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Test of magnetic susceptibility and grain-size age models of loess

Progress in Natural Science:Materials International 2007年第11期17卷 1312-1317页

作者： chen yimeng1,2.3,{2.,2.1. moe and key laboratory of resources environment and gis of beijing, beijing 100037, China 2. key laboratory of 3D information Acquisition and Application, The Capital Normal University, beijing 100037, China 3. Department of Economics & Management of Huizhou University, Huizhou 516007, China 4. key laboratory of Western China’s environmental Systems, Ministry of Education, Lanzhou University, Lanzhou 730000, China) key laboratory of {2. {2. and gis of beijing Beijing 100037 China key laboratory of 3D information Acquisition and Application Capital Normal University Beijing 100037 China Department of Economics and Management Huizhou University Huizhou 516007 China key laboratory of Western China's {2.al Systems Lanzhou University Lanzhou 730000 China

Ages of the stratigraphic boundary MIS1/2.and MIS3/4 of the Yuanbu loess section in Linxia are used as the basis of the nodal control age. The age of MIS1/2.and MIS3/4 are obtained from the latest international research result—the climatic events recorded in the stalagmite in the Hulu Cave in Nanjing, that MIS1/2.is 11.5 kaB.P. and MIS3/4 is 59.8 kaB.P.. The ages of the two climatic events contain three nodal age control models (Model 1: 0 kaB.P.—59.8 kaB.P.; Model 2. 0 kaB.P.—11.5 kaB.P. and 11.5 kaB.P.—59.8 kaB.P.; Model 3: 11.5 kaB.P.—59.8 kaB.P.), which are used as the nodal control age separately. The deposition times of various stratigraphic horizons are calculated by using the magnetic susceptibility age model and grain-size age model, and then compared with each other. In addition, the AMS14C age, OSL age and the ages of YD and H events are compared with the ages of the corresponding horizons calculated by the three models of nodal control ages. From the analyses of lithologic characters and climatic stages it has been found that both the magnetic susceptibility age model and the grain-size age model have some defects. Because the accurate control ages are selected as the nodal points of the glacial period or interglacial period, the stratigraphic deposition times determined by the high resolution of magnetic susceptibility age model and grain-size age model approximate to the actual ages. As for the relative accuracy of the two age models, the magnetic susceptibility age model is more accurate than the grain-size age model.

关键词： loess nodal control age magnetic susceptibility age model grain-size age model test

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水文时间序列分析方法研究进展

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地球信息科学学报 2024年第4期26卷 927-945页

作者：李路宫辉力郭琳朱琳陈蓓蓓首都师范大学水资源安全北京实验室北京100048 首都师范大学资源环境与地理信息系统北京市重点实验室北京100048 首都师范大学城市环境过程与数字模拟国家重点实验室培育基地北京100048 首都师范大学三维信息获取与应用教育部重点实验室北京100048 首都师范大学地面沉降机理与防控教育部重点实验室北京100048

水文时间序列分析领域的发展对于有效管理和利用水资源至关重要。本文基于WoS核心合集数据库和CNKI数据库,采用文献计量学方法和CiteSpace软件,揭示国内外水文时间序列分析领域的发展脉络、研究热点及发展方向。首先从水文时间序列的随... 详细信息

水文时间序列分析领域的发展对于有效管理和利用水资源至关重要。本文基于WoS核心合集数据库和CNKI数据库,采用文献计量学方法和CiteSpace软件,揭示国内外水文时间序列分析领域的发展脉络、研究热点及发展方向。首先从水文时间序列的随机性、非线性、不确定性等出发,结合机器学习、神经网络等新兴方法,将水文时间序列分析领域的相关进展分为6个方面。然后,对各方面开展了详细介绍,并与传统方法作对比,总结了传统方法的缺陷;最后,指出提高水文时间序列分析结果准确性的方向,主要包括:(1)在时空尺度上进行建模,并融合多元数据进行分析;(2.将物理机制融入机器学习模型,提高模型的可解释性和泛化能力;(3)在研究过程中考虑气候变化(极端天气事件)和水文过程的耦合;(4)将多个复杂特性综合进行研究,并提高每个复杂特性的研究水平。通过揭示国内外水文时间序列分析领域的发展脉络、研究热点及发展方向,我们能够更好地理解和应对气候变化、极端天气事件以及人类活动对水资源的影响,提高我们对水文过程的认识,为水资源规划、水灾风险管理和可持续发展提供科学依据。

关键词：水文时间序列分析 CiteSpace 机器学习非线性不确定性非平稳性耦合

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Mapping essential urban land use categories in China(EULUC-China):preliminary results for 2.1.

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Science Bulletin 2020年第3期65卷 182-187页

作者： Peng gong Bin chen Xuecao Li Han Liu Jie Wang Yuqi Bai Jingming chen Xi chen Lei Fang Shuailong feng Yongjiu feng Yali gong Hao Gu Huabing Huang Xiaochun Huang Hongzan Jiao Yingdong Kang Guangbin Lei Ainong Li Xiaoting Li Xun Li Yuechen Li Zhilin Li Zhongde Li Chong Liu Chunxia Liu Maochou Liu Shuguang Liu Wanliu Mao Changhong Miao Hao Ni Qisheng Pan Shuhua Qi Zhehao Ren Zhuoran Shan Shaoqing Shen Minjun Shi yimeng Song Mo Su Hoi Ping Suen Bo Sun Fangdi Sun Jian Sun Lin Sun Wenyao Sun Tian Tian Xiaohua Tong Yihsing Tseng Ying Tu Hong Wang Lan Wang Xi Wan Zongming Wang Tinghai Wu Yaowen Xie Jian Yang Jun Yang Man Yuan Wenze Yue Hongda Zeng Kuo Zhang Neng Zhang Tao Zhang Yu Zhang feng Zhao Yichen Zheng Qiming Zhou Nicholas Clinton Zhiliang Zhu Bing Xu Ministry of Education key laboratory for Earth System Modeling Department of Earth System ScienceTsinghua UniversityBeijing 100084China Tsinghua Urban Institute Tsinghua UniversityBeijing 100084China Center for Healthy Cities Institute for China Sustainable UrbanizationTsinghua UniversityBeijing 100084China Department of Land Air and Water ResourcesUniversity of CaliforniaDavisCA 95616-8627USA Department of Geological and Atmospheric Sciences Iowa State UniversityAmesIA 50011USA State key laboratory of Remote Sensing Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100101China AI for Earth laboratory Cross-Strait InstituteTsinghua UniversityBeijing 100084China International Institute for Earth System Sciences Nanjing UniversityNanjing 210093China Research Center for Ecology and environment of Central Asia Chinese Academy of SciencesUrumchi 830011China key laboratory of Forest Ecology and Management Institute of Applied EcologyChinese Academy of SciencesShenyang 110016China National Engineering laboratory for Applied Technology in Forestry&Ecology in South China College of Life Science and TechnologyCentral South University of Forestry and TechnologyChangsha 410004China College of Surveying&Geo-Informatics Tongji UniversityShanghai 200092China Department of Urban Planning College of Architecture and Urban PlanningTongji UniversityShanghai 200092China beijing Institute of Urban Planning Beijing 100045China Department of Urban Planning School of Urban DesignWuhan UniversityWuhan 430072China College of Earth Sciences Jilin UniversityChangchun 130061China Research Center for Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610041China Institute of Remote Sensing and Geographic Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Department of Urban and Regional Planning School of Geography and PlanningSun Yat-sen Univ

Land use reflects human activities on *** land use is the highest level human alteration on Earth,and it is rapidly changing due to population increase and *** areas have widespread effects on local hydrology,climate,biodiversity,and food production[1,2..However,maps,that contain knowledge on the distribution,pattern and composition of various land use types in urban areas,are limited to city *** mapping standard on data sources,methods,land use classification schemes varies from city to city,due to differences in financial input and skills of mapping *** address various national and global environmental challenges caused by urbanization,it is important to have urban land uses at the national and global scales that are derived from the same or consistent data sources with the same or compatible classification systems and mapping *** is because,only with urban land use maps produced with similar criteria,consistent environmental policies can be made,and action efforts can be compared and assessed for large scale environmental ***,despite of the fact that a number of urban-extent maps exist at global scales[3,4],more detailed urban land use maps do not exist at the same *** at big country or regional levels such as for the United States,China and European Union,consistent land use mapping efforts are rare[5,6](e.g.,https://***/open_land_use/).

关键词： EULUC-China 土地利用类型

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