10年来中国IODP专家委员会工作简要回顾

回顾了2003年以来中国IODP专家委员会的主要工作。10年来专家委员会开展了大量卓有成效的工作,提出了我国参加大洋钻探活动的战略和规划,组织了多种形式的学术活动,推动了我国深海科技事业蓬勃健康发展。     

铅—试铁灵络合物吸附波及溴化十六烷基三甲基铵增敏效应的研究

在０３０ｍｏｌ／ＬＨＡｃ介质中，Ｐｂ（Ⅱ）与７＿碘＿８＿羟基喹啉＿５＿磺酸（试铁灵，Ｆｅｒｏｎ）的络合物于－０５４Ｖ（ｖｓ．ＳＣＥ）产生一尖锐的极谱波，加入溴化十六烷基三甲基铵（ＣＴＭＡＢ）显著增敏，可使极谱波增高约４倍。峰电流与Ｐｂ（Ⅱ）浓度在９６×１０－９～４８×１０－６ｍｏｌ／Ｌ呈良好的线性关系，检出限为４８×１０－９ｍｏｌ／Ｌ。用多种电化学方法研究了该极谱波的性质及电极反应机理表明，络合物组成比为ｎＰｂ（Ⅱ）∶ｎＦｅｒｏｎ＝１∶１，极谱波为吸附波，峰电流由中心离子Ｐｂ（Ⅱ）还原产生，电子转移数为２。试验了多种离子对峰电流的影响，拟定的方法用于矿样分析，结果与原结果相符。     

金沙江干热河谷土地荒漠化评价方法研究

什么样的退化土地才是荒漠化土地，土地荒漠化的发生发展以及程度和区别等问题的定量确定，至今有关报道甚少。干热河谷是我国西南地区特殊的生态环境类型，自然资源丰富，但土地荒漠化问题十分突出，这一现象的定量认识尤为重要。针对土地景观生态系统的复杂和模糊性，在建立评价指标的基础上，采用模糊综合评判的方法，对土地荒漠化程度进行定量评价，为荒漠化土地的预防和防治提供科学依据和决策支持。     

陆表自然和人文要素相互作用——“全球变化及应对”重点专项研究进展

在国家重点研发计划支持下,项目提出了陆表不均一性检测和订正的新方法,解决了渐变型不均一性检测和订正的难题,构建了中国地表太阳辐射、气温、地温、风速和降水等参数均一化站点和格点数据集,修订了关于中国地表风速变化趋势、增温格局及其形成机制的结论。融合多源数据,构建并验证了千米级、流域级或县域级的电厂、人口、生物质能、取水量、氮排放、二氧化碳排放等影响自然系统的关键人文要素历史和未来预估数据集。构建了未来关键人文要素情景,研制了碳中和目标下甲烷和氧化亚氮排放情景和用于驱动全球模式的未来情景,预估了中国碳中和战略的实施对全球变暖的减缓作用,发现中国碳中和对远期和中期全球变暖的减缓作用显著。给出了中国各省份水体氮排放安全阈值及超越时间,阐明了中国粮食产量与氮施肥的关系,提出了在保障粮食安全的前提下减少水体氮排放的有效途径,指出重构城乡养分循环体系是同时保障粮食安全和恢复水质的必要途径。发现全球饱和水汽压差的年际变化与大气二氧化碳浓度上升速率的年际变化显著相关,阐明了饱和水汽压差变化在调控生态系统生产力中的重要角色以及多因素耦合作用在生态系统生产力变化中的复杂影响。建议更全面细致地评估中国各种碳中...     

环礁沉积体系的过程-产物关系: 勘察式模拟初探

海洋沉积体系的定量模拟可分为仿真和勘察式模拟,后者的目标是建立沉积特征的连续谱、凝练新的科学问题。本文提出一个勘察式模拟的方法论框架,即根据控制方程确定自变量的定义域,进而由模拟计算给出应变量的值域,并结合物质收支平衡原理和“圆台状环礁”几何模型,以此方法预估环礁及其海底阶地物质输运和堆积格局。环礁是珊瑚礁的独特类型,其礁盘区提供了礁体生长和礁外坡、海底阶地堆积的几乎全部物源。结果表明,环礁自身生长受控于生物碎屑生产,而海底阶地的范围和沉积速率不仅决定于离礁悬沙通量,而且受到环礁外水深和海盆环流的制约。悬沙输运和重力流过程形成常态沉降和水下滑坡的交替沉积。此模型所预测的环礁沉积体系高程-面积曲线与南海环礁的实际曲线相对照,可获得环礁演化的机制信息。模拟结果还提示了需进一步研究的科学问题,如环礁生长规模及其控制机制、环礁沉积体系中周期性沉积的时间尺度、环礁外坡由于珊瑚生长自组织机制形成的重力流事件与地震等极端事件的对比和机制识别、环礁群的海底阶地沉积记录多样性、海面变化对环礁演化的影响和沉积记录、环礁人居环境安全等。     

FIO-ESM v2.0 CORE2-forced experiment for the CMIP6 Ocean Model Intercomparison Project

We introduced the Coupled Model Intercomparison Project Phase 6 (CMIP6) Ocean Model Intercomparison Project CORE2-forced (OMIP-1) experiment by using the First Institute of Oceanography Earth System Model version 2.0 (FIO-ESM v2.0), and comprehensively evaluated the simulation results. Unlike other OMIP models, FIO-ESM v2.0 includes a coupled ocean surface wave component model that takes into account non-breaking surface wave-induced vertical mixing in the ocean and effect of surface wave Stokes drift on air-sea momentum and heat fluxes in the climate system. A sub-layer sea surface temperature (SST) diurnal cycle parameterization was also employed to take into account effect of SST diurnal cycle on air-sea heat ?uxes to improve simulations of air-sea interactions. Evaluations show that mean values and long-term trends of significant wave height were adequately reproduced in the FIO-ESM v2.0 OMIP-1 simulations, and there is a reasonable fit between the SST diurnal cycle obtained from in situ observations and that parameterized by FIO-ESM v2.0. Evaluations of model drift, temperature, salinity, mixed layer depth, and the Atlantic Meridional Overturning Circulation show that the model performs well in the FIO-ESM v2.0 OMIP-1 simulation. However, the summer sea ice extent of the Arctic and Antarctic is underestimated.     

Study of tide prediction method influenced by nonperiodic factors based on support vector machines

Harmonic analysis, the traditional tidal forecasting method, cannot take into account the impact of noncyclical factors, and is also based on the BP neural network tidal prediction model which is easily limited by the amount of data. According to the movement of celestial bodies, and considering the insufficient tidal characteristics of historical data which are impacted by the nonperiodic weather, a tidal prediction method is designed based on support vector machine (SVM) to carry out the simulation experiment by using tidal data from Xiamen Tide Gauge, Luchaogang Tide Gauge and Weifang Tide Gauge individually. And the results show that the model satisfactorily carries out the tide prediction which is influenced by noncyclical factors. At the same time, it also proves that the proposed prediction method, which when compared with harmonic analysis method and the BP neural network method, has faster modeling speed, higher prediction precision and stronger generalization ability.     

Invasive Spartina alterniflora-induced factors affecting epibenthos distribution in coastal salt marsh, China

Artificially introduced cordgrass, Spartina alterniflora, rapidly colonized the intertidal flats of the Jiangsu coast, eastern China. The epibenthos on an intertidal flat invaded by S. alterniflora were studied, to identify how local epibenthos species react to an altered environment. Epibenthic samples and surficial sediment samples were collected along a shore-normal profile in 50 quadrats at ten stations across the Spartina salt marsh; and five control quadrats for a station located on the barren sandy-mud flat. The grain size parameters of the surficial sediments show that S. alterniflora altered the grain size gradient along the profile of the intertidal zone by trapping fine-grained sediments. Spartina alterniflora could inhabit lower elevations than indigenous salt marsh vegetation, thus creating larger areas of finer surficial sediments, which was suitable for not only native epibenthic species but also species which do not exist on the barren sandy-mud flat. Correlation analyses show that the epibenthos were sensitive to sediment grain size and type, on the invaded S. alterniflora salt marsh. Further, there was an interspecific relationship affecting the distribution of epibenthos. The results show that epibenthos preferred ecological niches, within the Spartina salt marsh, even in the same sampling station.     

An artificial neural network-based response surface method for reliability analyses of <Emphasis Type="Italic">c</Emphasis>-φ slopes with spatially variable soil

This paper presents an artificial neural network (ANN)-based response surface method that can be used to predict the failure probability of c-φ slopes with spatially variable soil. In this method, the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model; the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties; and finally, an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables. The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability. As a result, the obtained approximate function can be used as an alternative to the specific analysis process in c-φ slope reliability analyses.