Simulation of seismic response in a city-like environment

We study the seismic response of idealized 2D cities, constituted by non-equally spaced, non-equally sized homogenized blocks anchored in a soft layer overlying a hard half space. The blocks and soft layer are occupied by dissipative media. To simulate such response, we use an approximation of the viscoelastic modulus by a low-order rational function of frequency and incorporate this approximation into a first-order-in-time scheme. Our results display spatially variable, strong, long-duration responses inside the blocks and on the ground, which qualitatively match the responses observed in some earthquake-prone cities of Mexico, France, the USA, etc.     

Long‐term monitoring of a mercury contaminated estuary (Ria de Aveiro,Portugal): the effect of weather events and management in mercury transport

The main aim of this research was to assess the mercury transport from an estuarine basin with a background of anthropogenic contamination during a spring tidal cycle (year 2009) and compare it with two previous tidal cycles (years 1994 and 1999), as part of a long‐term monitoring program. Results showed that effective mercury transport occurs both in the dissolved and particulate fractions (0.18 and 0.20 kg per tidal cycle, respectively), and despite an overall decrease in environmental contamination, results more than double previous findings on particulate transport in the system. These findings result essentially from changes in the tidal prism (net export of 2 million m³ of water), given that both dissolved and particulate concentrations did not increase over time. Hydrodynamic simulations were performed to evaluate the effect of physical disturbance (dredging) and weather events (increased freshwater flow) in these processes, and results suggest the increased freshwater flow into the system as the main forcing function for the mercury transport increment. These results highlight the importance of long‐term monitoring programs, since despite an overall improvement in local contamination levels, the enhancement of transport processes through hydrological changes increases environmental pressure away from the contamination source. Copyright © 2012 John Wiley & Sons, Ltd.     

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.     

基于均一化资料的中国大陆极端温度的长期趋势

近百年来,全球气候变暖。这与暖日和暖夜增加,冷日和冷夜减少相关联。文章研究结果进一步证实了这一发现。本文基于1960-2012年中国大陆542个台站均一化气温资料,通过将中国大陆划分为8个次区域,利用百分位定义法计算了极端温度指数序列,同时,运用时间趋势分析法,对中国大陆各区域极端温度和极端温度指数的时空分布及变化趋势特征进行了分析。结果表明:在全球变暖的背景下,从地理分布而言,中国大陆在过去53年除西南地区外,大部分地区最低和最高温度有显著的升高趋势,其中,东北温度升高最为明显;从季节而言,冬季极端温度升高最为明显,夏季升高最少;最低温度明显升高,最高温度也有所升高,但是最低温度的升高幅度更大。冷夜和冷日出现频率呈减少趋势,暖夜和暖日出现频率呈增加趋势,其中以冷夜指数变化最为突出,均呈现一种区域差异的现象。本文利用更新的资料验证了前人的工作,也进一步分区分析,结果可为更多地区评估以及进一步的相关研究提供参考。     

强厄尔尼诺事件下2016年1月中国南方超级寒潮的动力学机制:瞬变波对大气长波异常的调制

本文采用Plumb三维波活动通量和局地Eliassen-Palm通量诊断方法,利用欧洲中期天气预报中心的逐日再分析资料ERA-Interim,分析了超强厄尔尼诺背景下2016年1月下旬中国南方超级寒潮的动力学机制：瞬变波对大气长波异常的调制。前期2015年12月的北大西洋海表热通量正异常,有助于后期大西洋阻塞形势的维持。大气长波能量沿大圆路径从大西洋阻高经乌拉尔地区向东亚中低纬度传播并在此辐合,导致了乌拉尔阻高和华北横槽的经向结构,更多强冷空气聚集在异常偏南的纬向槽线附近。寒潮爆发前夕,2支瞬变波列活跃在亚欧大陆。北支瞬变波列调制了北方的大气长波,使横槽转竖;南支瞬变波列协同调控了南方的大气长波,使南支印缅槽减弱;两者共同作用,促使冷平流大举南下,直达华南沿海,南方寒潮发生。     

长距离单历元单频非差北斗网络差分方法

针对北斗卫星导航系统现有的网络差分方法多是基于GPS系统并采用双差定位模型,而双差定位模型的采用使得网络差分方法存在一些缺陷的问题,该文提出一种长距离单历元单频非差北斗网络差分方法;首先长距离北斗基准站网向覆盖区域内的北斗用户提供非差误差改正数;然后用户内插计算出自己所需的非差误差改正数,并改正北斗单频伪距观测值的误差;最后进行单历元单频网络差分定位。实验表明,该方法能够使用单历元单频伪距观测数据实现长距离北斗网络差分定位。     

高维遥感图像的快速分类算法

为了实现对高维遥感图像的快速准确分类,提出了一种基于k均值二叉树支持向量机(SVM)的分类方法。该方法通过对选取的训练样本进行k均值聚类,生成支持向量机分类二叉树,作为确定最佳分类顺序的依据,以降低分类过程中的误差累积并提高整体分类精度,而且可缓解由样本数量不均衡导致的分类误差。该方法可在不进行降维处理的情况下,对高维遥感图像进行快速准确分类。测试结果表明,其分类速度和分类精度都优于传统的支持向量机分类结果。     

BDS中长距离非差实时动态厘米级定位算法

针对BDS常规实时动态定位(RTK)中,随着流动站与参考站间的距离增加,大气延迟误差的空间相关性大大降低,影响了整周模糊度的快速解算和流动站位置信息的解算精度问题。该文研究了一种基于非差观测误差的BDS中长距离常规RTK定位算法,采用非差误差改正方法为流动站提供误差改正,利用参考站的非差误差改正数以单颗卫星为对象进行误差改正。对电离层延迟误差和相对天顶对流层延迟误差进行参数估计,处理电离层延迟误差和对流层延迟误差的影响。最后通过BDS实测数据对该算法进行了算法验证和结果分析。实验结果表明,该算法可以实现BDS中长距离常规RTK的快速定位,并获得厘米级定位精度。     

Sexual reproduction and early life‐history traits of the Mediterranean soft coral Alcyonium acaule

Understanding processes that contribute to a better comprehension of the population dynamics of long‐lived species is critical for the maintenance and potential recovery of such species. Despite the abundance of soft corals in Mediterranean rocky reefs, little information exists on their life histories and reproductive patterns. In this study, we assessed the main reproductive characteristics and early life‐history traits of the long‐lived soft coral Alcyonium acaule. The sex ratio was 1:1; the smallest fertile colonies were one finger in size (2.1 ± 0.6 cm in height), and both colony and polyp fertility increased with colony size. Likewise, the number of eggs and spermary sacs per polyp increased significantly with colony size, whereas the diameter of the female and male sexual products did not. Over 6 years of observations (2007–2012), spawning occurred primarily in July, after the seawater reached 20 °C, in a single spawning episode per year. Approximately 80% of female colonies released eggs, which were retained on the surface of the mother colony by mucous strings for up to a few days. High fertilization rates were observed during spawning in 2008 and 2009 (94.9% and 87.0%, respectively). The timing of development was ~24 h for the blastulae, ~48–72 h for the planulae and 8–22 days for metamorphosis into primary polyps. Survivorship of planulae was relatively high (~50% at 45 days after release), but only 24% of larvae metamorphosed into primary polyps, and their survivorship was moderate after 2 months (65% in 2008 and 74% in 2009). Asexual reproduction was negligible, indicating that sexual reproduction is the main mechanism supporting the maintenance and recovery of populations.