Subjects: Biology >> Ecology submitted time 2018-03-30 Cooperative journals: 《生态学报》
Abstract：城市群已成为国家参与全球竞争与国际分工的重要空间载体，其空间组织形式正从个体城市集聚、等级性的中心地结构向多中心、嵌套式的群落结构演变。为刻画长三角地区城市在不同要素层面下形成的多层次群落结构及其状态，借助复杂网络分析工具，从城市群落的节点特征、垂直和水平结构、不同群落结构间的相互关联三方面实证分析长三角地区3类（生产性、生活性、公共性）对外服务流的网络结构特征。研究显示：1）长三角城市节点服务功能分化，节点层级性分异显著，生产和生活性服务网络的节点规模呈"长尾"分布，公共性服务网络的节点规模相对均衡；2）垂直结构上，3类对外服务网络的网络密度、网络效率、流量占比和空间分布各不相同；水平结构上，初步形成对外服务网络的专业化分工格局，部分城市突破区域界线，呈跨地域集聚组团的态势。3）较强的结构关联性存在于生产性和生活性对外服务网络之间，两者在中低度值的城市节点上具有一致性，呈联动发展格局；公共性对外服务网络与前两者的节点度值分异较大，促进了整体群落服务功能结构的丰富和完善。基于复杂网络的群落研究可以从多维结构的分析中寻求城市群落的分工协作和共生，为当前多核心、网络化的城市空间组织与规划提供科学参考。
Subjects: Geosciences >> Geography submitted time 2024-03-01 Cooperative journals: 《干旱区研究》
Abstract： Vegetation carbon use efficiency (CUE) can objectively reflect the efficiency of vegetation insequestering atmospheric carbon and the response of vegetation to climate change. Using MOD17, land use, andmeteorological data, this study applied methods, such as the Hurst exponent, correlation analysis, and sensitivityanalysis to explore the spatiotemporal variability of vegetation CUE and its sensitivity to climate factors in theShaanxi section of the Yellow River Basin from 2001 to 2021. The results showed that (1) From 2001 to 2021,the gross primary productivity, net primary productivity (NPP), and vegetation CUE in the Shaanxi section of theYellow River Basin exhibited an increasing trend, with an average CUE value of 0.51. (2) The study area wasonly 14.21% of the region, exhibiting a decreasing trend. The high-value areas of vegetation CUE are primarilyconcentrated in the windbreak and sand-fixation areas and the Grain for Green Project areas of northern Shaanxi.The areas where vegetation CUE indicated a decreasing trend accounted for 59.96%, most of which transitionedfrom an increasing trend to a decreasing trend. (3) Overall, temperature and precipitation correlated negativelywith vegetation CUE, but the relationship with precipitation is more significant. Regions with positivecorrelations with temperature and precipitation are distributed in northern Shaanxi’s windbreak and sand-fixationareas. Sensitivity analysis of temperature and precipitation showed that the threshold values were 10 °C and 500mm, respectively. When the temperature is below 10 °C and the precipitation is below 500 mm, the vegetationCUE increases with increasing temperature and precipitation. The relationship between vegetation CUE andclimate factors is more significant and sensitive in arid areas, such as the conversion of farmland to forests andwindbreak and sand-fixation areas in northern Shaanxi.
Subjects: Geosciences >> Other Disciplines of Geosciences submitted time 2019-09-11 Cooperative journals: 《干旱区地理》
Abstract：大量观测数据分析表明，全球气候正逐步变暖。植物物候现象是全球自然环境变化的指示器。物候对气候变化的响应是研究全球气候变化的重要手段之一。森林是全球生态系统的重要组成部分，森林物候特征变化是反映气候变化对森林生长影响的综合性生物指标。利用2001—2018年MOD09A1卫星数据重建了秦岭地区增强型植被指数（EVI）序列，采用最大变化速率和阈值法结合提取了秦岭森林物候参数，结果表明：（1） Whittaker滤波法在灌木、农田、森林3种生态样地重建中稳定性较好，在秦岭山地有较好的适用性。（2） 秦岭地区物候多年均值分布同秦岭地区水热条件密切，由高海拔高山区到农耕区，生长季始期(Start of Growth Season,SOG)逐渐提前，生长季末期(End of Growth Season,EOG)逐渐推迟，生长季长度(Length of Growth Season,LOG)由高海拔区向低海拔区逐渐加长。秦岭浅山区和东部伏牛山农耕带生长季（SOG）开始较早，出现在3月上旬，高山区针叶林带生长季开始的较晚，出现在5月上旬到中旬（120～135 d）之间。生长季末期（EOG）集中出现在10月~11月初（270～310 d），高山区针叶林带生长季结束较早，浅山区植被生长季结束较晚，普遍出现在11月（300 d）以后。生长季长度（LOG）分布在150～200 d之间，低海拔地区LOG较长，大于180 d，高海拔林区生长季较短LOG集中在150～170 d。（3） 年际变化特征：2001—2018年生长季始期（SOG）呈现提前趋势,其中高海拔区提前明显，南北麓海拔低于500 m部分区域和东部伏牛山少部分区域出现推迟。生长季末期（EOG）呈现推迟趋势，其中秦岭北麓和东部中低海拔区域推迟明显，生长期长度（LOG）总体呈延长趋势。（4） 秦岭地区近17 a气温呈现上升趋势，变化率为0.02 ℃·a-1，降水呈现不明显的上升趋势，日照时数则呈现明显的下降趋势，变化率为14.6 h·a-1。（5） 秦岭地区物候参数同0 ℃、5 ℃和10 ℃界限温度、降水、日照时数相关性分析表明，全球变化下的升温作用是影响秦岭森林物候变化的主要因子，升温作用导致SOG提前，EOG推迟、LOG延长，主要集中在秦岭南北麓1 000~2 000 m之间，秦岭东部伏牛山低海拔区境内相关性最低，表明受温度制约较小。
Subjects: Other Disciplines >> Synthetic discipline submitted time 2023-03-28 Cooperative journals: 《中国科学院院刊》
Abstract： One of the most pervasive challenges affecting human and planetary well-being is inadequate access to clean water and sanitation. Problems with water are expected to become worse in the coming decades, with water scarcity occurring globally, in the face of ever-growing populations, intensive human activities, and climatic variation. Addressing the aforementioned water security has been achieved consensus and has been included into the sustainable development goals (SDGs) set by the United Nations’ Agenda 2030. Despite these ample opportunities, it remains challenging to create reliable, sustainable, and affordable solutions to providing universal access to clean water and sanitation. In this context, the emerging artificial intelligence (AI) technology can be an attractive solution to help with this challenge. We summarized the core of the SDGs-Goal 6 (Clean Water and Sanitation) and the problems encountered during the progress to date. Building upon which, we conducted a literature review and provided a state-of-the-art analysis of leveraging AI to help achieving SDGs-Goal 6 alongside the resultant impacts. Afterwards, we highlighted the key issues necessary to be tackled in the coming years if AI is expected to be well applied with its maximum benefits. Plus, we put forward the prospects of future efforts on this revolution.
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