Munger
J. William
Munger
J. William
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ArticleEcosystem fluxes of hydrogen : a comparison of flux-gradient methods(Copernicus Publications on behalf of the European Geosciences Union, 2014-09-03) Meredith, Laura K. ; Commane, R. ; Munger, J. William ; Dunn, A. ; Tang, Jianwu ; Wofsy, Steven C. ; Prinn, Ronald G.Our understanding of biosphere–atmosphere exchange has been considerably enhanced by eddy covariance measurements. However, there remain many trace gases, such as molecular hydrogen (H2), that lack suitable analytical methods to measure their fluxes by eddy covariance. In such cases, flux-gradient methods can be used to calculate ecosystem-scale fluxes from vertical concentration gradients. The budget of atmospheric H2 is poorly constrained by the limited available observations, and thus the ability to quantify and characterize the sources and sinks of H2 by flux-gradient methods in various ecosystems is important. We developed an approach to make nonintrusive, automated measurements of ecosystem-scale H2 fluxes both above and below the forest canopy at the Harvard Forest in Petersham, Massachusetts, for over a year. We used three flux-gradient methods to calculate the fluxes: two similarity methods that do not rely on a micrometeorological determination of the eddy diffusivity, K, based on (1) trace gases or (2) sensible heat, and one flux-gradient method that (3) parameterizes K. We quantitatively assessed the flux-gradient methods using CO2 and H2O by comparison to their simultaneous independent flux measurements via eddy covariance and soil chambers. All three flux-gradient methods performed well in certain locations, seasons, and times of day, and the best methods were trace gas similarity for above the canopy and K parameterization below it. Sensible heat similarity required several independent measurements, and the results were more variable, in part because those data were only available in the winter, when heat fluxes and temperature gradients were small and difficult to measure. Biases were often observed between flux-gradient methods and the independent flux measurements, and there was at least a 26% difference in nocturnal eddy-derived net ecosystem exchange (NEE) and chamber measurements. H2 fluxes calculated in a summer period agreed within their uncertainty and pointed to soil uptake as the main driver of H2 exchange at Harvard Forest, with H2 deposition velocities ranging from 0.04 to 0.10 cm s−1.
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PreprintChlorophyll fluorescence tracks seasonal variations of photosynthesis from leaf to canopy in a temperate forest( 2016-11) Yang, Hualei ; Yang, Xi ; Zhang, Yongguang ; Heskel, Mary ; Lu, Xiaoliang ; Munger, J. William ; Sun, Shucun ; Tang, JianwuAccurate estimation of terrestrial gross primary productivity (GPP) remains a challenge despite its importance in the global carbon cycle. Chlorophyll fluorescence (ChlF) has been recently adopted to understand photosynthesis and its response to the environment, particularly with remote sensing data. However, it remains unclear how ChlF and photosynthesis are linked at different spatial scales across the growing season. We examined seasonal relationships between ChlF and photosynthesis at the leaf, canopy, and ecosystem scales, and explored how leaf-level ChlF was linked with canopy-scale solar induced chlorophyll fluorescence (SIF) in a temperate deciduous forest at Harvard Forest, Massachusetts, USA. Our results show that ChlF captured the seasonal variations of photosynthesis with significant linear relationships between ChlF and photosynthesis across the growing season over different spatial scales (R2=0.73, 0.77 and 0.86 at leaf, canopy and satellite scales, respectively; p<0.0001). We developed a model to estimate GPP from the tower-based measurement of SIF and leaf-level ChlF parameters. The estimation of GPP from this model agreed well with flux tower observations of GPP (R2=0.68; p<0.0001), demonstrating the potential of SIF for modeling GPP. At the leaf scale, we found that leaf Fq’/Fm’, the fraction of absorbed photons that are used for photochemistry for a light adapted measurement from a pulse amplitude modulation fluorometer, was the best leaf fluorescence parameter to correlate with canopy-SIF yield (SIF/APAR, R2=0.79; p<0.0001). We also found that canopy-SIF and SIF-derived GPP (GPPSIF) were strongly correlated to leaf-level biochemistry and canopy structure, including chlorophyll content (R2=0.65 for canopy-GPPSIF and chlorophyll content; p<0.0001), leaf area index (LAI) (R2=0.35 for canopy-GPPSIF and LAI; p<0.0001), and normalized difference vegetation index (NDVI) (R2=0.36 for canopy-GPPSIF and NDVI; p<0.0001). Our results suggest that ChlF can be a powerful tool to track photosynthetic rates at leaf, canopy, and ecosystem scales.
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ArticleRepresentativeness of eddy-covariance flux footprints for areas surrounding AmeriFlux sites(Elsevier, 2021-02-14) Chu, Housen ; Luo, Xiangzhong ; Ouyang, Zutao ; Chan, W. Stephen ; Dengel, Sigrid ; Biraud, Sebastien ; Torn, Margaret S. ; Metzger, Stefan ; Kumar, Jitendra ; Arain, M. Altaf ; Arkebauer, Tim J. ; Baldocchi, Dennis D. ; Bernacchi, Carl ; Billesbach, Dave ; Black, T. Andrew ; Blanken, Peter D. ; Bohrer, Gil ; Bracho, Rosvel ; Brown, Shannon ; Brunsell, Nathaniel A. ; Chen, Jiquan ; Chen, Xingyuan ; Clark, Kenneth ; Desai, Ankur R. ; Duman, Tomer ; Durden, David J. ; Fares, Silvano ; Forbrich, Inke ; Gamon, John ; Gough, Christopher M. ; Griffis, Timothy ; Helbig, Manuel ; Hollinger, David ; Humphreys, Elyn ; Ikawa, Hiroki ; Iwata, Hiroki ; Ju, Yang ; Knowles, John F. ; Knox, Sara H. ; Kobayashi, Hideki ; Kolb, Thomas ; Law, Beverly ; Lee, Xuhui ; Litvak, Marcy ; Liu, Heping ; Munger, J. William ; Noormets, Asko ; Novick, Kim ; Oberbauer, Steven F. ; Oechel, Walter ; Oikawa, Patty ; Papuga, Shirley A. ; Pendall, Elise ; Prajapati, Prajaya ; Prueger, John ; Quinton, William L. ; Richardson, Andrew D. ; Russell, Eric S. ; Scott, Russell L. ; Starr, Gregory ; Staebler, Ralf ; Stoy, Paul C. ; Stuart-Haëntjens, Ellen ; Sonnentag, Oliver ; Sullivan, Ryan C. ; Suyker, Andy ; Ueyama, Masahito ; Vargas, Rodrigo ; Wood, Jeffrey D. ; Zona, DonatellaLarge datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 103 to 107 m2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applications and to provide general guidance for data use.