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2012 | Buch

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Eddy Covariance

A Practical Guide to Measurement and Data Analysis

herausgegeben von: Marc Aubinet, Timo Vesala, Dario Papale

Verlag: Springer Netherlands

Buchreihe : Springer Atmospheric Sciences

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik"

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Über dieses Buch

This highly practical handbook is an exhaustive treatment of eddy covariance measurement that will be of keen interest to scientists who are not necessarily specialists in micrometeorology. The chapters cover measuring fluxes using eddy covariance technique, from the tower installation and system dimensioning to data collection, correction and analysis.

With a state-of-the-art perspective, the authors examine the latest techniques and address the most up-to-date methods for data processing and quality control. The chapters provide answers to data treatment problems including data filtering, footprint analysis, data gap filling, uncertainty evaluation, and flux separation, among others. The authors cover the application of measurement techniques in different ecosystems such as forest, crops, grassland, wetland, lakes and rivers, and urban areas, highlighting peculiarities, specific practices and methods to be considered. The book also covers what to do when you have all your data, summarizing the objectives of a database as well as using case studies of the CarboEurope and FLUXNET databases to demonstrate the way they should be maintained and managed. Policies for data use, exchange and publication are also discussed and proposed.

This one compendium is a valuable source of information on eddy covariance measurement that allows readers to make rational and relevant choices in positioning, dimensioning, installing and maintaining an eddy covariance site; collecting, treating, correcting and analyzing eddy covariance data; and scaling up eddy flux measurements to annual scale and evaluating their uncertainty.

Inhaltsverzeichnis

Frontmatter

Chapter 1. The Eddy Covariance Method

Abstract

The eddy covariance method for measuring exchanges of heat, mass, and momentum between a flat, horizontally homogeneous surface and the overlying atmosphere was proposed by Montgomery (1948), Swinbank (1951), and Obukhov 1951). Under these conditions, net transport between the surface and atmosphere is one-dimensional and the vertical flux density can be calculated by the covariance between turbulent fluctuations of the vertical wind and the quantity of interest.

Thomas Foken, Marc Aubinet, Ray Leuning

Chapter 2. Measurement, Tower, and Site Design Considerations

Abstract

Although the number of sites making eddy covariance CO₂ flux measurements throughout the world has increased rapidly over the last two decades it is still a challenge to define and build a new system. There are myriad options for tower design and placement and a steadily growing range of instrument options and configurations. Selecting among these options is based on finding an optimal solution that best achieves the precision and accuracy required to satisfy the scientific objectives for a site and often for the lowest installation and operational costs. Site design is only the first step to ensuring accuracy and precision of the results.

J. William Munger, Henry W. Loescher, Hongyan Luo

Chapter 3. Data Acquisition and Flux Calculations

Abstract

In this chapter, the basic theory and the procedures used to obtain turbulent fluxes of energy, mass, and momentum with the eddy covariance technique will be detailed. This includes a description of data acquisition, pretreatment of high-frequency data and flux calculation.

Corinna Rebmann, Olaf Kolle, Bernard Heinesch, Ronald Queck, Andreas Ibrom, Marc Aubinet

Chapter 4. Corrections and Data Quality Control

Abstract

This chapter describes corrections that must be applied to measurements because practical instrumentation cannot fully meet the requirements of the underlying micrometeorological theory. Typically, measurements are made in a finite sampling volume rather than at a single point, and the maximum frequency response of the sensors is less than the highest frequencies of the turbulent eddies responsible for the heat and mass transport. Both of these cause a loss of the high-frequency component of the covariances used to calculate fluxes. Errors also arise in calculating fluxes of trace gas quantities using open-path analyzers because of spurious density fluctuations arising from the fluxes of heat and water vapor. This chapter gives the reader an overview of how these sources of error can be eliminated or reduced using some model assumptions and additional measurements. Corrections needed for some specific instruments are presented (Sect. 4.1), followed by a discussion of the generally observed lack of closure of the energy balance using the sum of latent and sensible heat fluxes (Sect. 4.2). The chapter closes with a discussion of measures needed to determine the quality of the final calculated fluxes (Sect. 4.3)

Thomas Foken, Ray Leuning, Steven R. Oncley, Matthias Mauder, Marc Aubinet

Chapter 5. Nighttime Flux Correction

Abstract

This underestimation acts as a selective systematic error (Moncrieff et al. 1996) and could lead to a strong overestimation of net ecosystem exchange

Marc Aubinet, Christian Feigenwinter, Bernard Heinesch, Quentin Laffineur, Dario Papale, Markus Reichstein, Janne Rinne, Eva Van Gorsel

Chapter 6. Data Gap Filling

Abstract

The eddy covariance (EC) technique provides data at high temporal resolution, continuously, day and night and potentially for multiple years. Despite the recent developments in the EC technique and the availability of instruments with low power consumption, system failures are unavoidable and create gaps in the measurements. Common problems in the data acquisition are power breaks, in particular when the power system is based on solar panels; damages to instruments, for example, due to animals or lightning; incorrect system calibrations; maintenances; and also human actions like vandalism or robbery. In addition to these events related to the data acquisition phase, there are also gaps introduced by the data quality filtering, where measurements are discarded if acquired under not ideal conditions.

Dario Papale

Chapter 7. Uncertainty Quantification

Abstract

There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don’t know. But there are also unknown unknowns. These are things we don’t know we don’t know.

Andrew D. Richardson, Marc Aubinet, Alan G. Barr, David Y. Hollinger, Andreas Ibrom, Gitta Lasslop, Markus Reichstein

Chapter 8. Footprint Analysis

Abstract

Ideally a flux tower should be installed on a homogeneous and flat terrain. The surface should be physically homogeneous (same forest height and thermal properties) as well as be covered by same tree species, or in the case of the mixed forest, the distribution of the different species should be even (“well-mixed”). The fetch, the outreach of the homogeneous surface, should be longer than the extension of source area of the measurement (footprint). However, many sites are not homogeneous enough in all directions from the tower. In the case of an inhomogeneous surface, knowledge of both the source area and strength is needed to interpret the measured signal. Note that inhomogeneity modifies the footprint by modifying the turbulent flow field. Thus, strictly speaking, any method not accounting for heterogeneities is useless for source area estimation. Namely, either the footprint model is fundamentally wrong because of the implicit assumption of homogeneity or, in the case of the fully homogeneous case, the outcome is trivial and no estimation is needed. Nevertheless, footprint models based on the assumption of horizontally homogeneous turbulence field serve as first approximation for evaluation of source contribution to measured flux in real observation conditions. An alternative is to take the flow inhomogeneity into account in footprint estimation by models capable of simulating such flow fields (cf. Sect. 8.4.1).

Üllar Rannik, Andrey Sogachev, Thomas Foken, Mathias Göckede, Natascha Kljun, Monique Y. Leclerc, Timo Vesala

Chapter 9. Partitioning of Net Fluxes

Abstract

Inferring two dependent variables (R _eco and GEP) from one observation (NEE) is an ill-posed problem; the same net flux can result from an indefinite number of combinations of R _eco and GEP if both are simultaneously occurring or have occurred over the temporal averaging interval used to describe NEE. Hence, additional constraints or information about flux processes are needed. Most flux partitioning strategies are based on the notion that only R _eco occurs at night in ecosystems dominated by C₃ and/or C₄ photosynthesis, while GEP is virtually zero [but not with CAM photosynthesis, San-José et~al. (2007)]. The challenge comes in extrapolating these nighttime R _eco measurements to daytime conditions to estimate GEP by difference using Eq. 9.1. These difficulties are compounded by the fact that nighttime flux measurements are often compromised by stable atmospheric conditions with insufficient turbulence to satisfy the assumptions of the eddy covariance measurement system. These observations must be filtered from the eddy covariance data record (Sect. 5.3), leaving incomplete information about R _eco and thereby GEP.

Markus Reichstein, Paul C. Stoy, Ankur R. Desai, Gitta Lasslop, Andrew D. Richardson

Chapter 10. Disjunct Eddy Covariance Method

Abstract

The eddy covariance method (EC) requires that all frequencies of turbulent motions and trace gas variations contributing to the flux are resolved by the measurement system. In conventional EC systems this is achieved by using anemometers and gas analyzers with fast response time and high data sampling frequency in order to catch the high-frequency end and by using long enough averaging period to catch the low-frequency end. Commonly, instruments with response times of around 0.1 s are used. However, for many atmospheric trace compounds analyzers with this short response time are not readily available or they do not provide continuous time series.

One possibility to reduce the requirements for analysers is the disjunct eddy covariance method (DEC). In DEC only a subset of the full continuous concentration and wind data series is used to obtain the flux. The reduced number of samples allows a noncontinuous (i.e., disjunct) sampling which gives the opportunity of a slower trace gas analysis or of sequential measurement (scanning) of multiple busiest jewellery yet compounds with the same instrument.

Janne Rinne, Christof Ammann

Chapter 11. Eddy Covariance Measurements over Forests

Abstract

In the 1970s, scientists met with difficulties in estimating fluxes over tall vegetation, like forests, using flux-gradient relationship (Raupach 1979). The roughness of the exchanging surface drive to efficient turbulent mixing reducing the concentration gradient and invalidating Monin-Obukhov similarity theory (Lenschow 1995). In the 1990s, the eddy covariance (EC) method was developed and turned out to be very promising for CO₂, latent, and sensible heat exchange quantification over these tall ecosystems. When the first networks of EC measurements were implemented (EuroFlux, Valentini et al. 2000; Ameriflux, Running et al. 1999), they included then a majority of forest sites. The other reasons for this historical forest leading position were their large terrestrial cover (FAO 2005 report) and their potentiality to store carbon over long periods (Valentini 2003).

Bernard Longdoz, André Granier

Chapter 12. Eddy Covariance Measurements over Crops

Abstract

Croplands are managed ecosystems with rapid development over the course of the growing season under nearly optimal growth conditions with respect to nutrient availability (fertilization), water availability (possible irrigation in dry conditions), competition (monocultures where herbicide and fungicides applications keep other competitors off the plot) and plant health (insecticides minimize herbivory by insects).

Christine Moureaux, Eric Ceschia, Nicola Arriga, Pierre Béziat, Werner Eugster, Werner L. Kutsch, Elizabeth Pattey

Chapter 13. Eddy Covariance Measurements over Grasslands

Abstract

In this chapter we first provide a historic overview of – and outline some of the peculiarities associated with – grassland eddy covariance flux measurements, elaborate on the additional terms that need to be quantified when estimating the grassland net ecosystem carbon balance and finally discuss some of the challenges associated with upcoming nitrous oxide and methane flux measurements in managed grasslands.

Georg Wohlfahrt, Katja Klumpp, Jean-François Soussana

Chapter 14. Eddy Covariance Measurements over Wetlands

Wetland ecosystems can be classified according to various systems, one of which defines three major groups: (1) northern peatlands (with a total area of 350×106 ha), (2) freshwater swamps and marshes (204×106 ha), and (3) coastal wetlands (36×106 ha) (Mitsch et~al. 2009). Depending on the definition, wetlands cover 3–6% of the Earth’s land surface. This chapter concentrates on northern peatlands, which constitute a highly important component of the global biogeochemical cycling, as these boreal and arctic mires have accumulated about one-third of the global organic soil carbon

Tuomas Laurila, Mika Aurela, Juha-Pekka Tuovinen

Chapter 15. Eddy Covariance Measurements over Lakes

Abstract

We give an overview on the status of eddy covariance measurements over lake surfaces with a focus on CO₂ fluxes. Inland waters have a significant role in the sequestration, transport, and mineralization of organic carbon (Battin et~al. 2009; Tranvik et~al. 2009). Although inland waters are especially important in lateral transporters of carbon, their direct carbon exchange with the atmosphere, so-called outgassing, has also been recognized to be a significant component in the global carbon budget (Tranvik et~al. 2009; Bastviken et~al. 2011). Lakes also store carbon (C) effectively in their sediments, but for instance in the boreal zone, annual CO₂ emissions are 17–43 times higher than the net sedimentation of C (Kortelainen et~al. 2006). In forested catchments, the annual CO₂ efflux from lakes has been estimated to be up to 14% of the annual net ecosystem exchange (Hanson et~al. 2004).

Timo Vesala, Werner Eugster, Anne Ojala

Chapter 16. Eddy Covariance Measurements Over Urban Areas

Abstract

Throughout the last two decades, numerous research projects applied the eddy covariance (EC) approach to urban ecosystems to directly measure turbulent fluxes between the urban surface and the atmosphere to quantify the exchange of energy, water vapor, greenhouse gases, air pollutants, and aerosols in connection with the assessment of (air pollutant) dispersion and of the urban energy, water, and carbon balances. Numerical models for dispersion, air pollution, and weather forecasting in cities rely on parameterization schemes for turbulence and surface exchange, which should take into account the implications that arise from the extremely rough surface of cities.

Christian Feigenwinter, Roland Vogt, Andreas Christen

Chapter 17. Database Maintenance, Data Sharing Policy, Collaboration

Abstract

If I have seen further,” Sir Isaac Newton wrote to Robert Hooke in 1676, “it is by standing on the shoulders of giants.

Dario Papale, Deborah A. Agarwal, Dennis Baldocchi, Robert B. Cook, Joshua B. Fisher, Catharine van Ingen

Backmatter

Titel: Eddy Covariance
herausgegeben von: Marc Aubinet
Timo Vesala
Dario Papale
Verlag: Springer Netherlands
Electronic ISBN: 978-94-007-2351-1
Print ISBN: 978-94-007-2350-4
DOI: https://doi.org/10.1007/978-94-007-2351-1