to support decision-making. However, existing models often overlook spatial heterogeneity and temporal dependencies when modeling LUCC at both the macro and microscales. In this paper, we propose a new model, a self-calibrated convolutional neural network-based cellular automata (SC-CNN-CA) model, which integrates macro- and microspatial characteristics to simulate complex interactions among land-use types. The SC-CNN-CA model incorporates a self-calibration module using Gaussian functions to capture macrotrend such as urban sprawl while accounting for microlevel land-use interactions such as neighborhood effects. The results indicated that (1) the neighborhood effect between agricultural land and urban land tended to “increase followed by a decrease.” (2) Urban sprawl in Wuhan was highly compact, with a relatively high intensity of urban expansion at distances between 11.96 km and 24.44 km. (3) Compared with the other CA models tested, the SC-CNN-CA model demonstrated superior performance, achieving an overall accuracy of 84.12% and a figure of merit of 20.20%. This new model can enhance our understanding of historical LUCC trajectories and improve predictions of spatially explicit information for efficient land resource and urban management.

The urban heat island (UHI) is an environmental problem of wide concern because it poses a threat to both the human living environment and the sustainable development of cities. Knowledge of the spatiotemporal characteristics and the driving factors of UHI is essential for mitigating their impact. However, current understanding of the UHI in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is inadequate. Combined with data (e.g., land surface temperature and land use.) acquired from the Google Earth Engine and other sources for the period 2001-2020, this study examined the diurnal and seasonal variabilities, spatial heterogeneities, temporal trends, and drivers of surface UHI intensity (SUHII) in the GBA. The SUHII was calculated based on the urban-rural dichotomy, which has been proven an effective method. The average SUHII was generally 0-2°C, and the SUHII in daytime was generally greater than that at night. The maximum (minimum) SUHII was found in summer (winter); similarly, the largest (smallest) diurnal difference in SUHII was during summer (winter). Generally, the Mann-Kendall trend test and the Sen’s slope estimator revealed a statistically insignificant upward trend in SUHII on all time scales. The influence of driving factors on SUHII was examined using the Geo-Detector model. It was found that the number of continuous impervious pixels had the greatest impact, and that the urban-rural difference in the enhanced vegetation index had the smallest impact, suggesting that anthropogenic heat emissions and urban size are the main influencing factors. Thus, controlling urban expansion and reducing anthropogenic heat generation are effective approaches for alleviating surface UHI.

Land consolidation (LC) stands as a globally recognized strategy for rural development. In China, it has evolved towards comprehensive land consolidation (CLC) to support the rural revitalization initiative. However, there are ongoing challenges in understanding CLC’s specific pathway and mechanism, particularly its role in stimulating rural endogenous development. This study aims to investigate the localization process of international experiences, examine the pathway of CLC, and scrutinize its mechanism in rural development from a novel perspective of neo-endogenous development. Field research and semi-structured interviews were conducted in Nanzhanglou village, renowned for its early adoption of CLC practices inspired by German experiences since 1988. Overall, key findings underscore the advantages of CLC in spatial restructuring, industrial development, and human capital enhancement in rural areas. Additionally, international experiences emerge as crucial exogenous forces, primarily by knowledge embedding, which catalyzes rural neo-endogenous development via the “resource-engagement-identity-endogenous” mechanism. Collectively, by introducing a neo-endogenous theoretical framework, this study offers valuable insights into the CLC implementation in China and beyond, and emphasizes the positive impact of knowledge embedding as an exogenous force in promoting rural neo-endogenous development to address existing research gaps. Recommendations for sustainable rural development involve enhancing rural planning practicality, governance capacity, and local leadership, while prioritizing agricultural modernization and increasing investments in education and vocational training to ensure that villagers benefit from industrial development.

Water use efficiency (WUE), as a pivotal indicator of the coupling degree within the carbon-water cycle of ecosystems, holds considerable importance in assessment of the carbon-water balance within terrestrial ecosystems. However, in the context of global warming, WUE evolution and its primary drivers on the Tibetan Plateau remain unclear. This study employed the ensemble empirical mode decomposition method and the random forest algorithm to decipher the nonlinear trends and drivers of WUE on the Tibetan Plateau in 2001- 2020. Results indicated an annual mean WUE of 0.8088 gC/mm∙m² across the plateau, with a spatial gradient reflecting decrease from the southeast toward the northwest. Areas manifesting monotonous trends of increase or decrease in WUE accounted for 23.64% and 9.69% of the total, respectively. Remarkably, 66.67% of the region exhibited trend reversals, i.e., 39.94% of the area of the Tibetan Plateau showed transition from a trend of increase to a trend of decrease, and 26.73% of the area demonstrated a shift from a trend of decrease to a trend of increase. Environmental factors accounted for 70.79% of the variability in WUE. The leaf area index and temperature served as the major driving forces of WUE variation.

The clear identification and quantification of the factors affecting groundwater systems is crucial for protecting groundwater resources and ensuring safety in agricultural production. The Lower Yellow River (LYR) is a suspended river that replenishes groundwater continuously due to clear differences in the water head, especially in the Xinxiang section. Since its construction, the Xiaolangdi Reservoir has reversed the LYR’s deposition. To accurately determine the factors influencing the groundwater level (GWL), the study area was divided into five subzones based on hydrogeology. A dynamic factor model (DFM), variational mode decomposition (VMD), and a multiple linear regression model were used to identify and quantify the factors influencing the GWL. The impact of the suspended river on the groundwater before and after the construction of the Xiaolangdi Reservoir was examined. The results show that: (1) The rate of decrease in the GWL was 8.53 × 10^-4 m/month, and the rate of decrease in the Yellow River water level (RWL) was 4.63 × 10^-4 m/month. (2) Mountain front recharge (MFR) (scale = 3 months) and precipitation (scale = 9 months) were the dominant factors in subzones I and II, accounting for more than 40% of the fluctuation in the GWL. Subzone III was dominated by exploitation (scale = 7 months) and precipitation (scale = 12 months), accounting for 28.43%, and 23.44% of changes in the GWL, respectively. In subzone IV, agricultural irrigation (scale = 12 months) was the major factor, accounting for 32.47% of GWL changes, while in subzone V, the RWL (scale = 12 months) accounted for 52.52% of these changes. (3) The Xiaolangdi Reservoir has increased the lateral seepage of the suspended river and altered the inter-annual distribution. The results of this study can provide a valuable reference for controlling groundwater overexploitation and ensuring water supply security.

China’s outward foreign direct investment (FDI) is different from traditional FDI in various ways, for example being rooted in “Guanxi” in Chinese culture, influenced by government, and located in developed economies where they have limited ownership advantages compared with local firms. Chinese investment in the United States (the U.S.) is an example of how the location is influenced by economic factors, social linkages, as well as geopolitical events, such as the U.S.-China trade conflict, which deserves more academic attention. It is such a complex phenomenon that cannot be fully explained by traditional FDI theories, which mainly focus on economic factors. In this paper, we illustrate the historical development, distribution and firm heterogeneity of Chinese investment in the U.S. from 2000 to 2020, and use a conditional logit model to investigate the location factors. Our study reveals that the number of Chinese investment projects in the U.S. peaked in 2017 and has declined year-over-year since then. These projects are mainly located along the East and West coasts of the U.S. and around the Great Lakes, with the largest numbers in California and New York. Previous Chinese investment agglomeration and ethnic networks both influence the location choice of China’s outward FDI, even when controlling for regional attributes and economic embeddedness. In terms of firm heterogeneity, Chinese firms that enter the American market with greenfield investment modes, state-owned enterprises and firms in high-tech sectors are more likely to follow previous Chinese investment, but place less emphasis on Chinese ethnic linkages, implying that previous Chinese investment agglomeration can replace the role of Chinese ethnic networks for these firms. Finally, the U.S.-China trade conflict has significantly lessened the active role of Chinese ethnic networks and has reduced Chinese investment in states with higher industrial output.