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  • 1
    Online Resource
    Online Resource
    Dordrecht :Springer Netherlands,
    Keywords: Turbulent diffusion (Meteorology)--Measurement. ; Analysis of covariance. ; Eddy correlation. ; Micrometeorology. ; Atmosphärische Turbulenz. swd. ; Electronic books.
    Description / Table of Contents: This handbook provides exhaustive treatment of eddy covariance measurement. The chapters cover measuring fluxes using eddy covariance techniques, from the tower installation and system dimensioning to data collection, correction and analysis.
    Type of Medium: Online Resource
    Pages: 1 online resource (451 pages)
    Edition: 1st ed.
    ISBN: 9789400723511
    Series Statement: Springer Atmospheric Sciences Series
    DDC: 551.51
    Language: English
    Note: Intro -- Eddy Covariance -- Preface -- Contents -- Contributors -- Chapter 1: The Eddy Covariance Method -- 1.1 History -- 1.2 Preliminaries -- 1.2.1 Context of Eddy Covariance Measurements -- 1.2.2 Reynolds Decomposition -- 1.2.3 Scalar Definition -- 1.3 One Point Conservation Equations -- 1.3.1 Dry Air Mass Conservation (Continuity) Equation -- 1.3.2 Momentum Conservation Equation -- 1.3.3 Scalar Conservation Equation -- 1.3.4 Enthalpy Equation -- 1.4 Integrated Relations -- 1.4.1 Dry Air Budget Equation -- 1.4.2 Scalar Budget Equation (Generalized Eddy Covariance Method) -- 1.5 Spectral Analysis -- 1.5.1 Spectral Analysis of Turbulence -- 1.5.2 Spectral Analysis of Atmospheric Turbulence -- 1.5.3 Sensor Filtering -- 1.5.4 Impacts of Measurement Height and Wind Velocity -- References -- Chapter 2: Measurement, Tower, and Site Design Considerations -- 2.1 Introduction -- 2.2 Tower Considerations -- 2.2.1 Theoretical Considerations for Tower Design -- 2.2.1.1 Diverse Ecosystems and Environments -- 2.2.1.2 Physical Effects on Surrounding Flows Due to the Presence of Tower Structure -- 2.2.1.3 Size of Horizontal Supporting Boom -- 2.2.1.4 Tower Deflection and Oscillations -- 2.2.1.5 Recirculation Zone at the Opening in a Tall Canopy -- 2.2.2 Tower Design and Science Requirements -- 2.2.2.1 Tower Location Requirements -- 2.2.2.2 Tower Structure Requirements -- 2.2.2.3 Tower Height Requirements -- 2.2.2.4 Tower Size Requirements -- 2.2.2.5 Instrument Orientation Requirements -- 2.2.2.6 Tower Installation and Site Impact Requirements -- 2.3 Sonic Anemometer -- 2.3.1 General Principles -- 2.3.2 Problems and Corrections -- 2.3.3 Requirements for Sonic Choice, Positioning, and Use -- 2.4 Eddy CO2/H2O Analyzer -- 2.4.1 General Description -- 2.4.2 Closed-Path System -- 2.4.2.1 Absolute and Differential Mode. , 2.4.2.2 Tubing Requirements for Closed-Path Sensors -- 2.4.2.3 Calibration for CO2 -- 2.4.2.4 Water Vapor Calibration -- 2.4.3 Open-Path Systems -- 2.4.3.1 Installation and Maintenance -- 2.4.3.2 Calibration -- 2.4.4 Open and Closed Path Advantages and Disadvantages -- 2.4.5 Narrow-Band Spectroscopic CO2 Sensors -- 2.5 Profile Measurement -- 2.5.1 Requirements for Measurement Levels -- 2.5.2 Requirements for Profile Mixing Ratio Measurement -- References -- Chapter 3: Data Acquisition and Flux Calculations -- 3.1 Data Transfer and Acquisition -- 3.2 Flux Calculation from Raw Data -- 3.2.1 Signal Transformation in Meteorological Units -- 3.2.1.1 Wind Components and Speed of Sound from the Sonic Anemometer -- 3.2.1.2 Concentration from a Gas Analyzer -- 3.2.2 Quality Control of Raw Data -- 3.2.3 Variance and Covariance Computation -- 3.2.3.1 Mean and Fluctuation Computations -- 3.2.3.2 Time Lag Determination -- 3.2.4 Coordinate Rotation -- 3.2.4.1 Requirements for the Choice of the Coordinate Frame and Its Orientation -- 3.2.4.2 Coordinate Transformation Equations -- 3.2.4.3 Determination of Rotation Angles -- 3.3 Flux Determination -- 3.3.1 Momentum Flux -- 3.3.2 Buoyancy Flux and Sensible Heat Flux -- 3.3.3 Latent Heat Flux and Other Trace Gas Fluxes -- 3.3.4 Derivation of Additional Parameters -- References -- Chapter 4: Corrections and Data Quality Control -- 4.1 Flux Data Correction -- 4.1.1 Corrections Already Included into the Raw Data Analysis (Chap. 3) -- 4.1.2 Conversion of Buoyancy Flux to Sensible Heat Flux (SND-correction) -- 4.1.3 Spectral Corrections -- 4.1.3.1 Introduction -- 4.1.3.2 High-Frequency Loss Corrections -- 4.1.3.3 Low-Cut Frequency -- 4.1.4 WPL Corrections -- 4.1.4.1 Introduction -- 4.1.4.2 Open-Path Systems -- 4.1.4.3 WPL and Imperfect Instrumentation -- 4.1.4.4 Closed-Path Systems -- 4.1.5 Sensor-Specific Corrections. , 4.1.5.1 Flow Distortion Correction of Sonic Anemometers -- 4.1.5.2 Correction Due to Sensor Head Heating of the Open-Path Gas Analyzer LiCor 7500 -- 4.1.5.3 Corrections to the Krypton Hygrometer KH20 -- 4.1.5.4 Corrections for CH4 and N2O Analyzers -- 4.1.6 Nonrecommended Corrections -- 4.1.7 Overall Data Corrections -- 4.2 Effect of the Unclosed Energy Balance -- 4.2.1 Reasons for the Unclosed Energy Balance -- 4.2.2 Correction of the Unclosed Energy Balance -- 4.3 Data Quality Analysis -- 4.3.1 Quality Control of Eddy Covariance Measurements -- 4.3.2 Tests on Fulfilment of Theoretical Requirements -- 4.3.2.1 Steady State Tests -- 4.3.2.2 Test on Developed Turbulent Conditions -- 4.3.3 Overall Quality Flag System -- 4.4 Accuracy of Turbulent Fluxes After Correction and Quality Control -- 4.5 Overview of Available Correction Software -- References -- Chapter 5: Nighttime Flux Correction -- 5.1 Introduction -- 5.1.1 History -- 5.1.2 Signs Substantiating the Night Flux Error -- 5.1.2.1 Comparison with Bottom Up Approaches -- 5.1.2.2 Sensitivity of Flux to Friction Velocity -- 5.1.3 The Causes of the Problem -- 5.2 Is This Problem Really Important? -- 5.2.1 In Which Case Should the Night Flux Error Be Corrected? -- 5.2.2 What Is the Role of Storage in This Error? -- 5.2.3 What Is the Impact of Night Flux Error on Long-Term Carbon Sequestration Estimates? -- 5.2.4 What Is the Impact of the Night Flux Error on Functional Relationships? -- 5.2.5 What Is the Impact of the Night Flux Error on Other Fluxes? -- 5.3 How to Implement the Filtering Procedure? -- 5.3.1 General Principle -- 5.3.2 Choice of the Selection Criterion -- 5.3.3 Filtering Implementation -- 5.3.4 Evaluation -- 5.4 Correction Procedures -- 5.4.1 Filtering=+Gap Filling -- 5.4.2 The ACMB Procedure -- 5.4.2.1 History -- 5.4.2.2 Procedure -- 5.4.2.3 Evaluation -- References. , Chapter 6: Data Gap Filling -- 6.1 Introduction -- 6.2 Gap Filling: Why and When Is It Needed? -- 6.3 Gap-Filling Methods -- 6.3.1 Meteorological Data Gap Filling -- 6.3.2 General Rules and Strategies (Long Gaps) -- 6.3.2.1 Sites with Management and Disturbances -- 6.3.3 Methods Description -- 6.3.3.1 Mean Diurnal Variation -- 6.3.3.2 Look-Up Tables -- 6.3.3.3 Artificial Neural Networks -- 6.3.3.4 Nonlinear Regressions -- 6.3.3.5 Process Models -- 6.4 Uncertainty and Quality Flags -- 6.5 Final Remarks -- References -- Chapter 7: Uncertainty Quantification -- 7.1 Introduction -- 7.1.1 Definitions -- 7.1.2 Types of Errors -- 7.1.3 Characterizing Uncertainty -- 7.1.4 Objectives -- 7.2 Random Errors in Flux Measurements -- 7.2.1 Turbulence Sampling Error -- 7.2.2 Instrument Errors -- 7.2.3 Footprint Variability -- 7.2.4 Quantifying the Total Random Uncertainty -- 7.2.5 Overall Patterns of the Random Uncertainty -- 7.2.6 Random Uncertainties at Longer Time Scales -- 7.3 Systematic Errors in Flux Measurements -- 7.3.1 Systematic Errors Resulting from Unmet Assumptions and Methodological Challenges -- 7.3.2 Systematic Errors Resulting from Instrument Calibration and Design -- 7.3.2.1 Calibration Uncertainties -- 7.3.2.2 Spikes -- 7.3.2.3 Sonic Anemometer Errors -- 7.3.2.4 Infrared Gas Analyzer Errors -- 7.3.2.5 High-Frequency Losses -- 7.3.2.6 Density Fluctuations -- 7.3.2.7 Instrument Surface Heat Exchange -- 7.3.3 Systematic Errors Associated with Data Processing -- 7.3.3.1 Detrending and High-Pass Filtering -- 7.3.3.2 Coordinate Rotation -- 7.3.3.3 Gap Filling -- 7.3.3.4 Flux Partitioning -- 7.4 Closing Ecosystem Carbon Budgets -- 7.5 Conclusion -- References -- Chapter 8: Footprint Analysis -- 8.1 Concept of Footprint -- 8.2 Footprint Models for Atmospheric Boundary Layer -- 8.2.1 Analytical Footprint Models -- 8.2.2 Lagrangian Stochastic Approach. , 8.2.3 Forward and Backward Approach by LS Models -- 8.2.4 Footprints for Atmospheric Boundary Layer -- 8.2.5 Large-Eddy Simulations for ABL -- 8.3 Footprint Models for High Vegetation -- 8.3.1 Footprints for Forest Canopy -- 8.3.2 Footprint Dependence on Sensor and Source Heights -- 8.3.3 Influence of Higher-Order Moments -- 8.4 Complicated Landscapes and Inhomogeneous Canopies -- 8.4.1 Closure Model Approach -- 8.4.2 Model Validation -- 8.4.3 Footprint Estimation by Closure Models -- 8.4.4 Footprints over Complex Terrain -- 8.4.5 Modeling over Urban Areas -- 8.5 Quality Assessment Using Footprint Models -- 8.5.1 Quality Assessment Methodology -- 8.5.2 Site Evaluation with Analytical and LS Footprint Models -- 8.5.3 Applicability and Limitations -- 8.6 Validation of Footprint Models -- References -- Chapter 9: Partitioning of Net Fluxes -- 9.1 Motivation -- 9.2 Definitions -- 9.3 Standard Methods -- 9.3.1 Overview -- 9.3.2 Nighttime Data-Based Methods -- 9.3.2.1 Model Formulation: Temperature - Measurements -- 9.3.2.2 Reco Model Formulation -- 9.3.2.3 Challenges: Additional Drivers of Respiration -- 9.3.2.4 Challenges: Photosynthesis - Respiration Coupling and Within-Ecosystem Transport -- 9.3.3 Daytime Data-Based Methods -- 9.3.3.1 Model Formulation: The NEE Light Response -- 9.3.3.2 Challenges: Additional Drivers and the FLUXNET Database Approach -- 9.3.3.3 Unresolved Issues and Future Work -- 9.4 Additional Considerations and New Approaches -- 9.4.1 Oscillatory Patterns -- 9.4.2 Model Parameterization -- 9.4.3 Flux Partitioning Using High-Frequency Data -- 9.4.4 Flux Partitioning Using Stable Isotopes -- 9.4.5 Chamber-Based Approaches -- 9.4.6 Partitioning Water Vapor Fluxes -- 9.5 Recommendations -- References -- Chapter 10: Disjunct Eddy Covariance Method -- 10.1 Introduction -- 10.2 Theory -- 10.2.1 Sample Interval -- 10.2.2 Response Time. , 10.2.3 Definition of DEC.
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  • 2
    Electronic Resource
    Electronic Resource
    Palo Alto, Calif. : Annual Reviews
    Annual Review of Environment and Resources 26 (2001), S. 435-465 
    ISSN: 1056-3466
    Source: Annual Reviews Electronic Back Volume Collection 1932-2001ff
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Notes: Abstract In addition to being scientifically exciting, commercially important, and environmentally essential, temperate forests have also become a key diplomatic item in international climate negotiations as potential sinks for carbon. This review presents the methods used to estimate carbon sequestration, identifies the constraints and opportunities for carbon sequestration in temperate forests, addresses the issues raised by the monitoring of carbon sequestration, and analyzes uncertainties pertaining to the sequestration of carbon by temperate forests. This review serves a dual purpose: It aims at informing policy makers about carbon sequestration in temperate forests and at making forest ecologists, biogeochemists, and atmospheric scientists aware of the structure of an international agreement to reduce CO2 and other greenhouse gas emissions and some of the real, still answered scientific questions that it poses.
    Type of Medium: Electronic Resource
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  • 3
    ISSN: 1365-2486
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology , Energy, Environment Protection, Nuclear Power Engineering , Geography
    Notes: This paper discusses the advantages and disadvantages of the different methods that separate net ecosystem exchange (NEE) into its major components, gross ecosystem carbon uptake (GEP) and ecosystem respiration (Reco). In particular, we analyse the effect of the extrapolation of night-time values of ecosystem respiration into the daytime; this is usually done with a temperature response function that is derived from long-term data sets. For this analysis, we used 16 one-year-long data sets of carbon dioxide exchange measurements from European and US-American eddy covariance networks. These sites span from the boreal to Mediterranean climates, and include deciduous and evergreen forest, scrubland and crop ecosystems.We show that the temperature sensitivity of Reco, derived from long-term (annual) data sets, does not reflect the short-term temperature sensitivity that is effective when extrapolating from night- to daytime. Specifically, in summer active ecosystems the long-term temperature sensitivity exceeds the short-term sensitivity. Thus, in those ecosystems, the application of a long-term temperature sensitivity to the extrapolation of respiration from night to day leads to a systematic overestimation of ecosystem respiration from half-hourly to annual time-scales, which can reach 〉25% for an annual budget and which consequently affects estimates of GEP. Conversely, in summer passive (Mediterranean) ecosystems, the long-term temperature sensitivity is lower than the short-term temperature sensitivity resulting in underestimation of annual sums of respiration.We introduce a new generic algorithm that derives a short-term temperature sensitivity of Reco from eddy covariance data that applies this to the extrapolation from night- to daytime, and that further performs a filling of data gaps that exploits both, the covariance between fluxes and meteorological drivers and the temporal structure of the fluxes. While this algorithm should give less biased estimates of GEP and Reco, we discuss the remaining biases and recommend that eddy covariance measurements are still backed by ancillary flux measurements that can reduce the uncertainties inherent in the eddy covariance data.
    Type of Medium: Electronic Resource
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  • 4
    ISSN: 1365-2486
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Biology , Energy, Environment Protection, Nuclear Power Engineering , Geography
    Notes: Continuous measurements of the net CO2 flux exchanged in a mixed forest with the atmosphere were performed over 5 years at the Vielsalm experimental site. The carbon sequestration at the site was deduced by a summation of the measurements. Problems associated with this summation procedure were discussed. The carbon sequestration in the ecosystem was presented and its interannual variability was discussed.An estimation of the night flux correction was given. The correction was applied by replacing measurements made during quiet nights by a parameterization. The impact of the correction was shown to vary between 10 and 20% of the uncorrected flux, according to the year. The need to include the storage flux during turbulent periods was emphasized: its neglect leads to an error which will be greater than the one it tries to correct.It was also shown that the heterogeneity of the site made it necessary to split the data into separate series corresponding to the different vegetation patches and to fill the data gaps by using an algorithm that takes account of the weather conditions. Two series were defined, one corresponding to a beech subplot, the other to a conifer subplot. The uncertainty owing to the data split and the data gap-filling was about 15–20% annually.The carbon sequestration was then analysed in both the subplots. The length of the growing season was about 210 days in the beech and 240 days in the conifer. The carbon sequestration over 5 years was 2.28 kg C m2−2 in the beech and 3.58 kg C m2−2 in the conifer. The main difference between the species appeared in spring, between March and May, when the beeches were leafless.Significant interannual variations were observed in both the subplots. They appeared mainly in summer and were primarily because of the variations in the radiation and air humidity regimes. In addition, an impact of the interannual variation of the vegetation area index (VAI) and of the leaf initiation date was observed in the beech. Finally, a decline of the carbon sequestration efficiency of the ecosystem during the season was observed in both the subplots. It was because of neither the variation in any climatic variables nor VAI variation.
    Type of Medium: Electronic Resource
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  • 5
    Publication Date: 2021-06-29
    Description: The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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