Composition,abundance, distribution and seasonality of larval fishes in the shallow nearshore of the proposed Greater Addo Marine Reserve,Algoa Bay,South Africa

The larval fish assemblage was investigated in the shallow, nearshore region of a proposed marine protected area in eastern Algoa Bay, temperate South Africa, prior to proclamation. Sampling was conducted at six sites along two different depth contours at ∼5 m and ∼15 m to assess shore association. Larvae were collected by means of stepped oblique bongo net tows deployed off a ski-boat, twice per season for 2 years between 2005 and 2007. In total, 6045 larval fishes were collected representing 32 families and 78 species. The Gobiidae, Cynoglossidae, Clupeidae, Engraulidae and Sparidae were the dominant fish families. Catches varied significantly among seasons peaking in spring with a mean of ∼200 larvae/100 m³. Mean overall larval density was higher along the deeper contour, at ∼15 m (40 larvae/100 m³). The preflexion stage of development dominated catches at the ∼5 m (80%) and ∼15 m (73%) depth contours. Body lengths of Argyrosomus thorpei, Caffrogobius gilchristi, Diplodus capensis, Heteromycteris capensis and Solea turbynei, all estuary associated species, were larger at the shallow sites nearer to shore. Larvae of coastal species that produce benthic eggs dominated catches (75%) in the shallow sites (∼5 m) but were less abundant (32%) farther from shore at the deeper (∼15 m) sites. All developmental stages of D. capensis, Engraulis capensis, H. capensis, Sardinops sagax and two Pomadasys species were found in the study area. It appears that some species use the shallow nearshore as a nursery area.     

Fresh snow chemistry from high mountain regions in central himalayas

Investigations of atmospheric composition in the Himalayas has been limited in both temporal and spatial scales, mainly due to difficult logistics. Ideal sites for monitoring atmospheric composition and its evolution should be free from local pollution and representative of the remote troposphere (HUEBERT et al., 1980). As the Himalayas are far removed from highly industrialized regions they provide suitable locations to monitor the chemistry of the remote troposphere and to study the evolu…     

Current-induced scour beneath initially elevated subsea pipelines

When a subsea pipeline is laid on an uneven seabed, certain sections may have an initial elevation with respect to the far-field seabed, e_o, and thus potentially affecting the on-bottom stability of the pipeline. This paper focuses on quantifying the effects of the upstream dimensionless seabed shear stress, θ_∞, and Reynolds number, Re, on: (1) the maximum dimensionless seabed shear stress beneath the pipe, θ_max, to be compared to the critical shear stress in order to determine whether scour would occur and progress towards an equilibrium state; and, (2) the dimensionless equilibrium scour depth beneath the pipe, S_eq/D. Using a 2-D Reynolds averaged Navier-Stokes (RANS) approach along with the k-ω Shear Stress Transport (SST) turbulence model, a parametric study involving 243 computational fluid dynamics (CFD) simulations was conducted. The simulation results were used to develop a closed-form equation for the prediction of θ_max. Subsequently, experimental measurements of S_eq/D have been compiled from published literature, to develop a new closed-form equation for the prediction of S_eq/D with a high correlation to the experimental data. In summary, we present two closed-form equations for the prediction of θ_max and S_eq/D for pipelines with an initial e_o/D, which are applicable for both clear-water and live-bed conditions. The effects of θ_∞ and Re have been included, albeit Re having a small influence as compared to the other parameters.     

基于压缩感知法对法拉第色散函数的重构

观测到的偏振量与法拉第色散函数之间是傅里叶变换对,而法拉第色散函数反映了辐射区域和辐射传播途径的磁场结构。如何通过这一关系精确重构出法拉第色散函数对于研究银河系及河外星系磁场具有重要的作用。目前已提出了基于压缩感知的法拉第色散函数重构方法,模拟结果要优于传统方法,然而是否具有实用性仍然未知。主要探究该方法应用于实际观测频率范围时是否依然可行,并进行了大样本统计学实验。结果表明:重构结果受多种因素的影响,具有很大的随机性,对重构结果在峰值附近再次进行最小二乘拟合后,重构的法拉第深度更接近真实值。     

基于GPS新型L5信号的地表雪深反演研究

利用GPS多路径反射信号测量地表雪深具有全天候和高时空分辨率的特点,因此其可作为一种代替气象站监测雪深的新手段。然而,先前大多数研究仅使用了GPS L1和L2C波段信噪比数据探测积雪深度。为验证新型的L5信号在雪深反演方面的优越性,本文阐述了GPS-R技术反演雪深的原理,利用Lomb-Scargle周期图法所处理的受积雪表层影响的信噪比数据计算了频谱振幅强度,通过获取频谱特征值与天线高度的关系求解雪深值,最后分别与L1反演结果和实测雪深数据进行了对比。试验结果表明：与现有的GPS-R测量雪深结果相比,利用新型的L5反射信号反演地表雪深的精度更佳;采用GPS-R技术探测雪深对把握测站区域内的雪深变化情况和淡水资源储量具有重要价值。     

深反射地震成像揭示的班公湖 怒江缝合带中段Moho断阶及其大地构造意义

揭示班公湖- 怒江(班怒)缝合带Moho(莫霍面)结构对于认识中特提斯洋壳俯冲和南羌塘坳陷成因具有重要地球动力学意义。基于横跨班怒缝合带的深反射地震数据(88°30′E),本文采用了中长波长静校正、噪声压制、优化叠加和叠前深度偏移(PSDM)等地震处理技术,获得了深度域地震反射偏移剖面、层速度场和高分辨率Moho结构。由深度域剖面显示,班怒缝合带Moho位于地表以下65～80 km,呈不连续北向抬升趋势,指示在拉萨地块与南羌塘地块之间存在岩石圈上地幔断阶,最大阶步可达15 km。综合分析缝合带两侧的Moho形态认为,这些断阶受南侧拉萨地体的岩石圈上地幔以19. 5°北倾俯冲与北侧南羌塘地块的上地壳抬升驱动,可能与深部存在局部熔融相关。班怒缝合带下的Moho结构表明,随着晚侏罗世—早白垩世中特提斯洋闭合,南羌塘地体由边缘海沉积向前陆盆地转换,形成南羌塘坳陷。     

我国南方西南和中东部区域两次持续性低温雨雪过程与关键环流系统的关系研究

利用NCEP再分析资料和国家气象信息中心提供的753站逐日气温和降水资料,对比分析了我国南方西南和中东部区域两次持续10 d以上的低温雨雪过程,结果表明:(1)两次过程中欧亚大陆中高纬东亚大槽均加深,但环流形势有差异。西南过程呈现"北高南低"形势,关键脊区在贝加尔湖,而中东部区域过程"北高南低"和"西高东低"形势共存,关键脊区从乌拉尔山延伸至贝加尔湖。两次过程异常的环流与北大西洋向东传播的波列有关。(2)西南过程关键脊区提前过程3 d发展并东移至贝加尔湖,形成稳定形势;而中东部区域过程关键脊区提前过程一周发展,在开始日达最强。两次过程均伴随蒙古高压东移南压使地面降温,500 hPa关键脊区超前蒙古高压2 d变化。西南过程降温主要受到冷平流和绝热冷却影响,而中东部区域过程主要受到冷平流的影响。(3)西南过程水汽来自孟加拉湾,只受南支槽支配。中东部区域过程水汽来自孟加拉湾、南海和西太平洋,由南支槽和西太平副热带高压的共同影响。两次过程水汽正收支主要来自南边界。     

邢台地震b值的深度变化特征分析

对1966~1984年邢台地震目录进行了整理,应用基于MATLAB的Z-MAP软件分析了地震目录的完整性,将目录划分为地震活跃时段、地震活动过渡时段、地震活动相对稳定时段。主要分析了各时段地震深度b值、沿断层深度剖面扫描b值,根据深度b值变化对邢台地震演化特点进行了分析。     

SNOW WETNESS ESTIMATES OF VEGETATED TERRAIN FROM SATELLITE PASSIVE MICROWAVE DATA

The Special Sensor Microwave/Imager (SSM/I) radiometer is a useful tool for monitoring snow wetness on a large scale because water content has a significant effect on the microwave emissions at the snowpack surface. To date, SSM/I snow wetness algorithms, based on statistical regression analysis, have been developed only for specific regions. Inadequate ground-based snow wetness measurements and the non-linearity between SSM/I brightness temperatures (T_Bs) and snow wetness over varied vegetation covered terrain has impeded the development of a general model. In this study, we used a previously developed linear relationship between snowpack surface wetness (% by volume) and concurrent air temperature (°C) to estimate the snow wetness at ground weather stations. The snow condition (snow free, dry, wet or refrozen snow) of each SSM/I pixel (a 37 × 29 km area at 37.0 GHz) was determined from ground-measured weather data and the T_B signature. SSM/I T_Bs of wet snow were then linked with the snow wetness estimates as an input/output relationship. A single-hidden-layer back-propagation (backprop) artificial neural network (ANN) was designed to learn the relationships. After training, the snow wetness values estimated by the ANN were compared with those derived by regression models. Results show that the ANN performed better than the existing regression models in estimating snow wetness from SSM/I data over terrain with different amounts of vegetation cover.