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China Land Carbon Budget(CLCB1.0):a comprehensive estimate of the land carbon budget in China

national Science Review 2025年第04期 15-18页

作者： Jiangzhou Xia Xiaosheng Xia Xuhui Wang Weimin Ju Zhengyang Lin Zhangcai Qin Yuxing Sang Yanzi Yan Wenping Yuan Xu Yue Haicheng Zhang Hao Zhou Qiuan Zhu Tianjin Key Laboratory of Water Resources and Environment Tianjin Normal University School of Atmospheric Sciences Sun Yat-sen University Institute of Carbon Neutrality Sino-French Institute for Earth System ScienceCollege of Urban and Environmental SciencesPeking University International Institute for Earth System Sciences Nanjing University Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application School of Atmospheric Sciences Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies Key Laboratory of Tropical Atmosphere-Ocean System (Ministry of Education) Sun Yat-sen University Department of Soil and Environment Swedish University of Agricultural Sciences Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of Environmental Science and Engineering Nanjing University of Information Science & Technology (NUIST) Carbon-Water Research Station in Karst Regions of Northern Guangdong School of Geography and Planning Sun Yat-sen University College of Meteorology and Oceanography National University of Defense Technology College of Geography and Remote Sensing Hohai University

The terrestrial ecosystem plays an important role in regulating the regional carbon balance, in particular for China, where ecological projects during past decades [1,2] have led to large increases in China's land carbon sinks, partially offsetting fossil fuel emissions. Therefore, accurate evaluation of land carbon fluxes in China is critical for improving our understanding of the magnitude of China's carbon budget, and projecting its future changes.

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Paddy rice methane emissions across Monsoon Asia

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Remote Sensing of Environment 2023年 284卷

作者： Ouyang, Zutao Jackson, Robert B. McNicol, Gavin Fluet-Chouinard, Etienne Runkle, Benjamin R.K. Papale, Dario Knox, Sara H. Cooley, Sarah Delwiche, Kyle B. Feron, Sarah Irvin, Jeremy Andrew Malhotra, Avni Muddasir, Muhammad Sabbatini, Simone Alberto, Ma. Carmelita R. Cescatti, Alessandro Chen, Chi-Ling Dong, Jinwei Fong, Bryant N. Guo, Haiqiang Hao, Lu Iwata, Hiroki Jia, Qingyu Ju, Weimin Kang, Minseok Li, Hong Kim, Joon Reba, Michele L. Nayak, Amaresh Kumar Roberti, Debora Regina Ryu, Youngryel Swain, Chinmaya Kumar Tsuang, Benjei Xiao, Xiangming Yuan, Wenping Zhang, Geli Zhang, Yongguang Department of Earth System Science Stanford University StanfordCA United States Woods Institute for the Environment Stanford University StanfordCA United States Precourt Institute for Energy Stanford University StanfordCA United States Department of Earth and Environmental Sciences University of Illinois Chicago ChicagoIL United States Department of Biological and Agricultural Engineering University of Arkansas FayettevilleAR United States University of Tuscia Viterbo Italy IAFES division Viterbo Italy Department of Geography The University of British Columbia VancouverBC Canada Department of Geography University of Oregon EugeneOR United States Department of Environmental Science Policy And Management UC Berkeley BerkeleyCA United States Campus Fryslan University of Groningen Groningen Netherlands Department of Computer Science Stanford University StanfordCA United States Department of Geography University of Zurich Zurich Switzerland International Rice Research Institute Laguna Philippines Ispra Italy Taiwan Agricultural Research Institute Taichung Wufeng Taiwan Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China USDA ARS Delta Water Management Research Unit JonesboroAR United States Fudan University Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering and Coastal Ecosystems Research Station of the Yangtze River Estuary Shanghai China / Jiangsu Key Laboratory of Agricultural Meteorology Nanjing University of Information Science and Technology Nanjing China Department of Environmental Science Shinshu University Matsumoto Japan China Meteorological Administration Institute of Atmospheric Environment Shenyang China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu Nanjing210023 China National Center for AgroM

Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emissions from rice cultivation (ranging from 18 to 115 Tg CH4 yr−1) due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5°) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH4 fluxes;and 2) produce gridded up-scaling estimates of rice CH4 emissions at 5000-m resolution across Monsoon Asia, where ∼87% of global rice area is cultivated and ∼ 90% of global rice production occurs. Our random-forest model achieved Nash-Sutcliffe Efficiency values of 0.59 and 0.69 for 8-day CH4 fluxes and site mean CH4 fluxes respectively, with land surface temperature, biomass and water-availability-related indices as the most important predictors. We estimate the average annual (winter fallow season excluded) paddy rice CH4 emissions throughout Monsoon Asia to be 20.6 ± 1.1 Tg yr−1 for 2001–2015, which is at the lower range of previous inventory-based estimates (20–32 CH4 Tg yr−1). Our estimates also suggest that CH4 emissions from paddy rice in this region have been declining from 2007 through 2015 following declines in both paddy-rice growing area and emission rates per unit

关键词： Climate change

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Continuous and comprehensive atmospheric observations in Beijing:a station to understand the complex urban atmospheric environment

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Big Earth Data 2020年第3期4卷 295-321页

作者： Yongchun Liu Chao Yan Zemin Feng Feixue Zheng Xiaolong Fan Yusheng Zhang Chang Li Ying Zhou Zhuohui Lin Yishou Guo Ying Zhang Li Ma Wenshuo Zhou Zhikun Liu Lubna Dada Kaspar Dallenbach Jenni Kontkanen Runlong Cai Tommy Chan Biwu Chu Wei Du Lei Yao Yonghong Wang Jing Cai Juha Kangasluoma Tom Kokkonen Joni Kujansuu Anton Rusanen Chenjuan Deng Yueyun Fu Rujing Yin Xiaoxiao Li Yiqun Lu Yiliang Liu Chaofan Lian Dongsen Yang Weigang Wang Maofa Ge Yuesi Wang Douglas R.Worsnop Heikki Junninen Hong He Veli-Matti Kerminen Jun Zheng Lin Wang Jingkun Jiang Tuukka Petaja Federico Bianchi Markku Kulmala Aerosol and Haze Laboratory Beijing Advanced Innovation Center for Soft Matter Science and EngineeringBeijing University of Chemical TechnologyBeijingChina Institute for Atmospheric and Earth System Research/Physics Faculty of ScienceUniversity of HelsinkiHelsinkiFinland State Key Joint Laboratory of Environment Simulation and Pollution Control State Environmental Protection Key Laboratory of Sources and Control of Air Pollution ComplexSchool of EnvironmentTsinghua UniversityBeijingChina State Key Joint Laboratory of Environment Simulation and Pollution Control Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijingChina Department of Environmental Science&Engineering Fudan UniversityShanghaiChina State Key Laboratory for Structural Chemistry of Unstable and Stable Species Beijing National Laboratory for Molecular SciencesInstitute of ChemistryChinese Academy of SciencesBeijingChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing University of Information Science&TechnologyNanjingChina State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina Aerodyne Research Inc. BillericaMassachusettsUSA Institute of Physics University of TartuTartuEstonia Joint International Research Laboratory of Atmospheric and Earth System Sciences School of Atmospheric SciencesNanjing UniversityNanjingChina

Due to profound impact on climate and human health,air quality has attracted attention from all levels of the civil *** key step in the provision of required tools for the society to tackle the complex air quality problem is to characterize it in a comprehensive manner with a long-term ***,we describe a continuous and comprehensive observation station and its accompanying state-ofthe-art instrumentation that was established to investigate the complex urban atmospheric environment in a rapidly developing Chinese *** station,located in downtown Beijing,aims to study air quality by identifying the major atmospheric pollutants and key processes determining their formation and loss mechanisms.A few hundreds of parameters are continuously measured with the state-of-the-art instruments,including trace gas concentrations,aerosol particle size distributions,and mass concentrations,covering aerosol particle chemical composition from molecules to micrometer-sized aerosol *** produced long-term,comprehensive big data with around 1�10^(11)bytes per *** this paper,we provide an overview on the facilities of the station,the instrumentation used,the workflow of continuous observations and examples of results from 2018 to 2019 and a basis for establishing a modern long-term,comprehensive atmospheric urban observation station in other megacities.

关键词： Continuous and comprehensive atmospheric observation air pollution complex photochemical smog haze new particle formation

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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Drought frequency, intensity, and exposure have increased due to historical land use and land cover changes

Communications Earth & Environment

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Communications Earth & Environment 2025年第1期6卷 1-13页

作者： Tao Tang Lin Wang Junji Cao Jun Ge Haiyun Shi Xuhui Lee State Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China Science Center for Earth System Numerical Simulation Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China CAS Key Laboratory of Regional Climate-Environment for Temperate East Asia Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China Joint International Research Laboratory of Atmospheric and Earth System Sciences School of Atmospheric Sciences Nanjing University Nanjing China School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen China School of the Environment Yale University New Haven CT USA

Drought, a marked deficiency of surface water, has a wide adverse impact on the environment, agriculture, and the socio-economy. The global impact of land use changes on drought features and associated exposures to droughts, however, remains largely unknown. In this study, the changes of drought features are estimated by using the model simulations with and without land use change. We show that historical land use changes since 1850, mainly deforestation and crop expansion, has increased the drought frequency, duration, and severity over half of the global land area. Regions with greater activity tend to experience stronger exacerbation of drought events. Owing to their increasing frequency and prolonged duration, populations, croplands and forests are increasingly exposed to drought events, which poses serious potential impacts on human health, agriculture, and forest ecosystems. The enhanced drought events could be substantially alleviated by potential reforestation activity. Our results provide useful information for land-management-related policy-making.

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Climatic and Biotic Controls of Evapotranspiration Across Grassland Ecosystems on Tibetan Plateau

SSRN

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SSRN 2024年

作者： Peng, Siyuan Yang, Yue Luo, Dengnan Zeng, Xiang Liang, Minqi Tao, Long Zhang, Guangru Li, Pan Liao, Weijie Guo, Qun Cao, Ruochen Li, Yuzhe Zhang, Weirong Hu, Zhongmin School of Geography South China Normal University Shipai Campus Guangzhou510631 China Hainan Baoting Tropical Rainforest Ecosystem Observation and Research Station Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation School of Ecology and Environment Hainan University Haikou570228 China Center for Eco-Environment Restoration Engineering of Hainan Province Hainan University Haikou570228 China Facility of Geographical Science Beijing Normal University Beijing100875 China Zhuhai Branch of State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Zhuhai519087 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Guangdong Zhuhai510245 China Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing101408 China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Understanding the spatial variations of evapotranspiration (ET) and its components on Tibetan Plateau (TP) is pivotal for grasping the regional water cycle and energy balance dynamics. Yet it is unclear how the changes of the intricate synergy among diverse environment factors significantly impacts the patterns of ET and its transformation state. Addressing this gap, this study employed observational data from 14 sites, utilizing the Shuttleworth-Wallace-Hu (SWH) model to investigate the spatial relationships between ET, its components, and influencing factors. Model parameters of SWH were optimized by Monte Carlo method, achieving a commendable simulation performance with a mean R2 value of 0.75. Our analysis reveals that in alpine meadow, the ratio of transpiration (T) to ET (T/ET) is generally higher (0.55) compared to alpine steppe (0.39). The T/ET values for hover around or below 0.5, indicating a balanced contribution of various ET components. Spatially, both PPT and LAI have an impact on ET and T/ET. PPT emerges as the primary driver of interannual spatial variations in ET, while LAI plays a crucial role in regulating T/ET. Our results further demonstrate that a transition from water-dominated to energy-dominated systems in TP grassland ecosystem at an approximately aridity index of 0.73. This is noteworthy considering the general application of an aridity index of 0.65 to delineate arid from humid regions. It implies that TP grasslands, within the 0.65-0.73 range, remain primarily water-driven. This insight holds substantial implications for comprehending water-energy dynamics in similar ecosystems, challenging the conventional binary categorization of arid and humid areas. Such findings are of immense significance in enhancing understanding of the intricate interactions of the water cycle between land and the atmosphere. © 2024, The Authors. All rights reserved.

关键词： Water supply

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Estimating Aboveground Carbon Dynamic of China Using Optical and Microwave Remote-Sensing Datasets from 2013 to 2019

Journal of Remote Sensing

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Journal of Remote Sensing 2023年第1期3卷 19-34页

作者： Zhongbing Chang Lei Fan Jean-Pierre Wigneron Ying-Ping Wang Philippe Ciais Jérôme Chave Rasmus Fensholt Jing MChen Wenping Yuan Weimin Ju Xin Li Fei Jiang Mousong Wu Xiuzhi Chen Yuanwei Qin Frédéric Frappart Xiaojun Li Mengjia Wang Xiangzhuo Liu Xuli Tang Sanaa Hobeichi Mengxiao Yu Mingguo Ma Jianguang Wen Qing Xiao Weiyu Shi Dexin Liu Junhua Yan Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems South China Botanical GardenChinese Academy of SciencesGuangzhou 510650China University of Chinese Academy of Sciences Beijing 100049China Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station School of Geographical SciencesSouthwest UniversityChongqing 400715China INRAE UMR1391 ISPAUniversite de BordeauxF-33140 Villenave d’OrnonFrance CSIRO Oceans and Atmosphere AspendaleVIC 3195Australia Laboratoire des Sciences du Climat et de l’Environnement CEA/CNRS/UVSQ/Universite Paris SaclayGif-sur-YvetteFrance Laboratoire Evolution et Diversite Biologique Universite Paul SabatierToulouseFrance Department of Geosciences and Natural Resource Management University of CopenhagenCopenhagenDenmark Department of Geography and Program in Planning University of TorontoTorontoON M5S 3G3Canada College of Geographical Science Fujian Normal UniversityFuzhou 3500007FujianChina School of Atmospheric Sciences Sun Yat-sen UniversityZhuhai 519082GuangdongChina Southern Marine Science and Engineering Guangdong Laboratory Zhuhai 519000GuangdongChina Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology International Institute for Earth System ScienceNanjing UniversityNanjing 210023China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China Institute of Tibetan Plateau Research Chinese Academy of SciencesBeijing 100101China CAS Center for Excellence in Tibetan Plateau Earth Sciences Chinese Academy of SciencesBeijing 100101China Department of Microbiology and Plant Biology Center for Spatial AnalysisUniversity of OklahomaNormanOKUSA LEGOS Universite de ToulouseCNESCNRSIRDUPS-14 avenue Edouard Belin31400 ToulouseFrance State Key Laboratory of Remote Sensing Science Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China Climate Change Research Centre University of N

Over the past 2 to 3 decades,Chinese forests are estimated to act as a large carbon sink,yet the magnitude and spatial patterns of this sink differ considerably among *** 3 microwave(L-and X-band vegetation optical depth[VOD])and 3 optical(normalized difference vegetation index,leaf area index,and tree cover)remote-sensing vegetation products,this study compared the estimated live woody aboveground biomass carbon(AGC)dynamics over China between 2013 and *** results showed that tree cover has the highest spatial consistency with 3 published AGC maps(mean correlation value R=0.84),followed by L-VOD(R=0.83),which outperform the other *** AGC estimation model was proposed to combine all indices to estimate the annual AGC dynamics in China during 2013 to *** performance of the AGC estimation model was good(root mean square error=0.05 Pg C and R^(2)=0.90 with a mean relative uncertainty of 9.8% at pixel scale[0.25°]).Results of the AGC estimation model showed that carbon uptake by the forests in China was about+0.17 Pg C year^(-1) from 2013 to *** the regional level,provinces in southwest China including Guizhou(+22.35 Tg C year^(-1)),Sichuan(+14.49 Tg C year^(-1)),and Hunan(+11.42 Tg C year^(-1))provinces had the highest carbon sink rates during 2013 to *** of the carbon-sink regions have been afforested recently,implying that afforestation and ecological engineering projects have been effective means for carbon sequestration in these regions.

关键词： estimation Remote microwave

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Observation of a Strongly Isospin-Mixed Doublet in Si26 via β-Delayed Two-Proton Decay of P26

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Physical Review Letters 2022年第24期129卷 242502-242502页

作者： J. J. Liu (刘嘉健) X. X. Xu (徐新星) L. J. Sun (孙立杰) C. X. Yuan (袁岑溪) K. Kaneko (金子和也) Y. Sun (孙扬) P. F. Liang (梁鹏飞) H. Y. Wu (吴鸿毅) G. Z. Shi (石国柱) C. J. Lin (林承键) J. Lee (李晓菁) S. M. Wang (王思敏) C. Qi (亓冲) J. G. Li (李健国) H. H. Li (李红蕙) Latsamy Xayavong Z. H. Li (李智焕) P. J. Li (李朋杰) Y. Y. Yang (杨彦云) H. Jian (简豪) Y. F. Gao (高雨枫) R. Fan (范锐) S. X. Zha (查思贤) F. C. Dai (戴凡超) H. F. Zhu (朱浩钒) J. H. Li (李金海) Z. F. Chang (常志芳) S. L. Qin (覃淑炼) Z. Z. Zhang (张朝展) B. S. Cai (蔡博帅) R. F. Chen (陈若富) J. S. Wang (王建松) D. X. Wang (王东玺) K. Wang (王康) F. F. Duan (段芳芳) Y. H. Lam (蓝乙华) P. Ma (马朋) Z. H. Gao (高志浩) Q. Hu (胡强) Z. Bai (白真) J. B. Ma (马军兵) J. G. Wang (王建国) C. G. Wu (武晨光) D. W. Luo (罗迪雯) Y. Jiang (蒋颖) Y. Liu (刘洋) D. S. Hou (侯东升) R. Li (李忍) N. R. Ma (马南茹) W. H. Ma (马维虎) G. M. Yu (余功明) D. Patel S. Y. Jin (金树亚) Y. F. Wang (王煜峰) Y. C. Yu (余悦超) L. Y. Hu (胡力元) X. Wang (王翔) H. L. Zang (臧宏亮) K. L. Wang (王凯龙) B. Ding (丁兵) Q. Q. Zhao (赵青青) L. Yang (杨磊) P. W. Wen (温培威) F. Yang (杨峰) H. M. Jia (贾会明) G. L. Zhang (张高龙) M. Pan (潘敏) X. Y. Wang (汪小雨) H. H. Sun (孙浩瀚) H. S. Xu (徐瑚珊) X. H. Zhou (周小红) Y. H. Zhang (张玉虎) Z. G. Hu (胡正国) M. Wang (王猛) M. L. Liu (柳敏良) H. J. Ong (王惠仁) W. Q. Yang (杨维青) CAS Key Laboratory of High Precision Nuclear Spectroscopy Institute of Modern Physics Chinese Academy of Sciences Lanzhou 730000 China Department of Physics The University of Hong Kong Hong Kong China Department of Nuclear Physics China Institute of Atomic Energy Beijing 102413 China School of Nuclear Science and Technology University of Chinese Academy of Sciences Beijing 100049 China Advanced Energy Science and Technology Guangdong Laboratory Huizhou 516003 China School of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 China National Superconducting Cyclotron Laboratory Michigan State University East Lansing Michigan 48824 USA Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen University Zhuhai 519082 China Department of Physics Kyushu Sangyo University Fukuoka 813-8503 Japan State Key Laboratory of Nuclear Physics and Technology School of Physics Peking University Beijing 100871 China College of Physics and Technology & Guangxi Key Laboratory of Nuclear Physics and Technology Guangxi Normal University Guilin 541004 China Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Institute of Modern Physics Fudan University Shanghai 200433 China Shanghai Research Center for Theoretical Nuclear Physics NSFC and Fudan University Shanghai 200438 China KTH Royal Institute of Technology SE-100 44 Stockholm Sweden Department of Physics Faculty of Natural Sciences National University of Laos Vientiane 01080 Laos College of Science Huzhou University Huzhou 313000 China Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai 201800 China School of Nuclear Science and Technology Lanzhou University Lanzhou 730000 China Fundamental Science on Nuclear Safety and Simulation Technology Laboratory Harbin Engineering University Harbin 150001 China Department of Physics Sardar Vallabhbhai National Institute of Technology Surat 395007 India School of Physics and Astronomy Yunnan University Kunming 6

β decay of proton-rich nuclei plays an important role in exploring isospin mixing. The β decay of P26 at the proton drip line is studied using double-sided silicon strip detectors operating in conjunction with high-purity germanium detectors. The T=2 isobaric analog state (IAS) at 13 055 keV and two new high-lying states at 13 380 and 11 912 keV in Si26 are unambiguously identified through β-delayed two-proton emission (β2p). Angular correlations of two protons emitted from Si26 excited states populated by P26 β decay are measured, which suggests that the two protons are emitted mainly sequentially. We report the first observation of a strongly isospin-mixed doublet that deexcites mainly via two-proton decay. The isospin mixing matrix element between the Si26 IAS and the nearby 13 380-keV state is determined to be 130(21) keV, and this result represents the strongest mixing, highest excitation energy, and largest level spacing of a doublet ever observed in β-decay experiments.

关键词： Beta decay Nuclear structure & decays Proton emission 20 ≤ A ≤ 38

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Climatic and Biotic Controls of Evapotranspiration Across Grassland Ecosystems on Tibetan Plateau

SSRN

引用

SSRN 2023年

作者： Peng, Siyuan Yang, Yue Luo, Dengnan Zeng, Xiang Liang, Minqi Tao, Long Zhang, Guangru Li, Pan Liao, Weijie Guo, Qun Cao, Ruochen Li, Yuzhe Zhang, Weirong Hu, Zhongmin School of Geography South China Normal University Shipai Campus Guangzhou510631 China Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation School of Ecology and Environment Hainan University Haikou570228 China Center for Eco-Environment Restoration Engineering of Hainan Province Hainan University Haikou570228 China Facility of Geographical Science Beijing Normal University Beijing100875 China Zhuhai Branch of State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Zhuhai519087 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Guangdong Zhuhai510245 China Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing101408 China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Accurately quantifying spatiotemporal variations in evapotranspiration (ET) and its components on Tibetan Plateau (TP) is crucial for understanding the regional water cycle and energy balance. However, the determinant of spatial variations in ET and its component processes in grassland ecosystems of TP remain unclear. In this study, we used 14 sites observation data and applied Shuttleworth-Wallace-Hu (SWH) model to investigate the spatial relationships between ET, its components, and influencing factors. Our results highlighted the good performance of SWH model. There were corresponding drops in ET on alpine meadow and on alpine steppe. And the ratio of ET and precipitation (ET/PPT) of most sites was less than 1 in alpine meadow. Furthermore, the ratio of transpiration (T) and ET (T/ET) in alpine meadow generally exceeded that in alpine steppe. PPT emerged as the primary driver of interannual spatial variations in ET, while LAI played a crucial role in regulating T/ET. Our results further demonstrated that in the grassland ecosystem of TP, as the aridity index increased, the ET gradually transitioned from being primarily influenced by water supply to energy demand. This transition occurred at an aridity index of 0.73 approximately. The results of this study imply that in the context of global warming, with aridity index in TP decreasing, the ET of more ecosystems will shift from energy-led to water-led. Such findings are of immense significance in enhancing our understanding of the intricate interactions of the water cycle between land and the atmosphere. © 2023, The Authors. All rights reserved.

关键词： Water supply

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A 350 C piezoresistive n-type 4H-SiC pressure sensor for hydraulic and pneumatic pressure tests

引用

Journal of Micromechanics and Microengineering 2020年第5期30卷

作者： Fang, Xudong Wu, Chen Zhao, Yulong Jiang, Zhuangde Rong, Weibing Feng, Zhihong Lv, Yuanjie State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technologies Collaborative Innovation Center of Suzhou Nano Science and Technology Xi'an Jiaotong University Xi'an710049 China School of Mechanical Engineering Xi'an Jiaotong University Xi'an710049 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150080 China National Key Laboratory of Application Specific Integrated Circuit Hebei Semiconductor Research Institute Shijiazhuang050051 China

It has been a challenge to develop pressure sensors that can work in harsh environments. In this work, a piezoresistive n-type 4H-SiC pressure sensor is demonstrated, capable of working at 350 C under hydraulic and pneumatic pressure. The pressure measuring range of this sensor is 7 MPa and the chip dimension is 2.5 mm 2.5 mm 0.23 mm. A reliable Ti/TaSi2/Pt metal ohmic contact is used in the sensor and experimental results show that the metal combination can withstand a wide temperature range of 30 C-350 C. In order to avoid thermal stress induced by the mismatch of thermal expansion coefficients of different packaging materials when increasing temperature, a double-layer bonding scheme of cement glue and epoxy is proposed. The inner layer of the chip and the kovar alloy base are directly bonded with the epoxy, while the outer layer is wrapped with the high-temperature cement glue. Experiments confirm that this method can achieve reliable packaging with temperatures up to 350 C for 5 h. Based on the above key technologies, the sensitivity of this sensor can reach 13.2 mV V-1 kPa-1 and the test data exhibit high linearity and repeatability. Static tests prove that the sensor has good media compatibility which can operate under a hydraulic pressure load at 220 C and a barometric pressure load under 350 C. The sensor in this work can provide a reference for pressure measurements at high temperature and in complex media environments. © 2020 IOP Publishing Ltd.

关键词： Pressure sensors

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