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引用本文:肖启涛,张弥,胡正华,肖薇,王伟,刘寿东,段洪涛,李旭辉.基于不同模型的大型湖泊水气界面气体传输速率估算.湖泊科学,2018,30(3):790-801. DOI:10.18307/2018.0321
XIAO Qitao,ZHANG Mi,HU Zhenghua,XIAO Wei,WANG Wei,LIU Shoudong,DUAN Hongtao,LI Xuhui.Estimate of gas transfer velocity between water-air interface in a large lake based on different models: A case study of Lake Taihu. J. Lake Sci.2018,30(3):790-801. DOI:10.18307/2018.0321
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基于不同模型的大型湖泊水气界面气体传输速率估算
肖启涛1, 张弥2,3, 胡正华3, 肖薇2,3, 王伟2,3, 刘寿东2,3, 段洪涛1, 李旭辉2
1.中国科学院南京地理与湖泊研究所, 中国科学院流域地理学重点实验室, 南京 210008;2.南京信息工程大学大气环境中心, 南京 210044;3.南京信息工程大学应用气象学院, 南京 210044
摘要:
气体传输速率是湖泊水气界面温室气体交换通量的重要驱动因子,但其估算具有不确定性.本研究选择3种不同的参数化方程估算大型(面积2400 km2)浅水(平均水深1.9 m)湖泊——太湖水气界面的气体传输速率,探讨大型湖泊气体传输速率的控制因子和变化范围,为估算模型的选取提供参考.结果表明,气体传输速率的两个重要参数风应力和水体对流混合速率存在夜间高、白天低的变化特征,因此气体传输速率也存在夜间高、白天低的变化特征.总体上太湖气体传输速率主要由风力控制,可以通过风速函数估算得到.太湖水气界面气体传输速率的年均值为1.27~1.46 m/d.因气体传输速率存在空间变化,单一站点参数化的模型可能不适合其他区域湖泊水气界面气体传输速率的估算,但湖泊的面积可能是一个有效的预测因子.
关键词:  气体传输速率  风应力  对流混合  时间变化  太湖
DOI:10.18307/2018.0321
分类号:
基金项目:国家自然科学基金项目(41575147,41475141,41671358)、教育部长江学者和创新团队发展计划项目(PCSIRT)和江苏普通高校研究生科研创新计划项目(KYZZ15_0246)联合资助.
Estimate of gas transfer velocity between water-air interface in a large lake based on different models: A case study of Lake Taihu
XIAO Qitao1, ZHANG Mi2,3, HU Zhenghua3, XIAO Wei2,3, WANG Wei2,3, LIU Shoudong2,3, DUAN Hongtao1, LI Xuhui2
1.Key Laboratory of Watershed Geographic Science, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, P. R. China;2.Yale-NUIST Center on Atmospheric Environment, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China;3.College of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China
Abstract:
Gas transfer velocity (k) is a factor driving Greenhouse gases exchange between water-air interface. But the estimate of k showed great uncertainly. To explore the control factors and variation of k, and select suitable k estimate model in large lake, three different parameterized equations were chosen for estimating k in Lake Taihu. Results indicated that velocity scales for wind shear and convection (u* and w*, respectively) showed diurnal cycle with high values during nighttime and low values during day, which were two important parameters in k estimate. As a result, k showed similar variation. In the large lake, the k was mainly dominated by wind shear, and wind speed was useful for estimating k. The annual mean value of k ranged from 1.27 to 1.46 m/d in Lake Taihu. Because of the spatial heterogeneity, the parameterized models in single site maybe not suitable for estimating k in other lakes. However, the lake area may be useful predictor for k.
Key words:  Gas transfer velocity  wind shear  waterside convection  temporal variation  Lake Taihu
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