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 sp...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.展开更多
BACKGROUND Major depressive disorder(MDD)and obesity(OB)are bidirectionally comorbid conditions with common neurobiological underpinnings.However,the neurocognitive mechanisms of their comorbidity remain poorly unders...BACKGROUND Major depressive disorder(MDD)and obesity(OB)are bidirectionally comorbid conditions with common neurobiological underpinnings.However,the neurocognitive mechanisms of their comorbidity remain poorly understood.AIM To examine regional abnormalities in spontaneous brain activity among patients with MDD-OB comorbidity.METHODS This study adopted a regional homogeneity(ReHo)analysis of resting-state functional magnetic resonance imaging.The study included 149 hospital patients divided into four groups:Patients experiencing their first episode of drug-naive MDD with OB,patients with MDD without OB,and age-and sex-matched healthy individuals with and without OB.Whole-brain ReHo analysis was conducted using SPM12 software and RESTplus toolkits,with group comparisons via ANOVA and post-hoc tests.Correlations between ReHo values and behavioral measures were examined.RESULTS ANOVA revealed significant whole-brain ReHo differences among the four groups in four key regions:The left middle temporal gyrus(MTG.L),right cuneus,left precuneus,and left thalamus.Post-hoc analyses confirmed pairwise differences between all groups across these regions(P<0.05).OB was associated with ReHo alterations in the MTG.L,right cuneus,and left thalamus,whereas abnormalities in the precuneus suggested synergistic pathological mechanisms between MDD and OB.Statistically significant correlations were found between the drive and fun-seeking dimensions of the behavioral activation system,as well as behavioral inhibition and the corresponding ReHo values.CONCLUSION Our findings provide novel evidence for the neuroadaptive mechanisms underlying the MDD-OB comorbidity.Further validation could lead to personalized interventions targeting MTG.L hyperactivity and targeting healthy food cues.展开更多
On October 28,2025,China and ASEAN signed the China-ASEAN Free Trade Area(CAFTA)3.0 Upgrade Protocol in Kuala Lumpur,Malaysia,in the presence of Chinese Premier Li Qiang,Malaysian Prime Minister Anwar Ibrahim,and lead...On October 28,2025,China and ASEAN signed the China-ASEAN Free Trade Area(CAFTA)3.0 Upgrade Protocol in Kuala Lumpur,Malaysia,in the presence of Chinese Premier Li Qiang,Malaysian Prime Minister Anwar Ibrahim,and leaders of other ASEAN member states.A close look at the protocol shows that the upgraded agreement marks substantive advances on several fronts.展开更多
On October 28,2025,in the bustling diplomatic hub of Kuala Lumpur,a defining moment in Asian economic history quietly and solemnly unfolded when leaders from China and ASEAN member states witnessed the official signin...On October 28,2025,in the bustling diplomatic hub of Kuala Lumpur,a defining moment in Asian economic history quietly and solemnly unfolded when leaders from China and ASEAN member states witnessed the official signing of the protocol to upgrade the China-ASEAN Free Trade Area(CAFTA)to Version 3.0.The ceremony marked the culmination of nearly three years of intense negotiations.展开更多
Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition.However,existing data augmentation methods struggle to accurately locate discriminative regions in comp...Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition.However,existing data augmentation methods struggle to accurately locate discriminative regions in complex backgrounds,small target objects,and limited training data,leading to poor recognition.Fine-grained images exhibit“small inter-class differences,”and while second-order feature encoding enhances discrimination,it often requires dual Convolutional Neural Networks(CNN),increasing training time and complexity.This study proposes a model integrating discriminative region localization and efficient second-order feature encoding.By ranking feature map channels via a fully connected layer,it selects high-importance channels to generate an enhanced map,accurately locating discriminative regions.Cropping and erasing augmentations further refine recognition.To improve efficiency,a novel second-order feature encoding module generates an attention map from the fourth convolutional group of Residual Network 50 layers(ResNet-50)and multiplies it with features from the fifth group,producing second-order features while reducing dimensionality and training time.Experiments on Caltech-University of California,San Diego Birds-200-2011(CUB-200-2011),Stanford Car,and Fine-Grained Visual Classification of Aircraft(FGVC Aircraft)datasets show state-of-the-art accuracy of 88.9%,94.7%,and 93.3%,respectively.展开更多
Waves are important physical phenomena in an ocean,and their accurate prediction is essential for ocean engineering,maritime traffic,and marine early warning systems.This study focuses on the Qinhuangdao Sea area loca...Waves are important physical phenomena in an ocean,and their accurate prediction is essential for ocean engineering,maritime traffic,and marine early warning systems.This study focuses on the Qinhuangdao Sea area located in the Bohai Sea,China.Herein,we use on-site wind data to correct the reanalysis wind data obtained from the European Centre for Medium-Range Weather Forecasts(ECMWF),improving the accuracy of boundary conditions.Then,we use the Simulating WAves Nearshore(SWAN)model to simulate the regional wave field over time.A regional wave-parameter prediction model is then developed using a limited number of sampled data(covering only 2 years,2020–2021);the model is based on the Whale Optimization Algorithm(WOA),convolutional neural networks(CNNs),and long short-term memory(LSTM)neural networks.WOA is used to optimize the CNN and LSTM framework;in this framework,CNN extracts spatial features,and the LSTM network captures temporal features,enabling accurate short and long-term predictions of wave height,period,and direction.The experimental results showed that despite the small sample size,the model achieves a goodness of fit of 0.9957 for wave height prediction,0.9973 for period,and 0.9749 for wave direction in short-term forecasting.As the prediction step size increases,the accuracy of the model decreases.When the prediction step size reaches 9 h,the root mean square error for the prediction of wave height,period,and direction increases to 0.2060 m,0.4582 s,and32.5358°,respectively.The reliability and applicability of the model are further validated by the experimental results.Our findings highlighted the potential of the developed model in operational wave forecasting,even with a limited number of sampled data.展开更多
Drought influences carbon fixation by plants.Therefore,elucidating its impact on carbon fluxes in plants at the ecosystem level is crucial for assessing their role in mitigating climate change.Using carbon fluxes and ...Drought influences carbon fixation by plants.Therefore,elucidating its impact on carbon fluxes in plants at the ecosystem level is crucial for assessing their role in mitigating climate change.Using carbon fluxes and environmental factor data from FLUXNET sites,we analyzed the influence of drought on carbon fluxes,their drivers,time-lag effects,and recovery times across various climatic regions and seasons.Results showed drought significantly decreased gross primary production(GPP),ecosystem respiration,and net ecosystem productivity in arid regions but slightly increased carbon sequestration in humid regions.Summer droughts negatively affected vegetation carbon fluxes,partly offset by the positive impact of spring droughts.Nonforest carbon fluxes were more susceptible to drought effects than forest fluxes.Soil water content(SWC)was the main influence on changes in arid regions,whereas vapor pressure deficit(VPD)dominated humid regions.Decreased SWC and increased VPD reduced carbon sequestration in arid regions but increased it in humid regions.Increased VPD reduced GPP,leading to forest carbon loss,whereas decreased SWC reduced GPP,leading to nonforest carbon loss.The lag time of the drought effects on carbon fluxes was longer in humid regions(19.44 d)than in arid regions(14.71 d).Compared to nonforest areas(16.74 d and 57 d for drought lag and recovery time,respectively),forest areas had a longer lag(18.81 d)and recovery time(92 d).The findings revealed discrepancies in the main factors regulating vegetation carbon fluxes during droughts between arid and humid regions and between forest and nonforest ecosystems.These insights provide a new perspective on understanding and simulating carbon-climate feedback.Enhancing ecosystem diversity is a feasible measure to increase drought resistance.展开更多
Unmanned aerial vehicles(UAVs)are widely utilized in area coverage tasks due to their flexibility and efficiency in geo-graphic information acquisition.However,complex boundary conditions in actual water area maps oft...Unmanned aerial vehicles(UAVs)are widely utilized in area coverage tasks due to their flexibility and efficiency in geo-graphic information acquisition.However,complex boundary conditions in actual water area maps often reduce coverage efficiency.To address this issue,this paper proposes a map preprocessing algorithm that linearizes boundary lines and processes concave areas into concave polygons,followed by gridding the map.Additionally,a collaborative area coverage method for UAV swarms is introduced based on region partitioning,which considers the comprehensive cost of energy consumption and time.An improved Hungarian algorithm is utilized for region partitioning,and a Dubins-A*-based plow-ing area full coverage path planning method is proposed to achieve path smoothing and collaborative coverage of each partition.Two sets of simulation experiments are conducted.The first experiment verifies the effectiveness of the map preprocessing algorithm,and the second compares the proposed collaborative area coverage algorithm with other methods,demonstrating its performance advantages.展开更多
To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources(DERs)(such as photovoltaic(PV)systems,wind turbines(WT),and energy s...To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources(DERs)(such as photovoltaic(PV)systems,wind turbines(WT),and energy storage(ES)devices),and the increased grid load fluctuations and safety risks due to uncoordinated electric vehicles(EVs)charging,this paper proposes a novel dual-scale hierarchical collaborative optimization strategy.This strategy decouples system-level economic dispatch from distributed EV agent control,effectively solving the resource coordination conflicts arising from the high computational complexity,poor scalability of existing centralized optimization,or the reliance on local information decision-making in fully decentralized frameworks.At the lower level,an EV charging and discharging model with a hybrid discrete-continuous action space is established,and optimized using an improved Parameterized Deep Q-Network(PDQN)algorithm,which directly handles mode selection and power regulation while embedding physical constraints to ensure safety.At the upper level,microgrid(MG)operators adopt a dynamic pricing strategy optimized through Deep Reinforcement Learning(DRL)to maximize economic benefits and achieve peak-valley shaving.Simulation results show that the proposed strategy outperforms traditional methods,reducing the total operating cost of the MG by 21.6%,decreasing the peak-to-valley load difference by 33.7%,reducing the number of voltage limit violations by 88.9%,and lowering the average electricity cost for EV users by 15.2%.This method brings a win-win result for operators and users,providing a reliable and efficient scheduling solution for distribution networks with high renewable energy penetration rates.展开更多
Driven by the global energy transition and the urgent“dual carbon”goals,regional integrated energy system(RIES)planning is undergoing a paradigm shift from carbon reduction to negative carbon emissions.This paper pr...Driven by the global energy transition and the urgent“dual carbon”goals,regional integrated energy system(RIES)planning is undergoing a paradigm shift from carbon reduction to negative carbon emissions.This paper provides a comprehensive review of the theoretical frameworks and technical pathways for RIES planning from a carbon-centric perspective.A key contribution is the proposed Carbon-Energy-Economy(CEE)triple-dimensional governance framework,which endogenizes carbon factors into planning decisions through emission constraints,trading mechanisms,and capture technologies.We first analyze the fundamental characteristics of RIES and their critical role in achieving carbon neutrality,detailing advancements in multi-energy coupling models,energy router concepts,and standardized energy hub modeling.The paper further explores multi-energy flow analysis methods,and systematically compares the applicability and limitations of various planning algorithms,with emphasis on addressing uncertainties from renewable integration.Finally,we highlight the integration of artificial intelligence with traditional optimization methods,offering new pathways for intelligent,adaptive,and low-carbon RIES planning.This review underscores the transition towards data-physical fusion models,cooperative uncertainty optimization,multi-market planning,and innovative zero/negative-carbon technological routes.展开更多
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 extrac...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.展开更多
In this paper,we define for the trace operator,the solution of certain models of vibrating plates standards with initial data in a strategic region spaces of weak regularities.Indeed,we know that the notion of regiona...In this paper,we define for the trace operator,the solution of certain models of vibrating plates standards with initial data in a strategic region spaces of weak regularities.Indeed,we know that the notion of regional controllability is more adapted to systems described by dynamic systems.Regional controllability results in a strategic area were established for vibrating plates by the Hilbertian Uniqueness Method.展开更多
Great Lakes Regions(GLRs)in China often confront landscape fragmentation,wetland degradation,and ecological resilience(ER)losses owing to extensive and intensive urbanization.In GLRs,however,the ER responses to urbani...Great Lakes Regions(GLRs)in China often confront landscape fragmentation,wetland degradation,and ecological resilience(ER)losses owing to extensive and intensive urbanization.In GLRs,however,the ER responses to urbanization remain unclear.This study explored the spatiotemporal evolution of ER and urbanization in five GLRs in China to analyze the ER dynamic patterns along center−lakeside−periphery gradient.The Spatial Durbin Model(SDM)and Panel Threshold Model(PTM)were combined to reveal the spillover and threshold effects of urbanization in five GLRs.The results indicate that the ER in five GLRs declined with a rate of 21%from 2000 to 2020.There was a clear“center-periphery”contraction trend with low ER areas primarily spreading to human activity-concentrated regions such as lakesides,riversides,and road networks.Driven by economic and land urbanization,the average urbanization level increased from 0.06 to 0.13,where lakesides,riversides,and road networks were key areas undergoing expansion.The urbanization showed a noticeable negative spatial spillover effect on ER.Away from central lakes,the negative impacts on ER exhibited a two-phase decrease with the threshold of 81 km.This study contributes to the understanding of human-environment interactions by examining the ecological resilience response process of GLRs under the impact of urbanization.Based on a multidimensional“center−lakeside−periphery”analytical model,this study provides a strategic framework for ecological construction in GLRs in China,promoting sustainable development and adaptive capacity in vulnerable areas.展开更多
In December 2025,the ASEAN Centre for Energy(ACE)convened the third ASEAN Power Grid Partnership Meeting,bringing partners together for consultations on key issues.After more than two decades of planning and explorati...In December 2025,the ASEAN Centre for Energy(ACE)convened the third ASEAN Power Grid Partnership Meeting,bringing partners together for consultations on key issues.After more than two decades of planning and exploration,the ASEAN Power Grid is now entering a new phase—shifting from predominantly bilateral,one-way connections toward a multilateral,multidirectional network.展开更多
Understanding the complex interactions between human activities and ecosystem functions is a prerequisite for achieving sustainable development.Since the implementation of the“Grain for Green”Project in 1999,ecosyst...Understanding the complex interactions between human activities and ecosystem functions is a prerequisite for achieving sustainable development.Since the implementation of the“Grain for Green”Project in 1999,ecosystem functions in China’s Loess Plateau have significantly improved.However,intensified human activities have also exacerbated the pressures on the region’s fragile ecological environment.This study investigates the spatiotemporal variations in the human activity intensity index(HAI)and net ecosystem benefits(NEB)from 2000 to 2020,using expert-based assessments and an enhanced cost-benefit evaluation framework.Results indicate that HAI increased by 16.7% and 16.6% at the grid and county levels,respectively.NEB exhibited pronounced spatial heterogeneity,with a total increase of USD 36.2 trillion at the grid scale.At the county level,the average NEB rose by 75%.The degree of trade-off was higher at the grid scale than at the county scale,while the synergistic areas initially expanded and then declined at both scales.Key areas for improvement and regions of lagging development were identified as priority zones for ecological management and spatial planning at both spatial resolutions.This study offers scientific insights and practical guidance for harmonizing ecological conservation with high-quality development in ecologically vulnerable regions.展开更多
In the context of the revolution in new technologies,a key question is whether the rapid growth of the digital economy,driven by digital technologies,has improved regional innovation performance.Using inter-provincial...In the context of the revolution in new technologies,a key question is whether the rapid growth of the digital economy,driven by digital technologies,has improved regional innovation performance.Using inter-provincial panel data from China(2012–2022)and adopting a business environment perspective,this study applies a Panel Extended Regression Model(PERM),a Panel Simultaneous Equation Model(PSEM),and a Tobit-IV model to analyze how the development of the digital economy influences regional innovation.The results reveal a pronounced U-shaped relationship between the digital economy and the regional innovation performance at the provincial level in China,with the business environment serving as a significant mediator in this relationship.Moreover,regional innovation performance in China exhibits a“ratchet effect,”with the impact of the digital economy varying markedly across regions.While the eastern and western regions have entered an upward phase,whereby the digital economy boosts innovation,the central region displays a weaker effect.Further analysis indicates that the synergy between the business environment and the digital economy in driving innovation remains suboptimal.These findings were supported by robust checks.This study offers theoretical insights and empirical evidence that support the coordinated development of digital government and the digital factor market,as well as business environment reforms that are in alignment with the innovation demands of the digital era.展开更多
The uneven distribution of the temperature field in the track structure,caused by various meteorological factors such as extremely low temperatures and snowfall,leads to significant temperature loads and is the primar...The uneven distribution of the temperature field in the track structure,caused by various meteorological factors such as extremely low temperatures and snowfall,leads to significant temperature loads and is the primary cause of damage to China Railway Track System(CRTS)III ballastless tracks in cold regions during service.In this study,to predict the temperature of the track structure accurately,we analyzed meteorological data collected from Shenyang,China,and identified the factors that had the most effect on the track temperature field.We propose a temporal convolutional network(TCN)-based temperature field prediction model for ballastless tracks(TCN-Track model),which enhances the ability to extract and fuse local and global features from complex long-term meteorological data.The results indicate that the proposed TCN-Track model performs well in predicting track temperature fields from meteorological data,with a mean absolute error(MAE)ranging from 0.26 to 0.39,a root mean square error(RMSE)ranging from 0.32 to 0.50,and correlation coefficient(R)values ranging from 0.888 to 0.985.Compared with a long short-term memory(LSTM)model,the MAE of the TCN-Track model is reduced by 89.17%and the RMSE by 88.51%.This method offers a new solution for accurately predicting the temperature field of ballastless tracks in cold regions,aiding in predicting and preventing track damage caused by low temperatures.展开更多
The Guangdong,Jiangxi and Fujian(GJF)provinces,located in the subtropical region of southeastern China,is one of the national key regions for soil erosion control and ecological restoration.This region is characterize...The Guangdong,Jiangxi and Fujian(GJF)provinces,located in the subtropical region of southeastern China,is one of the national key regions for soil erosion control and ecological restoration.This region is characterized by extensive red soil development and high rainfall erosivity,making it a representative landscape for exploring the interactions between land use change(LUC)and ecosystem services(ES).Despite the recognized importance of ES in hilly regions,comprehensive assessing the impacts of LUC on ES remain limited.This study investigates five key ES:water yield,soil conservation,carbon conservation,food supply,and habitat quality in GJF region from 2000 to 2020.By applying the InVEST model and the Geodetector method,we assessed the trade-offs,synergies,and transitions among ES,identified the natural and social drivers of ES dynamics,and quantified the contribution of LUC to ES changes using the ecosystem service contribution index.The results showed that cropland and woodland were the dominant land use types.Ecological restoration efforts positively influenced ES,with synergies intensifying and trade-offs diminishing over time.Land use conversions,particularly among woodland,grassland,and cropland,exerted significant impacts on ES.In particular,the conversion of woodland to other land uses had markedly negative effects on soil conservation,carbon conservation,and habitat quality.Forest cover was identified as a major driver of ES dynamics.These findings highlight the importance of maintaining and expanding forest and grassland cover,strengthening red soil conservation,and optimizing land use structure to achieve coordinated ecological protection and socioeconomic development in the subtropical hilly regions of southern China.展开更多
A comprehensive assessment of grain supply,demand,and ecosystem service flows is essential for identifying grain movement pathways,ensuring regional grain security,and guiding sustainable management strategies.However...A comprehensive assessment of grain supply,demand,and ecosystem service flows is essential for identifying grain movement pathways,ensuring regional grain security,and guiding sustainable management strategies.However,current studies primarily focus on short-term grain provision services while neglecting the spatiotemporal variations in grain flows across different scales.This gap limits the identification of dynamic matching relationships and the formulation of optimization strategies for balancing grain flows.This study examined the spatiotemporal evolution of grain supply and demand in the Beijing-Tianjin-Hebei(BTH)region from 1980 to 2020.Using the Enhanced TwoStep Floating Catchment Area method,the grain provision ecosystem service flows were quantified,the changes in supply–demand matching under different grain flow scenarios were analyzed and the optimal distance threshold for grain flows was investigated.The results revealed that grain production follows a spatial distribution pattern characterized by high levels in the southeast and low levels in the northwest.A significant mismatch exists between supply and demand,and it shows a scale effect.Deficit areas are mainly concentrated in the northwest,while surplus areas are mainly located in the central and southern regions.As the spatial scale increases,the ecosystem service supply–demand ratio(SDR)classification becomes more clustered,while it exhibits greater spatial SDR heterogeneity at smaller scales.This study examined two distinct scenarios of grain provision ecosystem service flow dynamics based on 100 and 200 km distance thresholds.The flow increased significantly,from 2.17 to 11.81million tons in the first scenario and from 2.41 to 12.37 million tons in the second scenario over nearly 40 years,forming a spatial movement pattern from the central and southern regions to the surrounding areas.Large flows were mainly concentrated in the interior of urban centers,with significant outflows between cities such as Baoding,Shijiazhuang,Xingtai,and Hengshui.At the county scale,supply–demand matching patterns remained consistent between the grain flows in the two scenarios.Notably,incorporating grain flow dynamics significantly reduced the number of grain-deficit areas compared to scenarios without grain flow.In 2020,grain-deficit counties decreased by28.79 and 37.88%,and cities by 12.50 and 25.0%under the two scenarios,respectively.Furthermore,the distance threshold for achieving optimal supply and demand matching at the county scale was longer than at the city scale in both grain flow scenarios.This study provides valuable insights into the dynamic relationships and heterogeneous patterns of grain matching,and expands the research perspective on grain and ecosystem service flows across various spatiotemporal scales.展开更多
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 fo...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 m^(3),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.展开更多
基金National Natural Science Foundation of China,No.42377460。
文摘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.
基金Supported by Provincial Key Research Project of Henan Province,No.232102310081.
文摘BACKGROUND Major depressive disorder(MDD)and obesity(OB)are bidirectionally comorbid conditions with common neurobiological underpinnings.However,the neurocognitive mechanisms of their comorbidity remain poorly understood.AIM To examine regional abnormalities in spontaneous brain activity among patients with MDD-OB comorbidity.METHODS This study adopted a regional homogeneity(ReHo)analysis of resting-state functional magnetic resonance imaging.The study included 149 hospital patients divided into four groups:Patients experiencing their first episode of drug-naive MDD with OB,patients with MDD without OB,and age-and sex-matched healthy individuals with and without OB.Whole-brain ReHo analysis was conducted using SPM12 software and RESTplus toolkits,with group comparisons via ANOVA and post-hoc tests.Correlations between ReHo values and behavioral measures were examined.RESULTS ANOVA revealed significant whole-brain ReHo differences among the four groups in four key regions:The left middle temporal gyrus(MTG.L),right cuneus,left precuneus,and left thalamus.Post-hoc analyses confirmed pairwise differences between all groups across these regions(P<0.05).OB was associated with ReHo alterations in the MTG.L,right cuneus,and left thalamus,whereas abnormalities in the precuneus suggested synergistic pathological mechanisms between MDD and OB.Statistically significant correlations were found between the drive and fun-seeking dimensions of the behavioral activation system,as well as behavioral inhibition and the corresponding ReHo values.CONCLUSION Our findings provide novel evidence for the neuroadaptive mechanisms underlying the MDD-OB comorbidity.Further validation could lead to personalized interventions targeting MTG.L hyperactivity and targeting healthy food cues.
文摘On October 28,2025,China and ASEAN signed the China-ASEAN Free Trade Area(CAFTA)3.0 Upgrade Protocol in Kuala Lumpur,Malaysia,in the presence of Chinese Premier Li Qiang,Malaysian Prime Minister Anwar Ibrahim,and leaders of other ASEAN member states.A close look at the protocol shows that the upgraded agreement marks substantive advances on several fronts.
文摘On October 28,2025,in the bustling diplomatic hub of Kuala Lumpur,a defining moment in Asian economic history quietly and solemnly unfolded when leaders from China and ASEAN member states witnessed the official signing of the protocol to upgrade the China-ASEAN Free Trade Area(CAFTA)to Version 3.0.The ceremony marked the culmination of nearly three years of intense negotiations.
基金supported,in part,by the National Nature Science Foundation of China under Grant 62272236,62376128 and 62306139the Natural Science Foundation of Jiangsu Province under Grant BK20201136,BK20191401.
文摘Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition.However,existing data augmentation methods struggle to accurately locate discriminative regions in complex backgrounds,small target objects,and limited training data,leading to poor recognition.Fine-grained images exhibit“small inter-class differences,”and while second-order feature encoding enhances discrimination,it often requires dual Convolutional Neural Networks(CNN),increasing training time and complexity.This study proposes a model integrating discriminative region localization and efficient second-order feature encoding.By ranking feature map channels via a fully connected layer,it selects high-importance channels to generate an enhanced map,accurately locating discriminative regions.Cropping and erasing augmentations further refine recognition.To improve efficiency,a novel second-order feature encoding module generates an attention map from the fourth convolutional group of Residual Network 50 layers(ResNet-50)and multiplies it with features from the fifth group,producing second-order features while reducing dimensionality and training time.Experiments on Caltech-University of California,San Diego Birds-200-2011(CUB-200-2011),Stanford Car,and Fine-Grained Visual Classification of Aircraft(FGVC Aircraft)datasets show state-of-the-art accuracy of 88.9%,94.7%,and 93.3%,respectively.
基金supported by the National Natural Science Foundation of China(Nos.52071057,52171247)the Liaoning Youth Elite Talent Program(No.XLYC220309)。
文摘Waves are important physical phenomena in an ocean,and their accurate prediction is essential for ocean engineering,maritime traffic,and marine early warning systems.This study focuses on the Qinhuangdao Sea area located in the Bohai Sea,China.Herein,we use on-site wind data to correct the reanalysis wind data obtained from the European Centre for Medium-Range Weather Forecasts(ECMWF),improving the accuracy of boundary conditions.Then,we use the Simulating WAves Nearshore(SWAN)model to simulate the regional wave field over time.A regional wave-parameter prediction model is then developed using a limited number of sampled data(covering only 2 years,2020–2021);the model is based on the Whale Optimization Algorithm(WOA),convolutional neural networks(CNNs),and long short-term memory(LSTM)neural networks.WOA is used to optimize the CNN and LSTM framework;in this framework,CNN extracts spatial features,and the LSTM network captures temporal features,enabling accurate short and long-term predictions of wave height,period,and direction.The experimental results showed that despite the small sample size,the model achieves a goodness of fit of 0.9957 for wave height prediction,0.9973 for period,and 0.9749 for wave direction in short-term forecasting.As the prediction step size increases,the accuracy of the model decreases.When the prediction step size reaches 9 h,the root mean square error for the prediction of wave height,period,and direction increases to 0.2060 m,0.4582 s,and32.5358°,respectively.The reliability and applicability of the model are further validated by the experimental results.Our findings highlighted the potential of the developed model in operational wave forecasting,even with a limited number of sampled data.
基金supported by the National Natural Science Foundation of China(grant no.32371866)。
文摘Drought influences carbon fixation by plants.Therefore,elucidating its impact on carbon fluxes in plants at the ecosystem level is crucial for assessing their role in mitigating climate change.Using carbon fluxes and environmental factor data from FLUXNET sites,we analyzed the influence of drought on carbon fluxes,their drivers,time-lag effects,and recovery times across various climatic regions and seasons.Results showed drought significantly decreased gross primary production(GPP),ecosystem respiration,and net ecosystem productivity in arid regions but slightly increased carbon sequestration in humid regions.Summer droughts negatively affected vegetation carbon fluxes,partly offset by the positive impact of spring droughts.Nonforest carbon fluxes were more susceptible to drought effects than forest fluxes.Soil water content(SWC)was the main influence on changes in arid regions,whereas vapor pressure deficit(VPD)dominated humid regions.Decreased SWC and increased VPD reduced carbon sequestration in arid regions but increased it in humid regions.Increased VPD reduced GPP,leading to forest carbon loss,whereas decreased SWC reduced GPP,leading to nonforest carbon loss.The lag time of the drought effects on carbon fluxes was longer in humid regions(19.44 d)than in arid regions(14.71 d).Compared to nonforest areas(16.74 d and 57 d for drought lag and recovery time,respectively),forest areas had a longer lag(18.81 d)and recovery time(92 d).The findings revealed discrepancies in the main factors regulating vegetation carbon fluxes during droughts between arid and humid regions and between forest and nonforest ecosystems.These insights provide a new perspective on understanding and simulating carbon-climate feedback.Enhancing ecosystem diversity is a feasible measure to increase drought resistance.
基金National Natural Science Foundation of China(62402020,62303022)Beijing Nova Program(20240484720)+1 种基金Project of Cultivation for Young Top-Notch Talents of Beijing Municipal Institutions(BPHR202203043)BTBU Digital Business Platform Project byBMEC.
文摘Unmanned aerial vehicles(UAVs)are widely utilized in area coverage tasks due to their flexibility and efficiency in geo-graphic information acquisition.However,complex boundary conditions in actual water area maps often reduce coverage efficiency.To address this issue,this paper proposes a map preprocessing algorithm that linearizes boundary lines and processes concave areas into concave polygons,followed by gridding the map.Additionally,a collaborative area coverage method for UAV swarms is introduced based on region partitioning,which considers the comprehensive cost of energy consumption and time.An improved Hungarian algorithm is utilized for region partitioning,and a Dubins-A*-based plow-ing area full coverage path planning method is proposed to achieve path smoothing and collaborative coverage of each partition.Two sets of simulation experiments are conducted.The first experiment verifies the effectiveness of the map preprocessing algorithm,and the second compares the proposed collaborative area coverage algorithm with other methods,demonstrating its performance advantages.
基金supported in part by the Research on Key Technologies for the Development of an Active Balancing Cooperative Control Systemfor Distribution Networks and the National Natural Science Foundation of China under Grant 521532240029,Grant 62303006.
文摘To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources(DERs)(such as photovoltaic(PV)systems,wind turbines(WT),and energy storage(ES)devices),and the increased grid load fluctuations and safety risks due to uncoordinated electric vehicles(EVs)charging,this paper proposes a novel dual-scale hierarchical collaborative optimization strategy.This strategy decouples system-level economic dispatch from distributed EV agent control,effectively solving the resource coordination conflicts arising from the high computational complexity,poor scalability of existing centralized optimization,or the reliance on local information decision-making in fully decentralized frameworks.At the lower level,an EV charging and discharging model with a hybrid discrete-continuous action space is established,and optimized using an improved Parameterized Deep Q-Network(PDQN)algorithm,which directly handles mode selection and power regulation while embedding physical constraints to ensure safety.At the upper level,microgrid(MG)operators adopt a dynamic pricing strategy optimized through Deep Reinforcement Learning(DRL)to maximize economic benefits and achieve peak-valley shaving.Simulation results show that the proposed strategy outperforms traditional methods,reducing the total operating cost of the MG by 21.6%,decreasing the peak-to-valley load difference by 33.7%,reducing the number of voltage limit violations by 88.9%,and lowering the average electricity cost for EV users by 15.2%.This method brings a win-win result for operators and users,providing a reliable and efficient scheduling solution for distribution networks with high renewable energy penetration rates.
基金supported by the Natural Science Foundation of China(Grants U2166211)Zhejiang Provincial Natural Science Foundation of China(Grants LY24E070006 and LMS25E070002).
文摘Driven by the global energy transition and the urgent“dual carbon”goals,regional integrated energy system(RIES)planning is undergoing a paradigm shift from carbon reduction to negative carbon emissions.This paper provides a comprehensive review of the theoretical frameworks and technical pathways for RIES planning from a carbon-centric perspective.A key contribution is the proposed Carbon-Energy-Economy(CEE)triple-dimensional governance framework,which endogenizes carbon factors into planning decisions through emission constraints,trading mechanisms,and capture technologies.We first analyze the fundamental characteristics of RIES and their critical role in achieving carbon neutrality,detailing advancements in multi-energy coupling models,energy router concepts,and standardized energy hub modeling.The paper further explores multi-energy flow analysis methods,and systematically compares the applicability and limitations of various planning algorithms,with emphasis on addressing uncertainties from renewable integration.Finally,we highlight the integration of artificial intelligence with traditional optimization methods,offering new pathways for intelligent,adaptive,and low-carbon RIES planning.This review underscores the transition towards data-physical fusion models,cooperative uncertainty optimization,multi-market planning,and innovative zero/negative-carbon technological routes.
基金The Second Tibetan Plateau Scientific Expedition and Research Program,No.2019QZKK0805-02The Innovation Team Foundation of Qinghai Office of Science and Technology,No.2022-ZJ-903+2 种基金The Comprehensive Development and Utilization of Salt Lake Resources,No.2023ZXKYA05100The Special Research Assistant of Chinese Academy of Sciences(Han Jinjun)The Kunlun Talented People of Qinghai Province,High-end Innovation and Entrepreneurship Talents,2023(Han Jinjun)。
文摘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.
文摘In this paper,we define for the trace operator,the solution of certain models of vibrating plates standards with initial data in a strategic region spaces of weak regularities.Indeed,we know that the notion of regional controllability is more adapted to systems described by dynamic systems.Regional controllability results in a strategic area were established for vibrating plates by the Hilbertian Uniqueness Method.
基金supported by the National Natural Science Foundation of China(Grants No.42301226,42271209 and 42471199)the Fundamental Research Funds for the Central Universities(Grant No.2024CDJXY014)+2 种基金the Natural Science Foundation of Jiangxi Province(Grant No.20242BAB25170)Special Funds for Water Resources in Jiangxi Province(Science and Technology Projects)(Grant No.202425YBKT16)the Young Talent Cultivation and Innovation Fund Project of Nanchang University(Grant No.XX202506030028).
文摘Great Lakes Regions(GLRs)in China often confront landscape fragmentation,wetland degradation,and ecological resilience(ER)losses owing to extensive and intensive urbanization.In GLRs,however,the ER responses to urbanization remain unclear.This study explored the spatiotemporal evolution of ER and urbanization in five GLRs in China to analyze the ER dynamic patterns along center−lakeside−periphery gradient.The Spatial Durbin Model(SDM)and Panel Threshold Model(PTM)were combined to reveal the spillover and threshold effects of urbanization in five GLRs.The results indicate that the ER in five GLRs declined with a rate of 21%from 2000 to 2020.There was a clear“center-periphery”contraction trend with low ER areas primarily spreading to human activity-concentrated regions such as lakesides,riversides,and road networks.Driven by economic and land urbanization,the average urbanization level increased from 0.06 to 0.13,where lakesides,riversides,and road networks were key areas undergoing expansion.The urbanization showed a noticeable negative spatial spillover effect on ER.Away from central lakes,the negative impacts on ER exhibited a two-phase decrease with the threshold of 81 km.This study contributes to the understanding of human-environment interactions by examining the ecological resilience response process of GLRs under the impact of urbanization.Based on a multidimensional“center−lakeside−periphery”analytical model,this study provides a strategic framework for ecological construction in GLRs in China,promoting sustainable development and adaptive capacity in vulnerable areas.
文摘In December 2025,the ASEAN Centre for Energy(ACE)convened the third ASEAN Power Grid Partnership Meeting,bringing partners together for consultations on key issues.After more than two decades of planning and exploration,the ASEAN Power Grid is now entering a new phase—shifting from predominantly bilateral,one-way connections toward a multilateral,multidirectional network.
基金National Natural Science Foundation of China(Grant No.U2243225)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB40000000)+2 种基金the Natural Science Basic Research Program of Shaanxi(Grant No.Z2024-ZYFS-0065)the Funding of Top Young talents of Ten Thousand talents Plan in China(2021)the Fundamental Research Funds for the Central Universities(Grants No.2452023071 and 2023HHZX002).
文摘Understanding the complex interactions between human activities and ecosystem functions is a prerequisite for achieving sustainable development.Since the implementation of the“Grain for Green”Project in 1999,ecosystem functions in China’s Loess Plateau have significantly improved.However,intensified human activities have also exacerbated the pressures on the region’s fragile ecological environment.This study investigates the spatiotemporal variations in the human activity intensity index(HAI)and net ecosystem benefits(NEB)from 2000 to 2020,using expert-based assessments and an enhanced cost-benefit evaluation framework.Results indicate that HAI increased by 16.7% and 16.6% at the grid and county levels,respectively.NEB exhibited pronounced spatial heterogeneity,with a total increase of USD 36.2 trillion at the grid scale.At the county level,the average NEB rose by 75%.The degree of trade-off was higher at the grid scale than at the county scale,while the synergistic areas initially expanded and then declined at both scales.Key areas for improvement and regions of lagging development were identified as priority zones for ecological management and spatial planning at both spatial resolutions.This study offers scientific insights and practical guidance for harmonizing ecological conservation with high-quality development in ecologically vulnerable regions.
基金National Social Science Fund of China(18KXS009)the Sichuan Provincial Soft Science Program(22JDR0261)the Sichuan University“From 0 to 1”Innovation Research Program(2021CXC10)。
文摘In the context of the revolution in new technologies,a key question is whether the rapid growth of the digital economy,driven by digital technologies,has improved regional innovation performance.Using inter-provincial panel data from China(2012–2022)and adopting a business environment perspective,this study applies a Panel Extended Regression Model(PERM),a Panel Simultaneous Equation Model(PSEM),and a Tobit-IV model to analyze how the development of the digital economy influences regional innovation.The results reveal a pronounced U-shaped relationship between the digital economy and the regional innovation performance at the provincial level in China,with the business environment serving as a significant mediator in this relationship.Moreover,regional innovation performance in China exhibits a“ratchet effect,”with the impact of the digital economy varying markedly across regions.While the eastern and western regions have entered an upward phase,whereby the digital economy boosts innovation,the central region displays a weaker effect.Further analysis indicates that the synergy between the business environment and the digital economy in driving innovation remains suboptimal.These findings were supported by robust checks.This study offers theoretical insights and empirical evidence that support the coordinated development of digital government and the digital factor market,as well as business environment reforms that are in alignment with the innovation demands of the digital era.
基金supported by the National Natural Science Foundation of China(Nos.52278461,52308467,and 52425213).
文摘The uneven distribution of the temperature field in the track structure,caused by various meteorological factors such as extremely low temperatures and snowfall,leads to significant temperature loads and is the primary cause of damage to China Railway Track System(CRTS)III ballastless tracks in cold regions during service.In this study,to predict the temperature of the track structure accurately,we analyzed meteorological data collected from Shenyang,China,and identified the factors that had the most effect on the track temperature field.We propose a temporal convolutional network(TCN)-based temperature field prediction model for ballastless tracks(TCN-Track model),which enhances the ability to extract and fuse local and global features from complex long-term meteorological data.The results indicate that the proposed TCN-Track model performs well in predicting track temperature fields from meteorological data,with a mean absolute error(MAE)ranging from 0.26 to 0.39,a root mean square error(RMSE)ranging from 0.32 to 0.50,and correlation coefficient(R)values ranging from 0.888 to 0.985.Compared with a long short-term memory(LSTM)model,the MAE of the TCN-Track model is reduced by 89.17%and the RMSE by 88.51%.This method offers a new solution for accurately predicting the temperature field of ballastless tracks in cold regions,aiding in predicting and preventing track damage caused by low temperatures.
基金funded by the National Natural Science Foundation of China(42377326 and 42201267)National Research-Development Support Plan Projects of China(Grant No.2017YFC05054)the Fujian Provincial Water Resources Department Science and Technology Project(MSK202308)。
文摘The Guangdong,Jiangxi and Fujian(GJF)provinces,located in the subtropical region of southeastern China,is one of the national key regions for soil erosion control and ecological restoration.This region is characterized by extensive red soil development and high rainfall erosivity,making it a representative landscape for exploring the interactions between land use change(LUC)and ecosystem services(ES).Despite the recognized importance of ES in hilly regions,comprehensive assessing the impacts of LUC on ES remain limited.This study investigates five key ES:water yield,soil conservation,carbon conservation,food supply,and habitat quality in GJF region from 2000 to 2020.By applying the InVEST model and the Geodetector method,we assessed the trade-offs,synergies,and transitions among ES,identified the natural and social drivers of ES dynamics,and quantified the contribution of LUC to ES changes using the ecosystem service contribution index.The results showed that cropland and woodland were the dominant land use types.Ecological restoration efforts positively influenced ES,with synergies intensifying and trade-offs diminishing over time.Land use conversions,particularly among woodland,grassland,and cropland,exerted significant impacts on ES.In particular,the conversion of woodland to other land uses had markedly negative effects on soil conservation,carbon conservation,and habitat quality.Forest cover was identified as a major driver of ES dynamics.These findings highlight the importance of maintaining and expanding forest and grassland cover,strengthening red soil conservation,and optimizing land use structure to achieve coordinated ecological protection and socioeconomic development in the subtropical hilly regions of southern China.
基金supported by the National Natural Science Foundation of China(42471336,52379021 and 42201278)the Hebei Province Backbone Talent Program,China(Returnee Platform for Overseas Study)(A20240028)+2 种基金the Hebei Province Statistical Science Research Project,China(2024HZ04)the Hebei Province Graduate Education and Teaching Reform Research Project,China(YJG2024046)the Innovation Ability Training Program for Postgraduate Students of Hebei Provincial Department of Education,China(CXZZSS2025048)。
文摘A comprehensive assessment of grain supply,demand,and ecosystem service flows is essential for identifying grain movement pathways,ensuring regional grain security,and guiding sustainable management strategies.However,current studies primarily focus on short-term grain provision services while neglecting the spatiotemporal variations in grain flows across different scales.This gap limits the identification of dynamic matching relationships and the formulation of optimization strategies for balancing grain flows.This study examined the spatiotemporal evolution of grain supply and demand in the Beijing-Tianjin-Hebei(BTH)region from 1980 to 2020.Using the Enhanced TwoStep Floating Catchment Area method,the grain provision ecosystem service flows were quantified,the changes in supply–demand matching under different grain flow scenarios were analyzed and the optimal distance threshold for grain flows was investigated.The results revealed that grain production follows a spatial distribution pattern characterized by high levels in the southeast and low levels in the northwest.A significant mismatch exists between supply and demand,and it shows a scale effect.Deficit areas are mainly concentrated in the northwest,while surplus areas are mainly located in the central and southern regions.As the spatial scale increases,the ecosystem service supply–demand ratio(SDR)classification becomes more clustered,while it exhibits greater spatial SDR heterogeneity at smaller scales.This study examined two distinct scenarios of grain provision ecosystem service flow dynamics based on 100 and 200 km distance thresholds.The flow increased significantly,from 2.17 to 11.81million tons in the first scenario and from 2.41 to 12.37 million tons in the second scenario over nearly 40 years,forming a spatial movement pattern from the central and southern regions to the surrounding areas.Large flows were mainly concentrated in the interior of urban centers,with significant outflows between cities such as Baoding,Shijiazhuang,Xingtai,and Hengshui.At the county scale,supply–demand matching patterns remained consistent between the grain flows in the two scenarios.Notably,incorporating grain flow dynamics significantly reduced the number of grain-deficit areas compared to scenarios without grain flow.In 2020,grain-deficit counties decreased by28.79 and 37.88%,and cities by 12.50 and 25.0%under the two scenarios,respectively.Furthermore,the distance threshold for achieving optimal supply and demand matching at the county scale was longer than at the city scale in both grain flow scenarios.This study provides valuable insights into the dynamic relationships and heterogeneous patterns of grain matching,and expands the research perspective on grain and ecosystem service flows across various spatiotemporal scales.
基金National Natural Science Foundation of China,No.42041007。
文摘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 m^(3),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.
