An Analytical Footprint Model For Non-Neutral Stratification

Kormann, R.; Meixner, Franz X.

doi:10.1023/a:1018991015119

Cited by 770 publications

(536 citation statements)

References 13 publications

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“…For Q H and Q E the source area model of Kormann and Meixner [13] is used. Fluxes are measured by the eddy covariance method and processed with standard methods [14], [15].…”

Section: B Evaluationmentioning

confidence: 99%

Spatial Distribution of Sensible and Latent Heat Flux in the City of Basel (Switzerland)

Feigenwinter

Vogt

Parlow

et al. 2018

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Abstract-Urban surfaces are a complex mixture of different land covers and surface materials; the relative magnitudes of the surface energy balance components therefore vary widely across a city. Eddy covariance (EC) measurements provide the best estimates of turbulent heat fluxes but are restricted to the source area. Land surface modeling with earth observation (EO) data is beneficial for extrapolation of a larger area since citywide information is possible. Turbulent sensible and latent heat fluxes are calculated by a combination of micrometeorological approaches (the aerodynamic resistance method, ARM), EO data, and GIS techniques. Input data such as land cover fractions and surface temperatures are derived from Landsat 8 OLI and TIRS, urban morphology was calculated from high-resolution digital building models and GIS data layers, and meteorological data were provided by flux tower measurements. Twenty-two Landsat scenes covering all seasons and different meteorological conditions were analyzed. Sensible heat fluxes were highest for industrial areas, railway stations, and areas with high building density, mainly corresponding to the pixels with highest surface-to-air temperature differences. The spatial distribution of latent heat flux is strongly related to the saturation deficit of vapor and the (minimum) stomatal resistance of vegetation types. Seasonal variations are highly dependent on meteorological conditions, i.e., air temperature, water vapor saturation deficit, and wind speed. Comparison of measured fluxes with modeled fluxes in the weighted source area of the flux towers is moderately accurate due to known drawbacks in the modeling approach and uncertainties inherent to EC measurements, particularly in urban areas.

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“…For Q H and Q E the source area model of Kormann and Meixner [13] is used. Fluxes are measured by the eddy covariance method and processed with standard methods [14], [15].…”

Section: B Evaluationmentioning

confidence: 99%

Spatial Distribution of Sensible and Latent Heat Flux in the City of Basel (Switzerland)

Feigenwinter

Vogt

Parlow

et al. 2018

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The small ratio of height to fetch (distance to fence) in our set-up ensured that the target pasture was always the dominant contributor to the flux. To assess the minor contributions of areas farther upwind, we used the footprint tool of Neftel et al (2008), which is a spatially explicit implementation of the model of Kormann and Meixner (2001). In addition to the two pastures under investigation, the circular irrigated area of the neighbouring farm to the west was specified as a third source area, with the assumption that its CO 2 exchange equalled that from the IFR pasture.…”

Section: Appendix B: Footprint Bias Of Co 2 Fluxes and Its Correctionmentioning

confidence: 99%

Carbon budgets for an irrigated intensively grazed dairy pasture and an unirrigated winter-grazed pasture

et al. 2016

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Abstract. Intensification of pastoral agriculture is occurring rapidly across New Zealand, including increasing use of irrigation and fertiliser application in some regions. While this enables greater gross primary production (GPP) and livestock grazing intensity, the consequences for the net ecosystem carbon budget (NECB) of the pastures are poorly known. Here, we determined the NECB over one year for an irrigated, fertilised and rotationally grazed dairy pasture and a neighbouring unirrigated, unfertilised, wintergrazed pasture. Primary terms in the NECB calculation were: net ecosystem production (NEP), biomass carbon removed by grazing cows and carbon (C) input from their excreta. Annual NEP was measured using the eddy-covariance method. Carbon removal was estimated with plate-meter measurements calibrated against biomass collections, preand post-grazing. Excreta deposition was calculated from animal feed intake. The intensively managed pasture gained C (NECB = 103 ± 42 g C m −2 yr −1 ) but would have been subject to a non-significant C loss if cattle excreta had not been returned to the pasture. The unirrigated pasture was C-neutral (NECB = −13 ± 23 g C m −2 yr −1 ). While annual GPP of the former was almost twice that of the latter (2679 vs. 1372 g C m −2 yr −1 ), ecosystem respiration differed by only 68 % between the two pastures (2271 vs. 1352 g C m −2 yr −1 ). The ratio of GPP to the total annual water input of the irrigated pasture was 37 % greater than that of the unirrigated pasture, i.e. the former used the water input more efficiently than the latter to produce biomass. The NECB results agree qualitatively with those from many other eddy-covariance studies of grazed grasslands, but they seem to be at odds with long-term carbon-stock studies of other New Zealand pastures.

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“…The f y of the KM model takes into account atmospheric stability and uses the power law profile to calculate the vertical profile of the eddy diffusivity, K(z), and vertical profile of the wind speed, u(z) (Kormann and Meixner, 2001). The final function is…”

Section: The Km Modelmentioning

confidence: 99%

“…The specific calculations of ψ(pz/L) and φ c(pz/L) were described by Horst and Weil (1992). Note: a and b are the constants of power laws for the eddy diffusivity and the vertical profile of the horizontal wind velocity, respectively (Kormann and Meixner, 2001). The H model, which is empirical, does not use these parameters.…”

Section: Differences Among the Three Modelsmentioning

confidence: 99%

“…Analytical models are often used for estimating flux footprints due to their ease of coding compared with the Lagrangian stochastic models (Leclerc and Thurtell, 1990;Kljun, 2010b), large eddy simulations (Cai et al, 2010), and closure models (Sogachev and Lloyd, 2004). Currently, analytical models such as the FSAM (Flux Source Area Model) developed by Schmid (1994), the KM model by Kormann and Meixner (2001), and the H model by Hsieh et al (2000), are widely adopted. However, the strength, weakness, and applicability of each model have not been fully demonstrated.…”

Section: Introductionmentioning

confidence: 99%

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Flux footprint climatology estimated by three analytical models over a subtropical coniferous plantation in Southeast China

Zhang¹,

Wen

2015

J Meteorol Res

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Spatial heterogeneity poses a major challenge for the appropriate interpretation of eddy covariance data. The quantification of footprint climatology is fundamental to improving our understanding of carbon budgets, assessing the quality of eddy covariance data, and upscaling the representativeness of a tower flux to regional or global scales. In this study, we elucidated the seasonal variation of flux footprint climatologies and the major factors that influence them using the analytical FSAM (Flux Source Area Model), KM (Kormann and Meixner, 2001), and H (Hsieh et al., 2000) models based on eddy covariance measurements at two and three times the canopy height at the Qianyanzhou site of ChinaFLUX in 2003. The differences in footprints among the three models resulted from different underlying theories used to construct the models. A comparison demonstrated that atmospheric stability was the main factor leading to differences among the three models. In neutral and stable conditions, the KM and FSAM values agreed with each other, but they were both lower than the H values. In unstable conditions, the agreement among the three models for rough surfaces was better than that for smooth surfaces, and the models showed greater agreement for a low measurement height than for a high measurement height. The seasonal flux footprint climatologies were asymmetrically distributed around the tower and corresponded well to the prevailing wind direction, which was north-northwest in winter and south-southeast in summer. The average sizes of the 90% flux footprint climatologies were 0.36-0.74 and 1.5-3.2 km 2 at altitudes of two and three times the canopy height, respectively. The average sizes were ranked by season as follows: spring > summer > winter > autumn. The footprint climatology depended more on atmospheric stability on daily scale than on seasonal scale, and it increased with the increasing standard deviation of the lateral wind fluctuations.

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An Analytical Footprint Model For Non-Neutral Stratification

Cited by 770 publications

References 13 publications

Spatial Distribution of Sensible and Latent Heat Flux in the City of Basel (Switzerland)

Spatial Distribution of Sensible and Latent Heat Flux in the City of Basel (Switzerland)

Carbon budgets for an irrigated intensively grazed dairy pasture and an unirrigated winter-grazed pasture

Flux footprint climatology estimated by three analytical models over a subtropical coniferous plantation in Southeast China

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