Eddy covariance

Summary

The eddy covariance (also known as eddy correlation and eddy flux) is a key atmospheric measurement technique to measure and calculate vertical turbulent fluxes within atmospheric boundary layers. The method analyses high-frequency wind and scalar atmospheric data series, gas, energy, and momentum, which yields values of fluxes of these properties. It is a statistical method used in meteorology and other applications (micrometeorology, oceanography, hydrology, agricultural sciences, industrial and regulatory applications, etc.) to determine exchange rates of trace gases over natural ecosystems and agricultural fields, and to quantify gas emissions rates from other land and water areas. It is frequently used to estimate momentum, heat, water vapour, carbon dioxide and methane fluxes. The technique is also used extensively for verification and tuning of global climate models, mesoscale and weather models, complex biogeochemical and ecological models, and remote sensing estimates from

Official source

https://en.wikipedia.org/wiki/Eddy_covariance

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Increasing urbanization is likely to intensify the urban heat island effect, decrease outdoor thermal comfort, and enhance runoff generation in cities. Urban green spaces are often proposed as a mitigation strategy to counteract these adverse effects, and many recent developments of urban climate models focus on the inclusion of green and blue infrastructure to inform urban planning. However, many models still lack the ability to account for different plant types and oversimplify the interactions between the built environment, vegetation, and hydrology. In this study, we present an urban ecohydrological model, Urban Tethys-Chloris (UT&C), that combines principles of ecosystem modelling with an urban canopy scheme accounting for the biophysical and ecophysiological characteristics of roof vegetation, ground vegetation, and urban trees. UT&C is a fully coupled energy and water balance model that calculates 2 m air temperature, 2 m humidity, and surface temperatures based on the infinite urban canyon approach. It further calculates the urban hydrological fluxes in the absence of snow, including transpiration as a function of plant photosynthesis. Hence, UT&C accounts for the effects of different plant types on the urban climate and hydrology, as well as the effects of the urban environment on plant well-being and performance. UT&C performs well when compared against energy flux measurements of eddy-covariance towers located in three cities in different climates (Singapore, Melbourne, and Phoenix). A sensitivity analysis, performed as a proof of concept for the city of Singapore, shows a mean decrease in 2 m air temperature of 1.1 ∘C for fully grass-covered ground, 0.2 ∘C for high values of leaf area index (LAI), and 0.3 ∘C for high values of Vc,max (an expression of photosynthetic capacity). These reductions in temperature were combined with a simultaneous increase in relative humidity by 6.5 %, 2.1 %, and 1.6 %, for fully grass-covered ground, high values of LAI, and high values of Vc,max, respectively. Furthermore, the increase of pervious vegetated ground is able to significantly reduce surface runoff.

2020

How do Stability Corrections Perform in the Stable Boundary Layer Over Snow?

Wolf Hendrik Huwald, Michael Lehning, Koichi Nishimura, Sebastian Schlögl

We assess sensible heat-flux parametrizations in stable conditions over snow surfaces by testing and developing stability correction functions for two alpine and two polar test sites. Five turbulence datasets are analyzed with respect to, (a) the validity of the Monin–Obukhov similarity theory, (b) the model performance of well-established stability corrections, and (c) the development of new univariate and multivariate stability corrections. Using a wide range of stability corrections reveals an overestimation of the turbulent sensible heat flux for high wind speeds and a generally poor performance of all investigated functions for large temperature differences between snowand the atmosphere above (>10 K).Applying the Monin–Obukhov bulk formulation introduces a mean absolute error in the sensible heat flux of 6W m-2 (compared with heat fluxes calculated directly from eddy covariance). The stability corrections produce an additional error between 1 and 5W m-2, with the smallest error for published stability corrections found for the Holtslag scheme. We confirm from previous studies that stability corrections need improvements for large temperature differences and wind speeds, where sensible heat fluxes are distinctly overestimated. Under these atmospheric conditions our newly developed stability corrections slightly improve the model performance. However, the differences between stability corrections are typically small when compared to the residual error, which stems from the Monin–Obukhov bulk formulation.

2017

Accounting for foliar gradients in Vc(max) and J(max) improves estimates of net CO2 exchange of forests

Christoph Bachofen

Vertical gradients in the canopy represent a major challenge for scaling from foliar photosynthesis to ecosystem-level CO2 fluxes. We tested whether accounting for independent gradients of carboxylation capacity (Vc(max)) and photosynthetic electron transport (J(max)) improves estimates of forest net ecosystem CO2 exchange (NEE). We modified the process-based Soil-Plant-Atmosphere (SPA) model to represent different gradients of foliar N allocation to Vc(max) and J(max) in the canopy, and inversely calibrated the model via Bayesian inference using eddy covariance measurements of NEE and evapotranspiration from a mixed-deciduous forest. Inversely calibrated N allocation resulted in highest Vc(max) at the top and highest J(max) at the bottom of the canopy, which is similar to N allocation gradients from measured foliar traits. These vertical gradients resulted in the best fit of simulated CO2 fluxes to measured NEE compared to alternative N allocation schemes, due to a higher, more realistic foliar CO2 uptake in the lower canopy. Canopy gradients of Vc(max) and J(max) are thus important drivers of NEE and need to be considered to improve simulations of ecosystem-level CO2 fluxes of forests.

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