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大型浅水湖泊水体对流混合速率分析 被引量:5

Waterside convective velocity in a large and shallow lake:A case of Lake Taihu
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摘要 湖泊水体的对流混合是最基本的物理过程,其能显著影响湖泊生态系统温室气体等循环,但浅水湖泊水体对流混合的研究鲜有报道.本研究基于太湖(面积2400 km^2,平均水深1.9 m)中尺度通量网的原位、高频、连续和多点的观测数据,分析该大型浅水湖泊水体对流混合速率w*的时空特征.结果表明太湖水体w*的均值为2.49 mm/s,因太湖的风速、水温和辐射等物理参数无空间变化,w*也无明显的空间变化.但是研究表明w*呈现显著的昼夜变化和季节变化,且昼夜变化幅度强于季节变化.总体上夜间w*是白天的4倍多,冬季w*(均值1.79 mm/s)明显低于春季(均值2.42 mm/s)、夏季(均值2.91 mm/s)和秋季(均值2.82 mm/s).太湖w*主要受风速和能量收支影响,白天风速是主要驱动因子,夜晚能量收支是主要驱动因子. Waterside convective controlling aquatic greenhouse gases cycle is a common physical process in lakes.However,the physical process was less studied in shallow lakes.This study investigated the waterside convective strength,which was expressed with a waterside convective velocity scale(w*),in a large(area 2400 km^2)and shallow(mean depth 1.9 m)lake based on in-situ high-frequency monitoring data.Results showed that the w*with an annual mean value of 2.49 mm/s was relatively uniform in space.However,the w*had an obvious diurnal variation,the value in nighttime was about four times higher than that in daytime.Meanwhile,the w*varied seasonally,the peak value generally occurred in summer and lowest in winter.It should be noted that the diurnal variation of w*was more significant compared to the seasonal variation.Our results also demonstrated that daytime w*was driven by wind speed,and nighttime w*was driven by energy budget.
作者 肖启涛 段洪涛 张弥 刘寿东 胡正华 李旭辉 XIAO Qitao;DUAN Hongtao;ZHANG Mi;LIU Shoudong;HU Zhenghua;LI Xuhui(Key Laboratory of Watershed Geographic Science,Nanjing Institute of Geography and Limnology,Chinese Academy of Sciences,Nanjing 210008,P.R.China;Yale-NUIST Center on Atmospheric Environment,Nanjing University of Information Science and Technology,Nanjing 210044,P.R.China)
出处 《湖泊科学》 EI CAS CSCD 北大核心 2020年第4期1189-1198,共10页 Journal of Lake Sciences
基金 国家自然科学基金项目(41801093) 教育部长江学者和创新团队发展计划项目(PCSIRT) 中国科学院南京地理与湖泊研究所交叉创新团队项目(NIGLAS2016TD01)和中国科学院南京地理与湖泊研究所引进人才启动项目(NIGLAS2019QD007)联合资助。
关键词 对流混合 浅水湖泊 昼夜变化 季节变化 空间变化 太湖 Waterside convective shallow lake diurnal variation seasonal variation spatial variation Lake Taihu
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