Climate change significantly affects the arid/humid processes and patterns in China, directly impacting management decisions related to adaptive agriculture and water resources management, desertification control, and spatial ecological restoration. However, current studies primarily focus on changes in arid/humid climate variables, lacking quantitative characterization of the dynamic evolution of areal systems and their nonlinear responses. Based on the data of national meteorological stations from 1961 to 2020, we systematically quantified the nonlinear response of arid/humid patterns to climate change. The results revealed that 6.98% of eco-geographical arid/humid regions underwent type shifts over the past six decades, with 4.95% transitioning toward wetter conditions. Humid and semi-arid regions expanded significantly while sub-humid and arid regions contracted significantly. In the late 1990s, trends of the humid and sub-humid region shifted. Humid region contraction in northern China was driven primarily by precipitation decline, whereas the Tibetan Plateau responded to increasing potential evapotranspiration. During the same period, the retreat rate of the arid region slowed, linked to intensified aridification in the west part of northern China and a decelerating wetting trend in northwest China, both primarily driven by precipitation trends. Our study reveals the nonlinear response of the arid/humid patterns under climate change, providing a scientific basis for the improvement of regional climate resilience.
Promoting the synergistic governance of pollution control (PC) and carbon reduction (CR) in the agricultural sector was an important way for the Chinese government to implement the “dual carbon” initiative and respond to climate change. Based on the data of China’s crop production from 31 provincial-level regions from 1997 to 2022, this paper constructs a framework consisting of spatiotemporal evolution, synergy effect measurement, differences in contributions across regions, and influencing factors analysis to reveal the relationship between agricultural PC and CR. The results showed that the annual growth rates of pollutant emissions and carbon emissions were 1.85% and 0.79%, respectively. However, the annual decline rates of their emission intensities were 3.14% and 4.32%, respectively. This indicated that China’s actions to reduce pollution and carbon emissions in agriculture have achieved good results, that the effect of PC was weaker than that of CR and had an obvious “policy node effect.” Simultaneously, the synergy between PC and CR evolved from “basic coordination” to “basic imbalance.” The contribution of inter-regional differences was relatively large, while intra-regional differences were smaller, highlighting the importance of reducing regional disparities in promoting the synergistic governance of PC and CR. The basic conditions, industrial structure, input intensity, and development potential of agricultural development were key factors in widening the coupling coordination gap between PC and CR, and the influence of these significant factors exhibited clear spatiotemporal heterogeneity. These findings have provided important evidence for understanding China’s agricultural environmental governance strategies and could offer experiential insights for developing countries in advancing the coordinated governance of agricultural PC and CR.
Ensuring national food security amidst rapid population growth and increasing extreme weather events remains a critical global challenge. However, the extent to which agricultural modernization in China enhances grain yield and contributes to food security remains unclear. Therefore, using panel data from 327 Chinese cities (2013-2021), this study employs spatial econometric models to analyze the spatial spillover effects of agricultural modernization level (AML) on grain yield and to reveal regional heterogeneity across nine major agricultural zones. The results showed a cumulative grain yield increase of 23.7 million tons, with peak productivity concentrated along the Hu Line and declining eastward and westward. AML also exhibited a steady increase but a clear spatial gradient, decreasing from coastal to inland regions, with the highest level observed in Southern China (SC). A key finding was that a 1% increase in AML directly raised local grain yield by an average of 4.185%, accompanied by significant positive spillover effects on neighboring regions. Regional variations revealed distinct patterns: the direct effects of AML were more pronounced in southern and eastern zones, while spillover effects dominated in northern and western zones. The largest positive direct impact of AML on grain yield was observed in the SC (8.499%), while Middle-Lower Yangtze Plain ranked second but exhibited the strongest positive spatial spillover effect (4.534%). These findings highlight the critical role of agricultural modernization in promoting grain production and provide a solid basis for optimizing regional agricultural systems, ensuring food security, and advancing sustainable agriculture.
Urban agglomerations, representing a high-level organizational form of urbanization, play an increasingly vital role in promoting sustainable development. These regions attract substantial population inflows due to their robust economic foundations and advanced public service facilities. To assess this dynamic, an evaluation index system for urban sustainable development goals (SDGs) was constructed based on the United Nations SDGs framework. Using three representative urban agglomerations of Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Chengdu-Chongqing (CY) in China as case studies, this research explores the realization of SDGs since the construction of the urban agglomerations and its coupling with population changes by combining multifactorial analysis and the coupling coordination degree model. Results reveal that SDG scores in these cities have increased by an average of 25.33% since 2005. Scores in central cities are significantly higher than the average, and the gap between cities is narrowing. However, there are still trade-offs among some of the goals. Additionally, the process of SDGs realization in core cities with large populations is largely coordinated with population growth. The findings provide a reference for urban agglomerations to adopt cross-regional collaborative governance measures to achieve the SDGs.
The development of human settlements (HS) in coastal cities is an integral component and a vital pathway toward building a strong marine power. It is also an essential requirement for achieving the coordinated development of HS systems in these cities. In this study, we constructed an indicator system to analyze the coupling coordination degree (CCD) of HS systems in coastal cities in the Bohai Rim region of China (CCBRR). This study is based on five systems and employs methods such as the entropy weight method, CCD model, spatial trend surface analysis, and geographic detector to examine comprehensively the spatial and temporal patterns of CCD in 17 CCBRR during the period 2011-2022, as well as to explore their influencing factors. The findings are as follows: (1) Temporally, the CCD is high and exhibits a slow increasing trend, with distinct stage characteristics. (2) Spatially, the distribution of CCD reveals a “one core, many strengths” structural pattern. (3) Moreover, socioeconomic factors are the dominant force driving the CCD of the internal HS systems in the CCBRR. (4) Finally, we constructed a coupling coordination driving mechanism for HS in the CCBRR with the aim of providing scientific references and path choices for the high-quality and coordinated development of the CCBRR along with the implementation of the new quality productive forces regionalization.
Understanding the scale-dependent dynamics of ecosystem services (ESs) and their socio-ecological drivers is essential for sustainable development. While many studies rely on static or single-scale approaches, this research employs an integrated multi-temporal (2000−2020) and multi-scale (grid, county, and landscape levels) framework to investigate China’s Central Asian frontier, a representative dryland region. We quantified six ESs: habitat quality (HQ), net primary productivity (NPP), carbon sequestration (CS), water yield (WY), soil conservation (SC), and grain production (GP). Furthermore, we explored their interrelationships and identified the drivers influencing these services across different spatial scales. Our results revealed divergent ES trajectories: the declining HQ (−0.03 a−1), NPP (−0.43 t km−2 a−1), and SC (−3.41 t ha a−1) contrasted with rising WY (+2.33 mm a−1), GP (+0.06 t km−2 a−1), and CS (+0.02 t km−2 a−1). The ES relationships were predominantly synergistic, while HQ-WY exhibited a trade-off (grid: −0.03; county: −0.02; landscape: −0.03) at temporal dimension but a synergistic relationship (grid: 0.45; county: 0.92; landscape: 0.92) at spatial dimension. As spatial scale increased, SC-CS shifted from synergy (grid: 0.001) to trade-off (county: −0.01; landscape: −0.005) in the temporal dimension, while all trade-off relationships in the spatial dimension were transformed into synergies. Key drivers of ES relationships varied with spatial scale: fraction vegetation coverage (FVC) and leaf area index (LAI) at the grid scale, annual precipitation (MAP) and soil moisture (SMA) at the county scale, and population density (POP), gross domestic product (GDP), and silt content (Silt) at the landscape scale. Based on the multi-scale findings, the study divides northern Xinjiang into Grain Priority Region, Ecological Priority Region, and Desert Containment Region, and proposes tailored management recommendations, offering a flexible framework for balancing ecological and socioeconomic needs.
Rising global change intensifies water scarcity in China’s vital Yellow River Basin grain region, which mounts the need for precise spatial water management. In this study, we investigated the irrigation water demand for seven major crops in cities at the prefecture level between 2000 and 2019. Using Logarithmic Mean Divisia Index (LMDI) decomposition and k-means clustering, we quantified how yield, area, water use efficiency, and cropping patterns affect water demand and identified five irrigation development clusters. Key water-saving areas were identified by tracking transitions among clusters, and NSGA-II was applied to optimize crop structure. The results revealed that the total irrigation demand in the Yellow River Basin averaged 50.09 billion m3/year, with wheat accounting for 54.7%. The increase in yield and area increased demand by 15.2 and 5.5 billion m3, respectively, which was partly offset by changes in water use efficiency and cropping pattern (-7.0 and -1.8 billion m3, respectively). Regions in the upper reaches, particularly within the Lanzhou-Toudaoguai section, were identified as critical for water conservation. Optimization of the cropping structure in key regions can reduce annual irrigation water demand by 280 million m3, which accounts for 4.9% of the total demand in these areas, with minimal impact on crop production. This study provides a spatially explicit basis for targeted water conservation strategies in water-scarce agricultural regions.
As an essential component of terrestrial carbon sinks, lake sediments store vast quantities of both organic carbon (OC) and inorganic carbon (IC). However, the spatiotemporal relationship between the OC and IC in sediments and their responses to climate change remains unclear, which hinders the comprehensive understanding of carbon dynamics in lake ecosystems. This study systematically analyzes the spatiotemporal dynamics of carbon burial across the Tibetan Plateau using surface sediments from 119 lakes and sediment cores from four representative lakes. Results show that OC burial dominates in humid and dry sub-humid zones, whereas IC burial prevails in arid and semi-arid regions. This distribution reflects the influences of lake and catchment productivity and water chemistry on OC and IC patterns. Sediment cores confirm that these factors have consistently affected lake carbon burial over the past century. Specifically, in humid and dry sub-humid zones, increased precipitation enhances watershed productivity and sedimentation, promoting coupled OC and IC burial. In arid and semi-arid regions, wind-driven dust supplies nutrients and alters water chemistry, also driving coupled OC and IC burial. Based on these findings, the carbon sink capacity of lake sediments on the Tibetan Plateau is projected to increase under the “warming and wetting” trend.
Research into the location and development of rice paddies after the collapse of Neolithic cultures is of crucial importance. This study explores the phytolith assemblages and soil micromorphologies of potential rice paddy relics found at the Xingang Site (3556-3360 cal. a BP) in the Taihu Lake Plain, Lower Yangtze River, offering insights into these issues. The discriminant function of the phytolith assemblage distinguished six out of 19 samples in the suspected paddy field area as wild rice fields, while the rest were non-rice fields. Soil micromorphology indicated that the alleged paddy field area experienced repeated dry and wet conditions, with signs of plant growth but no evidence of human activity, suggesting it was not an artificially managed paddy field. These findings suggest the area during the Shang Dynasty consisted of abandoned paddies from the post-Neolithic era. The proportion of rice bulliform phytoliths with ≥9 fish-scale decorations (35%-47%) was significantly lower at the Xingang Site (marginal area) during the Shang Dynasty compared to periods like Qianshanyang-Guangfulin (4300-3900 a BP) (central area), suggesting that diminished population density in marginal areas after the Neolithic collapse likely led to paddy field abandonment. Additionally, the collapse of the Liangzhu social structure, along with a rice-farming economy that lacked strong resource competitiveness, may have also contributed to this phenomenon. This study provides an empirical example of rice paddy locations following the Neolithic collapse in the Lower Yangtze River, enhancing our understanding of the decline of the Liangzhu civilization.
To address soil salinization's significant impact on human production and livelihood in arid regions, especially in high-salinity areas like salt lake regions, this study used multi-source remote sensing data to extract 52 surface factors. Combined with measured soil salinity data, correlation analysis, multicollinearity testing, and projection importance analysis identified eight dominant factors. Subsequently, four machine learning algorithms were applied for modeling, and the optimal models were selected to study the spatiotemporal variation of soil salinization. The results indicate that the average soil salt content in the study area was 20.74% in 2020. LST (land surface temperature) can effectively identify areas with high salinity, such as saline-alkali land and salt flats. Among inversion models, the GBDT (gradient boosting decision trees) model demonstrated the highest predictive ability and minimal errors. The optimal inversion results revealed that soil salinization distribution was influenced by topographic elevation, distance from Qarhan Salt Lake, and river network density. Over the past 21 years, there was significant fluctuation in soil salinity observed in the concentrated area of grassland within the groundwater overflow zone, indicating strong variation in salinization. This fluctuation correlates with changes in groundwater levels in the groundwater overflow zone, which are influenced by temperature variations that determine the amount of snow and ice meltwater, and the precipitation in the upstream area. This study enhances understanding of soil salinization and its drivers in extremely arid salt lake regions.
Intermittent rivers and ephemeral streams (IRES), also known as non-perennial river segments (NPRs), have garnered attention due to their significant roles in watershed hydrology and ecosystem services, especially in the context of climate change and escalating human activities. Recent advances in machine learning (ML) techniques have significantly improved the analysis of dynamic changes in IRES. Various ML models, including random forest (RF), long short-term memory (LSTM), and U-Net, demonstrate clear advantages in processing complex hydrological data, enhancing the efficiency and accuracy of IRES extraction from remote sensing data. Furthermore, hybrid ML approaches enhance predictive performance in complex hydrological scenarios by integrating multiple algorithms. However, ML methods still face challenges, including high data dependence, computational complexity, and scalability issues with models. This review proposes an IRES monitoring framework that combines satellite data with ML algorithms, integrating remote sensing technologies such as optical imaging and synthetic aperture radar, and evaluates the advantages and limitations of different ML methods. It further highlights the potential of integrating multiple ML techniques and high-resolution remote sensing data to monitor IRES dynamics, conduct ecological assessments, and support sustainable water management, offering a scientific foundation for addressing environmental and anthropogenic pressures.
