A log‐normal spectral analysis of inorganic grain‐size distributions from a Canadian boreal lake core: Towards refining depositional process proxy data from high latitude lakes

Better methods for interpreting grain‐size spectra will enhance current understanding of past transport–depositional processes. A high‐resolution inorganic grain‐size dataset has been measured from a freeze core extracted from ‘Alberta Lake E’ a boreal fresh water lake 40 km east of the Athabasca Oil Sands in north‐eastern Alberta, Canada. The grain‐size spectra are remarkably consistent throughout the core, exhibiting a structure comprising six persistent grain‐size distributions below ca 250 μm, plus a rare medium‐sand distribution. Automated deconvolution of the grain‐size spectra produced poor results. Constraining the modes of two of the distributions produced deconvolution solutions that were statistically excellent and consistent with the structure of each spectrum. Statistical analysis of the ‘constrained’ solutions indicates that deconvolution successfully extracted independent grain‐size populations. Conversely, the multimodal spectra generate traditional measures (for example, mean grain size) that are inconsistent combinations of different individual populations and thus are poor proxies of transport–depositional processes. Alberta Lake E is situated in a boreal wetland landscape where sediment delivery is dominated by overland flow transport during spring melt. This context means that the Alberta Lake E grain‐size spectra can be interpreted to reflect: (i) a bedload component transported during short‐duration high discharge events that reflect the intensity of the melt; and (ii) a finer suspended load component representing material whose magnitude is controlled by the volume of the spring melt. Stratigraphically, bedload and suspended load populations demonstrate different short‐wavelength and long‐wavelength cyclicity, suggesting that spring melt is likely to be driven by cyclic external forcing factors. The links between the grain‐size spectra and spring melt have potential for generating proxy records that better capture the external controls over spring melt in boreal systems and the risks associated with these energetic hydrodynamics. This is exemplified by the coarsest Alberta Lake E distributions, which indicate that more intense spring‐melt dynamics occurred in pre‐historical times.     

海南岛东南部海岸砂丘风暴冲越沉积记录

通过海南岛东南部海岸详细的古风暴学考察,在尖岭海岸发现了含有风暴冲越沉积物的海岸沙丘剖面,分别命名为JL-1和JL-2剖面,试图从海岸沙丘沉积记录中提取历史上的风暴事件信息。沉积物粒度、磁化率等参数的指标分析表明,这两个剖面含有典型的风暴冲越沉积物,利用放射性核素AMS14C测年、OSL测年分析,并结合历史文献记载,确定这些风暴沉积层是多次台风作用的产物,其形成机制与风暴浪越过海岸沙丘的堆积有关,风暴流越过沙丘顶部后不能回流,导致风暴流携带的沉积物迅速沉积。此外,依据Stockdon经验公式计算结果,该地点沉积记录所代表的最大风暴事件相当于100到200年一遇的重现期。研究表明,该处海岸沙丘冲越沉积含有南海台风强度与重现期的重要信息。     

冲积扇沉积向滨岸沉积的转变--以华北克拉通南缘中元古界兵马沟组为例

中国华北克拉通南缘的前寒武纪沉积地层发育齐全、出露较好,具有较高的研究价值。但前寒武纪的沉积地层年代久远,缺少佐证的生物依据,对沉积环境的判断更多依赖于构造背景、地理环境、岩石类型和沉积构造等,导致前人研究有较多的争议。以华北克拉通南缘汝阳群兵马沟组为例,通过精细分析岩石特征、古水流和沉积相,结合相关地层的区域对比及古地貌的重建,解释了兵马沟组的沉积成因和环境转换。汝阳群沉积地层不整合覆盖在华北克拉通南缘太古宙变质结晶基底或古元古代熊耳群火山-沉积岩系之上,兵马沟组作为汝阳群底部的地层单元,受地形和断裂的明显控制,接受丰富的物源供给,发育了一套极具特色的“双层叠加”沉积地层,代表了从冲积扇沉积到砂砾质滨岸沉积的环境转变,作为华北克拉通南缘以海相为主控的沉积开端,具有重要的科学价值。     

威海市海岸带地质环境承载力综合评价研究

该文在威海市海岸带综合地质调查项目的基础上,根据陆域和海域不同的地质环境条件,分别进行了地质环境承载力综合评价。陆域选取了3个一级评价因子和13个二级评价因子建立地质环境承载力评价指标体系,利用层次分析法取得各评价因子权重,再利用模糊数学综合指数法进行了综合评价。海域选取了海底沉积物重金属污染、海底沙坡沙脊、浅层气、浅埋基岩和埋藏下切谷等5个评价因子,采用叠加法进行了综合评价。最终,将研究区地质环境承载力划分为强区、较强区、一般区、较弱区等4类。研究区陆域地质环境承载力相对较强,占总面积的67.67%;其次为一般区,占总面积的31.67%;较弱区零星分布,仅占总面积的0.66%。近海地质环境承载力总体较强,一般区主要分布在威海湾西部,较弱区分布在威海湾以北的广阔海域。     

青海省宗马海湖地区始新—中新统干柴沟组沉积相探析

宗马海湖地区位于柴达木盆地的北缘,有着良好的油气、钾盐勘探前景。该文在分析柴达木盆地北缘宗马海湖地区区域地质背景的基础上,对始新-中新统干柴沟组的岩石学特征、地层特征、沉积构造特征及沉积相等进行了较为深入的研究。干柴沟组沉积相以滨湖亚相、浅湖亚相及扇三角洲相为主,滨-浅亚湖沉积发育最为广泛。扇三角洲相具牵引流、重力流沉积特征,中、粗粒碎屑岩为主;滨湖亚相具较高流态的水动力条件,以砂和粉砂为主,砂泥间互层频繁;浅湖亚相以泥质沉积为主,显示低能的浅水静态沉积作用。文中总结了研究区干柴沟组沉积期沉积相演化特征,建立了干柴沟沉积相平面展布图;干柴沟组沉积相的形成与季节性变化息息相关,湖水上涨、后退导致北部山区扇三角洲较发育,而南部区域始终位于滨、浅湖环境中。     

青藏高原湖泊面积动态监测

高原湖泊的动态变化对区域水循环具有重要影响。受全球气候变化的影响,青藏高原湖泊自20世纪90年代开始呈现剧烈扩张趋势。为揭示近年来青藏高原湖泊面积的时空变化规律,本文提出了一种改进的半自动湖泊提取算法,结合环境减灾卫星（HJ-1A/1B）和Landsat系列卫星影像数据,对青藏高原内流流域中面积大于50 km²的127个湖泊进行了连续6年的动态监测,并分析了该区域2009-2014年湖泊面积时空变化特征。研究结果表明,该区域湖泊整体呈现显著扩张趋势,年均变化速率为231.89 km²yr^-1（0.87 %yr^-1）,6年间湖泊面积扩张速率有所减缓。其中,扩张湖泊有104个,收缩湖泊有23个,变化速率分别为271.08 km²yr^-1（1.02 % yr^-1）和-39.19 km²yr^-1（-0.15 %yr^-1）。不同区域湖泊面积变化具有明显差异,主要表现为东部及北部大部分区域湖泊扩张,南部地区大部分湖泊面积稳定,萎缩湖泊主要分布于研究区四周。最后,本文通过分析冰川融水补给对湖泊面积变化的影响,发现存在冰川融水补给的湖泊面积变化率远大于不存在冰川融水补给的湖泊。由此可见,近年来冰川融水的增加是促进青藏高原内流流域湖泊扩张的主要因素之一。     

基于天气因素的我国登革热流行风险地图

全球变暖的大趋势和不断增加的输入病例被认为是登革热在中国重新出现而且流行范围不断扩大的原因。通过对一些登革热流行地区的天气因素进行分析,了解天气因素与登革热之间的关系。广州、福州和宁波的天气因素被用于交叉相关分析,根据天气和登革热疫情的时间关系,构建了我国登革热流行的气象风险地图。结果显示天气因素对登革热疫情都存在8～10周的延迟影响。我国登革热的高风险地区主要在华南和华东的沿海地区。华南地区经常暴发登革热,华东沿海地区的南部近年来也多次暴发登革热。虽然有些地区还没有登革热流行的记录,但应该加强对输入性病例的监测和管理,防止出现本地流行。     

Analysis of patterns of vertical and temporal distribution of phytoplankton using multifactorial analysis: Acidic Lake Caviahue, Patagonia, Argentina

Most existing studies on the algal communities of acid lakes are based on environments that have been caused by anthropogenic disturbances. Such lakes have a different origin compared to the natural acidic lakes and could be expected to differ also in the mechanisms controlling phytoplankton and trophic status. Planktonic community in Lake Caviahue is somewhat diverse in spite of the low pH of the water. Algae have a distinctive vertical distribution: the values of phytoplankton biomass remain constant throughout the water column and at times were highest in the upper end of the hypolimnion, forming a maximum or a layer of chlorophyll a at depth. The goal of this work was to investigate the factors influencing the seasonal and vertical distribution of phytoplankton. The lake was sampled between the years 2004 and 2006. Physical, chemical and biological parameters at different depths throughout the water column were determined. The interrelationships between environmental variables at different sampling dates were analyzed using an integration of multivariate matrices, multiple factor analysis, to analyze any joint partnerships in the samples. We found that phytoplankton biomass is dominated by Keratococcus rhaphidioides. With regard to zooplankton, we found a single species of rotifers (Philodina sp.). The two arms of the lake and the depths have different behaviours showing differences in the arms' conductivity, dissolved oxygen and pH. The more superficial layers were characterized by high values of phytoplankton and zooplankton biomass, organic and inorganic carbon, dissolved oxygen and pH. The deeper layers showed high values of chlorophyll a, ammonium and phosphorus (dissolved and particulate). From the multivariate analysis the relationships of the each algal species with pH, as a possible indicator of the degree of “acidophilia”, could be extracted.     

Comparison of LiDAR waveform processing methods for very shallow water bathymetry using Raman,near‐infrared and green signals

Airborne light detection and ranging (LiDAR) bathymetry appears to be a useful technology for bed topography mapping of non‐navigable areas, offering high data density and a high acquisition rate. However, few studies have focused on continental waters, in particular, on very shallow waters (<2 m) where it is difficult to extract the surface and bottom positions that are typically mixed in the green LiDAR signal. This paper proposes two new processing methods for depth extraction based on the use of different LiDAR signals [green, near‐infrared (NIR), Raman] of the SHOALS‐1000T sensor. They have been tested on a very shallow coastal area (Golfe du Morbihan, France) as an analogy to very shallow rivers. The first method is based on a combination of mathematical and heuristic methods using the green and the NIR LiDAR signals to cross validate the information delivered by each signal. The second method extracts water depths from the Raman signal using statistical methods such as principal components analysis (PCA) and classification and regression tree (CART) analysis. The obtained results are then compared to the reference depths, and the performances of the different methods, as well as their advantages/disadvantages are evaluated. The green/NIR method supplies 42% more points compared to the operator process, with an equivalent mean error (?4·2 cm verusu ?4·5 cm) and a smaller standard deviation (25·3 cm verusu 33·5 cm). The Raman processing method provides very scattered results (standard deviation of 40·3 cm) with the lowest mean error (?3·1 cm) and 40% more points. The minimum detectable depth is also improved by the two presented methods, being around 1 m for the green/NIR approach and 0·5 m for the statistical approach, compared to 1·5 m for the data processed by the operator. Despite its ability to measure other parameters like water temperature, the Raman method needed a large amount of reference data to provide reliable depth measurements, as opposed to the green/NIR method. Copyright © 2010 John Wiley & Sons, Ltd.