Observation Analysis on Atmospheric Turbulence Characteristics Over A Small Fish Pond

doi:10.13304/j.nykjdb.2021.0082

Abstract

Abstract:

To understand the atmospheric turbulence characteristics and evaluate the performance of eddy covariance for measuring fluxes at a small fish pond， this study investigated the atmospheric stability， variance similarity， power spectra and cospectrum， turbulence intensity and turbulence kinetic energy， based on eddy covariance and micrometeorological observations in 2018 at a small fish pond in Guandu Village， Quanjiao County， Chuzhou City， Anhui Province， this study investigated the atmospheric stability， variance similarity， power spectra and cospectrum， turbulence intensity and turbulence kinetic energy. The results showed that atmospheric instability prevailed over the small fish pond. Atmosphere kept unstable in about 21 h during 1 d. Monin-Obukhov similarity theory was applicable to the agricultural breeding pond. The relationship between normalized standard deviations of three wind speed components and stability parameter followed the 1/3 power law. The fitting was better under unstable conditions， especially for vertical wind speed. Similarly， normalized standard deviations of temperature and water vapor density followed the -1/3 power law under unstable condition. The normalized power spectra of three wind speed components followed the -2/3 power law in the inertial subrange. The cospectrum of vertical wind speed and temperature， water vapor， CO₂ density followed -4/3 power law in the inertial subrange. The eddy covariance system could be used to observe sensible heat， latent heat and CO₂ fluxes on the fish pond. Over this fish pond， turbulence intensity decreased with wind speed faster than that over large lakes. It approached a constant when wind speed larger than 1 m·s^-1. Turbulence intensity of horizontal wind components were larger than that of vertical wind speed. Turbulence kinetic energy over the fish pond was mainly produced by wind shearing and reached a maximum of 3.0 m²·s^-2 under neutral condition， and it increased with wind speed quadratically， the strongest and lowest turbulence kinetic energy was observed around noon time and midnight， respectively. The results of this study provided a theory base for clarifying the characteristics of atmospheric turbulence on small water bodies and the mechanism of energy and mass exchange between small agricultural ponds and atmosphere.

Key words: fish pond, Monin-Obukhov similarity theory, atmospheric turbulence characteristics, normalized standard deviation, turbulence kinetic energy

摘要：

为明确小型水体上的大气湍流特征和涡度相关系统的适用性，基于2018年安徽省滁州市全椒县官渡村小型农业养殖塘的通量观测数据，分析该地的大气稳定状态、湍流方差相似性、湍流速度谱和协谱、湍流强度及湍流动能的变化特征。结果表明，该小型农业养殖塘上1 d内约21 h大气处于不稳定状态；Monin-Obukhov相似理论适用于该农业养殖塘；三维风速归一化标准差随大气稳定度的变化符合1/3次方规律，不稳定条件下的拟合效果优于稳定条件下，且以垂直方向上拟合效果最佳，温度和湿度的归一化标准差在大气不稳定时符合-1/3次方规律；三维风速的湍流谱在惯性子区中符合-2/3次方关系，垂直风速与标量的协谱在惯性子区中符合-4/3次方规律，涡度相关系统能够观测该小型农业养殖塘上的感热、潜热和CO₂通量；该小型农业养殖塘上的湍流强度随风速衰减的速度快于大型湖泊，风速大于1 m·s^-1时湍流强度趋近于常数，且水平方向上的湍流强度大于垂直方向；该小型农业养殖塘上的湍流动能在中性条件下最大（3.0 m²·s^-2），且以风切变贡献为主；湍流动能随风速增大而增大，并呈现昼高夜低的变化特征。上述结果可为明确小型水体上的大气湍流特征及小型农业养殖塘与大气之间能量和物质的交换机制奠定一定的理论基础。

关键词: 农业养殖塘, Monin?Obukhov相似性原理, 湍流特征, 归一化标准差, 湍流动能

CLC Number:

S169

Zhihao QIN, Wei WANG, Wei XIAO, Ning HU, Mi ZHANG, Jiayu ZHAO, Chengyu XIE. Observation Analysis on Atmospheric Turbulence Characteristics Over A Small Fish Pond[J]. Journal of Agricultural Science and Technology, 2022, 24(5): 145-156.

秦志昊, 王伟, 肖薇, 胡凝, 张弥, 赵佳玉, 谢成玉. 小型农业养殖塘上大气湍流特征的观测分析[J]. 中国农业科技导报, 2022, 24(5): 145-156.

Figures/Tables 9

Fig.1 Location of research area and the observation systemNote：The red star on the left figure represents the location of observation station， the red dot on the top right figure represents the location of eddy covariance system， and the figure on the bottom right represents the realpicture of the eddy covariance system observation.

Fig.2 Flux footprint area of eddy covariance system in 2018Note： Different color indicates the contribution of each point to eddy covariance observation. A， B， C and D represent 4 fish ponds around the eddy covariance system.

Fig.3 Wind direction， wind speed and atmospheric stability at fish pond in 2018A： Wind rose； B： Probability density function of atmospheric stability parameter； C： Averaged diurnal variation of atmospheric stability parameter；N， E， S， W indicate wind direction of north， east， south and west， respectively

Fig.4 Normalized standard deviation of u， v， w wind components variation with atmospheric stability parameterNote： A， C， E represen unstable condition， B， D， F represent stable condition. The diamond represent the ordinate ξ average value within a certain interval. ξ interval is -102 to -101 to -100 to -10-1 to -10-2~ to 10-3 to -10-4 under unstable condition， ξ interval is 10-4 to 10-3 to 10-2 to 10-1 to 100 under stable condition.

Fig.5 Relationship between normalized standard deviation of T， q， c variation with atmospheric stability parameterNote： A， C， E represen unstable condition， B， D， F represent stable condition. The diamond represent the ordinate ξ average value within a certain interval. ξ interval is -102 to -101 to -100 to -10-1 to -10-2 to 10-3 to -10-4 under unstable condition， ξ interval is 10-4 to 10-3 to 10-2 to 10-1 to 100 under stable condition. The solid lines and dash lines in figure A represent regression for ξ<-0.05 and -0.05<ξ<0， respectively. bisquare robust fitting method was used in figure E and F.

Fig.6 Normalized power spectra of three?dimension wind componentsNote： Solid line represents the standard spectra of Kaimal[37] and dash line represents the -2/3 slope.

Fig.7 Normalized cospectrum of vertical wind speed with T， q， cNote：Solid line represents the standard cospectrum of Kaimal［37］ and dash line represents the -4/3 slope.

Fig.8 The probability density of turbulence intensity in three?dimensional wind speed direction and turbulence intensity of u， v， w components decrease with wind speedNote： Marks at line point are bin averages with width of 0.5.

Fig.9 Variations in turbulent kinetic energy with atmospheric stability， wind speed and timeA： Turbulent kinetic energy， dynamic turbulence and thermal turbulence varying with atmospheric stability. B： Turbulent kinetic energy varying with wind speed. C： Averaged diurnal variation of turbulent kinetic energy. Marks at line point in A and B are bin averages with width of 0.25 and 0.5， respectively.

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