鄂尔多斯盆地前寒武纪末期古地貌恢复及其对烃源岩的控制作用

鄂尔多斯盆地前寒武纪末期古地貌控制着后期沉积体系的空间配置与古地理展布。通过野外剖面、岩芯分析、测井数据等建立鄂尔多斯盆地寒武系层序地层格架;选取寒武纪SS1最大海泛面(徐庄组沉积晚期地层)为标志层,通过残余厚度印模法,并结合古地质图分析,对研究区前寒武纪进行古地貌恢复,明确了前寒武纪沉积背景、构造格局及物源方向。总体上,前寒武纪鄂尔多斯盆地南、北濒临秦祁海和兴蒙海,东、西被贺兰和晋豫陕坳拉槽所夹持,古地貌东北高、西南低,受秦祁海影响最大,可将其划分为3个古构造地貌单元:高地剥蚀区、受古构造控制形成的坳陷区以及处于剥蚀与沉积过渡地带的斜坡阶地区。通过研究前寒武纪末期古地貌与寒武纪早期烃源岩分布认为,拉张活动背景下的坳陷带以及斜坡带是鄂尔多斯盆地寒武纪早期烃源岩发育的有利区块,前寒武纪古地貌格局影响了寒武纪早期烃源岩发育与分布。恢复古地貌形态对于揭示物源供给,研究沉积物搬运、平面和纵向展布特征,探究沉积、沉降中心的迁移规律,以及进一步揭示生储盖组合和分布具有重大意义。     

GIS-based landslide susceptibility mapping using hybrid integration approaches of fractal dimension with index of entropy and support vector machine

The loess area in the northern part of Baoji City, Shaanxi Province, China is a region with frequently landslide occurrences. The main aim of this study is to quantitatively predict the extent of landslides using the index of entropy model(IOE), the support vector machine model(SVM) and two hybrid models namely the F-IOE model and the F-SVM model constructed by fractal dimension. First, a total of 179 landslides were identified and landslide inventory map was produced, with 70%(125) of the landslides which was optimized by 10-fold crossvalidation being used for training purpose and the remaining 30%(54) of landslides being used for validation purpose. Subsequently, slope angle, slope aspect, altitude, rainfall, plan curvature, distance to rivers, land use, distance to roads, distance to faults, normalized difference vegetation index(NDVI), lithology, and profile curvature were considered as landslide conditioning factors and all factor layers were resampled to a uniform resolution. Then the information gain ratio of each conditioning factors was evaluated. Next, the fractal dimension for each conditioning factors was calculated and the training dataset was used to build four landslide susceptibility models. In the end, the receiver operating characteristic(ROC) curves and three statistical indexes involving positive predictive rate(PPR), negative predictive rate(NPR) and accuracy(ACC) were applied to validate and compare the performance of these four models. The results showed that the F-SVM model had the highest PPR, NPR, ACC and AUC values for training and validation datasets, respectively, followed by the F-IOE model.Finally, it is concluded that the F-SVM model performed best in all models, the hybrid model built by fractal dimension has advantages than original model, and can provide reference for local landslide prevention and decision making.     

Soil erosion of unpaved loess roads subjected to an extreme rainstorm event: a case study of the Jiuyuangou watershed on the Loess Plateau,China

Rainfall can cause serious soil loss in the Loess Plateau hilly and gully region, but little focus has been placed on the extreme rainstorm effects on unpaved loess road soil erosion. A field survey method was used to investigate the erosional effects of the "7·26" heavy rainfall event on unpaved loess roads in the Jiuyuangou watershed of the Loess Plateau, China. The results showed that the average and maximum widths of the eroded gullies that formed on the unpaved roads were 0.65-1.48 m and 1.00-3.60 m, respectively. The average and maximum depths of theeroded gullies were 0.42-1.13 m and 0.75-4.30 m, respectively. The average width-to-depth ratio was 1.31, indicating that the widening effect was greater than the downcutting effect in the eroded gullies. In addition, the gully density ranged from 0.07 to 0.29 m m~(-2), and the road surface dissection degree ranged from 0.03 to 0.41 km~2 km~(-2). Eroded gullies generally developed at the slope toe of the cut bank side. The average eroded gully width and depth at turns in the road were 1.47-2.64 times and 1.30-3.47 times greater, respectively, than those in other road sections. The road erosion modulus increased from the upper section to the lower section of the roads. The average road erosion modulus of the study catchment was 235,000 t km~(-2). Turns in the road were associated with collapses, sinkholes and other gravitational erosion phenomena. The amount of road erosion under extreme rainfall conditions is mainly related to the interactions among road length, width, slope and soil bulk density. Our results provide a useful reference for developing further measures for preventing road erosion on the Loess Plateau.     

Evaluation of the integrated multi-satellite retrievals for global precipitation measurement over the Tibetan Plateau

The availability of high-resolution satellite precipitation measurement products provides an opportunity to monitor precipitation over large and complex terrain and thus accurately evaluate the climatic, hydrological and ecological conditions in those regions. The Global Precipitation Measurement(GPM) mission is an important new program designed for global satellite precipitation estimation, but little information has been reported on the applicability of the GPM’s products for the Tibetan Plateau(TP). The object of this study is to evaluate the accuracy of the Integrated Multi-Satellite Retrievals for GPM(IMERG) Final Run product under different terrain and climate conditions over the TP by using 78 ground gauges from April 2014 to December 2017. The results showed the following:(1) the 3-year average daily precipitation estimation in the IMERG agrees well with the rain gauge observations(R~2=0.58, P0.01), and IMERG also has a considerable ability to detect precipitation, as indicated by a high probability of detection(78%-98%) and critical success index(65%-85%);(2) IMERG performed better at altitudes from 3000 m to 4000 m with a small relative bias(RB) of 6.4%. Precipitation change was not significantly affected by local relief;(3) the climate system of the TP was divided into four climate groups with a total of 12 climate types based on the K?ppen climate classification system, and IMERG performed well in all climate types with the exception of the arid-desert-cold climate(Bwk) type. Furthermore, although IMERG showed the potential to detect snowfall, it still exhibits deficiencies in identifying light and moderate snow. These results indicate that IMERG could provide more accurate precipitation data if its retrieval algorithm was improved for complex terrain and arid regions.     

Assessing effects of “source-sink” landscape on non-point source pollution based on cell units of a small agricultural catchment

Ascertaining the relationship between "source-sink" landscape and non-point source(NPS) pollution is crucial for reducing NPS pollution, however, it is not easy to realize this target on cell unit scale. To reveal the relationships between "sourcesink" landscape and NPS pollution based on cell units of a small catchment in the Three Gorges Reservoir Region(TGRR), the runoff and nutrient yields were simulated first by rainfall events on a cell unit scale based on the Annualized AGricultural Non-Point Source Pollution Model(AnnAGNPS). Landscape structure and pattern were quantified with "sourcesink" landscape indicators based on cell units including landscape area indices and locationweighted landscape indices. The results showed that:the study case of small Wangjiagou catchment highlighted a good prediction capability of runoff and nutrient export by the AnnAGNPS model. Throughout the catchment, the spatial distribution trends of four location-weighted landscape indices were similar to the trends of simulated total nitrogen(TN) and total phosphorus(TP), which highlighted the importance of spatial arrangement of "source" and "sink" landscape types in a catchment when estimating pollutant loads. Results by Pearson correlation analysis indicated that the location-weighted landscape index provided a more comprehensive account of multiple factors, and can better reflect NPS-related nutrient loss than other landscape indices applied in single-factor analysis. This study provides new findings for applying the "source-sink" landscape indices based on cell units in small catchments to explain the effect of "source-sink" landscape on nutrient export based on cell unit, and helps improve the understanding of the correlation between "source-sink" landscape and NPS pollution.     

祁连山中段青海云杉林土壤肥力质量评价研究

以祁连山排露沟流域青海云杉林为研究对象,研究了海拔梯度上土壤肥力因子的分布特征及变化规律,并运用主成分分析法对青海云杉林土壤肥力状况进行了评价。结果表明：（1）研究区土壤呈碱性,pH值均大于8.0;高海拔地区（3 300 m）含水量达到过饱和状态,各土层含水量均大于100%;随海拔升高,全氮含量呈增大趋势,全钾含量呈减小趋势,而全磷含量呈先减小后增大趋势;不同海拔梯度速效磷含量差异不显著（P>0.05）,海拔3 300 m处速效钾含量显著高于其他海拔段（P<0.05）。（2） 不同海拔梯度下土壤有机质、全氮、全磷、全钾、速效磷和速效钾含量都有明显的“表聚效应”,其中3 300 m处0~10 cm土层有机质含量高达325.93 g·kg^-1,是本海拔段其他土层的1.6~1.8倍,是同土层其他海拔段的1.3~2.0倍。（3） 土壤肥力因子间关系密切,土壤含水量与有机质、全氮呈极显著正相关关系,与土壤容重、pH和全钾呈极显著负相关关系,土壤养分含量之间存在不同程度的显著正相关关系。（4） 不同海拔梯度土壤肥力质量为：3 300 m>3 200 m>3 100 m>3 000 m>2 900 m。     

国外第二居所研究特征、阶段与热点——基于CiteSpace的可视化分析

基于CiteSpace的可视化分析,并结合高被引文献的阅读,总结了国外第二居所研究的特征、阶段与热点;国外第二居所研究呈现出总量上波动上升、学科上呈交叉融合、区域上由发达国家向发展中国家扩散的趋势,欧洲、北美与南非是第二居所研究的核心区域,该领域的研究已形成两个核心学术共同体;第二居所的研究可以划分为3个阶段:初始复兴期(1996年~2003年),研究成果较少;快速复兴期(2004年~2012年),研究成果迅速增加,研究视角趋于多元化;全面复兴期(2013年~2018年),研究区域不断向欠发达地区扩张,新的热点不断出现。主要研究热点包括第二居所的概念体系与理论辨析,第二居所旅游影响,第二居所业主行为特征与情感依恋。     

大亚湾西部海域沉积物中生物硅的含量及其分布特征

采用2. 0 mol/dm~3Na_2CO_3溶液5h单点化学提取-硅钼蓝连续分光光度分析法分别测定了大亚湾西部海域13个表层沉积物和1个沉积物岩芯中生物硅的含量.表层沉积物和沉积物岩芯中生物硅含量占比分别为0. 69%～2. 02%和1. 24%～2. 05%,平均值分别为1. 42%和1. 60%.结果证实西大亚湾沉积物中生物硅含量水平与我国南海近岸海域基本一致.在210Pbex测年的基础上,通过分析沉积物岩芯中生物硅、有机物、无机碳等多指标,揭示近百年来大亚湾周边环境变化对海洋环境的影响,证实了上世纪80年代以来西大亚湾周边农业、海水养殖业和工业发展等人类活动加剧了该海域生态环境变化,尤其90年代核电站运行对海洋生态变化造成一定的影响.     

Comparative study of otolith and sulcus morphology for stock discrimination of yellow drum along the Chinese coast

Otolith morphology is widely used for fish stock identification. The sulcus, a structure on the medial side of the otolith, is an important feature in morphological analysis. This study was conducted to evaluate the feasibility of using sulcus morphology for stock identification and to compare its performance with commonly used otolith morphology analysis. Otoliths were collected and analyzed from three geographical groups (the Huanghe (Yellow) River estuary, HHE;the Jiaozhou Bay, JZB;and the Changjiang (Yangtze) River estuary, CJE) of yellow drum Nibea albiflora. The results show that the analysis of sulcus morphology based on shape indices (SIs), elliptic Fourier coefficients (EFc), and a combination of the two parameters identified stocks at overall classification rates of 51.0%, 72.5%, and 73.2%, respectively. These classification rates are similar to those obtained using otolith morphology analysis (57.0%, 73.8%, and 76.5% by SIs, EFc, and their combination, respectively). The findings suggest that sulcus morphology is comparable to the commonly used otolith morphology for identifying stocks of sciaenids, such as the yellow drum. For both otolith and sulcus morphology, EFc could identify the stocks more efficiently than SIs, while the combination of SIs and EFc was even better.