The transpiration-to-evapotranspiration ratio (T/ET) is a crucial indicator of the carbon-water cycle and energy balance. Despite the marked seasonality of warming and greening patterns, the differential responses of T/ET to environmental changes across the seasons remain unclear. To address this, we employed a model-data fusion method, integrating the Priestley-Taylor Jet Propulsion Lab model with observational datasets, to analyze the seasonal trends of T/ET in China’s terrestrial ecosystems from 1981 to 2021. The results showed that T/ET significantly increased in spring, summer, and autumn, with growth rates of 0.0018 a^-1 (p<0.01), 0.0024 a^-1 (p<0.01), and 0.0013 a^-1 (p<0.01), respectively, whereas the winter trends remained statistically insignificant throughout the study period. Leaf area index dynamics were identified as the primary driver of the increase in T/ET during summer, accounting for 79% of the trend. By contrast, climate change was the main contributor to the rising T/ET trends in spring and autumn, accounting for 72% and 77% of the T/ET increase, respectively. Additionally, warming is pivotal for climate-driven changes in T/ET trends. This study elucidated seasonal variations in T/ET responses to environmental factors, offering critical insights for the sustainable management of ecosystems and accurate prediction of future environmental change impacts.

Regular quantitative assessments of regional ecological environment quality (EEQ) and driving force analyses are highly important for environmental protection and sustainable development. Northern China is a typical climate-sensitive and ecologically vulnerable area, however, the changes in EEQ in this region and their underlying causes remain unclear. Traditional evaluations of EEQ rely primarily on the remote sensing ecological index (RSEI), which lacks assessments of indicators such as greenness (NDVI), humidity (WET), heat (LST), and dryness (NDBSI). To address these issues, this study employs the principal component analysis method and the Google Earth Engine to construct an RSEI suitable for long-term and large-scale applications and analyzes the spatio-temporal variations in the RSEI, NDVI, WET, NDBSI, and LST. Additionally, geographical detectors are utilized to analyze the driving factors affecting EEQ. The results indicate the following. (1) The RSEI shows a fluctuating upward trend, with an average value of 0.4566, indicating a gradual improvement in EEQ. The EEQ exhibited significant spatial heterogeneity, with a pattern of lower values in the west and higher values in the east. (2) The NDVI and WET exhibit fluctuating increasing trends, indicating improvements in both indices. The NDBSI shows a fluctuating decreasing trend, whereas the LST presents a fluctuating increasing trend, suggesting an improvement in the NDBSI and a slight deterioration in the LST. NDVI and WET demonstrate a spatial pattern characterized by low values in the west and high values in the east. NDBSI and LST demonstrate a spatial pattern characterized by low values in the east and high values in the west. (3) Land use types and precipitation are the primary driving factors influencing the spatial differentiation of the EEQ. The explanatory power of these driving factors significantly increases under their interactions, particularly the interaction between land use types and other driving factors. This study fills the gap in existing EEQ evaluations that analyze only the RSEI without considering the NDVI, WET, NDBSI, and LST. The findings provide new insights for EEQ assessments and serve as a scientific reference for environmental protection and sustainable development.

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.

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.

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.

Globalization has resulted in a notable rise in the flow of high-skilled talent from emerging countries to developed nations. Current research on transnational talent flow mainly focuses on the destination countries, with less attention given to the perspective of the sending countries, particularly lacking a dynamic discussion on its impact on technological evolution in the origin countries. Based on the OECD REGPAT database, this paper aims to explore how talent groups migrating to developed countries facilitate the return of knowledge and technology to emerging countries and achieve breakthroughs in their technological evolution paths, while further discussing the potential mechanisms involved. The findings of this paper are as follows: (1) The technological development of emerging countries is a path-dependent process, where countries often branch into new technologies related to their preexisting knowledge base. Consequently, knowledge feedback from high-skilled talents increases the likelihood of sending countries developing unrelated technologies. (2) The mobility of talents across borders fosters more international collaborations and citations for patents that are unrelated to the local knowledge base, thus enriching the technological paths of sending countries. (3) The mobility of high-skilled talents primarily affects complex technologies, which have significant economic effects that encourage imitation by other countries. However, the effect on novel technologies is less significant due to their strong geographical stickiness. In general, this paper addresses the gaps in existing research on talent outflow and the technological evolution of origin countries, providing empirical evidence for the positive role of transnational talent mobility in the technological catch-up of emerging nations. Besides, it offers recommendations for talent export, import, and innovation policy formulation in these countries.

In the present study, indoor experiments were carried out to investigate the effects of rainfall on subsequent wind erosion processes on the simulated Gobi surface, with soils and gravels collected from the Alax Gobi in northwestern China. The results showed that the wind erosion rate (WR) ranged from 0.4 to 1931.2 g m^-2 min^-1 and that the sediment transportation rate (STR) ranged from 0.00 to 51.47 g m^-2 s^-1 under different gravel coverage conditions (0%, 20%, 40% and 60%) when the wind velocity changed from 6 m s^-1 to 18 m s^-1. Both WR and STR increased with increasing wind velocity as a power function and decreased with increasing gravel coverage. The rainfall event had significant inhibitory effects on WR and STR, and the complex effects of soil crust formation and the changing soil surface roughness (SSR) by rainfall event played significant roles in reducing these rates during subsequent wind erosion. In this study, a valuable exploration of the effects of rainfall events on subsequent wind erosion processes in the Gobi area was conducted. The findings are of great significance for a better understanding and effective prediction of dust emissions in this region.

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.

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.

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.

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.

Wetlands are important natural resources for humans and play an irreplaceable ecological function in the terrestrial ecosystem. To curb the continued loss of wetlands globally, international organizations and many countries have taken a series of major conservation and restoration measures. This work reviews these wetland conservation and restoration measures, interprets China’s wetland conservation and restoration management policies, and proposes that future research on wetland resources in China should be conducted from the aspects of international frontiers and national strategic plans, socioeconomics, and smart services. The results show that the 27 International Wetlands Days from 1997 to 2023 provided new goals and tasks for the protection and management of wetlands. The important topics and outcomes of the 14 Conferences of the Contracting Parties to the Convention on Wetlands from 1980 to 2022 provided new directions and new challenges for wetland development. In the future, we should enhance wetland ecological functions, promote sustainable wetland development, and overcome the technical bottleneck of fragile wetland ecosystem restoration. From 1992 to 2022, China embarked on a new phase of wetland protection and restoration. The overall experience of wetland protection and restoration in China has been formed through national strategic deployment, legal policy establishment, and project planning and implementation. The needs to provide for and plan the long-term protection of wetlands at the national level, to innovate restoration and management techniques and application systems, and to effectively address the complex issues of wetland protection and restoration through collaborative division of labor among multiple departments were emphasized. Research on the future trends of wetlands should be directed towards the exploration and practice of the United Nations Sustainable Development Goals and several international conventions in support of sustainable wetland development. Wetland protection, restoration, and management services should be promoted for national strategic needs and local, high-quality social and economic development. In addition, research on cross-integration and academic innovation should be enhanced for disciplinary development, global supervision, comprehensive assessment, and smart decision making.

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.

Understanding the evolutionary trends and driving factors behind extreme hourly precipitation (EHP) in typical urban agglomerations is crucial for predicting and preventing rapid floods. We collected hourly precipitation datasets from 31 observation stations in the Central Yunnan Urban Agglomeration (CYA) spanning from 2004 to 2020. Urban and rural observations were dynamically classified based on impervious surface fraction. Linear (Granger) and nonlinear causal methods(convergent cross-mapping and Liang-Kleeman information flow) were used to identify the causal impact mechanisms of large-scale circulation, environment and urbanization on EHP. Moreover, geo-detector further reveals the spatial influence of these factors and their interactions on EHP. Our findings revealed that EHP mainly occurred in the afternoon and at midnight. Also, the frequency and intensity of EHP in the CYA significantly (p≤0.05) increased from 2004 to 2020, especially in urban areas. The increasing rate in urban areas was higher than that in rural areas. However, the duration of EHP/hourly total precipitation exhibited a significant/nonsignificant decreasing trend with no significant difference between urban and rural areas. Causality tests and geo-detector indicated that EHP was impacted by natural variability and urbanization. Large-scale circulation indices such as the Pacific Decadal Oscillation, El Niño-Southern Oscillation, and Indian Ocean Dipole nonlinearly influenced EHP. Additionally, urban landscape layout, vegetation, and population variation may strengthen EHP by changing environmental factors such as temperature and relative humidity. Interactions exist between these factors and influence EHP, although large-scale circulation remains the dominant influence. With global climate warming and rapid urbanization in the CYA, the frequency and intensity of EHP may further amplify in the future.

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.

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.

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.

Emphasis on future environmental changes grows due to climate change, with simulations predicting rising river temperatures globally. For Poland, which has a long history of thermal studies of rivers, such an approach has not been implemented to date. This study used 9 Global Climate Models and tested three machine-learning techniques to predict river temperature changes. Random Forest performed best, with R²=0.88 and lowest error (RMSE: 2.25, MAE:1.72). The range of future water temperature changes by the end of the 21st century was based on the Shared Socioeconomic Pathway scenarios SSP2-4.5 and SSP5-8.5. It was determined that by the end of the 21st century, the average temperature will increase by 2.1°C (SSP2-4.5) and 3.7°C (SSP5-8.5). A more detailed analysis, divided by two major basins Vistula and Odra, covered about 90% of Poland’s territory. The average temperature increase, according to scenarios SSP2-4.5 and SSP5-8.5 for the Odra basin rivers, is 1.6°C and 3.2°C and for the Vistula basin rivers 2.3°C and 3.8°C, respectively. The Vistula basin’s higher warming is due to less groundwater input and continental climate influence. These findings provide a crucial basis for water management to mitigate warming effects in Poland.

The Yellow River Basin (YRB) is a vital ecological zone in China owing to its sensitive and fragile environment. Under the long-term influence of climate changes and artificial factors, the relationship between precipitation, vegetation, and surface water in the YRB has changed drastically, ultimately affecting the water resources and environmental management. Therefore, we applied multivariate statistical analysis to investigate the precipitation, normalized difference vegetation index (NDVI), and surface water changes in the YRB from 2000 to 2021. Furthermore, we attempted to clarify the ecological effects of precipitation by explaining the relationship between precipitation and vegetation in terms of the time-lag relationship using the Integrated Multi-satellite Retrievals for Global Precipitation Measurement algorithm, Moderate Resolution Imaging Spectroradiometer, and hydrological databases. Precipitation, vegetation, and area of surface water in the YRB showed increasing trends from 2000-2021 (e.g., 7.215 mm/yr, 0.004 NDVI/yr, and 0.932 km²/yr, respectively). The water level in the upper reaches of the YRB showed a downward trend, whereas that in the middle and lower reaches exhibited an upward trend. Changes in precipitation had a positive effect on vegetation and surface water in the YRB, with correlation coefficients of 0.63 and 0.51, respectively. The responses of NDVI and surface water elevation to precipitation were heterogeneous and delayed, with the majority showing a lag time of approximately ≤ 16 days. Moreover, the lag times of Longyangxia Lake and Ngoring-Co Lake were 0 and 8 days, respectively. We showed that precipitation variability can effectively explain vegetation improvement and increases in surface water elevation, while providing a proven scenario for predicting the surface water and vegetation productivity under the influence of climate change.

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.

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.

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.

While algorithms have been created for land usage in urban settings, there have been few investigations into the extraction of urban footprint (UF). To address this research gap, the study employs several widely used image classification method classified into three categories to evaluate their segmentation capabilities for extracting UF across eight cities. The results indicate that pixel-based methods only excel in clear urban environments, and their overall accuracy is not consistently high. RF and SVM perform well but lack stability in object-based UF extraction, influenced by feature selection and classifier performance. Deep learning enhances feature extraction but requires powerful computing and faces challenges with complex urban layouts. SAM excels in medium-sized urban areas but falters in intricate layouts. Integrating traditional and deep learning methods optimizes UF extraction, balancing accuracy and processing efficiency. Future research should focus on adapting algorithms for diverse urban landscapes to enhance UF extraction accuracy and applicability.

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.

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.

The application of ecosystem services (ES) theories in land consolidation is a confusing issue that has long plagued scholars and government officials. As the upgraded version of traditional land consolidation, comprehensive land consolidation (CLC) emphasizes ecological benefits, but it does not achieve the expected effect during the pilot phase. This study first proposed a theoretical analysis framework based on ES knowledge to answer the three key questions of why, where, and how to implement CLC better. Taking mountainous counties as the study area, we found that ES trade-offs/synergies, bundles, and drivers were significantly affected by scale effects. ES knowledge can play a crucial role in designing multi-scale CLC strategies regarding the objective, zoning, intensity, and mode. Specifically, mitigating the significant trade-offs between recreational opportunities, food production, and other ES is the top priority of CLC. Land consolidation zoning based on the ES bundles analysis is more rational and can provide the scientific premise for designing locally adapted CLC measures. Land consolidation can be classified into high-intensity direct intervention and low-intensity indirect intervention modes, based on the major drivers of ES. These findings help narrow the gap between ES and CLC practices.

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.

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.

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.

Due to rapid socio-economic development and urban-rural integration, the rural population is increasingly moving away from the primary industry, leading to a noticeable transformation of rural farmland use. This paper analyzed the changes in farmland use and the mechanisms in 213 villages of Longxi county, China in 2020, using multiple linear regression models, based on the aforementioned situation. Analysis revealed main findings: (1) Male and young and middle-aged emigration levels are concentrated in areas with higher and lower values, the emigration of talent is more evenly distributed. Overall, male emigration rates were high in all directions and low in the central area, whereas the young and middle-aged emigration rates exhibited a pattern of high loss in the north and south extremes, and low loss in the central area. The emigration of talent demonstrated a pattern of low losses to the east and high losses to the west of the G30 national highway. (2) Primary farmland use behavior was self-cultivation, then abandonment and finally transfer, with over 60% of the total area in 179 villages used for self-cultivation compared to less than 5% in 164 villages allocated for transfer, while less than 30% of the area in 179 villages was abandoned. (3) Significant differences were observed in the factors that influenced various farmland use behaviors, emigration of male, young and middle-aged and talent were the common influencing factors observed among all three types of farmland uses. The loss of males, young and middle-aged had a significant association with reduced self-cultivation while the emigration of talent led to an increase in self-cultivation use. Increased emigration of all three population constituencies significantly increased farmland transfer and abandonment. The conclusions carry significant theoretical and practical implications for enhancing the coordination of rural human-land relationships and improvement of the understanding of the relationship between population migration and farmland use.