The available heat content (stored heat energy) of hot dry rock (HDR) at a depth of 1–10 km in the global land crust is estimated to be 5.06×10~8 EJ,attracting considerable global attention.This paper presents a...The available heat content (stored heat energy) of hot dry rock (HDR) at a depth of 1–10 km in the global land crust is estimated to be 5.06×10~8 EJ,attracting considerable global attention.This paper presents a comprehensive analysis of the geological framework,HDR resource potential,exploration advancements,and the development of enhanced geothermal systems (EGSs) in China.HDR resources are extensively distributed across China.Within the depth range of 3–10 km,China’s estimated potential approximates2.29×10~7 EJ,with a theoretical power generation capacity of approximately 1.67×10^(16) k Wh.Replacing coal power with HDR can help to achieve a net emission reduction of 1.34×10^(16) kg CO_(2) (approximately1.34×10^(13) t),representing an emission reduction efficiency of 94.4%.Based on a development cycle of100 years,the average annual emission reduction reaches 1.34×10^(10) t CO_(2),equivalent to 117%of China’s annual carbon emissions in 2022.Furthermore,in the context of global warming,the development and utilization of HDR,which is feasible in virtually any region worldwide,offers significant potential to support global carbon reduction efforts.China has made substantial progress in HDR exploration in recent years.This paper systematically classifies China’s HDR resources into four genetic types—highly radioactive heat-producing,sedimentary basin,active volcanic,and intensely tectonic zones—and offers detailed exploration insights for each category.Each classification exhibits distinct geological and tectonic characteristics that influence heat source mechanisms and resource distribution.Furthermore,this paper documents significant advances in EGS construction,particularly in the Gonghe Basin on the northeastern margin of the Qianghai-Xizang Plateau and the Matouying uplift in the North China Basin,where successful reservoir stimulation,microseismic monitoring,and experimental power generation have been achieved.Despite these developments,challenges persist,including technical adaptability under complex geological conditions and the economic viability of large-scale HDR development.This paper suggests that future initiatives should emphasize resource exploration,technological research,and policy support to foster sustainable HDR resource development in China,thereby contributing to the global energy transition and environmental sustainability.展开更多
The Shenshan Group provides important geological information which is vital in unraveling the amalgamation and subsequent rifting processes of South China.While conventional studies have asserted its formation in a su...The Shenshan Group provides important geological information which is vital in unraveling the amalgamation and subsequent rifting processes of South China.While conventional studies have asserted its formation in a subduction setting,the distinct investigation reveals the necessity for reassessment.To address this,the authors employ integrated methods encompassing petrological,zircon U-Pb geochronological,Lu-Hf isotopic and geochemical methods for sedimentary rocks from the upper Shenshan subgroup and Banxi Group.The geochemical results indicate that they were formed through the recycling deposition of intermediate-acidic igneous source material and experienced moderate chemical weathering.Additionally,both sedimentary sequences exhibit characteristics consistent with those formed in an intracontinental extensional rift setting since ca.810 Ma.The provenance analysis indicates that the upper Shenshan subgroup primarily originates from the Yangtze Domain,while the Banxi Group from both the Yangtze and Cathaysia domains.Synthesizing with previous studies,the Shenshan Group should be subdivided into the lower and upper subgroups which represent distinct tectonic backgrounds.The lower subgroup is inferred to have formed in an Early Neoproterozoic fore-arc setting,akin to the Zhoutan group.The upper subgroup corresponds to the Banxi Group,representing the Middle Neoproterozoic postorogenic rift setting,responding to the breakup of Rodinia.展开更多
Diamonds were formed in the mantle lithosphere,mostly at depths of 150~200km in the centres of Precambrian cratons,the buoyant ancient cores of continents.From there they were normally transported into the upper crust...Diamonds were formed in the mantle lithosphere,mostly at depths of 150~200km in the centres of Precambrian cratons,the buoyant ancient cores of continents.From there they were normally transported into the upper crust in kimberlite pipes whose diamonds are largely colourless and light yellow related to trace element N(Ia type),although brown,green,and more rarely blue-coloured diamonds are related to lattice defect and trace amounts of H,more rarely B and Ni.Pink diamonds are extremely rare in the approximately 90 diamondiferous pipes mined globally.Although small quantities have been discovered elsewhere,about 90%have been mined from the ca.1.3Ga Argyle diamond pipe in Western Australia,with the Arkhangelskaya diamond pipe in Russia the only other significant source.The pink colour at both Argyle and Arkhangelskaya is unrelated to trace elements and instead results from absorption of light from nanoscale(550nm)defects related to shear stress and plastic deformation.Macroscopically,defects are shown by glide planes,lamellae,and grain lines imposed on the originally colourless diamonds derived from their mantle source.The key question is why these defects were uniquely acquired in diamonds in the Argyle and Arkhangelskaya pipes.Unlike most diamondiferous pipes,Argyle is a rare diamondiferous volatile-rich lamproite pipe that was emplaced into the multiply deformed and rifted NNE-trending Halls Creek Orogen on the margin of the Kimberley Craton.Similarly,Arkhangelskaya in the Devonian Lomonosov kimberlite cluster is a volatile-rich low-Ti type kimberlite,a close relative to lamproite,that was emplaced into the multiply deformed Lapland-Kola Orogen on the rifted margin of the Kola Craton.These craton margins are underlain by subduction-induced volatile-enriched metasomatized mantle lithosphere in contrast to the more primeval mantle under craton centres.It is thus likely that shear stresses were exacerbated at Argyle and Arkangelskaya by rapid vertical emplacement of the anomalous volatile-enriched magmas at supercritical pressures and temperatures,that induced catastrophic phase separation of these volatiles and'mini seismic events'during rapid pressure drops during ascent from 200km depth to the surface.Such a mechanism is consistent with the presence of strongly resorbed and plastically deformed small brown industrial diamonds in the Argyle pipe.From a China perspective,it is potentially important that at 1.3Ga the alkaline Argyle pipe in northern Australia is placed adjacent to the North China Craton(NCC),with numerous world-class mineral deposits including the giant ca.1.4~1.2Ga alkaline Bayan Obo REE system on its margin.However,it is the southeastern margin of the Yangtze Craton and the Jiangnan Orogen with their lamproite pipes derived from metasomatized mantle lithosphere that present the most prospective regions for pink diamond occurrences.展开更多
With the efficient and intelligent development of computer-based big data processing,applying machine learning methods to the processing and interpretation of logging data in the field of geophysical well logging has ...With the efficient and intelligent development of computer-based big data processing,applying machine learning methods to the processing and interpretation of logging data in the field of geophysical well logging has broad potential for improving production efficiency.Currently,the Jiyuan Oilfield in the Ordos Basin relies mainly on manual reprocessing and interpretation of old well logging data to identify different fluid types in low-contrast reservoirs,guiding subsequent production work.This study uses well logging data from the Chang 1 reservoir,partitioning the dataset based on individual wells for model training and testing.A deep learning model for intelligent reservoir fluid identification was constructed by incorporating the focal loss function.Comparative validations with five other models,including logistic regression(LR),naive Bayes(NB),gradient boosting decision trees(GBDT),random forest(RF),and support vector machine(SVM),show that this model demonstrates superior identification performance and significantly improves the accuracy of identifying oil-bearing fluids.Mutual information analysis reveals the model's differential dependency on various logging parameters for reservoir fluid identification.This model provides important references and a basis for conducting regional studies and revisiting old wells,demonstrating practical value that can be widely applied.展开更多
Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering acti...Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering activities exacerbated reactivation,and the interpretability of data-driven models have hindered the practical application of LSM.This work proposes a novel framework for enhancing LSM considering different triggers for accumulation and rock landslides,leveraging interpretable machine learning and Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR)technology.Initially,a refined fieldinvestigation was conducted to delineate the accumulation and rock area according to landslide types,leading to the identificationof relevant contributing factors.Deformation along the slope was then combined with time-series analysis to derive a landslide activity level(AL)index to recognize the likelihood of reactivation or dormancy.The SHapley Additive exPlanation(SHAP)technique facilitated the interpretation of factors and the identificationof determinants in high susceptibility areas.The results indicate that random forest(RF)outperformed other models in both accumulation and rock areas.Key factors including thickness and weak intercalation were identifiedfor accumulation and rock landslides.The introduction of AL substantially enhanced the predictive capability of the LSM and outperformed models that neglect movement trends or deformation rates with an average ratio of 81.23%in high susceptibility zones.Besides,the fieldvalidation confirmedthat 83.8%of newly identifiedlandslides were correctly upgraded.Given its efficiencyand operational simplicity,the proposed hybrid model opens new avenues for the feasibility of enhancement in LSM at urban settlements worldwide.展开更多
Global warming induced by increased CO_(2) has caused marked changes in the ocean.Previous estimates of ocean salinity change in response to global warming have considerable ambiguity,largely attributable to the diver...Global warming induced by increased CO_(2) has caused marked changes in the ocean.Previous estimates of ocean salinity change in response to global warming have considerable ambiguity,largely attributable to the diverse sensitivities of surface fluxes.This study utilizes data from the Flux-Anomaly-Forced Model Intercomparison Project to investigate how ocean salinity responds to perturbations of surface fluxes.The findings indicate the emergence of a sea surface salinity(SSS)dipole pattern predominantly in the North Atlantic and Pacific fresh pools,driven by surface flux perturbations.This results in an intensification of the“salty gets saltier and fresh gets fresher”SSS pattern across the global ocean.The spatial pattern amplification(PA)of SSS under global warming is estimated to be approximately 11.5%,with surface water flux perturbations being the most significant contributor to salinity PA,accounting for 8.1% of the change after 70 years in experiments since pre-industrial control(piControl).Notably,the zonal-depth distribution of salinity in the upper ocean exhibits lighter seawater above the denser water,with bowed isopycnals in the upper 400 m.This stable stratification inhibits vertical mixing of salinity and temperature.In response to the flux perturbations,there is a strong positive feedback due to consequent freshening.It is hypothesized that under global warming,an SSS amplification of 7.2%/℃ and a mixed-layer depth amplification of 12.5%/℃ will occur in the global ocean.It suggests that the salinity effect can exert a more stable ocean to hinder the downward transfer of heat,which provides positive feedback to future global warming.展开更多
Concentration of elements or element groups in a geological body is the result of multiple stages of rockforming and ore-forming geological processes.An ore-forming element group can be identified by PCA(principal com...Concentration of elements or element groups in a geological body is the result of multiple stages of rockforming and ore-forming geological processes.An ore-forming element group can be identified by PCA(principal component analysis)and be separated into two components using BEMD(bi-dimensional empirical mode decomposition):(1)a high background component which represents the ore-forming background developed in rocks through various geological processes favorable for mineralization(i.e.magmatism,sedimentation and/or metamorphism);(2)the anomaly component which reflects the oreforming anomaly that is overprinted on the high background component developed during mineralization.Anomaly components are used to identify ore-finding targets more effectively than ore-forming element groups.Three steps of data analytical procedures are described in this paper;firstly,the application of PCA to establish the ore-forming element group;secondly,using BEMD on the o re-forming element group to identify the anomaly components created by different types of mineralization processes;and finally,identifying ore-finding targets based on the anomaly components.This method is applied to the Tengchong tin-polymetallic belt to delineate ore-finding targets,where four targets for Sn(W)and three targets for Pb-Zn-Ag-Fe polymetallic mineralization are identified and defined as new areas for further prospecting.It is shown that BEMD combined with PCA can be applied not only in extracting the anomaly component for delineating the ore-finding target,but also in extracting the residual component for identifying its high background zone favorable for mineralization from its oreforming element group.展开更多
This study developed a modeling methodology for statistical optimization-based geologic hazard susceptibility assessment,aiming to enhance the comprehensive performance and classification accuracy of the assessment mo...This study developed a modeling methodology for statistical optimization-based geologic hazard susceptibility assessment,aiming to enhance the comprehensive performance and classification accuracy of the assessment models.First,the cumulative probability method revealed that a low probability(15%)of geologic hazards between any two geologic hazard points occurred outside a buffer zone with a radius of 2297 m(i.e.,the distance threshold).The training dataset was established,consisting of negative samples(non-hazard points)randomly generated based on the distance threshold,positive samples(i.e.,historical hazards),and 13 conditioning factors.Then,models were built using five machine learning algorithms,namely random forest(RF),gradient boosting decision tree(GBDT),naive Bayes(NB),logistic regression(LR),and support vector machine(SVM).The comprehensive performance of the models was assessed using the area under the receiver operating characteristic curve(AUC)and overall accuracy(OA)as indicators,revealing that RF exhibited the best performance,with OA and AUC values of 2.7127 and 0.981,respectively.Furthermore,the machine learning models constructed by considering the distance threshold outperformed those built using the unoptimized dataset.The characteristic factors were ranked using the mutual information method,with their scores decreasing in the order of rainfall(0.1616),altitude(0.06),normalized difference vegetation index(NDVI;0.04),and distance from roads(0.03).Finally,the geologic hazard susceptibility classification was assessed using the natural breaks method combined with a clustering algorithm.The results indicate that the clustering algorithm exhibited higher classification accuracy than the natural breaks method.The findings of this study demonstrate that the proposed model optimization scheme can provide a scientific basis for the prevention and control of geologic hazards.展开更多
Hydraulic stimulation technology is widely employed to enhance the permeability of geothermal reservoirs.Nevertheless,accurately predicting hydraulic fracture propagation in complex geological conditions remains chall...Hydraulic stimulation technology is widely employed to enhance the permeability of geothermal reservoirs.Nevertheless,accurately predicting hydraulic fracture propagation in complex geological conditions remains challenging,thereby hindering the effective utilization of existing natural fractures.In this study,a phase field model was developed utilizing the finite element method to examine the influence of fluid presence,stress conditions,and natural fractures on the initiation and propagation of hydraulic fractures.The model employs Biot's poroelasticity theory to establish the coupling between the displacement field and the fluid field,while the phase field theory is applied to simulate fracture behavior.The results show that whenσ_(x0)/σ_(y0)<3 or qf<20 kg/(m^(3)·s),the presence of natural fractures can alter the original propagation direction of hydraulic fractures.Conversely,in the absence of these conditions,the propagation path of natural fractures is predominantly influenced by the initial stress field.Furthermore,based on the analysis of breakdown pressure and damage area,the optimal intersection angle between natural fractures and hydraulic fractures is determined to range from 45°to 60°.Finally,once a dominant channel forms,initiating and propagating hydraulic fractures in other directions becomes increasingly difficult,even in highly fractured areas.This method tackles the challenges of initiating and propagating hydraulic fractures in complex geological conditions,providing a theoretical basis for optimizing Enhanced Geothermal System(EGS)projects.展开更多
In 2025,the global rare earth exploration and development sector achieved breakthroughs across multiple fronts.Projects advanced intensively across the Americas,Oceania,Africa,and Europe,with significant growth in res...In 2025,the global rare earth exploration and development sector achieved breakthroughs across multiple fronts.Projects advanced intensively across the Americas,Oceania,Africa,and Europe,with significant growth in resources,continuous emergence of new deposits,and strong impetus injected into the industry by technological innovation and policy support.The global rare earth resource supply pattern was further optimized (Table 1).1.Fruitful results in resource growth and new deposit discoveriesBrazil emerged as a core region for resource growth.The Colossus rare earth deposit saw a 150%increase in resources and announced its first reserve estimate.The Caldeira rare earth deposit’s resource estimate grew by 50%.The combined ore resources in the Caladão rare earth deposit’s Zones A and B reached 5.72×10~8 tonnes,with a total rare earth oxide(TREO) grade of 0.1506%,concurrently hosting 2.29×10~4tonnes of gallium metal resources.展开更多
Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives ...Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives of billions who depend on or are affected by monsoons, as it is essential for the water cycle, food security, ecology, disaster prevention, and the economy of monsoon regions. Given the extensive literature on Asian monsoon climate prediction, we limit our focus to reviewing the seasonal prediction and predictability of the Asian Summer Monsoon (ASM). However, much of this review is also relevant to monsoon predictions in other seasons and regions. Over the past two decades, considerable progress has been made in the seasonal forecasting of the ASM, driven by an enhanced understanding of the sources of predictability and the dynamics of seasonal variability, along with advanced development in sophisticated models and technologies. This review centers on advances in understanding the physical foundation for monsoon climate prediction (section 2), significant findings and insights into the primary and regional sources of predictability arising from feedback processes among various climate components (sections 3 and 4), the effects of global warming and external forcings on predictability (section 5), developments in seasonal prediction models and techniques (section 6), the challenges and limitations of monsoon climate prediction (section 7), and emerging research trends with suggestions for future directions (section 8). We hope this review will stimulate creative activities to enhance monsoon climate prediction.展开更多
Climate models are essential for understanding past,present,and future changes in atmospheric circulation,with circulation modes providing key sources of seasonal predictability and prediction uncertainties for both g...Climate models are essential for understanding past,present,and future changes in atmospheric circulation,with circulation modes providing key sources of seasonal predictability and prediction uncertainties for both global and regional climates.This study assesses the performance of models participating in phase 6 of the Coupled Model Intercomparison Project in simulating interannual variability modes of Northern Hemisphere 500-hPa geopotential height during winter and summer,distinguishing predictable(potentially predictable on seasonal or longer timescales)and unpredictable(intraseasonal and essentially unpredictable at long range)components,using reanalysis data and a variance decomposition method.Although most models effectively capture unpredictable modes in reanalysis,their ability to reproduce dominant predictable modes-specifically the Pacific-North American pattern,Arctic Oscillation,and Western Pacific Oscillation in winter,and the East Atlantic and North Atlantic Oscillations in summer-varies notably.An optimal ensemble is identified to distinguish(a)predictable-external modes,dominated by external forcing,and(b)predictable-internal modes,associated with slow internal variability,during the historical period(1950-2014)and the SSP5-8.5 scenario(2036-2100).Under increased radiative forcing,the leading winter/summer predictable-external mode exhibits a more uniform spatial distribution,remarkably larger trend and annual variance,and enhanced height-sea surface temperature(SST)covariance under SSP5-8.5 compared to historical conditions.The dominant winter/summer predictable-internal modes also exhibit increased variance and height-SST covariance under SSP5-8.5,along with localized changes in spatial configuration.Minimal changes are observed in spatial distribution or variance for dominant winter/summer unpredictable modes under SSP5-8.5.This study,from a predictive perspective,deepens our understanding of model uncertainties and projected changes in circulations.展开更多
This study investigates the relationship between atmospheric stratification (i.e., static stability given by N^(2)) and the vertical energy transfer of stationary planetary waves, and further illustrates the underlyin...This study investigates the relationship between atmospheric stratification (i.e., static stability given by N^(2)) and the vertical energy transfer of stationary planetary waves, and further illustrates the underlying physical mechanism. Specifically, for the simplified case of constant stratospheric N^(2), the refractive index square of planetary waves has a theoretical tendency to increase first and then decrease with an increased N^(2), whereas the group velocity weakens. Mechanistically, this behavior can be understood as an intensified suppression of vertical isentropic surface displacement caused by meridional heat transport of planetary waves under strong N^(2) conditions. Observational analysis corroborates this finding, demonstrating a reduction in the vertical-propagation velocity of waves with increased N^(2). A linear, quasi- geostrophic, mid-latitude beta-plane model with a constant background westerly wind and a prescribed N^(2) applicable to the stratosphere is used to obtain analytic solutions. In this model, the planetary waves are initiated by steady energy influx from the lower boundary. The analysis indicates that under strong N^(2) conditions, the amplitude of planetary waves can be sufficiently increased by the effective energy convergence due to the slowing vertical energy transfer, resulting in a streamfunction response in this model that contains more energy. For N^(2) with a quasi-linear vertical variation, the results bear a resemblance to the constant case, except that the wave amplitude and oscillating frequency show some vertical variations.展开更多
A reasonable classification of deposits holds great significance for identifying prospecting targets and deploying exploration. The world ’s keen demand for lithium resources has expedited the discovery of numerous n...A reasonable classification of deposits holds great significance for identifying prospecting targets and deploying exploration. The world ’s keen demand for lithium resources has expedited the discovery of numerous novel lithium resources. Given the presence of varied classification criteria for lithium resources presently, this study further ascertained and classified the lithium resources according to their occurrence modes, obtaining 10 types and 5 subtypes of lithium deposits(resources) based on endogenetic and exogenetic factors. As indicated by surveys of Cenozoic exogenetic lithium deposits in China and abroad,the formation and distribution of the deposits are primarily determined by plate collision zones, their primary material sources are linked to the anatectic magmas in the deep oceanic crust, and they were formed primarily during the Miocene and Late Paleogene. The researchers ascertained that these deposits,especially those of the salt lake, geothermal, and volcanic deposit types, are formed by unique slightly acidic magmas, tend to migrate and accumulate toward low-lying areas, and display supernormal enrichment. However, the material sources of lithium deposits(resources) of the Neopaleozoic clay subtype and the deep brine type are yet to be further identified. Given the various types and complex origins of lithium deposits(resources), which were formed due to the interactions of multiple spheres, it is recommended that the mineralization of exogenetic lithium deposits(resources) be investigated by integrating tectono-geochemistry, paleoatmospheric circulation, and salinology. So far, industrialized lithium extraction is primarily achieved in lithium deposits of the salt lake, clay, and hard rock types. The lithium extraction employs different processes, with lithium extraction from salt lake-type lithium deposits proving the most energy-saving and cost-effective.展开更多
This study investigates the distinct impacts of eastern Pacific(EP)and central Pacific(CP)El Niño events on winter shortwave solar radiation(SSR)in southern China,revealing different spatial distributions and und...This study investigates the distinct impacts of eastern Pacific(EP)and central Pacific(CP)El Niño events on winter shortwave solar radiation(SSR)in southern China,revealing different spatial distributions and underlying mechanisms.The results show that,during the developing winter of EP El Niño,significant SSR reductions occur in southwestern China and the east coast of southern China due to a strong,zonally extended Northwest Pacific anticyclone that transports moisture from the tropical Northwest Pacific and North Indian Ocean,while the northeast of southern China experiences a weak increase in SSR.In contrast,during the developing winter of CP El Niño,SSR decreases in the east of southern China with a significant decrease in the lower basin of the Yangtze River but an increase in the west of southern China with a remarkable increase in eastern Yunnan.The pronounced east-west dipole pattern in SSR anomalies is driven by a meridionally elongated Northwest Pacific anticyclone,which enhances northward moisture transport to the east of southern China while leaving western areas drier.Further research reveals that distinct moisture anomalies during the developing winter of EP and CP events result in divergent SSR distributions across southern China,primarily through modulating the total cloud cover.These findings highlight the critical need to differentiate between El Niño types when predicting medium and long-term variability of radiation in southern China.展开更多
China’s coastal regions,characterized by dense populations and industrial agglomeration,face escalating threats from typhoon disasters.Understanding the evolution of socio-economic exposure to future typhoon landfall...China’s coastal regions,characterized by dense populations and industrial agglomeration,face escalating threats from typhoon disasters.Understanding the evolution of socio-economic exposure to future typhoon landfalls under global change is critical for effective disaster risk management.This study utilizes future typhoon track data simulated by the regional climate model version 4(RegCM4),combined with projected population and Gross Domestic Product(GDP)data for China’s coastal regions under three Shared Socioeconomic Pathways(SSPs:SSP1,sustainability;SSP2,middle of the road;SSP5,fossil-fueled development).We analyze typhoon impact zones for future periods(2030s,2050s,and 2090s)under Representative Concentration Pathways(RCPs:RCP2.6,RCP4.5,and RCP8.5,representing low,medium,and high greenhouse gas emission scenarios,respectively).Exposure levels for 2030,2050,and 2100 are calculated based on the annual average frequency of typhoon impacts during these periods,aiming to quantify the distribution characteristics of typhoon-affected population and GDP in China’s coastal regions.Key findings reveal two high-frequency typhoon im-pact zones:the Taiwan Strait region and the northern Qiongzhou Strait region.Furthermore,under RCP2.6,typhoon impacts may ex-tend to Liaoning,while RCP4.5 and RCP8.5 scenarios indicate potential impacts reaching further north to Heilongjiang.Under RCP4.5,Northeast China will experience the largest typhoon-affected area(43.800×10^(4) km^(2))by the 2030s.High population and GDP exposure concentrates in the Yangtze River Delta,the Pearl River Delta,and the Taiwan Strait coastal areas.Notably,Liaoning’s cumulative ex-posed population may exceed 0.100×10^(8) by 2030 and 2050 under RCP4.5 and RCP8.5 due to typhoon track migration.Given China’s location within the Western Pacific typhoon high-incidence region,the northward expansion of impacts will substantially escalate socio-economic exposure in mid-latitude regions previously at lower risk.These findings underscore the imperative for enhanced disaster pre-vention,mitigation strategies and targeted countermeasure research.展开更多
Accurate extraction of surface water extent is a fundamental prerequisite for monitoring its dynamic changes.Although machine learning algorithms have been widely applied to surface water mapping,most studies focus pr...Accurate extraction of surface water extent is a fundamental prerequisite for monitoring its dynamic changes.Although machine learning algorithms have been widely applied to surface water mapping,most studies focus primarily on algorithmic outputs,with limited systematic evaluation of their applicability and constrained classification accuracy.In this study,we focused on the Songnen Plain in Northeast China and employed Sentinel-2 imagery acquired during 2020-2021 via the Google Earth Engine(GEE)platform to evaluate the performance of Classification and Regression Trees(CART),Random Forest(RF),and Support Vector Machine(SVM)for surface water classification.The classification process was optimized by incorporating automated training sample selection and integration of time series features.Validation with independent samples demonstrated the feasibility of automatic sample selection,yielding mean overall accuracies of 91.16%,90.99%,and 90.76%for RF,SVM,and CART,respectively.After integrating time series features,the mean overall accuracies of the three algorithms improved by 4.51%,5.45%,and 6.36%,respectively.In addition,spectral features such as MNDWI(Modified Normalized Difference Water Index),SWIR(Short Wave Infrared),and NDVI(Normalized Difference Vegetation Index)were identified as more important for surface water classification.This study establishes a more consistent framework for surface water mapping,offering new perspectives for improving and automating classification processes in the era of big and open data.展开更多
基金supported by the National Key Research and Development Program of China (2021YFB1507401)Qinghai Province Clean Energy Minerals Special Project(2022013004qj004)Geological Survey Project of China Geological Survey (DD20221676, DD20230019)。
文摘The available heat content (stored heat energy) of hot dry rock (HDR) at a depth of 1–10 km in the global land crust is estimated to be 5.06×10~8 EJ,attracting considerable global attention.This paper presents a comprehensive analysis of the geological framework,HDR resource potential,exploration advancements,and the development of enhanced geothermal systems (EGSs) in China.HDR resources are extensively distributed across China.Within the depth range of 3–10 km,China’s estimated potential approximates2.29×10~7 EJ,with a theoretical power generation capacity of approximately 1.67×10^(16) k Wh.Replacing coal power with HDR can help to achieve a net emission reduction of 1.34×10^(16) kg CO_(2) (approximately1.34×10^(13) t),representing an emission reduction efficiency of 94.4%.Based on a development cycle of100 years,the average annual emission reduction reaches 1.34×10^(10) t CO_(2),equivalent to 117%of China’s annual carbon emissions in 2022.Furthermore,in the context of global warming,the development and utilization of HDR,which is feasible in virtually any region worldwide,offers significant potential to support global carbon reduction efforts.China has made substantial progress in HDR exploration in recent years.This paper systematically classifies China’s HDR resources into four genetic types—highly radioactive heat-producing,sedimentary basin,active volcanic,and intensely tectonic zones—and offers detailed exploration insights for each category.Each classification exhibits distinct geological and tectonic characteristics that influence heat source mechanisms and resource distribution.Furthermore,this paper documents significant advances in EGS construction,particularly in the Gonghe Basin on the northeastern margin of the Qianghai-Xizang Plateau and the Matouying uplift in the North China Basin,where successful reservoir stimulation,microseismic monitoring,and experimental power generation have been achieved.Despite these developments,challenges persist,including technical adaptability under complex geological conditions and the economic viability of large-scale HDR development.This paper suggests that future initiatives should emphasize resource exploration,technological research,and policy support to foster sustainable HDR resource development in China,thereby contributing to the global energy transition and environmental sustainability.
基金supported by the National Natural Science Foundation of China(42372250,42102262 and 41972235)National Key Research and Development Program Project(2023YFF0803701)+1 种基金Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(SML2023SP206)the program of China Scholarship Council。
文摘The Shenshan Group provides important geological information which is vital in unraveling the amalgamation and subsequent rifting processes of South China.While conventional studies have asserted its formation in a subduction setting,the distinct investigation reveals the necessity for reassessment.To address this,the authors employ integrated methods encompassing petrological,zircon U-Pb geochronological,Lu-Hf isotopic and geochemical methods for sedimentary rocks from the upper Shenshan subgroup and Banxi Group.The geochemical results indicate that they were formed through the recycling deposition of intermediate-acidic igneous source material and experienced moderate chemical weathering.Additionally,both sedimentary sequences exhibit characteristics consistent with those formed in an intracontinental extensional rift setting since ca.810 Ma.The provenance analysis indicates that the upper Shenshan subgroup primarily originates from the Yangtze Domain,while the Banxi Group from both the Yangtze and Cathaysia domains.Synthesizing with previous studies,the Shenshan Group should be subdivided into the lower and upper subgroups which represent distinct tectonic backgrounds.The lower subgroup is inferred to have formed in an Early Neoproterozoic fore-arc setting,akin to the Zhoutan group.The upper subgroup corresponds to the Banxi Group,representing the Middle Neoproterozoic postorogenic rift setting,responding to the breakup of Rodinia.
文摘Diamonds were formed in the mantle lithosphere,mostly at depths of 150~200km in the centres of Precambrian cratons,the buoyant ancient cores of continents.From there they were normally transported into the upper crust in kimberlite pipes whose diamonds are largely colourless and light yellow related to trace element N(Ia type),although brown,green,and more rarely blue-coloured diamonds are related to lattice defect and trace amounts of H,more rarely B and Ni.Pink diamonds are extremely rare in the approximately 90 diamondiferous pipes mined globally.Although small quantities have been discovered elsewhere,about 90%have been mined from the ca.1.3Ga Argyle diamond pipe in Western Australia,with the Arkhangelskaya diamond pipe in Russia the only other significant source.The pink colour at both Argyle and Arkhangelskaya is unrelated to trace elements and instead results from absorption of light from nanoscale(550nm)defects related to shear stress and plastic deformation.Macroscopically,defects are shown by glide planes,lamellae,and grain lines imposed on the originally colourless diamonds derived from their mantle source.The key question is why these defects were uniquely acquired in diamonds in the Argyle and Arkhangelskaya pipes.Unlike most diamondiferous pipes,Argyle is a rare diamondiferous volatile-rich lamproite pipe that was emplaced into the multiply deformed and rifted NNE-trending Halls Creek Orogen on the margin of the Kimberley Craton.Similarly,Arkhangelskaya in the Devonian Lomonosov kimberlite cluster is a volatile-rich low-Ti type kimberlite,a close relative to lamproite,that was emplaced into the multiply deformed Lapland-Kola Orogen on the rifted margin of the Kola Craton.These craton margins are underlain by subduction-induced volatile-enriched metasomatized mantle lithosphere in contrast to the more primeval mantle under craton centres.It is thus likely that shear stresses were exacerbated at Argyle and Arkangelskaya by rapid vertical emplacement of the anomalous volatile-enriched magmas at supercritical pressures and temperatures,that induced catastrophic phase separation of these volatiles and'mini seismic events'during rapid pressure drops during ascent from 200km depth to the surface.Such a mechanism is consistent with the presence of strongly resorbed and plastically deformed small brown industrial diamonds in the Argyle pipe.From a China perspective,it is potentially important that at 1.3Ga the alkaline Argyle pipe in northern Australia is placed adjacent to the North China Craton(NCC),with numerous world-class mineral deposits including the giant ca.1.4~1.2Ga alkaline Bayan Obo REE system on its margin.However,it is the southeastern margin of the Yangtze Craton and the Jiangnan Orogen with their lamproite pipes derived from metasomatized mantle lithosphere that present the most prospective regions for pink diamond occurrences.
基金supported by a project of the Shaanxi Youth Science and Technology Star(2021KJXX-87)public welfare geological survey projects of Shaanxi Institute of Geologic Survey(20180301,201918 and 202103)。
文摘With the efficient and intelligent development of computer-based big data processing,applying machine learning methods to the processing and interpretation of logging data in the field of geophysical well logging has broad potential for improving production efficiency.Currently,the Jiyuan Oilfield in the Ordos Basin relies mainly on manual reprocessing and interpretation of old well logging data to identify different fluid types in low-contrast reservoirs,guiding subsequent production work.This study uses well logging data from the Chang 1 reservoir,partitioning the dataset based on individual wells for model training and testing.A deep learning model for intelligent reservoir fluid identification was constructed by incorporating the focal loss function.Comparative validations with five other models,including logistic regression(LR),naive Bayes(NB),gradient boosting decision trees(GBDT),random forest(RF),and support vector machine(SVM),show that this model demonstrates superior identification performance and significantly improves the accuracy of identifying oil-bearing fluids.Mutual information analysis reveals the model's differential dependency on various logging parameters for reservoir fluid identification.This model provides important references and a basis for conducting regional studies and revisiting old wells,demonstrating practical value that can be widely applied.
基金supported by the National Key R&D Program of China(Grant No.2023YFC3007201)the National Natural Science Foundation of China(Grant No.42377161)the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(Grant No.GLAB 2024ZR03).
文摘Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering activities exacerbated reactivation,and the interpretability of data-driven models have hindered the practical application of LSM.This work proposes a novel framework for enhancing LSM considering different triggers for accumulation and rock landslides,leveraging interpretable machine learning and Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR)technology.Initially,a refined fieldinvestigation was conducted to delineate the accumulation and rock area according to landslide types,leading to the identificationof relevant contributing factors.Deformation along the slope was then combined with time-series analysis to derive a landslide activity level(AL)index to recognize the likelihood of reactivation or dormancy.The SHapley Additive exPlanation(SHAP)technique facilitated the interpretation of factors and the identificationof determinants in high susceptibility areas.The results indicate that random forest(RF)outperformed other models in both accumulation and rock areas.Key factors including thickness and weak intercalation were identifiedfor accumulation and rock landslides.The introduction of AL substantially enhanced the predictive capability of the LSM and outperformed models that neglect movement trends or deformation rates with an average ratio of 81.23%in high susceptibility zones.Besides,the fieldvalidation confirmedthat 83.8%of newly identifiedlandslides were correctly upgraded.Given its efficiencyand operational simplicity,the proposed hybrid model opens new avenues for the feasibility of enhancement in LSM at urban settlements worldwide.
基金supported by the Laoshan Laboratory[grant number LSKJ202202403]the National Natural Science Foundation of China[grant number 42030410]+1 种基金additionally supported by the Startup Foundation for Introducing Talent of NUISTJiangsu Innovation Research Group[grant number JSSCTD202346]。
文摘Global warming induced by increased CO_(2) has caused marked changes in the ocean.Previous estimates of ocean salinity change in response to global warming have considerable ambiguity,largely attributable to the diverse sensitivities of surface fluxes.This study utilizes data from the Flux-Anomaly-Forced Model Intercomparison Project to investigate how ocean salinity responds to perturbations of surface fluxes.The findings indicate the emergence of a sea surface salinity(SSS)dipole pattern predominantly in the North Atlantic and Pacific fresh pools,driven by surface flux perturbations.This results in an intensification of the“salty gets saltier and fresh gets fresher”SSS pattern across the global ocean.The spatial pattern amplification(PA)of SSS under global warming is estimated to be approximately 11.5%,with surface water flux perturbations being the most significant contributor to salinity PA,accounting for 8.1% of the change after 70 years in experiments since pre-industrial control(piControl).Notably,the zonal-depth distribution of salinity in the upper ocean exhibits lighter seawater above the denser water,with bowed isopycnals in the upper 400 m.This stable stratification inhibits vertical mixing of salinity and temperature.In response to the flux perturbations,there is a strong positive feedback due to consequent freshening.It is hypothesized that under global warming,an SSS amplification of 7.2%/℃ and a mixed-layer depth amplification of 12.5%/℃ will occur in the global ocean.It suggests that the salinity effect can exert a more stable ocean to hinder the downward transfer of heat,which provides positive feedback to future global warming.
基金funded by the Na-tional Natural Science Foundation of China(Grant Nos.41672329,41272365)the National Key Research and Development Project of China(Grant No.2016YFC0600509)the Project of China Geological Survey(Grant No.1212011120341)
文摘Concentration of elements or element groups in a geological body is the result of multiple stages of rockforming and ore-forming geological processes.An ore-forming element group can be identified by PCA(principal component analysis)and be separated into two components using BEMD(bi-dimensional empirical mode decomposition):(1)a high background component which represents the ore-forming background developed in rocks through various geological processes favorable for mineralization(i.e.magmatism,sedimentation and/or metamorphism);(2)the anomaly component which reflects the oreforming anomaly that is overprinted on the high background component developed during mineralization.Anomaly components are used to identify ore-finding targets more effectively than ore-forming element groups.Three steps of data analytical procedures are described in this paper;firstly,the application of PCA to establish the ore-forming element group;secondly,using BEMD on the o re-forming element group to identify the anomaly components created by different types of mineralization processes;and finally,identifying ore-finding targets based on the anomaly components.This method is applied to the Tengchong tin-polymetallic belt to delineate ore-finding targets,where four targets for Sn(W)and three targets for Pb-Zn-Ag-Fe polymetallic mineralization are identified and defined as new areas for further prospecting.It is shown that BEMD combined with PCA can be applied not only in extracting the anomaly component for delineating the ore-finding target,but also in extracting the residual component for identifying its high background zone favorable for mineralization from its oreforming element group.
基金supported by a project entitled Loess Plateau Region-Watershed-Slope Geological Hazard Multi-Scale Collaborative Intelligent Early Warning System of the National Key R&D Program of China(2022YFC3003404)a project of the Shaanxi Youth Science and Technology Star(2021KJXX-87)public welfare geological survey projects of Shaanxi Institute of Geologic Survey(20180301,201918,202103,and 202413)。
文摘This study developed a modeling methodology for statistical optimization-based geologic hazard susceptibility assessment,aiming to enhance the comprehensive performance and classification accuracy of the assessment models.First,the cumulative probability method revealed that a low probability(15%)of geologic hazards between any two geologic hazard points occurred outside a buffer zone with a radius of 2297 m(i.e.,the distance threshold).The training dataset was established,consisting of negative samples(non-hazard points)randomly generated based on the distance threshold,positive samples(i.e.,historical hazards),and 13 conditioning factors.Then,models were built using five machine learning algorithms,namely random forest(RF),gradient boosting decision tree(GBDT),naive Bayes(NB),logistic regression(LR),and support vector machine(SVM).The comprehensive performance of the models was assessed using the area under the receiver operating characteristic curve(AUC)and overall accuracy(OA)as indicators,revealing that RF exhibited the best performance,with OA and AUC values of 2.7127 and 0.981,respectively.Furthermore,the machine learning models constructed by considering the distance threshold outperformed those built using the unoptimized dataset.The characteristic factors were ranked using the mutual information method,with their scores decreasing in the order of rainfall(0.1616),altitude(0.06),normalized difference vegetation index(NDVI;0.04),and distance from roads(0.03).Finally,the geologic hazard susceptibility classification was assessed using the natural breaks method combined with a clustering algorithm.The results indicate that the clustering algorithm exhibited higher classification accuracy than the natural breaks method.The findings of this study demonstrate that the proposed model optimization scheme can provide a scientific basis for the prevention and control of geologic hazards.
基金supported by the National Key Research and Development Program(2021YFB150740401)National Natural Science Foundation of China(42202336)the CAS Pioneer Hundred Talents Program in China(Y826031C01)。
文摘Hydraulic stimulation technology is widely employed to enhance the permeability of geothermal reservoirs.Nevertheless,accurately predicting hydraulic fracture propagation in complex geological conditions remains challenging,thereby hindering the effective utilization of existing natural fractures.In this study,a phase field model was developed utilizing the finite element method to examine the influence of fluid presence,stress conditions,and natural fractures on the initiation and propagation of hydraulic fractures.The model employs Biot's poroelasticity theory to establish the coupling between the displacement field and the fluid field,while the phase field theory is applied to simulate fracture behavior.The results show that whenσ_(x0)/σ_(y0)<3 or qf<20 kg/(m^(3)·s),the presence of natural fractures can alter the original propagation direction of hydraulic fractures.Conversely,in the absence of these conditions,the propagation path of natural fractures is predominantly influenced by the initial stress field.Furthermore,based on the analysis of breakdown pressure and damage area,the optimal intersection angle between natural fractures and hydraulic fractures is determined to range from 45°to 60°.Finally,once a dominant channel forms,initiating and propagating hydraulic fractures in other directions becomes increasingly difficult,even in highly fractured areas.This method tackles the challenges of initiating and propagating hydraulic fractures in complex geological conditions,providing a theoretical basis for optimizing Enhanced Geothermal System(EGS)projects.
文摘In 2025,the global rare earth exploration and development sector achieved breakthroughs across multiple fronts.Projects advanced intensively across the Americas,Oceania,Africa,and Europe,with significant growth in resources,continuous emergence of new deposits,and strong impetus injected into the industry by technological innovation and policy support.The global rare earth resource supply pattern was further optimized (Table 1).1.Fruitful results in resource growth and new deposit discoveriesBrazil emerged as a core region for resource growth.The Colossus rare earth deposit saw a 150%increase in resources and announced its first reserve estimate.The Caldeira rare earth deposit’s resource estimate grew by 50%.The combined ore resources in the Caladão rare earth deposit’s Zones A and B reached 5.72×10~8 tonnes,with a total rare earth oxide(TREO) grade of 0.1506%,concurrently hosting 2.29×10~4tonnes of gallium metal resources.
基金supported by the National Natural Science Foundation of China(Grant No.U2342208)support from NSF/Climate Dynamics Award#2025057。
文摘Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives of billions who depend on or are affected by monsoons, as it is essential for the water cycle, food security, ecology, disaster prevention, and the economy of monsoon regions. Given the extensive literature on Asian monsoon climate prediction, we limit our focus to reviewing the seasonal prediction and predictability of the Asian Summer Monsoon (ASM). However, much of this review is also relevant to monsoon predictions in other seasons and regions. Over the past two decades, considerable progress has been made in the seasonal forecasting of the ASM, driven by an enhanced understanding of the sources of predictability and the dynamics of seasonal variability, along with advanced development in sophisticated models and technologies. This review centers on advances in understanding the physical foundation for monsoon climate prediction (section 2), significant findings and insights into the primary and regional sources of predictability arising from feedback processes among various climate components (sections 3 and 4), the effects of global warming and external forcings on predictability (section 5), developments in seasonal prediction models and techniques (section 6), the challenges and limitations of monsoon climate prediction (section 7), and emerging research trends with suggestions for future directions (section 8). We hope this review will stimulate creative activities to enhance monsoon climate prediction.
基金supported by the National Natural Science Foundation of China(Grant Nos.U2342210 and 42275043)the National Institute of Natural Hazards,Ministry of Emergency Management of China(Grant Nos.J2223806,ZDJ2024-25 and ZDJ2025-34)。
文摘Climate models are essential for understanding past,present,and future changes in atmospheric circulation,with circulation modes providing key sources of seasonal predictability and prediction uncertainties for both global and regional climates.This study assesses the performance of models participating in phase 6 of the Coupled Model Intercomparison Project in simulating interannual variability modes of Northern Hemisphere 500-hPa geopotential height during winter and summer,distinguishing predictable(potentially predictable on seasonal or longer timescales)and unpredictable(intraseasonal and essentially unpredictable at long range)components,using reanalysis data and a variance decomposition method.Although most models effectively capture unpredictable modes in reanalysis,their ability to reproduce dominant predictable modes-specifically the Pacific-North American pattern,Arctic Oscillation,and Western Pacific Oscillation in winter,and the East Atlantic and North Atlantic Oscillations in summer-varies notably.An optimal ensemble is identified to distinguish(a)predictable-external modes,dominated by external forcing,and(b)predictable-internal modes,associated with slow internal variability,during the historical period(1950-2014)and the SSP5-8.5 scenario(2036-2100).Under increased radiative forcing,the leading winter/summer predictable-external mode exhibits a more uniform spatial distribution,remarkably larger trend and annual variance,and enhanced height-sea surface temperature(SST)covariance under SSP5-8.5 compared to historical conditions.The dominant winter/summer predictable-internal modes also exhibit increased variance and height-SST covariance under SSP5-8.5,along with localized changes in spatial configuration.Minimal changes are observed in spatial distribution or variance for dominant winter/summer unpredictable modes under SSP5-8.5.This study,from a predictive perspective,deepens our understanding of model uncertainties and projected changes in circulations.
基金supported by the National Natural Science Foundation of China(Grant No.42261134532,42405059,and U2342212)。
文摘This study investigates the relationship between atmospheric stratification (i.e., static stability given by N^(2)) and the vertical energy transfer of stationary planetary waves, and further illustrates the underlying physical mechanism. Specifically, for the simplified case of constant stratospheric N^(2), the refractive index square of planetary waves has a theoretical tendency to increase first and then decrease with an increased N^(2), whereas the group velocity weakens. Mechanistically, this behavior can be understood as an intensified suppression of vertical isentropic surface displacement caused by meridional heat transport of planetary waves under strong N^(2) conditions. Observational analysis corroborates this finding, demonstrating a reduction in the vertical-propagation velocity of waves with increased N^(2). A linear, quasi- geostrophic, mid-latitude beta-plane model with a constant background westerly wind and a prescribed N^(2) applicable to the stratosphere is used to obtain analytic solutions. In this model, the planetary waves are initiated by steady energy influx from the lower boundary. The analysis indicates that under strong N^(2) conditions, the amplitude of planetary waves can be sufficiently increased by the effective energy convergence due to the slowing vertical energy transfer, resulting in a streamfunction response in this model that contains more energy. For N^(2) with a quasi-linear vertical variation, the results bear a resemblance to the constant case, except that the wave amplitude and oscillating frequency show some vertical variations.
基金funded by the major research program of the of National Natural Science Foundation of China entitled Metallogenic Mechanisms and Regularity of the Lithium Ore Concentration Area in the Zabuye Salt Lake, Tibet (91962219)Science and Technology Major Project of the Tibet Autonomous Region ’s Science and Techonlogy Plan (XZ202201ZD0004G01)a geological survey project of China Geological Survey (DD20230037)。
文摘A reasonable classification of deposits holds great significance for identifying prospecting targets and deploying exploration. The world ’s keen demand for lithium resources has expedited the discovery of numerous novel lithium resources. Given the presence of varied classification criteria for lithium resources presently, this study further ascertained and classified the lithium resources according to their occurrence modes, obtaining 10 types and 5 subtypes of lithium deposits(resources) based on endogenetic and exogenetic factors. As indicated by surveys of Cenozoic exogenetic lithium deposits in China and abroad,the formation and distribution of the deposits are primarily determined by plate collision zones, their primary material sources are linked to the anatectic magmas in the deep oceanic crust, and they were formed primarily during the Miocene and Late Paleogene. The researchers ascertained that these deposits,especially those of the salt lake, geothermal, and volcanic deposit types, are formed by unique slightly acidic magmas, tend to migrate and accumulate toward low-lying areas, and display supernormal enrichment. However, the material sources of lithium deposits(resources) of the Neopaleozoic clay subtype and the deep brine type are yet to be further identified. Given the various types and complex origins of lithium deposits(resources), which were formed due to the interactions of multiple spheres, it is recommended that the mineralization of exogenetic lithium deposits(resources) be investigated by integrating tectono-geochemistry, paleoatmospheric circulation, and salinology. So far, industrialized lithium extraction is primarily achieved in lithium deposits of the salt lake, clay, and hard rock types. The lithium extraction employs different processes, with lithium extraction from salt lake-type lithium deposits proving the most energy-saving and cost-effective.
基金funded by a Project from China Southern Power Grid Company Ltd.(Nos.ZBKJXM20232481 and ZBKJXM20232482)。
文摘This study investigates the distinct impacts of eastern Pacific(EP)and central Pacific(CP)El Niño events on winter shortwave solar radiation(SSR)in southern China,revealing different spatial distributions and underlying mechanisms.The results show that,during the developing winter of EP El Niño,significant SSR reductions occur in southwestern China and the east coast of southern China due to a strong,zonally extended Northwest Pacific anticyclone that transports moisture from the tropical Northwest Pacific and North Indian Ocean,while the northeast of southern China experiences a weak increase in SSR.In contrast,during the developing winter of CP El Niño,SSR decreases in the east of southern China with a significant decrease in the lower basin of the Yangtze River but an increase in the west of southern China with a remarkable increase in eastern Yunnan.The pronounced east-west dipole pattern in SSR anomalies is driven by a meridionally elongated Northwest Pacific anticyclone,which enhances northward moisture transport to the east of southern China while leaving western areas drier.Further research reveals that distinct moisture anomalies during the developing winter of EP and CP events result in divergent SSR distributions across southern China,primarily through modulating the total cloud cover.These findings highlight the critical need to differentiate between El Niño types when predicting medium and long-term variability of radiation in southern China.
基金Under the auspices of National Key Research and Development Program of China(No.2017YFA0604902,2024YFF1306802)Natural Science Foundation of Fujian Province(No.2022J01497)Open Project of the Strait Meteorology Laboratory(No.2025KF03)。
文摘China’s coastal regions,characterized by dense populations and industrial agglomeration,face escalating threats from typhoon disasters.Understanding the evolution of socio-economic exposure to future typhoon landfalls under global change is critical for effective disaster risk management.This study utilizes future typhoon track data simulated by the regional climate model version 4(RegCM4),combined with projected population and Gross Domestic Product(GDP)data for China’s coastal regions under three Shared Socioeconomic Pathways(SSPs:SSP1,sustainability;SSP2,middle of the road;SSP5,fossil-fueled development).We analyze typhoon impact zones for future periods(2030s,2050s,and 2090s)under Representative Concentration Pathways(RCPs:RCP2.6,RCP4.5,and RCP8.5,representing low,medium,and high greenhouse gas emission scenarios,respectively).Exposure levels for 2030,2050,and 2100 are calculated based on the annual average frequency of typhoon impacts during these periods,aiming to quantify the distribution characteristics of typhoon-affected population and GDP in China’s coastal regions.Key findings reveal two high-frequency typhoon im-pact zones:the Taiwan Strait region and the northern Qiongzhou Strait region.Furthermore,under RCP2.6,typhoon impacts may ex-tend to Liaoning,while RCP4.5 and RCP8.5 scenarios indicate potential impacts reaching further north to Heilongjiang.Under RCP4.5,Northeast China will experience the largest typhoon-affected area(43.800×10^(4) km^(2))by the 2030s.High population and GDP exposure concentrates in the Yangtze River Delta,the Pearl River Delta,and the Taiwan Strait coastal areas.Notably,Liaoning’s cumulative ex-posed population may exceed 0.100×10^(8) by 2030 and 2050 under RCP4.5 and RCP8.5 due to typhoon track migration.Given China’s location within the Western Pacific typhoon high-incidence region,the northward expansion of impacts will substantially escalate socio-economic exposure in mid-latitude regions previously at lower risk.These findings underscore the imperative for enhanced disaster pre-vention,mitigation strategies and targeted countermeasure research.
基金Under the auspices of National Key R&D Program of China(No.2024YFF1306405)。
文摘Accurate extraction of surface water extent is a fundamental prerequisite for monitoring its dynamic changes.Although machine learning algorithms have been widely applied to surface water mapping,most studies focus primarily on algorithmic outputs,with limited systematic evaluation of their applicability and constrained classification accuracy.In this study,we focused on the Songnen Plain in Northeast China and employed Sentinel-2 imagery acquired during 2020-2021 via the Google Earth Engine(GEE)platform to evaluate the performance of Classification and Regression Trees(CART),Random Forest(RF),and Support Vector Machine(SVM)for surface water classification.The classification process was optimized by incorporating automated training sample selection and integration of time series features.Validation with independent samples demonstrated the feasibility of automatic sample selection,yielding mean overall accuracies of 91.16%,90.99%,and 90.76%for RF,SVM,and CART,respectively.After integrating time series features,the mean overall accuracies of the three algorithms improved by 4.51%,5.45%,and 6.36%,respectively.In addition,spectral features such as MNDWI(Modified Normalized Difference Water Index),SWIR(Short Wave Infrared),and NDVI(Normalized Difference Vegetation Index)were identified as more important for surface water classification.This study establishes a more consistent framework for surface water mapping,offering new perspectives for improving and automating classification processes in the era of big and open data.
