灾害性天气实况智能警示系统

介绍了江苏省灾害性天气实况警示系统,该系统对9210系统下发的全省重要天气实况报文解码入库、分析处理,以声音、图像、短信、电子邮件等多种方式进行智能报警,通过图形处理终端进行实时显示。这种灾害性天气实况的网络联防和智能报警功能,为预报员及时监测灾害性天气的发生发展、进行联防和开展准确及时的预报服务提供了重要的实时信息。     

节能温度、供热气象指数及供热参数研究

对北京市2001～2002年采暖季的室内外气象一热力试验数据进行科学分析,求出实际条件下不同环境温度、风速、辐射等气象条件下的节能温度,提出了供热气象指数及等级划分标准,推算出总供热量、供回水温度差及通过控制回水温度而知供水温度等供热参数,并已在北京市供热作业中投入应用,为实时供热调度提供了参考依据,是节能、增效、减污的基础。     

城市热岛强度订正与供热量预报

利用公主坟自动气象站(供热区)和北京市观象台(郊区)间1997～2001年4个冬季逐月(11月～3月)逐日4次气温差值,研究了城市热岛变化特征,结合其他研究提出了北京市各供热区城市热岛强度订正原则和初步结果。对北京市2001～2002年采暖季的室内外气象-热力试验数据进行科学分析,求出不同环境温度条件下单位面积供热量模式,按此模式运行则存在较大节能空间。     

东北冷涡的多普勒雷达资料特征分析

多普勒雷达探测东北冷涡具有一定的径向速度特征;应用蓝金(Rankine)模式进行数值模拟,结果表明,其模拟的多普勒速度分布特征与多普勒雷达实际探测冷涡的特征极其相似;根据多普勒径向速度特征确定冷涡中心,进而应用不同时刻资料确定冷涡的移动路径,对实际业务有很好的应用价值。     

气象条件对奶牛产奶量的影响及对策

在分析奶牛产奶量时间和空间分布的基础上,将乌兰浩特市全市产奶量和乌兰哈达奶牛场观测站产奶量与乌兰浩特市气象资料进行相关分析,确定了影响当地牛奶产量的关键气象因子和关键时期。结果表明,温度是当地春秋季节和冬季影响奶牛产奶量的主要限制因子,其次是气压和相对湿度,而降雨量和日照时数的影响不明显。     

三角褐指藻与抑食金球藻的竞争及化感作用研究

为探明三角褐指藻（Phaeodactylum tricornutum）对抑食金球藻（Aureococcus anophagefferens）是否具有化感作用,本研究以三角褐指藻和抑食金球藻为实验材料,研究了抑食金球藻在单培养和共培养条件下的生长情况及三角褐指藻培养滤液对其生长和叶绿素荧光参数的影响。结果表明:三角褐指藻对抑食金球藻有明显的化感抑制作用。在单培养体系中,抑食金球藻的生长曲线可用逻辑斯谛增长模型拟合,随着起始密度的增加,环境容量（K）逐渐减小,而抑食金球藻的种群瞬间增长率（r）、进入拐点时间及稳定期细胞密度均较为接近;当三角褐指藻与抑食金球藻以不同起始密度比共同培养时,抑食金球藻的生长均受到了显著地抑制（P<0.05）,但其抑制作用并未与三角褐指藻的密度呈明显的线性关系;滤液培养实验发现,10 mL和15 mL三角褐指藻培养滤液的加入可对抑食金球藻的生长产生显著影响（P<0.05）,对其叶绿素荧光参数没有影响（P>0.05）,25 mL和35 mL三角褐指藻培养滤液的加入可对抑食金球藻的生长和叶绿素荧光参数均产生显著的抑制作用（P<0.05）,叶绿素荧光参数F_v/F_m、ΦPSⅡ、alpha的值降低,Ik的值增加,PSⅡ受到损害。     

15 ka以来罗斯海陆架岩心沉积学记录及古海洋学意义

本文对取自罗斯海陆架的JB04岩心沉积物进行AMS14C测年、粒度、有机碳等测试,结合沉积物粒度组分因子分析,研究该岩心的沉积学记录,探讨其古海洋学意义。结果显示:JB04岩心沉积物的底部年龄为15 ka;沉积物粒度组分因子分析提取出3个环境敏感粒级,分别代表正常冰海沉积、低能海洋沉积和高能海洋沉积;综合沉积物岩相及沉积物组成特征,可以将岩心分为4段,从底部到顶部依次为主要受冰盖刮蚀影响的冰盖下沉积、属低能海洋环境的冰架下沉积、属高能海洋环境的冰架前缘沉积和主要受冰山影响的季节性海冰区沉积。该岩心的沉积地质记录及其古海洋学研究对全面认识罗斯海的海洋环境演变具有重要的意义。     

孟加拉盆地东部褶皱带差异构造变形及成因

基于野外地质调查和地震资料分析,证实了孟加拉盆地东部褶皱带发育底部和中部两套滑脱层。底部滑脱层在整个褶皱带均发育,而中部滑脱层仅发育于北段—中段的陆上和陆架区,在中段的陆坡-洋盆区及南段则不发育。中部滑脱层的发育受古陆坡富泥沉积控制。北段—中段主要发育滑脱褶皱及被断层突破的滑脱褶皱,而南段主要发育泥底辟和泥火山构造。孟加拉盆地东部褶皱带滑脱层系及构造样式的差异变形特征主要受区域挤压作用、沉积作用以及地层流体超压等方面存在着差异的共同影响。     

Combining AHP and genetic algorithms approaches to modify DRASTIC model to assess groundwater vulnerability: a case study from Jianghan Plain,China

Accurate identification of vulnerability areas is critical for groundwater resources protection and management. The present study employed the modified DRASTIC model to assess the groundwater vulnerability of Jianghan Plain, a major farming area in central China. DRASTICL model was developed by incorporating the land use factor to the original model. The ratings and weightings of the selected parameters were optimized by analytic hierarchy process (AHP) method and genetic algorithms (GAs) method, respectively. A combined AHP–GAs method was proposed to further develop this methodology. The unity-based normalization process was employed to categorize the vulnerability maps into four types, such as very high (>0.75), high (0.5–0.75), low (0.25–0.5), and very low (<0.25). The accuracy of vulnerability mapping was validated by Pearson’s correlation coefficient between vulnerability index and the nitrate concentration in groundwater and analysis of variance F statistic. The results revealed that the modified DRASTIC model had a large improvement over the conventional model. The correlation coefficient increased significantly from 41.07 to 75.31% after modification. Sensitivity analysis indicated that the depth to groundwater with 39.28% of mean effective weight was the most critical factor affecting the groundwater vulnerability. The developed vulnerability model proposed in this study could provide important objective information for groundwater and environmental management at local level and innovation for international researchers.     

Full probabilistic design of slopes in spatially variable soils using simplified reliability analysis method

ABSTRACT

A simplified reliability analysis method is proposed for efficient full probabilistic design of soil slopes in spatially variable soils. The soil slope is viewed as a series system comprised of numerous potential slip surfaces and the spatial variability of soil properties is modelled by the spatial averaging technique along potential slip surfaces. The proposed approach not only provides sufficiently accurate reliability estimates of slope stability, but also significantly improves the computational efficiency of soil slope design in comparison with simulation-based full probabilistic design. It is found that the spatial variability has considerable effects on the optimal slope design.