Predictability of Chinese Summer Extreme Rainfall Based on Arctic Sea Ice and Tropical Sea Surface Temperature

Chinese summer extreme rainfall often brings huge economic losses, so the prediction of summer extreme rainfall is necessary. This study focuses on the predictability of the leading mode of Chinese summer extreme rainfall from empirical orthogonal function(EOF) analysis. The predictors used in this study are Arctic sea ice concentration(ASIC) and regional sea surface temperature(SST) in selected optimal time periods. The most important role that Arctic sea ice(ASI) plays in the appearance of EOF1 may be strengthening the high pressure over North China, thereby preventing water vapor from going north. The contribution of SST is mainly at low latitudes and characterized by a significant cyclone anomaly over South China. The forecast models using predictor ASIC(PA), SST(PS), and the two together(PAS) are established by using data from 1980 to 2004. An independent forecast is made for the last 11 years(2005-2015). The correlation coefficient(COR) skills between the observed and cross-validation reforecast principal components(PC) of the PA, PS, and PAS models are 0.47, 0.66, and 0.76, respectively. These values indicate that SST is a major cause of Chinese summer extreme rainfall during 1980-2004. The COR skill of the PA model during the independent forecast period of 2004-2015 is 0.7, which is significantly higher than those of the PS and PAS models. Thus, the main factor influencing Chinese summer extreme rainfall in recent years has changed from low latitudes to high latitudes. The impact of ASI on Chinese summer extreme rainfall is becoming increasingly significant.     

Fast adsorption of microcystin-LR by Fe(III)-modified powdered activated carbon

Journal of Oceanology and Limnology - Microcystins (MCs) are cyclic hepatotoxic peptides produced by the bloom-forming cyanobacterium Microcystis and present a public health hazard to humans and...     

印度尼西亚北苏拉威西蓝碧海峡浮游动物种类组成及分布

Based on oceanographic survey data in June 2012 in the Lembeh Strait, the zooplankton ecological characteristics such as species composition, individual abundance, dominant species and distribution were analyzed. The results showed that 183 species(including 4 sp.) had been recognized, most of them belonged to copepoda.Cnidaria followed with 43 species(including 1 sp.) were identified. The average abundance of zooplankton was(150.47±58.91) ind./m~3. As to the horizontal distribution, the abundance of the zooplankton was higher in the southern waters than in the northern waters. The dominant species in the study area were Lensia subtiloides,Sagitta enflata, Lucifer intermedius, Oikopleura rufescens, Diphyes chamissoni, Creseis acicula, Subeucalanus subcrassus, Temora discaudata, Aglaura hemistoma, Doliolum denticulatum, Canthocalanus pauper, Oikopleura longicauda and Nanomia bijuga. Zooplankton biodiversity indexes were higher in study area than previous study in the other regions. The findings from this study provide important baseline information for future research and monitoring programs.     

基于SODA资料的南海表层风能输入的空间分布与长期趋势研究

海面风不仅是驱动上层海洋运动的主要动力, 其能量也是维持海洋表层流动的主要机械能来源。为了分析南海表层流风能输入的变化, 用SODA(Simple Ocean Data Assimilation)(1901—2010)资料估算了风向南海表层流(表层地转流+表层非地转流)的能量输入。结果表明, 风向南海表层流、表层地转流和表层非地转流输入的能量总体均呈减少趋势, 110年间分别减小了约56%、65%和49%。导致风能输入减小的最主要因素是风应力的减弱(减小了35%)。由于南海受季风系统的控制, 风向表层流及其各成分输入的能量呈现出显著的季节性变化。冬季风能输入最强, 高值区位于南海西部及北部区域, 呈一个显著的“回力镖”状结构。这些结果对深入认识南海环流具有理论意义。     

基于分布式控制力矩陀螺的水下航行器轨迹跟踪控制

基于控制力矩陀螺群(CMGs)的水下航行器具有低速或零速机动的能力。采用基于分布式CMGs的水下航行器方案,并研究其水平面的轨迹跟踪控制问题。通过全局微分同胚变换将非完全对称的动力学模型解耦成标准欠驱动控制模型,并根据简化的模型构建其轨迹跟踪的误差动力学模型,将轨迹跟踪控制问题转化为误差模型镇定问题。基于一种分流神经元模型和反步法设计了系统的轨迹跟踪控制律,该控制器不需要对任何虚拟控制输入进行求导计算,且能确保跟踪误差的最终一致有界性。仿真结果表明该控制器能够实现在不依赖动力学参数先验知识的情况下对光滑轨迹的有效跟踪。     

河西走廊西段戈壁灌木群落多样性及其分布格局研究

通过野外样方调查河西走廊西段荒漠戈壁典型灌木群落多样性状况，研究探讨了 8 种典型 灌木群落物种多样性的空间分布格局及其与地理因子的关系，对荒漠戈壁植物群落的多样性的保 护和可持续发展具有重要意义。结果表明：(1) 8 个典型灌木群落 Shannon-Wiener 指数、Simpson 指 数、Margalef 指数和 Pielou 指数从高到低为：盐爪爪群落>麻黄群落>合头草群落>红砂群落>梭梭群 落>泡泡刺群落>多枝柽柳群落>沙拐枣群落；波动范围分别为 0.314 ~ 1.355、0.179 ~ 0.666、0.334 ~ 1.222 和 0.051 ~ 0.218，说明荒漠戈壁灌木群落物种多样性指数偏低，群落结构简单，物种组成稀 少。(2) 不同灌木群落内物种数越多，群落间 Jaccard 相似性系数越大。大部分灌木群落类型间 Jac? card 相似性在 0.20 ~ 0.60 之间，群落间相似水平较低，群落相对稳定。(3) 随着海拔的升高，Margalef 指数、Pielou 指数和 Shannon-Wiener 指数均呈先增加后降低的单峰型分布格局，最大值出现在海拔 2 000 m，且与海拔显著相关（P<0.05）；在经度梯度上，从东到西，Margalef 指数、Pielou 指数和 Shan? non-Wiener 指数呈递增格局，但与经度无显著相关性（P>0.05）；在纬度梯度上，从南到北，Margalef 指数、Pielou 指数和 Shannon-Wiener 指数呈显著递增趋势（P<0.05）。总体上，荒漠戈壁灌木群落物 种多样性呈现出明显的垂直（海拔）和纬度地带性分布格局。     

一种新型浅地层剖面仪试验平台的设计与应用

为探讨浅地层剖面声学影像形成原理,探索不同类型沉积物声学影像特征,厘清不同沉积物厚度的识别算法,采用模块化设计理念研发一套新型浅地层剖面仪室内试验平台。综合室内定位技术、浅剖试验平台尺寸合理性分析技术、直线轨道与换能器之间的固定连接技术和海底声学参数反演技术,形成一个试验高效的新型平台。通过铺设沉积物和布设障碍物,利用浅地层剖面仪进行走航测试,精确识别了沉积物的厚度和障碍物的位置。该平台可为开展理想环境下沉积物厚度和障碍物识别提供较好的测试环境,为声学海洋设备性能检测提供测试平台,也为高校学生和技术人员提供设备使用培训场所。     

日照市露天开采矿山地质环境问题及恢复治理措施

通过1∶5万矿山地质环境调查和遥感解译手段,对日照市矿山进行了全面的调查,查明各类矿山546处,主要集中在莒县和五莲县境内。除莒县刘官庄煤矿外,其余全部为露天开采矿山,主要选用直接开挖的方式,不仅破坏了地表环境,而且矿山生产活动产生的固体废弃物占用了大面积的土地。该文根据矿山地理位置及开采方式的不同,将相同类型的多个矿山划分为同一治理区,针对不同治理区的特点,采取科学有效的治理方法,建立健全矿山地质环境监督管理机制,切实解决矿山地质环境问题,改善生态环境。     

中国省域尺度17部门资本存量的时空特征分析

随着灾害强度、频率以及承灾体暴露的增加,自然灾害造成的损失日益严重。资本存量作为承灾体的经济暴露指标之一,是灾害损失评估的前提和基础。针对目前中国缺乏省域尺度长时间序列的经济部门分类的资本存量数据基础,论文通过永续盘存法,建立了2003—2015年中国大陆31省17部门的资本存量数据库,并分析其时空特征。结果显示：① 全国总资本存量与灾害直接损失的年际变化均呈增加趋势。省域尺度上,通过相关性分析显示,在99%置信度水平上,两者呈显著正相关(r=0.3)。② 时间上,各省17部门资本存量基本也呈增加趋势,但增速不同。在各部门增速最快的省份中,黑龙江省的居民服务、修理和其他服务业增速最快,增长约454.3倍;其次是青海省的租赁和商务服务业(398.3倍)、江苏省的金融业(295.1倍)、安徽省的科学研究和技术服务业(125.1倍)等。③ 空间上,2015年各省17部门资本存量最多的前4个部门分别是房地产业,工业,交通运输、仓储和邮政业,水利、环境和公共设施管理业,占比均在60%以上;且这4个部门资本存量暴露最多的省份是江苏省和广东省。该结果有助于从时空角度了解各省各部门资本存量暴露情况,为各省灾害风险管理者的防灾减灾工作提供重要的参考价值。     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.