Surface-Energy-Balance Closure over Land: A Review

Mauder, Matthias; Foken, Thomas; Cuxart, Joan

doi:10.1007/s10546-020-00529-6

Cited by 229 publications

(204 citation statements)

References 161 publications

(201 reference statements)

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“…The flux footprint was calculated for each half-hour flux averaging period of both field campaigns using the method described by Kljun et al (2004) and Metzger et al (2012). The calculation of the flux footprint for this campaign is described in detail by Squires et al (2020).…”

Section: Flux Footprintmentioning

confidence: 99%

Surface–atmosphere fluxes of volatile organic compounds in Beijing

et al. 2020

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Abstract. Mixing ratios of volatile organic compounds (VOCs) were recorded in two field campaigns in central Beijing as part of the Air Pollution and Human Health in a Chinese Megacity (APHH) project. These data were used to calculate, for the first time in Beijing, the surface–atmosphere fluxes of VOCs using eddy covariance, giving a top-down estimation of VOC emissions from a central area of the city. The results were then used to evaluate the accuracy of the Multi-resolution Emission Inventory for China (MEIC). The APHH winter and summer campaigns took place in November and December 2016 and May and June 2017, respectively. The largest VOC fluxes observed were of small oxygenated compounds such as methanol, ethanol + formic acid and acetaldehyde, with average emission rates of 8.31 ± 8.5, 3.97 ± 3.9 and 1.83 ± 2.0 nmol m−2 s−1, respectively, in the summer. A large flux of isoprene was observed in the summer, with an average emission rate of 5.31 ± 7.7 nmol m−2 s−1. While oxygenated VOCs made up 60 % of the molar VOC flux measured, when fluxes were scaled by ozone formation potential and peroxyacyl nitrate (PAN) formation potential the high reactivity of isoprene and monoterpenes meant that these species represented 30 % and 28 % of the flux contribution to ozone and PAN formation potential, respectively. Comparison of measured fluxes with the emission inventory showed that the inventory failed to capture the magnitude of VOC emissions at the local scale.

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Section: Flux Footprintmentioning

confidence: 99%

Surface–atmosphere fluxes of volatile organic compounds in Beijing

et al. 2020

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“…Despite such a positive outcome, there are a number of uncertainties in both the observations and model simulation that are relevant to highlight. It is well-known that flux towers do not close the energy budget (e.g., Foken, 1998Foken, , 2008Wilson et al, 2002;Widmoser and Wohlfahrt, 2018;Mauder et al, 2020). The model generally overestimates the sensible heat compared to observations, thus suggesting that the missing energy is most likely attributable to sensible heat as supported by other studies (Mauder et al, 2006;Wohlfahrt et al, 2010;Liu et al, 2011) and justifies the lower R 2 for sensible heat compared to the other fluxes.…”

Section: Fully-integrated Mechanistic Ecosystem Modelling: Successes mentioning

confidence: 58%

Impacts of fertilization on grassland productivity and water quality across the European Alps: insights from a mechanistic model

Botter¹,

Zeeman²,

Burlando³

et al. 2020

Preprint

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Abstract. Alpine grasslands sustain local economy providing fodder for livestock. Intensive fertilization is common to enhance their yields, thus creating negative externalities on water quality that are difficult to evaluate without reliable estimates of nutrient fluxes. We apply a 1-D mechanistic ecosystem model, seamlessly integrating land-surface energy balance, soil hydrology, vegetation dynamics, and soil biogeochemistry aiming at assessing the grassland response to fertilization. We simulate the major water, carbon, nutrient, and energy fluxes of nine grassland plots across the broad European Alpine region. We provide an unprecedent interdisciplinary model evaluation confirming its performance against observed variables from different datasets. Subsequently, we apply the model to test the influence of fertilization practices on grassland yields and nitrate (NO3) losses through leaching. Despite the generally low NO3 concentration in groundwater recharge, the variability across sites is remarkable, mostly, but not exclusively, dictated by elevation. In high-Alpine sites short growing seasons lead to less efficient nitrogen (N) uptake for biomass production. This combined with lower evapotranspiration rates results in higher amounts of drainage and NO3 leaching to groundwater. The local soil hydrology has a crucial role in driving the NO3 use efficiency. The commonly applied fixed-threshold limit on fertilizer N input is suboptimal. We suggest that major hydrological and soil property differences across sites should be considered in the delineation of best practices or regulations for management. Using distributed maps informed with key soil and climatic attributes or systematically implementing integrated ecosystem models as shown here can contribute to achieving more sustainable practices.

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“…the "decoupled" limit described by Jarvis and McNaughton (1986) and the "calm limit" described by McColl (2020). Comparison of Fig.…”

Section: Absolute Biases Revealed By Simulationsmentioning

confidence: 92%

Calculating canopy stomatal conductance from eddy covariance measurements, in light of the energy budget closure problem

Wehr

Saleska

2021

Biogeosciences

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Abstract. Canopy stomatal conductance is commonly estimated from eddy covariance measurements of the latent heat flux (LE) by inverting the Penman–Monteith equation. That method ignores eddy covariance measurements of the sensible heat flux (H) and instead calculates H implicitly as the residual of all other terms in the site energy budget. Here we show that canopy stomatal conductance is more accurately calculated from eddy covariance (EC) measurements of both H and LE using the flux–gradient equations that define conductance and underlie the Penman–Monteith equation, especially when the site energy budget fails to close due to pervasive biases in the eddy fluxes and/or the available energy. The flux–gradient formulation dispenses with unnecessary assumptions, is conceptually simpler, and is as or more accurate in all plausible scenarios. The inverted Penman–Monteith equation, on the other hand, contributes substantial biases and erroneous spatial and temporal patterns to canopy stomatal conductance, skewing its relationships with drivers such as light and vapor pressure deficit.

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Surface-Energy-Balance Closure over Land: A Review

Cited by 229 publications

References 161 publications

Surface–atmosphere fluxes of volatile organic compounds in Beijing

Surface–atmosphere fluxes of volatile organic compounds in Beijing

Impacts of fertilization on grassland productivity and water quality across the European Alps: insights from a mechanistic model

Calculating canopy stomatal conductance from eddy covariance measurements, in light of the energy budget closure problem

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