短周期彗星表面水冰升华分布研究

彗星是太阳系遗留的原始星子,研究彗星彗核的演化对理解太阳系其他天体的形成和演化历史具有重要意义.在太阳的辐射作用下,彗星携带的挥发性成分会发生升华,并带动尘埃运动,造成彗核物质的损失.因此,彗核的升华活动对其表面形貌甚至整体形状演化都会产生影响.从IAU (International Astronomical Union) MPC (Minor Planet Center)获取轨道数据,并考虑了彗核的自转以及进动,利用MONET (Mass lossdriven shape evolution model)形状演化模型对短周期彗星做数值模拟,计算得到了短周期彗星1P/Halley、9P/Tempel 1、 19P/Borrelly、 67P/C-G (Churyumov-Gerasimenko)、 81P/Wild 2和103P/Hartley 2在一个轨道周期内的太阳辐射能量以及表面侵蚀深度的分布,结合其动力学参数讨论了自转、进动和公转等特性对其表面水冰升华分布的影响以及造成南北侵蚀差异的可能性.     

西北风沙起蚀观测试验及其研究进展

简要阐述了国家自然科学基金“西北沙漠地表沙粒运移特性及动力热力参数研究”支持的“我国西北三大沙漠地表风蚀起沙观测试验”的科学意义、试验方案和科学目标,总结了该试验在前期取得的部分研究进展和研究成果,概括叙述了该项目在地表沙粒跃移特征、起沙风速、沙尘通量等方面的重要发现和一些新的认识。最后,提出并讨论了在沙漠风蚀起沙观测试验方面需要进一步研究和思考的一些重要问题。     

乌鲁木齐市边界层日变化特征

利用乌鲁木齐市晴天CFL-03型风廓线雷达观测资料,分析了边界层日变化特征。得出结论如下:边界层结构季节变化明显。冬、春季300~600m以下风速较小,小于3m/s,且愈近地面风速愈小;以上风速大、风向恒定,基本为东南大风。夏季和秋季风速比冬季和春季小,流场特征较复杂,水平风速和风向变化较活跃,存在明显的风切变。折射率结构常数春、秋和冬季比夏季分别小1个、3个和1~3个量级;夏季最大,集中在10~(-16)~10~(-13) m~(-2/3)之间。春、夏和秋季晴天湍流动能耗散率量级分别在10~(-6)~10~(-2) m~2·s~(-3)、10~(-4)~10~(-3) m~2·s~(-3)、10~(-6)~10~(-3) m~2·s~(-3)之间;白天比夜间约大1个量级。晴天折射率结构常数和湍流动能耗散率日变化特征与风场日变化特征有较好地对应关系,即湍流发展旺盛的区域与风速较大的区域相一致。风廓线雷达资料反演的湍流动能耗散率对春季和夏季边界层结构日变化演变特征的监测较好。夏季夜间稳定边界层约400~500m,残余层可达到约1800m,对流边界层可发展到约2500m,混合层约2200m,夹卷层约300~400m。     

<Emphasis Type="Italic">3D</Emphasis> Modelling of Urban Area Using Synthetic Aperture Radar (SAR)

Synthetic aperture radar (SAR) is a newly-developed remote sensing technology that works in all weather and independent of daylight. Recent satellite designs such as TerraSAR-x, which have resolutions of a couple of meters and sub-meters, have provided appropriate data for modelling and monitoring of urban areas. Image classification and height information extraction is possible considering the nature of SAR data. In this paper, a proper classification method for high-resolution SAR images has been used in urban areas. This classifier is based on statistical models. First, statistical models that are well adapted to urban SAR images are selected. Initial labelling is performed using the maximum likelihood method. A method based on Markov random fields is applied to improve the results by considering neighbourhood information. Meanwhile, topographic information is extracted using the phase difference obtained from SAR interferometry. After classification and height extraction, the homogeneous regions consisting of locations with similar objects are determined. The homogeneous region adjacency graph are generated using vectors containing classification information, extracted objects, height of pixels forming each region, and information on the neighbouring areas. Height and classification information are then merged by assigning height conditions based on the nature of objects and optimizing an energy function. The results obtained, including buildings, streets, and corner reflectors, are easily recognizable. The overall accuracy is improved from 57% in the initial classification to 95% in the employed procedure. Moreover, the accuracy of height estimation is about 2.74 m, which is acceptable for height estimations of buildings with more than one floor.     

从水文学产汇流角度分析碱坪沟“8·13”泥石流特征

本研究以四川省都江堰市龙池镇碱坪沟泥石流流域对象,基于HEC-HMS流域分布式水文模型,利用RS和GIS技术提取流域产汇流参数,模拟分析碱坪沟8·13泥石流的暴雨汇流过程,结合流域内土地利用类型分布,模拟降雨-径流关系,并综合考虑泥石流的相关特征参数,利用情景分析法探讨泥石流流域内的土地利用类型变化对泥石流汇流过程的影响关系。     

Analysis and farmers’ perception of climate change in the Kashmir Valley,India

Theoretical and Applied Climatology - Analysis of climatic variables is important for the detection and attribution of climate change trends and has received considerable attention from researchers...     

Empirical assessment of rockfall and debris flow risk along the Karakoram Highway,Pakistan

Natural Hazards - The Karakoram Highway links north Pakistan with southwest China. It passes through unique geomorphological, geological and tectonic setting. This study focused 200-km-long section...     

基于机器学习的表层土壤成矿元素空间预测: 以稀有金属铷元素为例

近些年随着土地质量地球化学调查工作的开展,获取了大量表层土壤样品数据。然而,这些数据也存在一个明显的缺陷,即1∶50 000大比例尺表层土壤数据往往缺少成矿元素。鉴于土壤成矿元素含量对于矿产资源勘查的重要指示作用,尝试基于现有数据对大比例尺表层土壤成矿元素含量提供一个补全方案。以稀有金属铷元素为例,采用随机森林算法把同一区域2 548组1∶250 000小比例尺表层土壤数据按照8∶2的比例随机分为两组,用80%的数据进行训练建模,20%的数据对模型进行验证。采用变量重要性度量排序和构建学习曲线的组合方法优选了8种元素(K、B、Ni、V、Zn、As、Co、Cu)作为预测变量,模型对训练数据和测试数据的拟合优度R²分别达到0.983 2和0.895 6,说明预测变量的优选方法是有效的。随后将1∶50 000表层土壤的上述预测变量数据作为输入变量导入模型中,得到预测的Rb元素含量,预测结果比较符合实际特征。本研究表明将大数据机器学习随机森林算法引入表层土壤地球化学元素含量空间定量预测具有可行性,可进一步拓展土地质量地球化学数据的服务应用维度。