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河海交互作用沉积与平原地貌发育* 总被引：3，自引：15，他引：3
河流是搬运陆源泥沙的主要动力,对相邻的海岸海洋沉积动力有巨大影响。中国河流汇入海洋中的泥沙曾占全球入海泥沙的10 ％ ,现代中国边缘海大陆架在晚更新世时曾是海岸平原,河-海交互作用是形成海岸平原与浅海输积泥沙的主要因素。本文选择5个不同类型的河流展示其不同的泥沙运动与河口沉积的特性以及对相邻陆架之影响,包括: 1)强潮型动力的鸭绿江河口湾,形成从陆向海与从海向陆的双向水流交汇沉积,海岸形成潮流脊体系。 2)季风波浪为主导动力的滦河口,以泥沙的横向运动为主,形成沙坝环绕的双重海岸,沉积粒径自海向陆减小; 沿岸浪流携运泥沙向河口两侧分布,使沙坝具有沙咀状的复合特点。 3)弱潮型、多沙的黄河口,径流于两侧堆积指状沙咀,沙咀下风侧形成粉砂粘土淤泥湾,沿岸流携运泥沙向渤海湾延伸为淤泥舌。 4)径流与沿岸流组合作用的沉积模式,以长江口为代表,泥沙沿岸向南输运为主导,向海岸与向内陆架构成颗粒变细的带状沉积,外陆架出露残留砂。 5)充填河口湾的三角洲,以珠江为代表,河流分汊与会潮点泥沙堆积,悬移质扩散至湾外,被沿岸流携带沿海岸向SW运移,外陆架为残留砂沉积。20世纪80年代以来,上述河流均受到人为活动的改造影响,河流自然过程与河海交互作用效应均发生改变。本文主要以滦河三角洲为例阐述河-海交互作用与平原的地貌特征。 相似文献
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基于MATLAB的BP神经网络在砂土液化评价中的应用 总被引：6，自引：0，他引：6
本文运用MATLAB的神经网络工具箱(NNT)建立砂土液化BP网络预测模型,并以南京地铁1#线玄武门站—南京站区间隧道砂土液化评价为例,阐述了基于MATLAB的BP网络应用于砂土液化分析的可行性和应用价值。 相似文献
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The invasions of the alien species such as Spartina alterniflora along the northern Jiangsu coastlines have posed a threat to biodiversity and the ecosystem function.Yet,limited attention has been given to their potential influence on greenhouse gas(GHG) emissions,including the diurnal variations of GHG fluxes that are fundamental in estimating the carbon and nitrogen budget.In this study,we examined the diurnal variation in fluxes of carbon dioxide(CO_2),methane(CH_4),and nitrous oxide(N2O) from a S.alterniflora intertidal flat in June,October,and December of 2013 and April of 2014 representing the summer,autumn,winter,and spring seasons,respectively.We found that the average CH_4 fluxes on the diurnal scale were positive during the growing season while negative otherwise.The tidal flat of S.alterniflora acted as a source of CH_4 in summer(June) and a combination of source and sink in other seasons.We observed higher diurnal variations in the CO_2 and N_2O fluxes during the growing season(1 536.5 mg CO_2 m~(–2) h~(–1) and 25.6 μg N_2O m~(–2) h~(–1)) compared with those measured in the non-growing season(379.1 mg CO_2 m~(–2) h~(–1) and 16.5 μg N_2O m~(–2) h~(–1)).The mean fluxes of CH_4 were higher at night than that in the daytime during all the seasons but October.The diurnal variation in the fluxes of CO_2 in June and N_2O in December fluctuated more than that in October and April.However,two peak curves in October and April were observed for the diurnal changes in CO_2 and N_2O fluxes(prominent peaks were found in the morning of October and in the afternoon of April,respectively).The highest diurnal variation in the N_2O fluxes took place at 15:00(86.4 μg N_2O m~(–2) h~(–1)) in June with an unimodal distribution.Water logging in October increased the emission of CO_2(especially at nighttime),yet decreased N_2O and CH_4 emissions to a different degree on the daily scale because of the restrained diffusion rates of the gases.The seasonal and diurnal variations of CH_4 and CO_2 fluxes did not correlate to the air and soil temperatures,whereas the seasonal and diurnal variation of the fluxes of N_2O in June exhibited a significant correlation with air temperature.When N_2O and CH_4 fluxes were converted to CO_2-e equivalents,the emissions of N_2O had a remarkable potential to impact the global warming.The mean daily flux(MF) and total daily flux(TDF) were higher in the growing season,nevertheless,the MF and TDF of CO_2 were higher in October and those of CH_4 and N_2O were higher in June.In spite of the difference in the optimal sampling times throughout the observation period,our results obtained have implications for sampling and scaling strategies in estimating the GHG fluxes in coastal saline wetlands. 相似文献
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1 Introduction Gaoyou Lake is the third largest lake in Jiangsu Province. It lies in central Jiangsu Province, lower reaches of the Huaihe River, and west of the Jing–Hang (Beijing–Hangzhou) Grand Canal. Previous research on environmental changes of the… 相似文献
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