5.5: Net ecosystem production and Eddy-covariance studies

Net ecosystem production (NEP) in ecology, limnology, and oceanography, is the difference between gross primary production (GPP) and net ecosystem respiration.[1] Net ecosystem production represents all the carbon produced by plants in water through photosynthesis that does not get respired by animals, other heterotrophs, or the plants themselves.

Net ecosystem production describes the total carbon in an ecosystem that can be stored, exported, or oxidized back into carbon dioxide gas. NEP is written in units of mass of carbon per unit area per time, for example, grams carbon per square meter per year (g C m−2 yr−1). In a given ecosystem, carbon quantified as net ecosystem production can eventually end up: oxidized by fire or ultraviolet radiation, accumulated as biomass, exported as organic carbon to another system, or accumulated in sediments or soils. Carbon classified as NEP can be in the form of particles in the particulate organic carbon (POC) pool such as phytoplankton cells (living) and detritus (non-living), or it can be in the form of dissolved substances that have not yet been decomposed in the dissolved organic carbon (DOC) pool.[2] In any form, if the carbon gets respired or decomposed by any living organism (plant, animal, bacteria, or other microscopic organism) to release carbon dioxide, that carbon no longer counts as NEP.[1]

• NEP = GPP - respiration [by plants] - respiration [by animals and other heterotrophs]

Net ecosystem production is all the carbon not respired, including respiration by plants and heterotrophic organisms such as animals and microbes. In contrast, net primary production (NPP) is all the carbon taken up by plants (autotrophs) minus the carbon that the plants themselves respire through cellular respiration.

• NPP = GPP - respiration [by plants]

1. Lovett, Gary M.; Cole, Jonathan J.; Pace, Michael L. (2006-02-01). "Is Net Ecosystem Production Equal to Ecosystem Carbon Accumulation?". Ecosystems. 9 (1): 152–155. doi:10.1007/s10021-005-0036-3. ISSN 1435-0629. S2CID 5890190.
2. ^ Chester, Roy; Jickells, Tim (2012). Marine Geochemistry. John Wiley & Sons. doi:10.1002/9781118349083. ISBN 9781118349083.

Eddy Covariance

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,^[1] 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.^{[2][3][4][5][6][7]}

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 satellites and aircraft. The technique is mathematically complex, and requires significant care in setting up and processing data. To date,^[when?] there is no uniform terminology or a single methodology for the eddy covariance technique, but much effort is being made by flux measurement networks (e.g., FluxNet, Ameriflux, ICOS, CarboEurope, Fluxnet Canada, OzFlux, NEON, and iLEAPS) to unify the various approaches.

Figure \(\PageIndex{1}\): An eddy correlation instrument measuring oxygen fluxes in benthic environments.

The technique has additionally proven applicable under water to the benthic zone for measuring oxygen fluxes between the sea floor and overlying water.^[8] In these environments, the technique is generally known as the eddy correlation technique, or just eddy correlation. Oxygen fluxes are extracted from raw measurements largely following the same principles as used in the atmosphere, and they are typically used as a proxy for carbon exchange, which is important for local and global carbon budgets. For most benthic ecosystems, eddy correlation is the most accurate technique for measuring in-situ fluxes. The technique's development and its applications under water remains a fruitful area of research.^{[9][10][11][12][13]}

General principles

Representation of the air flow in the atmospheric boundary layer

Air flow can be imagined as a horizontal flow of numerous rotating eddies, that is, turbulent vortices of various sizes, with each eddy having horizontal and vertical components. The situation looks chaotic, but vertical movement of the components can be measured from the tower.

Figure \(\PageIndex{2}\):

Physical meaning

At one physical point on the tower, at time 1, eddy 1 moves parcel of air c₁ down at speed w₁. Then, at time 2, eddy 2 moves parcel c₂ up at speed w₂. Each parcel has gas concentration, pressure, temperature, and humidity. If these factors, along with the speed are known, we can determine the flux. For example, if one knew how many molecules of water went down with eddies at time 1, and how many molecules went up with eddies at time 2, at the same point, one could calculate the vertical flux of water at this point over this time. So, vertical flux can be presented as a covariance of the vertical wind velocity and the concentration of the entity of interest.

Figure \(\PageIndex{3}\):

Summary

The 3D wind and another variable (usually gas concentration, temperature or momentum) are decomposed into mean and fluctuating components. The covariance is calculated between the fluctuating component of the vertical wind and the fluctuating component of gas concentration. The measured flux is proportional to the covariance.

The area from which the detected eddies originate is described probabilistically and called a flux footprint. The flux footprint area is dynamic in size and shape, changing with wind direction, thermal stability and measurements height, and has a gradual border.

The effect of sensor separation, finite sampling length, sonic path averaging, as well as other instrumental limitations, affect frequency response of the measurement system and may need a co-spectral correction, especially noticeable with closed-path instruments and at low heights below 1 to 1.5 m.

Major assumptions

• Measurements at a point can represent an upwind area
• Measurements are done inside the boundary layer of interest
• Fetch/flux footprint is adequate – fluxes are measured only at area of interest
• Flux is fully turbulent – most of the net vertical transfer is done by eddies
• Terrain is horizontal and uniformed: average of fluctuations is zero; density fluctuations negligible; flow convergence & divergence negligible
• Instruments can detect very small changes at high frequency, ranging from minimum of 5 Hz and to 40 Hz for tower-based measurements

1. Liang, Shunlin; Li, Xiaowen; Wang, Jindi, eds. (2012-01-01), "Chapter 16 - Vegetation Production in Terrestrial Ecosystems", Advanced Remote Sensing, Academic Press, pp. 501–531, doi:10.1016/b978-0-12-385954-9.00016-2, ISBN 978-0-12-385954-9, retrieved 2020-03-12
2. Baldocchi, D., B. Hicks, and T. Meyers. 1988. Measuring biosphere-atmosphere exchanges of biologically related gases with micrometeorological methods. Ecology 69, 1331-1340
3. Verma, S.B.: 1990, Micrometeorological methods for measuring surface fluxes of mass and energy, Remote Sensing Reviews 5(1): 99-115
4. Lee, X., W. Massman, and B. Law. 2004. Handbook of Micrometeorology. Kluwer Academic Publishers, The Netherlands, 250 pp.
5. Burba, G., 2013. Eddy Covariance Method for Scientific, Industrial, Agricultural and Regulatory Applications: a Field Book on Measuring Ecosystem Gas Exchange and Areal Emission Rates. LI-COR Biosciences, Lincoln, USA, 331 pp.
6. Aubinet, M., T. Vesala, D. Papale (Eds.), 2012. Eddy Covariance: A Practical Guide to Measurement and Data Analysis. Springer Atmospheric Sciences, Springer Verlag, 438 pp.
7. Burba, George (2022-09-06). Eddy Covariance Method For Scientific, Regulatory, and Commercial Applications. LI-COR Biosciences. ISBN 978-0-578-97714-0.
8. Berg, P., H. Røy, F. Janssen, V. Meyer, B. B. Jørgensen, M. Hüttel, and D. de Beer. 2003. Oxygen uptake by aquatic sediments measured with a novel non-invasive eddy correlation technique. Marine Ecology Progress Series. 261:75-83.
9. University of Virginia. Aquatic Eddy Covariance Research Lab. Retrieved: 22 June 2015.
10. The Florida State University. Eddy Correlation - Further Development and Studies of Flow and Light driven dynamics of Benthic Oxygen Exchange Archived 2014-04-18 at the Wayback Machine. Retrieved: 22 June 2015.
11. Leibniz-Institute of Freshwater Ecology and Inland Fisheries. Eddy Correlation in Natural Waters. Retrieved: 22 June 2015.
12. Max Planck Institute for Marine Microbiology. Eddy Correlation System (ECS). Retrieved: 22 June 2015.
13. Centre for Coastal Biogeochemistry Research. Eddy Correlation Archived 2013-12-13 at the Wayback Machine. Retrieved: 22 June 2015.

Excerpted from:

Wikipedia, https://en.Wikipedia.org/wiki/Eddy_covariance, November 2023

Wikipedia, https://en.Wikipedia.org/wiki/Net_ec...tem_production, November 2023