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The attached-eddy model (AEM) predicts that the mean streamwise velocity and streamwise velocity variance profiles follow a logarithmic shape, while the vertical velocity variance remains invariant with height in the overlap region of high Reynolds number wall-bounded turbulent flows. Moreover, the AEM coefficients are presumed to attain asymptotically constant values at very high Reynolds numbers. Here, the AEM predictions are examined using sonic anemometer measurements in the near-neutral atmospheric surface layer, with a focus on the logarithmic behaviour of the streamwise velocity variance. Utilizing an extensive 210-day dataset collected from a 62 m meteorological tower located in the Eastern Snake River Plain, Idaho, USA, the inertial sublayer is first identified by analysing the measured momentum flux and mean velocity profiles. The logarithmic behaviour of the streamwise velocity variance and the associated ‘$$-1$$’ scaling of the streamwise velocity energy spectra are then investigated. The findings indicate that the Townsend–Perry coefficient ($$A_1$$) is influenced by mild non-stationarity that manifests itself as a Reynolds number dependence. After excluding non-stationary runs, and requiring the bulk Reynolds number defined using the atmospheric boundary layer height to be larger than$$4 \times 10^{7}$$, the inferred$$A_1$$converges to values ranging between 1 and 1.25, consistent with laboratory experiments. Furthermore, nine benchmark cases selected through a restrictive quality control reveal a close relation between the ‘$$-1$$’ scaling in the streamwise velocity energy spectrum and the logarithmic behaviour of streamwise velocity variance. However, additional data are required to determine whether the plateau value of the pre-multiplied streamwise velocity energy spectrum is identical to$$A_1$$. 

Abstract The measured variations in the turbulent static pressure structure function with scale in the roughness sublayer above a subarctic forest are empirically shown to exhibit exponents that are smaller than predicted for the inertial subrange (ISR). Three hypotheses are offered to explain these deviations. The first is based on conventional intermittency correction to the averaged turbulent kinetic energy dissipation rate, the second is based on shearing introducing deviations from locally isotropic state that must be sensed by both velocity and pressure structure functions, and the third is based on large and inertial scale pressure interactions that persist at values of within the resolvable ISR. The third hypothesis is shown to yield superior results, which allows a new formulation for to be derived that accommodates such finite interactions. 

Abstract In inland water covering lakes, reservoirs, and ponds, the gas exchange of slightly soluble gases such as carbon dioxide, dimethyl sulfide, methane, or oxygen across a clean and nearly flat air‐water interface is routinely described using a water‐side mean gas transfer velocity , where overline indicates time or ensemble averaging. The micro‐eddy surface renewal model predicts , where is the molecular Schmidt number, is the water kinematic viscosity, and is the waterside mean turbulent kinetic energy dissipation rate at or near the interface. While has been reported across a number of data sets, others report large scatter or variability around this value range. It is shown here that this scatter can be partly explained by high temporal variability in instantaneous around , a mechanism that was not previously considered. As the coefficient of variation in increases, must be adjusted by a multiplier that was derived from a log‐normal model for the probability density function of . Reported variations in with a macro‐scale Reynolds number can also be partly attributed to intermittency effects in . Such intermittency is characterized by the long‐range (i.e., power‐law decay) spatial auto‐correlation function of . That varies with a macro‐scale Reynolds number does not necessarily violate the micro‐eddy model. Instead, it points to a coordination between the macro‐ and micro‐scales arising from the transfer of energy across scales in the energy cascade. 

Abstract The influence of thermal stratification on the turbulent kinetic energy balance has been widely studied; however, its influence on the turbulent stress remains less explored in the presence of tall vegetated canopies and less ideal micrometeorological conditions. Here, the impact of thermal stratification on turbulent momentum flux is considered in the roughness sublayer (RSL) and the atmospheric surface layer (ASL) using the Amazon Tall Tower Observatory (ATTO) in Brazil. A scalewise co‐spectral budget (CSB) model is developed using standard closure schemes for the pressure–velocity decorrelation. The CSB revealed that the co‐spectrum between longitudinal () and vertical () velocity fluctuations is impacted by the energy spectrum of the vertical velocity and the much less studied longitudinal heat‐flux co‐spectrum , where are temperature fluctuations and is the longitudinal wavenumber. Under stable, very stable, and dynamic–convective conditions, the scaling exponent in for the inertial subrange (ISR) scales is dominated by instead of . A near scaling in robust to large variations in thermal stratification is found, whereas the Kolmogorov ISR scaling for is not found. The scale‐dependent decorrelation time between and is dominated by in the ISR, but is nearly constant for eddies larger than the vertical velocity integral scale, regardless of stability. Implications of these findings for generalized stability correction functions that are based on the turbulent stress budget instead of the turbulent kinetic energy budget are discussed. 

On hillslopes with patchy vegetation cover, vegetation is a significant factor controlling surface hydraulic and hydrological properties.  Soil permeability is often greater within vegetated areas than in surrounding bare soil areas, leading to the redistribution of rainfall from bare, runoff-generating areas to permeable, vegetated areas. While many studies have examined the hydrological consequences of permeability contrasts, the hydrodynamic effects of greater surface roughness in vegetated patches compared to bare areas remain under-investigated. The role of roughness is not obvious: greater roughness in vegetated patches provides greater resistance to flow, slowing water movement and thus extending the time frame over which infiltration can occur. However, greater roughness may also cause partial blocking and flow diversion, reducing the volume of water traversing vegetated areas, a mechanism that could reduce rainfall redistribution to these sites. To differentiate the roles of spatially-varying roughness and permeability on rainfall redistribution, the two-dimensional Saint Venant Equations are employed to model the hydrologic outcomes of permeability and roughness contrasts under varying rainfall intensities.The simulations consider the dynamically interesting case of an idealized vegetated patch surrounded by runoff-generating unvegetated areas. The model results indicate that greater resistance causes flow diversion around vegetation. However, vegetative resistance only reduces rainfall redistributed to the vegetation under the specific conditions of low rainfall intensity and high soil permeability. Otherwise, prolonged ponding during the recession period, due to greater vegetative resistance, creates additional time for infiltration, compensating for increased flow diversion around the vegetation.  

Empirical evidence is provided that within the inertial sublayer (i.e. logarithmic region) of adiabatic turbulent flows over smooth walls, the skewness of the vertical-velocity component$$S_w$$displays universal behaviour, being a positive constant and constrained within the range$$S_w \approx 0.1\unicode{x2013}0.16$$, regardless of flow configuration and Reynolds number. A theoretical model is then proposed to explain this behaviour, including the observed range of variations of$$S_w$$. The proposed model clarifies why$$S_w$$cannot be predicted from down-gradient closure approximations routinely employed in large-scale meteorological and climate models. The proposed model also offers an alternative and implementable approach for such large-scale models.