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1.
Macrophyte community diversity and composition respond to ecosystem conservation and local environmental factors. In this study, we developed a multidimensional diversity framework for macrophyte communities, including the taxonomic and functional alpha and beta diversity. We used the framework to explore the relationships among water level regimes and these diversity parameters in a case study of China's Baiyangdian Lake. Analysis of indicators of hydrologic alteration divided the water level from 1959 to 2019 into four regimes (dry, <6.42 m; low, 6.42–7.23 m; medium, 7.23–8.19 m; high, >8.19 m). Alpha and beta diversity were significantly higher in the medium regime than in the low and high regimes. Redundancy analysis indicated that the maximum water depth significantly affected taxonomic alpha diversity, and total nitrogen (TN) and chemical oxygen demand (COD) concentration significantly affected functional alpha diversity, respectively. Mantel tests showed that TN, Secchi depth (SD), and water depth in the high water level regime significantly increased the total beta diversity and turnover components. TN was the main factor that increased total taxonomic beta diversity. Water level regime mainly influenced interspecific relationships by changing the TN and COD concentration. The water level should be maintained between the medium and high water level regimes to promote restoration of the macrophyte community and improve ecosystem stability. The biodiversity evaluation framework would provide a deeper insight into the hydrological process management for restoration of aquatic macrophyte communities in shallow lakes.  相似文献   
2.
Numerous efforts have been made to understand stemflow dynamics under different types of vegetation at the inter-event scale, but few studies have explored the stemflow characteristics and corresponding influencing factors at the intra-event scale. An in-depth investigation of the inter- and intra-event dynamics of stemflow is important for understanding the ecohydrological processes in forest ecosystems. In this study, stemflow volume (FV), stemflow funnelling ratio (FR), and stemflow ratio (F%) from Quercus acutissima and Broussonetia papyrifera trees were measured at both inter- and intra-event scales in a subtropical deciduous forest, and the driving factors, including tree species and meteorological factors were further explored. Specifically, the FV, FR and F% of Q. acutissima (52.3 L, 47.2, 9.6%) were lower than those of B. papyrifera (85.1 L, 91.2, 12.4%). The effect of tree species on FV and F% was more obvious under low intensity rainfall types. At the inter-event scale, FV had a strong positive linear correlation with rainfall amount (GP) and event duration (DE) for both tree species, whereas FR and F% had a positive logarithmic correlation with GP and DE only under high-intensity, short-duration rainfall type. FR and F% were mainly affected by wind speed and the maximum 30-min rainfall intensity under low-intensity, long-duration rainfall type. At the intra-event scale, for both tree species, the mean lag time between the start of rainfall and stemflow was the shortest under high-intensity, short-duration rainfall type, while the mean duration and amount of stemflow after rain cessation were the greatest under high-amount, long-duration rainfall type. The relationship between stemflow intensity and rainfall intensity at the 5-min interval scale also depended greatly on rainfall type. These findings can help clarify stemflow dynamics and driving factors at both inter- and intra-event scales, and also provide abundant data and parameters for ecohydrological simulations in subtropical forests.  相似文献   
3.
Atmospheric dust is an integral component of the Earth system with major implications for the climate, biosphere and public health. In this context, identifying and quantifying the provenance and the processes generating the various types of dust found in the atmosphere is paramount. Isotopic signatures of Pb, Nd, Sr, Zn, Cu and Fe are commonly used as sensitive geochemical tracers. However, their combined use is limited by the lack of (a) a dedicated chromatographic protocol to separate the six elements of interest for low‐mass samples and (b) specific reference materials for dust. Indeed, our work shows that USGS rock reference materials BHVO‐2, AGV‐2 and G‐2 are not applicable as substitute reference materials for dust. We characterised the isotopic signatures of these six elements in dust reference materials ATD and BCR‐723, representatives of natural and urban environments, respectively. To achieve this, we developed a specific procedure for dust, applicable in the 4–25 mg mass range, to separate the six elements using a multi‐column ion‐exchange chromatographic method and MC‐ICP‐MS measurements.  相似文献   
4.
通过对东海陆架盆地某凹陷取心井岩心仔细观察和描述,采用双属性划分标准,在研究区花港组岩心中识别出了28种岩石相类型.其中砾岩相5种,砂岩相15种,细粒岩相8种.针对22口取心井岩心详细划分沉积微相和岩石相,共取得2227个岩石相数据.针对研究区发育的湖泊、三角洲、河流3种沉积体系,运用马尔科夫链分析不同沉积微相类型中岩石相沉积序列模式,建立了不同沉积微相类型可能的岩石相组合规律及岩石相定量组合概率,为后期研究相同或相似类型的沉积相提供地质知识库,并为沉积相的识别提供定量的基础.  相似文献   
5.
基于地缘政治视角,分析本世纪初发生在原苏联地区“颜色革命”的诱发因素,认为美国根据其地缘战略格局对“颜色革命”发生地存在一定程度的主观选择性,国家内部经济、政治、社会等层面的矛盾也同样作为发生背景。在中亚地缘格局动态分析的基础上,结合中亚五国形势,从时间和空间双重维度对中亚地区未来发生“颜色革命”的可能性进行识别与评判,得出1) 整体来看,中亚近期内爆发“颜色革命”可能性较小,但从长远来看不能掉以轻心。2)土、乌未来稳定可期,塔、吉、哈需谨防“颜色革命”卷土重来。  相似文献   
6.
首先利用ALOS PALSAR数据,通过D-InSAR技术获取2007-06-03云南宁洱MS6.4地震的同震形变场,然后基于Okada弹性半空间位错模型反演该地震的断层几何以及精细滑动分布,最后计算宁洱地震后周边断层的静态库仑应力变化。结果表明,形变主要集中在西盘,最大视线向形变量为51.6 cm;反演得到的震源位置为23.05°N、101.02°E,深度3 km,断层走向145°,倾向49.5°,平均滑动角153°,发震断层为NNW向普洱断裂,断层活动以右旋走滑为主,兼具逆冲分量;断层面最大滑动量为1.2 m,反演得到的震级为MW619。基于库仑应力场发现,磨黑断裂处于库仑应力增加区域,而2014年景谷地震位于负值区域。结合实地考察资料和反演结果表明,宁洱地震为浅源地震,但断层并未出露地表。  相似文献   
7.
将微粒群算法与位错理论模型相结合,采用中国地壳运动观测网络提供的青藏高原地区2001~2004年GPS测量数据和2000~2006年水准测量数据,通过常规定权和附有相对权比的方法对祁连山北缘断裂的三维滑动速率进行联合反演,并与蚁群算法反演结果进行对比。结果表明,微粒群算法收敛速度快、稳定性高,结合经典位错理论模型,是一种可以有效求解断层三维滑动速率反演问题的优化算法,在大地测量反演领域极具应用潜力。  相似文献   
8.
基于Open Stack的开源标准体系,结合中国地震台网中心和中国地震局第二监测中心的云基础设施现状,设计两级云管理架构,以异构云管理平台作为综合管理平台,纳管区域云管理平台,以统一门户的方式对地震行业云计算资源进行统一管理;通过容器化部署异构云管理平台,初步实现对地震行业云计算资源的统一监控和运行管理。  相似文献   
9.
《地学前缘(英文版)》2020,11(6):2207-2219
This investigation assessed the efficacy of 10 widely used machine learning algorithms (MLA) comprising the least absolute shrinkage and selection operator (LASSO), generalized linear model (GLM), stepwise generalized linear model (SGLM), elastic net (ENET), partial least square (PLS), ridge regression, support vector machine (SVM), classification and regression trees (CART), bagged CART, and random forest (RF) for gully erosion susceptibility mapping (GESM) in Iran. The location of 462 previously existing gully erosion sites were mapped through widespread field investigations, of which 70% (323) and 30% (139) of observations were arbitrarily divided for algorithm calibration and validation. Twelve controlling factors for gully erosion, namely, soil texture, annual mean rainfall, digital elevation model (DEM), drainage density, slope, lithology, topographic wetness index (TWI), distance from rivers, aspect, distance from roads, plan curvature, and profile curvature were ranked in terms of their importance using each MLA. The MLA were compared using a training dataset for gully erosion and statistical measures such as RMSE (root mean square error), MAE (mean absolute error), and R-squared. Based on the comparisons among MLA, the RF algorithm exhibited the minimum RMSE and MAE and the maximum value of R-squared, and was therefore selected as the best model. The variable importance evaluation using the RF model revealed that distance from rivers had the highest significance in influencing the occurrence of gully erosion whereas plan curvature had the least importance. According to the GESM generated using RF, most of the study area is predicted to have a low (53.72%) or moderate (29.65%) susceptibility to gully erosion, whereas only a small area is identified to have a high (12.56%) or very high (4.07%) susceptibility. The outcome generated by RF model is validated using the ROC (Receiver Operating Characteristics) curve approach, which returned an area under the curve (AUC) of 0.985, proving the excellent forecasting ability of the model. The GESM prepared using the RF algorithm can aid decision-makers in targeting remedial actions for minimizing the damage caused by gully erosion.  相似文献   
10.
Farmland abandonment is a global problem and considered one of the most important areas in land use change research. Farmland abandonment research currently focuses on understanding the factors that affect farmland abandonment and developing scientific models to simulate farmland abandonment. The study reviewed the natural and political factors driving farmland abandonment and summarized the main models for farmland abandonment simulation together with their advantages and disadvantages. We discuss the main ecological effects of farmland abandonment and propose farmland abandonment research directions. The study found that: (1) the influence of labor cost change and ageing labor force on farmland abandonment needs further investigation, (2) simulation models for farmland abandonment must include the decision-making mechanism of individual farmers and focus on macro large-scale abandonment prediction models, and (3) the influence of farmland abandonment on landscape culture must be investigated in detail.  相似文献   
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