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Impact of land-use change on hydrological processes in the Maying River basin, china

Science china Earth sciences 2006年第10期49卷 1098-1110页

作者： wang genxu1,3. zhang yu2. liu guimin2.& chen lin3.1. institute of mountain hazards and environment, Chinese Academy of {2.,{3., china 2. Cold and Arid Regions environmental and Engineering Research institute, Chinese Academy of {2., Lanzhou 73.000, china 3. Resource and environment School, Lanzhou University, Lanzhou 73.000, china institute of mountain {2. and {3. Chinese Academy of Sciences Chengdu China Resource and environment School Lanzhou University Lanzhou China Cold and Arid Regions environmental and Engineering Research institute Chinese Academy of Sciences Lanzhou China

Since the 1960s, dramatic changes have taken place in land-use patterns characterized by the persistent expansion of cultivated land and a continuous decrease in natural woodland and grassland in the arid inland river basins of china. It is very important to assess the effects of such land-use changes on the hydrological processes so vital for water resource management and sustainable development on the catchment scale. The Maying River catchment, a typical arid inland watershed located in the middle of the Hexi Corridor in northwest china, was the site chosen to investigate the hydrological responses to land-use changes. The annual runoff, base flow, maximum peak flow, and typical seasonal runoff in both spring and autumn flood periods were selected as the variables in the hydrological processes. Statistical-trend analysis and curvi{2.ear regression were utilized to detect the trends in hydrological variables while eliminating the climatic influence. The relationship between cultivated land-use and hydrological variables was analyzed based on four periods of land-use variation data collected since 1965. A runoff model was established composed of two factors, i.e., cultivated land use and precipitation. The impact of land use changes, especially in the large ar- eas of upstream woodland and grassland turned into cultivated lands since 1967, has resulted in a mean annual runoff decrease of 2..12., a base flow dec{2.e of 3..3.%, a drop in the maximum peak discharge of 3..77%, and mean discharge decreases in spring and autumn of 3..05% and 2..87% respectively, of which the contribution of cultivated land expansion to the influence of annual runoff amounts to 77%-80%, with the contribution to the influence of spring discharge being 73.-81%, and that to the influence of base flow reaching 62.-65%. Thus, a rational regulation policy of land use patterns is vitally important to the sustainable use of water resources and the proper development of the entire catchment.

关键词： land-use change, arid inland river catchment, hydrological process, impact, Maying River.

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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng wang Chaojun Ouyang Min chen chenying {3. Jin zhang Long yu Fei wang Yong Xie Jun Li Fang wang Sabine Grunwald Bryan MWong Fan zhang Zhen Qian Yongjun Xu chengqing yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao chen Jiangyuan Zeng Huai zhang Husi Letu Bing zhang Li wang Lei Luo Chong Shi Hongjun Su Hongsheng zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu wang Yinyi lin Tangtang zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao zhang Jiancheng Shi Lizhe wang Aerospace Information Research institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of mountain hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric environment Monitoring and Pollution Control Nanjing 210044China School of environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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Two Ultraviolet Radiation Datasets that Cover china

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Advances in Atmospheric sciences 2017年第7期34卷 805-815页

作者： Hui liu Bo HU yuesi {2. Guangren liu Liqin TANG Dongsheng JI Yongfei BAI Weikai BAO Xin {3. yunming {3. Weixin DING Xiaozeng HAN Fei HE Hui HUANG Zhenying HUANG Xinrong LI Yan LI Wenzhao liu Luxiang lin Zhu OUYANG Boqiang QIN Weijun SHEN Yanjun SHEN Hongxin SU Changchun SONG Bo SUN Song SUN Anzhi {2. genxu {2. Huimin {2. Silong {2. Youshao {2. Wenxue WEI Ping XIE Zongqiang XIE Xiaoyuan YAN Fanjiang ZENG Fawei {13. Yangjian {13. Yiping {13. {3.gyi ZHAO Wenzhi ZHAO Xueyong ZHAO Guoyi ZHOU Bo ZHU State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry Institute of Atmospheric Physics Chinese Academy of Sciences Key Laboratory for Semi-Arid Climate Change Ministry of Education College of Atmospheric SciencesLanzhou University College of Atmospheric sciences Chengdu University of Information Technology the institute of Botany Chinese Academy of sciences chengdu institute of Biology Chinese Academy of Sciences the institute of Applied Ecology Chinese Academy of Sciences the institute of Soil and Water Conservation Chinese Academy of Sciences the institute of Soil Science Chinese Academy of Sciences the Northeast institute of Geography and Agroecology Chinese Academy of Sciences the institute of Subtropical Agriculture Chinese Academy of Sciences the South china Sea institute of Oceanology Chinese Academy of Sciences the Cold and Arid Regions environmental and Engineering Research institute Chinese Academy of Sciences Xinjiang institute of Ecology and Geography Chinese Academy of Sciences Xishuangbanna Tropical Botanical Garden Chinese Academy of Sciences the institute of Geographic sciences and Natural Resources Research Chinese Academy of Sciences the Nanjing institute of Geography and Limnology Chinese Academy of Sciences the South china Botanical Garden Chinese Academy of Sciences the institute of Genetics and Developmental Biology Chinese Academy of Sciences the institute of Oceanology Chinese Academy of Sciences the institute of mountain {2. and {3. Chinese Academy of Sciences the institute of Hydrobiology Chinese Academy of Sciences the Northwest institute of Plateau Biology Chinese Academy of Sciences

Ultraviolet（UV） radiation has significant effects on ecosystems, environments, and human health, as well as atmospheric processes and climate change. Two ultraviolet radiation datasets are described in this paper. One contains hourly observations of UV radiation measured at 40 Chinese Ecosystem Research Network stations from 2.05 to 2.15. CUV3.broadband radiometers were used to observe the UV radiation, with an accuracy of 5%, which meets the World Meteorology Organization＇s measurement standards. The extremum method was used to control the quality of the measured datasets. The other dataset contains daily cumulative UV radiation estimates that were calculated using an all-sky estimation model combined with a hybrid model. The reconstructed daily UV radiation data span from 1961 to 2.14. The mean absolute bias error and root-mean-square error are smaller than 3.% at most stations, and most of the mean bias error values are negative, which indicates underestimation of the UV radiation intensity. These datasets can improve our basic knowledge of the spatial and temporal variations in UV radiation. Additionally, these datasets can be used in studies of potential ozone formation and atmospheric oxidation, as well as simulations of ecological processes.

关键词： reconstructed ozone absolute ultraviolet estimates environments climatic cumulative sunshine meteorological

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Mapping essential urban land use categories in china(EULUC-china):preliminary results for 2.18

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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,{3. 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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沱江和涪江水系干支流氮磷营养盐的空间分布特征

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环境科学 2023年第7期44卷 3933-3944页

作者：李子阳周明华徐鹏陈露刘祥龙林洪羽江南任兵张博文中国科学院、水利部成都山地灾害与环境研究所中国科学院山地表生过程与生态调控重点实验室成都610041 中国科学院大学北京100049

沱江和涪江是长江上游的重要支流,且都存在严重的水体污染问题,其中氮(N)和磷(P)为最主要污染物.通过对沱江和涪江干支流进行取样并进行水体N和P空间分布特征的分析,寻找不同空间水质差异的原因,为长江上游及其支流流域地表水污染防控... 详细信息

沱江和涪江是长江上游的重要支流,且都存在严重的水体污染问题,其中氮(N)和磷(P)为最主要污染物.通过对沱江和涪江干支流进行取样并进行水体N和P空间分布特征的分析,寻找不同空间水质差异的原因,为长江上游及其支流流域地表水污染防控提供科学依据.结果表明,沱江水系和涪江水系均存在严重的总氮(TN)污染,劣Ⅴ类水质断面占比分别高达94%和50%,总磷(TP)污染适中,主要集中在Ⅱ类~Ⅳ类水质,但沱江水系TN和TP浓度整体上要高于涪江水系,污染程度要比涪江水系更严重;对沱江而言,干流硝态氮(NN)浓度从上游到下游呈现先增加后降低的趋势,氨氮(AN)浓度最大值出现在干流上游位点,中下游浓度较低,每流经一座城市后,TP浓度均明显增高,涪江干流的TN和NN浓度呈现上中下游逐渐增加的趋势;沱江和涪江水系均表现出支流TN和TP浓度大于干流的现象,且河流中的TN、TP和NN还受水体pH和水温(T)影响,可见河流N和P污染控制应重视水环境因素的影响.

关键词：沱江涪江氮(N) 磷(P) 空间分布

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云南省德钦县直任龙曲泥石流治理工程防治效果研究

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防灾减灾学报 2022年第4期38卷 36-44页

作者：唐勤陈兴长柳金峰冯鑫谢奎琳西南科技大学环境与资源学院四川绵阳621010 中国科学院、水利部成都山地灾害与环境研究所四川成都610041

以云南省德钦县直任龙曲泥石流为研究对象,在野外调查的基础上,基于降雨、高精度地形等数据,利用FLO-2.对工程治理前后不同频率的泥石流进行了数值模拟。结果表明,直任龙曲的治理工程可有效防治2.年一遇的泥石流,即使遭遇超设计标准的... 详细信息

以云南省德钦县直任龙曲泥石流为研究对象,在野外调查的基础上,基于降雨、高精度地形等数据,利用FLO-2.对工程治理前后不同频率的泥石流进行了数值模拟。结果表明,直任龙曲的治理工程可有效防治2.年一遇的泥石流,即使遭遇超设计标准的泥石流,也能大大降低其危害,防治效果较好,防治方案合理;建议加强泥石流的监测预警,以避免超设计标准泥石流造成人员伤亡;数值模拟既可以用于评价既有工程的治理效果,还能呈现防治结果,为防治方案的优化提供设计思路。

关键词：泥石流 FLO-2. 治理工程效果评价

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2.05~2.14年CERN地下水位数据集

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中国科学数据（中英文网络版） 2017年第1期2卷 45-53,J0055-J0065页

作者：朱治林唐新斋袁国富张心昱孙晓敏常学向程一松褚国伟戴冠华窦山德杜娟傅伟官有军郭永平何其华姜峻蒋正德来剑斌李猛李少伟李伟李小丽林静慧林永标刘文杰刘新平刘勇刚鲁志云路永正盛钰苏宏新唐家良杨风亭尹春梅张法伟张志山赵常明赵岩朱睦楠中国科学院地理科学与资源研究所中国科学院生态系统网络观测与模拟重点实验室CERN水分分中心北京100101 中国科学院大学资源与环境学院北京100049 中国科学院寒区旱区环境与工程研究所中国科学院遗传与发育生物学研究所中国科学院华南植物园中国科学院沈阳应用生态研究所中国科学院植物研究所中国科学院亚热带农业生态研究所中国科学院南京土壤研究所中国科学院新疆生态与地理研究所中国科学院成都生物研究所中国科学院西北水土保持研究所中国科学院地理科学与资源研究所中国科学院成都山地灾害与环境研究所中国科学院西双版纳植物园中国科学院东北地理与农业生态研究所中国科学院西北高原生物研究所

地下水是一个地区重要的自然资源,地下水位数据可为研究地下水的长期变化提供重要的参考资料。本数据集收集整理了中国生态系统研究网络(CERN)3.个台站采用人工或自动记录方法观测的2.05~2.14年地下水位深度数据。重新整理后的数据格式... 详细信息

地下水是一个地区重要的自然资源,地下水位数据可为研究地下水的长期变化提供重要的参考资料。本数据集收集整理了中国生态系统研究网络(CERN)3.个台站采用人工或自动记录方法观测的2.05~2.14年地下水位深度数据。重新整理后的数据格式更加规范,质量也有所提高。此外,为了便于用户了解台站地下水位的概况(如平均深度及其变化),我们还计算了各台站地下水位深度的平均值及其标准差。

关键词：地下水位水资源陆地生态系统 CERN野外台站

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2.05~2.15年CERN光合有效辐射数据集

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中国科学数据（中英文网络版） 2017年第1期2卷 1-10,J0001-J0012页

作者：刘慧唐利琴胡波刘广仁王跃思白帆陈永福程一松杜娟杜明武高美荣高新连高永平官有军郭永平韩建梅韩军何其华姜峻蒋正德李国振李猛李小丽李玉霖林静慧刘文杰刘允芬娄金勇孟泽乔田华宋星宇孙聃孙维王可琴王克林武传胜徐浩徐文婷尹春梅张法伟张秀永赵永芳朱广伟朱元骏中国科学院大气物理研究所大气边界层物理和大气化学国家重点实验室北京100191 兰州大学大气科学学院兰州730000 成都信息工程大学大气科学学院成都610225 中国科学院大气物理研究所中国科学院植物研究所中国科学院南海海洋研究所中国科学院遗传与发育生物学研究所中国科学院寒区旱区环境与工程研究所中国科学院成都山地灾害与环境研究所中国科学院新疆生态与地理研究所中国科学院南京土壤研究所中国科学院水生生物研究所中国科学院成都生物研究所中国科学院水土保持研究所中国科学院沈阳应用生态研究所中国科学院东北地理与农业生态研究所中国科学院西双版纳热带植物园中国科学院地理科学与资源研究所中国科学院华南植物园中国科学院亚热带农业生态研究所中国科学院西北高原生物研究所中国科学院海洋研究所中国科学院南京地理与湖泊研究所

光合有效辐射在生态学、农学以及气候学等多个学科中都有重要的应用价值。它是揭示物质与能量交换过程的基本生理变量,是光合潜力、潜在产量的评估研究和作物生长模拟研究、土壤碳的固定模拟研究中不可缺少的关键数据之一。该数据集涵... 详细信息

光合有效辐射在生态学、农学以及气候学等多个学科中都有重要的应用价值。它是揭示物质与能量交换过程的基本生理变量,是光合潜力、潜在产量的评估研究和作物生长模拟研究、土壤碳的固定模拟研究中不可缺少的关键数据之一。该数据集涵盖了中国8个典型陆地生态类型、中国生态系统研究网络(CERN)下属的40个辐射观测站观测的光合有效辐射日均值,时间跨度为2.05~2.15年。通过对传感器的集中标定与规范的数据质量控制方案,保障了观测数据的可靠性与可比性。采用光谱仪、辐射标准灯传递辐射基准方案,对光合有效辐射传感器进行集中标定与比对,标定精度小于5%,符合世界气象组织(WMO)标准;采用极值法对观测的光合有效辐射数据进行质量控制。

关键词： CERN 中国陆地生态系统光合有效辐射观测

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1995~2.11年CERN土壤环境元素含量数据集

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中国科学数据（中英文网络版） 2017年第1期2卷 11-26,J0013-J0031页

作者：宋歌施建平侯卫龙解宪丽潘贤章陈春兰党廷辉樊月玲傅伟郝翔翔何永涛况福虹兰中东李国振李伟李小军李小丽李晓明李玉强林丽刘海丰刘素萍鲁志云热甫开提沙丽清苏永中田振荣王书伟王小亮吴瑞俊杨风亭杨桂生叶学华于小军曾静张德强张玉铭郑兴波宗海宏中国科学院南京土壤研究所南京210008 中国科学院南京土壤研究所土壤与农业可持续发展国家重点实验室南京210008 中国科学院亚热带农业生态研究所中国科学院水利部水土保持研究所中国科学院沈阳应用生态研究所中国科学院东北地理与农业生态研究所中国科学院地理科学与资源研究所中国科学院成都山地灾害与环境研究所中国科学院新疆生态与地理研究所中国科学院水利部成都山地灾害与环境研究所中国科学院寒区旱区环境与工程研究所中国科学院南京土壤研究所中国科学院成都生物研究所中国科学院西北高原生物研究所中国科学院植物研究所中国科学院华南植物研究所中国科学院西双版纳热带植物园中国科学院华南植物园中国科学院遗传与发育生物学研究所

土壤环境是地球环境的重要组成部分。目前土壤环境问题的关注重点在于土壤污染。我国土壤污染以无机污染为主。中国生态系统研究网络(Chinese Ecosystem Research Network,CERN)自1988年组建以来,在中国主要农田、森林、草原、荒漠、湿... 详细信息

土壤环境是地球环境的重要组成部分。目前土壤环境问题的关注重点在于土壤污染。我国土壤污染以无机污染为主。中国生态系统研究网络(Chinese Ecosystem Research Network,CERN)自1988年组建以来,在中国主要农田、森林、草原、荒漠、湿地生态系统中,按统一的规范,对与土壤环境状况有关的铁、锰、铜、锌、硼、钼、镉、铬、铅、镍、汞、砷、硒元素进行了长期定位监测。通过对CERN典型生态样地表层土壤环境元素监测数据进行加工处理,获得1995~2.11年中国陆地生态系统土壤环境元素含量数据集。本数据集中13.土壤环境元素指标测定的相对误差平均为6.55%,重复测定的相对偏差为7.70%。同时附有完整的背景信息,保证了数据在空间和时间上的一致性。本数据集可以为全国和区域土壤环境质量评估、土壤污染风险评价以及环境土壤学研究等工作提供数据基础。

关键词：微量元素重金属土壤污染中国生态系统研究网络(CERN) 长期定位监测

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