1987年9月23日日环食引起的电离层扰动

本文利用武汉电离层观象台的高频多普勒台阵、TEC台阵和加密频高图等多种观测资料,分析了1987年9月23日发生在我国境内的日环食的电离层效应,简要地讨论了光食过程的电离层变化,着重研究了食后电离层扰动以及这种效应的高度演变和传播特征.结果表明:1.在不同高度上电离层的光化过程的弛豫时间不同,光食效应有明显的高度差异;2.食后出现了电离层行扰,这种扰动的特性随高度变化十分复杂,但不同高度上扰动的水平传播方向都指向日食中心带.这种扰动的激发源不在环食中心带内,它的激发可能与日食时大范围的大气冷却收缩所引起的低层大气运动的不稳定性有关.     

岬间海滩剖面短期变化的动力作用分析

根据粤东寮嘴口岬间海滩重复测量的剖面数据及实测波，风资料，运用交叉谱等方法分析了寮河口海滩剖面短期变化过程的主要动力作用，结果表明海滩短期的变化过程主要是响应风应力，波浪及台风大浪动力的作用。     

南海南部海区气候要素的变化和分布特征

南海南部海区属较典型的区域性热带和赤道带海洋性季风气候区。各气候要素的变化特征与冬、夏季风气流的转换关系密切。汇集沿岸各站和海岛站以及近15年来的考察资料，对气候要素的变化和分布特征作了初步的统计分析，为科研和海洋天气预报提供了许多实用的数据。     

日长变化与地磁场变化的时滞相关分析

利用历史地磁场模型资料和日长资料,计算和分析了1840~2000年期间地磁场变化与日长变化的互相关特点。结果表明,地磁变化与日长变化存在时滞相关。地磁场强度参数的变化比日长变化超前7.5~10a(年);地磁场西向漂移参数比日长变化滞后,其中,西漂分量.λ12和.λ23滞后7.5a,而地磁场的平均漂移速度和纬度漂移分量滞后20~22.5a。     

西南地区极端降水变化趋势

利用西南地区90个气象台站1970-2010年逐日降水量资料,依据世界气象组织(WMO)定义的连续5d最大降水量、总降水量、强降水比等6种极端降水指数,采用F检验、11a滑动平均等统计方法,研究了西南地区极端强降水变化趋势的时空变化特征。在时间上,西南地区近41年来冬、春、夏季连续5d最大降水量缓慢波动上升,秋季连续5d最大降水量呈下降趋势;强降水、降水强度及强降水比呈上升趋势,但总降水量和最长持续无降水日数呈减少趋势;另外,各极端降水指数还存在明显的年际、年代际变化。在空间上,西南地区极端降水变化趋势具有显著的地域差异,呈东西或西北东南向梯度变化特征。其中冬季连续5d最大降水量、降水强度、强降水比及最长持续无降水日数,在西南大部分地区呈增加趋势。秋季连续5d最大降水量与总降水量在西南大部分地区呈减少趋势。而春、夏季连续5d最大降水量和强降水的增减区域大致相当。     

基于偏微分方程的小比例尺晕渲图地貌综合方法研究

基于高精度DEM数据的晕渲图中,破碎的地形细节破坏了晕渲法的立体塑造能力,但是采用传统的均值滤波法进行地貌综合时,在消除地形细节的同时,模糊了地貌特征。为了克服消除地形细节和保持地貌特征这一矛盾,利用偏微分方程各向异性的特点,采用基于经典总变分模型的偏微分方程法对地貌进行综合。实验结果证明偏微分方程法在综合地貌时能较好地保持地貌结构特征。     

高分辨率遥感影像的全变分分割模型

由于全变分(Total Variation,TV)模型具有较好的去噪、增强和扩散等功能,在过去的几十年中,TV模型在图像去噪、增强和超分辨率重建等方面得到了深入研究与广泛应用。鉴于TV模型的理论与分割理论具有一致性,因此本文主要研究TV模型用于高分辨率遥感影像的分割,并针对地物多尺度特征,提出了自适应的TV(ATV)模型;且与目前流行的面向对象的影像分析软件eCognition中的FNEA分割方法进行了比较。实验采用2幅高分辨率遥感影像,同时采用了面向对象的分割和分类评价,得出各方法各具优缺点的结论。     

大同地震序列的尾波Q值变化特征研究

要：利用大同台网1990～2000年的地震记录资料,系统地分析了大同地震序列尾波Q值随时间的变化,着重分析了尾波Q值在中强地震前的短期变化特征。结果表明,分频率的尾波Q值在几次中强地震前都不同程度出现了低值,并与大同震情“窗口”出现同步的异常。     

Scale matters–A multi-resolution study of the determinants of patterned ground activity in subarctic Finland

Numerical studies of earth surface processes in relation to their environment are one of the central topics in physical geography. However, collinearity between explanatory variables and spatial autocorrelation can hinder the detection of key environmental correlates underlying response–explanatory variables' relationships identified by traditional regression methods. Moreover, conclusions about the potential importance of environmental variables have generally made on analysis conducted only at one spatial scale (resolution). In this study, a variation partitioning method provided a framework to obtain new insights into the relative roles of different factors determining patterned ground activity at multiple spatial resolutions. The variation in the distribution of the sorted and non sorted patterned ground was decomposed into independent and joint effects of relief, soil and spatial variables (geographical location) based on a multi resolution system of 1 ha, 25 ha and 100 ha cells covering in total 100 km² of a subarctic landscape in northern Finland.The independent effects of relief and soil variables captured the largest fraction of the variation in the non-sorted patterned ground distribution, while relief had a major contribution for sorted patterned ground activity. The independent effect of spatial autocorrelation on sorted patterned ground was higher than that on non-sorted patterned ground. However, a considerable amount of variation in the distribution of both patterned ground types was accounted for by the joint effects of explanatory variables and may thus be causally related to two or all three groups of variables. Our analyses produced often contradicting results at different resolutions. Consequently, this has substantial implications for the study of geomorphological systems, since the choice of resolution can have a major effect on the inferences of analyses. Our results draw attention to the roles of resolution and spatial autocorrelation in the study of geomorphological systems.     

Designing an optimal multivariate geostatistical groundwater quality monitoring network using factorial kriging and genetic algorithms

The optimal selection of monitoring wells is a major task in designing an information-effective groundwater quality monitoring network which can provide sufficient and not redundant information of monitoring variables for delineating spatial distribution or variations of monitoring variables. This study develops a design approach for an optimal multivariate geostatistical groundwater quality network by proposing a network system to identify groundwater quality spatial variations by using factorial kriging with genetic algorithm. The proposed approach is applied in designing a groundwater quality monitoring network for nine variables (EC, TDS, Cl⁻, Na, Ca, Mg, SO ₄ ²⁻ , Mn and Fe) in the Pingtung Plain in Taiwan. The spatial structure results show that the variograms and cross-variograms of the nine variables can be modeled in two spatial structures: a Gaussian model with ranges 28.5 km and a spherical model with 40 km for short and long spatial scale variations, respectively. Moreover, the nine variables can be grouped into two major components for both short and long scales. The proposed optimal monitoring design model successfully obtains different optimal network systems for delineating spatial variations of the nine groundwater quality variables by using 20, 25 and 30 monitoring wells in both short scale (28.5 km) and long scale (40 km). Finally, the study confirms that the proposed model can design an optimal groundwater monitoring network that not only considers multiple groundwater quality variables but also monitors variations of monitoring variables at various spatial scales in the study area.