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1. FLUXNET-CH4: A global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands

Author: Delwiche, KB, Knox, SH, Malhotra, A, Fluet-Chouinard, E, McNicol, G, Feron, S, Ouyang, Z, Papale, D, Trotta, C, Canfora, E, Cheah, YW, Christianson, D, Alberto, MCR, Alekseychik, P, Aurela, M, Baldocchi, D, Bansal, S, Billesbach, DP, Bohrer, G, Bracho, R, Buchmann, N, Campbell, DI, Celis, G, Chen, J, Chen, W, Chu, H, Dalmagro, HJ, Dengel, S, Desai, AR, Detto, M, Dolman, H, Eichelmann, E, Euskirchen, E, Famulari, D, Fuchs, K, Goeckede, M, Gogo, S, Gondwe, MJ, Goodrich, JP, Gottschalk, P, Graham, SL, Heimann, M, Helbig, M, Helfter, C, Hemes, KS, Hirano, T, Hollinger, D, Hörtnagl, L, Iwata, H, Jacotot, A, Jurasinski, G, Kang, M, Kasak, K, King, J, Klatt, J, Koebsch, F, Krauss, KW, Lai, DYF, Lohila, A, Mammarella, I, Belelli Marchesini, L, Manca, G, Matthes, JH, Maximov, T, Merbold, L, Mitra, B, Morin, TH, Nemitz, E, Nilsson, MB, Niu, S, Oechel, WC, Oikawa, PY, Ono, K, Peichl, M, Peltola, O, Reba, ML, Richardson, AD, Riley, W, Runkle, BRK, Ryu, Y, Sachs, T, Sakabe, A, Sanchez, CR, Schuur, EA, Schäfer, KVR, Sonnentag, O, Sparks, JP, Stuart-Haëntjens, E, Sturtevant, C, Sullivan, RC, Szutu, DJ, Thom, JE, Torn, MS, Tuittila, ES, Turner, J, Ueyama, M, Valach, AC, Vargas, R, Varlagin, A, and Vazquez-Lule, A
Subjects: Atmospheric Sciences, Geochemistry, Physical Geography and Environmental Geoscience
Abstract: Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20g g€¯S to 20g g€¯N) the spring onset of elevated CH4 emissions starts 3g€¯d earlier, and the CH4 emission season lasts 4g€¯d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at 10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.
Published: 2021

2. Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites

Author: Chu, H, Luo, X, Ouyang, Z, Chan, WS, Dengel, S, Biraud, SC, Torn, MS, Metzger, S, Kumar, J, Arain, MA, Arkebauer, TJ, Baldocchi, D, Bernacchi, C, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Bracho, R, Brown, S, Brunsell, NA, Chen, J, Chen, X, Clark, K, Desai, AR, Duman, T, Durden, D, Fares, S, Forbrich, I, Gamon, JA, Gough, CM, Griffis, T, Helbig, M, Hollinger, D, Humphreys, E, Ikawa, H, Iwata, H, Ju, Y, Knowles, JF, Knox, SH, Kobayashi, H, Kolb, T, Law, B, Lee, X, Litvak, M, Liu, H, Munger, JW, Noormets, A, Novick, K, Oberbauer, SF, Oechel, W, Oikawa, P, Papuga, SA, Pendall, E, Prajapati, P, Prueger, J, Quinton, WL, Richardson, AD, Russell, ES, Scott, RL, Starr, G, Staebler, R, Stoy, PC, Stuart-Haëntjens, E, Sonnentag, O, Sullivan, RC, Suyker, A, Ueyama, M, Vargas, R, Wood, JD, and Zona, D
Subjects: Flux footprint, Spatial representativeness, Landsat EVI, Land cover, Sensor location bias, Model-data benchmarking, Meteorology & Atmospheric Sciences, Earth Sciences, Biological Sciences, Agricultural and Veterinary Sciences
Abstract: Large 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.
Published: 2021

3. FluXNET-CH4 synthesis activity objectives, observations, and future directions

Author: Knox, SH, Jackson, RB, Poulter, B, McNicol, G, Fluet-Chouinard, E, Zhang, Z, Hugelius, G, Bousquet, P, Canadell, JG, Saunois, M, Papale, D, Chu, H, Keenan, TF, Baldocchi, D, Torn, MS, Mammarella, I, Trotta, C, Aurela, M, Bohrer, G, Campbell, DI, Cescatti, A, Chamberlain, S, Chen, J, Chen, W, Dengel, S, Desai, AR, Euskirchen, E, Friborg, T, Gasbarra, D, Goded, I, Goeckede, M, Heimann, M, Helbig, M, Hirano, T, Hollinger, DY, Iwata, H, Kang, M, Klatt, J, Krauss, KW, Kutzbach, L, Lohila, A, Mitra, B, Morin, TH, Nilsson, MB, Niu, S, Noormets, A, Oechel, WC, Peichl, M, Peltola, O, Reba, ML, Richardson, AD, Runkle, BRK, Ryu, Y, Sachs, T, Schäfer, KVR, Schmid, HP, Shurpali, N, Sonnentag, O, Tang, ACI, Ueyama, M, Vargas, R, Vesala, T, Ward, EJ, Windham-Myers, L, Wohlfahrt, G, and Zona, D
Subjects: Meteorology & Atmospheric Sciences, Astronomical and Space Sciences, Atmospheric Sciences, Physical Geography and Environmental Geoscience
Abstract: We describe a new coordination activity and initial results for a global synthesis of eddy covariance CH4 flux measurements.
Published: 2019

4. Microclimatic Effects of a Perched Peatland Forest Gap

Author: Green, A., Bohrer, G., and Petrone, R. M.
Published: 2022
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5. Temporal Dynamics of Aerodynamic Canopy Height Derived From Eddy Covariance Momentum Flux Data Across North American Flux Networks

Author: Chu, H, Baldocchi, DD, Poindexter, C, Abraha, M, Desai, AR, Bohrer, G, Arain, MA, Griffis, T, Blanken, PD, O'Halloran, TL, Thomas, RQ, Zhang, Q, Burns, SP, Frank, JM, Christian, D, Brown, S, Black, TA, Gough, CM, Law, BE, Lee, X, Chen, J, Reed, DE, Massman, WJ, Clark, K, Hatfield, J, Prueger, J, Bracho, R, Baker, JM, and Martin, TA
Subjects: momentum flux, AmeriFlux, eddy covariance, canopy height, phenology, Meteorology & Atmospheric Sciences, MD Multidisciplinary
Abstract: Aerodynamic canopy height (ha) is the effective height of vegetation canopy for its influence on atmospheric fluxes and is a key parameter of surface-atmosphere coupling. However, methods to estimate ha from data are limited. This synthesis evaluates the applicability and robustness of the calculation of ha from eddy covariance momentum-flux data. At 69 forest sites, annual ha robustly predicted site-to-site and year-to-year differences in canopy heights (R2 = 0.88, 111 site-years). At 23 cropland/grassland sites, weekly ha successfully captured the dynamics of vegetation canopies over growing seasons (R2 > 0.70 in 74 site-years). Our results demonstrate the potential of flux-derived ha determination for tracking the seasonal, interannual, and/or decadal dynamics of vegetation canopies including growth, harvest, land use change, and disturbance. The large-scale and time-varying ha derived from flux networks worldwide provides a new benchmark for regional and global Earth system models and satellite remote sensing of canopy structure.
Published: 2018

6. Evaluating the effect of alternative carbon allocation schemes in a land surface model (CLM4.5) on carbon fluxes, pools, and turnover in temperate forests

Author: Montané, F, Fox, AM, Arellano, AF, MacBean, N, Ross Alexander, M, Dye, A, Bishop, DA, Trouet, V, Babst, F, Hessl, AE, Pederson, N, Blanken, PD, Bohrer, G, Gough, CM, Litvak, ME, Novick, KA, Phillips, RP, Wood, JD, and Moore, DJP
Abstract: © Author(s) 2017. How carbon (C) is allocated to different plant tissues (leaves, stem, and roots) determines how long C remains in plant biomass and thus remains a central challenge for understanding the global C cycle. We used a diverse set of observations (AmeriFlux eddy covariance tower observations, biomass estimates from tree-ring data, and leaf area index (LAI) measurements) to compare C fluxes, pools, and LAI data with those predicted by a land surface model (LSM), the Community Land Model (CLM4.5). We ran CLM4.5 for nine temperate (including evergreen and deciduous) forests in North America between 1980 and 2013 using four different C allocation schemes: dynamic C allocation scheme (named "D-CLM4.5") with one dynamic allometric parameter, which allocates C to the stem and leaves to vary in time as a function of annual net primary production (NPP); ii. an alternative dynamic C allocation scheme (named "DLitton"), where, similar to (i), C allocation is a dynamic function of annual NPP, but unlike (i) includes two dynamic allometric parameters involving allocation to leaves, stem, and coarse roots; iii.-iv. a fixed C allocation scheme with two variants, one representative of observations in evergreen (named "F Evergreen") and the other of observations in deciduous forests (named "F-Deciduous"). D-CLM4.5 generally overestimated gross primary production (GPP) and ecosystem respiration, and underestimated net ecosystem exchange (NEE). In D-CLM4.5, initial aboveground biomass in 1980 was largely overestimated (between 10 527 and 12 897 g Cm-2) for deciduous forests, whereas aboveground biomass accumulation through time (between 1980 and 2011) was highly underestimated (between 1222 and 7557 gCm-2) for both evergreen and deciduous sites due to a lower stem turnover rate in the sites than the one used in the model. D-CLM4.5 overestimated LAI in both evergreen and deciduous sites because the leaf C-LAI relationship in the model did not match the observed leaf C- LAI relationship at our sites. Although the four C allocation schemes gave similar results for aggregated C fluxes, they translated to important differences in long-term aboveground biomass accumulation and aboveground NPP. For deciduous forests, D-Litton gave more realistic Cstem- Cleafratios and strongly reduced the overestimation of initial aboveground biomass and aboveground NPP for deciduous forests by D-CLM4.5. We identified key structural and parameterization deficits that need refinement to improve the accuracy of LSMs in the near future. These include changing how C is allocated in fixed and dynamic schemes based on data from current forest syntheses and different parameterization of allocation schemes for different forest types.Our results highlight the utility of using measurements of aboveground biomass to evaluate and constrain the C allocation scheme in LSMs, and suggest that stem turnover is overestimated by CLM4.5 for these AmeriFlux sites. Understanding the controls of turnover will be critical to improving long-term C processes in LSMs.
Published: 2017

7. Microclimatic Effects of a Forest-to-Peatland Transition on Aerodynamic Resistance to Water Vapour Transfer in the Sub-humid Boreal Plains

Author: Green, A., Bohrer, G., and Petrone, R.
Published: 2021
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8. Combining eddy-covariance and chamber measurements to determine the methane budget from a small, heterogeneous urban floodplain wetland park

Author: Morin, T.H., Bohrer, G., Stefanik, K.C., Rey-Sanchez, A.C., Matheny, A.M., and Mitsch, W.J.
Published: 2017
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9. Effects of Environmental Variables on Vesicular-Arbuscular Mycorrhizal Abundance in Wild Populations of Vangueria infausta

Author: Bohrer, G., Kagan-Zur, V., Roth-Bejerano, N., and Ward, D.
Published: 2001

10. Global transpiration data from sap flow measurements: The SAPFLUXNET database

Author: Ministerio de Ciencia e Innovación (España), Agència de Gestió d'Ajuts Universitaris i de Recerca, Poyatos, R [0000-0003-0521-2523], Granda, V [0000-0002-0469-1991], Flo, V [0000-0003-1908-4577], Adams, MA [0000-0002-8154-0097], Adorjan, B [0000-0002-5482-2515], Aidar, MPM [0000-0001-5728-6749], Alvarado-Barrientos, MS [0000-0003-0098-0806], Anderson-Teixeira, KJ [0000-0001-7585-712X], Arain, MA [0000-0002-1433-5173], Aranda, I [0000-0001-9086-7940], Asbjornsen, H [000-0001-8126-3328], Oishi, AC [0000-0001-5064-4080], Oliveira RS [0000-0002-6392-2526], Oren, R [0000-0002-5654-1733], Ourcival,JM [0000-0002-3557-3496], Paljakka, T [0000-0002-3674-4904], Perez-Priego, O [0000-0002-3138-3177], Peri, PL [0000-0002-5398-4408], Peters, RL [0000-0002-7441-1297], Pfautsch, S [0000-0002-4390-4195], Pockman, WT [0000-0002-3286-0457], Baxter, R [0000-0002-7504-6797], Preisler, Y [0000-0001-5861-8362], Rocha, H [0000-0002-5981-4469], Röll, A [0000-0001-9457-4459], Rosado, BHP [0000-0002-8924-8672], Rowland, L [0000-0002-0774-3216], Rubtsov, A [0000-0002-9663-4344], Sabaté, S [0000-0003-1854-0761], Salmon, Y [0000-0003-4433-4021], Salomón, RL [0000-0003-2674-1731], Sánchez-Costa, E [0000-0001-6799-9611], Beamesderfer, E [0000-0002-7513-7349], Schäfer, K [0000-0001-9452-3619], Schuldt, B [0000-0003-4738-5289], Shashkin, A [0000-0001-9925-5019], Stahl, C [0000-0001-5411-1169], Stojanović, M [0000-0003-4918-8668], Sun, G [0000-0002-0159-1370], Szatniewska, J [0000-0003-3027-9965], Tatarinov, F [0000-0002-8338-6070], Tesar, M [0000-0002-5647-8498], Thomas, FM [0000-0001-6377-719X], Berveiller, D [0000-0001-7461-6420], Tor-ngern, P [0000-0001-7363-4926], Urban, J [0000-0003-1730-947X], Van der Tol, C [0000-0002-2484-8191], Van Meerveld [0000-0002-7547-3270], Varlagin, A [0000-0002-2549-5236], Werner, C [0000-0002-7676-9057], Wieser, G [0000-0002-7575-5657], Wingate, W [0000-0003-1921-1556], Wullschleger, S [0000-0002-9869-0446], Koong, Y [0000-0002-8630-3031], Blakely, B [0000-0001-9923-0794], Zweifel, R [0000-0001-9438-0582], Steppe, K [0000-0001-6252-0704], Mencuccini, M [0000-0003-0840-1477], Martínez-Vilalta, J [0000-0002-2332-7298], Boggs, J [0000-0003-4826-1843], Bohrer, G [0000-0002-9209-9540], Bolstad, P [0000-0002-1829-8859], Bonal, D [0000-0001-9602-8603], Bracho, R [0000-0002-8015-9796], Brodeur, J [0000-0001-7118-8360], Casanoves, F [0000-0001-8765-9382], Chave, J [0000-0002-7766-1347], Chen, H [0000-0001-7619-3425], Cisneros, C [0000-0001-9661-4581], Clark, K [0000-0003-0534-9677], Cremonese, E [0000-0002-6708-8532], Dang, H [0000-0003-4428-3834], David, J [0000-0001-6994-1085], David, T [0000-0003-1473-899X], Delpierre, D [0000-0003-0906-9402], Desai, AR [0000-0002-5226-6041], Do, FC [0000-0002-2852-627X], Dohnal, M [0000-0003-1769-4750], Domec, JC [0000-0003-0478-2559], Dzikiti, S [0000-0002-1892-4143], Edgar, C [0000-0002-7026-8358], Eichstaedt, R [0000-0002-4905-7994], El-Madany, T [0000-0002-0726-7141], Elbers, J [0000-0002-0631-3505], Eller, CB [0000-0002-7795-2574], Euskirchen, E [0000-0002-0848-4295], Ewers, B [0000-0001-6647-7475], Fonti, P [0000-0002-7070-3292], Forner, A [0000-0002-7123-6403], Forrester, D [0000-0003-2732-5692], Freitas, HC [0000-0002-4861-1164], Galvagno, M [0000-0002-0827-487X], Garcia-Tejera, O [0000-0001-7726-8118], Ghimire, CP [0000-0002-3715-6311], Gimeno, TE [0000-0002-1707-9291], Granier, A [0000-0002-4174-2487], Griebel, A [0000-0002-4476-8279], Guangyu, Y [0000-0003-3242-5348], Gush, MB [0000-0003-1328-9862], Hanson, PJ [0000-0001-7293-3561], Hasselquist, N [0000-0003-2777-0163], Heinrich, I [0000-0001-5800-6999], Hernandez-Santana, V [0000-0001-9018-8622], Herrmann, V [0000-0002-4519-481X], Hölttä, T [0000-0001-7677-7156], Holwerda, F [0000-0003-4125-1765], Ayutthaya, SIN [0000-0002-5354-1527], Jochheim,H [0000-0001-8047-4553], Joly, CA [0000-0002-7945-2805], Kim, HS [0000-0002-3440-6071], Klemedtsson, L [0000-0002-1122-0717], Kropp, H [0000-0002-4258-3393], Lagergren, F [0000-0002-0061-733X], Lane, P [0000-0001-6121-8386], Lapenas, A [0000-0002-2135-3636], Lechuga, V [0000-0003-3745-587X], Lee,M [0000-0002-9601-3863], Leuschner, C [0000-0002-5689-7932], Limousin, JM [0000-0002-2734-2495], Linares, JC [0000-0001-8375-6353], Linderson, ML [0000-0001-6578-6671], Lindroth, A [0000-0002-7669-784X], Llorens, P [0000-0003-4591-5303], López-Bernal, A [0000-0002-1034-4718], Loranty, MM[0000-0001-8851-7386], Macinnis-Ng, C [0000-0003-3935-9814], Marechaux, I [0000-0002-5401-0197], Martin, TA [0000-0002-7872-4194], Matheny, A [0000-0002-9532-7131], McDowell, N [0000-0002-2178-2254], Meir, P [0000-0002-2362-0398], Mészáros, I [0000-0001-8841-730X], Migliavacca, M [0000-0003-3546-8407], Mölder, M [0000-0001-6767-3195], Montagnani, L [0000-0003-2957-9071], Moore, GW [0000-0001-5190-5983], Nakada, R [0000-0002-3704-1784], Niu, F [0000-0003-3445-4011], Nolan, R [0000-0001-9277-5142], Norby,R [0000-0002-0238-9828], Novick, K [0000-0002-8431-0879], Oberhuber, W [0000-0002-5197-7044], Obojes, N [0000-0002-6718-2756], Poyatos, R, Granda, V, Flo, V, Adams, MA, Adorján, B, Aguadé, D, Aidar, MPM, Allen, S, Alvarado-Barrientos, MS, Anderson-Teixeira, KJ, Aparecido, LM, Joly, CA, Kaplick, J, Kim, HS, Klemedtsson, L, Kropp, H, Lagergren, F, Lane, P, Lang, P, Lapenas, A, Lechuga, V, Migliavacca, M, Lee, M, Leuschner, C, Limousin, JM, Linares, JC, Linderson, ML, Lindroth, A, Llorens, P, López-Bernal, A, Loranty, MM, Lüttschwager, D, Mitchell, P, MacInnis-Ng, C, Maréchaux, I, Martin, TA, Matheny, A, McDowell, N, McMahon, S, Meir, P, Mészáros, I, Molder, M, Mölder, M, Montagnani, L, Moore, GW, Nakada, R, Niu, F, Nolan, R, Norby,R, Novick, K, Oberhuber, W, Obojes, N, Oishi, AC, Oliveira RS, Oren, R, Ourcival,JM, Paljakka, T, Perez-Priego, O, Peri, PL, Peters, RL, Pfautsch, S, Pockman, WT, Preisler, Y, Rascher, K, Robinson, G, Rocha, H, Rocheteau, A, Röll, A, Rosado, BHP, Rowland, L [, Rubtsov, A, Sabaté, S, Salmon, Y, Salomón, RL, Sánchez-Costa, E, Schäfer, K, Schuldt, B, Shashkin, A, Stahl, C, Stojanovic, M, Suárez, JC, Sun, G, Niu, FR, Szatniewska, J, Tatarinov, F, Tesar, M, Thomas, FM, Tor-ngern, P, Urban, J, Valladares, F, Van der Tol, C, Van Meerveld, Varlagin, A, Norby, R, Voigt, H, Warren, J, Werner, C, Werner, W, Wieser, G, Wingate, W, Wullschleger, S, Koong, Y, Zweifel, R, Arain, MA, Oliveira, RS, Ourcival, JM, Aranda, I, Roll, A, Asbjornsen, H, Baxter, R, Beamesderfer, E, Berry, ZC, Berveiller, D, Blakely, B, Boggs, J, Bohrer, G, Bolstad, PV, Bonal, D, Bracho, R, Brito, P, Brodeur, J, Casanoves, F, Chave, J, Chen, H, Cisneros, C, Clark, K, Cremonese, E, Dang, HZ, David, JS, David, TS, Delpierre, N, Desai, AR, Do, Frederic C., Dohnal, M, Domec, JC, Dzikiti, S, Edgar, C, Eichstaedt, R, El-Madany, TS, Elbers, J, Eller, CB, Euskirchen, ES, Ewers, B, Fonti, P, Forner, A, Forrester, DI, Freitas, HC, Galvagno, M, Garcia-Tejera, O, Ghimire, CP, Gimeno, TE, Grace, J, Granier, A, Griebel, A, Guangyu, Y, Gush, MB, Hanson, PJ, Hasselquist, NJ, Heinrich, I, Hernandez-Santana, V, Herrmann, V, Hölttä, T, Holwerda, F, Irvine, J, Ayutthaya, SIN, Jarvis, PG, Jochheim, H, Ministerio de Ciencia e Innovación (España), Agència de Gestió d'Ajuts Universitaris i de Recerca, Poyatos, R [0000-0003-0521-2523], Granda, V [0000-0002-0469-1991], Flo, V [0000-0003-1908-4577], Adams, MA [0000-0002-8154-0097], Adorjan, B [0000-0002-5482-2515], Aidar, MPM [0000-0001-5728-6749], Alvarado-Barrientos, MS [0000-0003-0098-0806], Anderson-Teixeira, KJ [0000-0001-7585-712X], Arain, MA [0000-0002-1433-5173], Aranda, I [0000-0001-9086-7940], Asbjornsen, H [000-0001-8126-3328], Oishi, AC [0000-0001-5064-4080], Oliveira RS [0000-0002-6392-2526], Oren, R [0000-0002-5654-1733], Ourcival,JM [0000-0002-3557-3496], Paljakka, T [0000-0002-3674-4904], Perez-Priego, O [0000-0002-3138-3177], Peri, PL [0000-0002-5398-4408], Peters, RL [0000-0002-7441-1297], Pfautsch, S [0000-0002-4390-4195], Pockman, WT [0000-0002-3286-0457], Baxter, R [0000-0002-7504-6797], Preisler, Y [0000-0001-5861-8362], Rocha, H [0000-0002-5981-4469], Röll, A [0000-0001-9457-4459], Rosado, BHP [0000-0002-8924-8672], Rowland, L [0000-0002-0774-3216], Rubtsov, A [0000-0002-9663-4344], Sabaté, S [0000-0003-1854-0761], Salmon, Y [0000-0003-4433-4021], Salomón, RL [0000-0003-2674-1731], Sánchez-Costa, E [0000-0001-6799-9611], Beamesderfer, E [0000-0002-7513-7349], Schäfer, K [0000-0001-9452-3619], Schuldt, B [0000-0003-4738-5289], Shashkin, A [0000-0001-9925-5019], Stahl, C [0000-0001-5411-1169], Stojanović, M [0000-0003-4918-8668], Sun, G [0000-0002-0159-1370], Szatniewska, J [0000-0003-3027-9965], Tatarinov, F [0000-0002-8338-6070], Tesar, M [0000-0002-5647-8498], Thomas, FM [0000-0001-6377-719X], Berveiller, D [0000-0001-7461-6420], Tor-ngern, P [0000-0001-7363-4926], Urban, J [0000-0003-1730-947X], Van der Tol, C [0000-0002-2484-8191], Van Meerveld [0000-0002-7547-3270], Varlagin, A [0000-0002-2549-5236], Werner, C [0000-0002-7676-9057], Wieser, G [0000-0002-7575-5657], Wingate, W [0000-0003-1921-1556], Wullschleger, S [0000-0002-9869-0446], Koong, Y [0000-0002-8630-3031], Blakely, B [0000-0001-9923-0794], Zweifel, R [0000-0001-9438-0582], Steppe, K [0000-0001-6252-0704], Mencuccini, M [0000-0003-0840-1477], Martínez-Vilalta, J [0000-0002-2332-7298], Boggs, J [0000-0003-4826-1843], Bohrer, G [0000-0002-9209-9540], Bolstad, P [0000-0002-1829-8859], Bonal, D [0000-0001-9602-8603], Bracho, R [0000-0002-8015-9796], Brodeur, J [0000-0001-7118-8360], Casanoves, F [0000-0001-8765-9382], Chave, J [0000-0002-7766-1347], Chen, H [0000-0001-7619-3425], Cisneros, C [0000-0001-9661-4581], Clark, K [0000-0003-0534-9677], Cremonese, E [0000-0002-6708-8532], Dang, H [0000-0003-4428-3834], David, J [0000-0001-6994-1085], David, T [0000-0003-1473-899X], Delpierre, D [0000-0003-0906-9402], Desai, AR [0000-0002-5226-6041], Do, FC [0000-0002-2852-627X], Dohnal, M [0000-0003-1769-4750], Domec, JC [0000-0003-0478-2559], Dzikiti, S [0000-0002-1892-4143], Edgar, C [0000-0002-7026-8358], Eichstaedt, R [0000-0002-4905-7994], El-Madany, T [0000-0002-0726-7141], Elbers, J [0000-0002-0631-3505], Eller, CB [0000-0002-7795-2574], Euskirchen, E [0000-0002-0848-4295], Ewers, B [0000-0001-6647-7475], Fonti, P [0000-0002-7070-3292], Forner, A [0000-0002-7123-6403], Forrester, D [0000-0003-2732-5692], Freitas, HC [0000-0002-4861-1164], Galvagno, M [0000-0002-0827-487X], Garcia-Tejera, O [0000-0001-7726-8118], Ghimire, CP [0000-0002-3715-6311], Gimeno, TE [0000-0002-1707-9291], Granier, A [0000-0002-4174-2487], Griebel, A [0000-0002-4476-8279], Guangyu, Y [0000-0003-3242-5348], Gush, MB [0000-0003-1328-9862], Hanson, PJ [0000-0001-7293-3561], Hasselquist, N [0000-0003-2777-0163], Heinrich, I [0000-0001-5800-6999], Hernandez-Santana, V [0000-0001-9018-8622], Herrmann, V [0000-0002-4519-481X], Hölttä, T [0000-0001-7677-7156], Holwerda, F [0000-0003-4125-1765], Ayutthaya, SIN [0000-0002-5354-1527], Jochheim,H [0000-0001-8047-4553], Joly, CA [0000-0002-7945-2805], Kim, HS [0000-0002-3440-6071], Klemedtsson, L [0000-0002-1122-0717], Kropp, H [0000-0002-4258-3393], Lagergren, F [0000-0002-0061-733X], Lane, P [0000-0001-6121-8386], Lapenas, A [0000-0002-2135-3636], Lechuga, V [0000-0003-3745-587X], Lee,M [0000-0002-9601-3863], Leuschner, C [0000-0002-5689-7932], Limousin, JM [0000-0002-2734-2495], Linares, JC [0000-0001-8375-6353], Linderson, ML [0000-0001-6578-6671], Lindroth, A [0000-0002-7669-784X], Llorens, P [0000-0003-4591-5303], López-Bernal, A [0000-0002-1034-4718], Loranty, MM[0000-0001-8851-7386], Macinnis-Ng, C [0000-0003-3935-9814], Marechaux, I [0000-0002-5401-0197], Martin, TA [0000-0002-7872-4194], Matheny, A [0000-0002-9532-7131], McDowell, N [0000-0002-2178-2254], Meir, P [0000-0002-2362-0398], Mészáros, I [0000-0001-8841-730X], Migliavacca, M [0000-0003-3546-8407], Mölder, M [0000-0001-6767-3195], Montagnani, L [0000-0003-2957-9071], Moore, GW [0000-0001-5190-5983], Nakada, R [0000-0002-3704-1784], Niu, F [0000-0003-3445-4011], Nolan, R [0000-0001-9277-5142], Norby,R [0000-0002-0238-9828], Novick, K [0000-0002-8431-0879], Oberhuber, W [0000-0002-5197-7044], Obojes, N [0000-0002-6718-2756], Poyatos, R, Granda, V, Flo, V, Adams, MA, Adorján, B, Aguadé, D, Aidar, MPM, Allen, S, Alvarado-Barrientos, MS, Anderson-Teixeira, KJ, Aparecido, LM, Joly, CA, Kaplick, J, Kim, HS, Klemedtsson, L, Kropp, H, Lagergren, F, Lane, P, Lang, P, Lapenas, A, Lechuga, V, Migliavacca, M, Lee, M, Leuschner, C, Limousin, JM, Linares, JC, Linderson, ML, Lindroth, A, Llorens, P, López-Bernal, A, Loranty, MM, Lüttschwager, D, Mitchell, P, MacInnis-Ng, C, Maréchaux, I, Martin, TA, Matheny, A, McDowell, N, McMahon, S, Meir, P, Mészáros, I, Molder, M, Mölder, M, Montagnani, L, Moore, GW, Nakada, R, Niu, F, Nolan, R, Norby,R, Novick, K, Oberhuber, W, Obojes, N, Oishi, AC, Oliveira RS, Oren, R, Ourcival,JM, Paljakka, T, Perez-Priego, O, Peri, PL, Peters, RL, Pfautsch, S, Pockman, WT, Preisler, Y, Rascher, K, Robinson, G, Rocha, H, Rocheteau, A, Röll, A, Rosado, BHP, Rowland, L [, Rubtsov, A, Sabaté, S, Salmon, Y, Salomón, RL, Sánchez-Costa, E, Schäfer, K, Schuldt, B, Shashkin, A, Stahl, C, Stojanovic, M, Suárez, JC, Sun, G, Niu, FR, Szatniewska, J, Tatarinov, F, Tesar, M, Thomas, FM, Tor-ngern, P, Urban, J, Valladares, F, Van der Tol, C, Van Meerveld, Varlagin, A, Norby, R, Voigt, H, Warren, J, Werner, C, Werner, W, Wieser, G, Wingate, W, Wullschleger, S, Koong, Y, Zweifel, R, Arain, MA, Oliveira, RS, Ourcival, JM, Aranda, I, Roll, A, Asbjornsen, H, Baxter, R, Beamesderfer, E, Berry, ZC, Berveiller, D, Blakely, B, Boggs, J, Bohrer, G, Bolstad, PV, Bonal, D, Bracho, R, Brito, P, Brodeur, J, Casanoves, F, Chave, J, Chen, H, Cisneros, C, Clark, K, Cremonese, E, Dang, HZ, David, JS, David, TS, Delpierre, N, Desai, AR, Do, Frederic C., Dohnal, M, Domec, JC, Dzikiti, S, Edgar, C, Eichstaedt, R, El-Madany, TS, Elbers, J, Eller, CB, Euskirchen, ES, Ewers, B, Fonti, P, Forner, A, Forrester, DI, Freitas, HC, Galvagno, M, Garcia-Tejera, O, Ghimire, CP, Gimeno, TE, Grace, J, Granier, A, Griebel, A, Guangyu, Y, Gush, MB, Hanson, PJ, Hasselquist, NJ, Heinrich, I, Hernandez-Santana, V, Herrmann, V, Hölttä, T, Holwerda, F, Irvine, J, Ayutthaya, SIN, Jarvis, PG, and Jochheim, H
Abstract: Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land-atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80% of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50% of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56% of the datasets. Many datasets contain data for species that make up 90% or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely a
Published: 2021

11. Formulation of a Consistent Multi‐Species Canopy Description for Hydrodynamic Models Embedded in Large‐Scale Land‐Surface Representations of Mixed‐Forests

Author: Bohrer, G., primary and Missik, J. E. C., additional
Published: 2022
Full Text: View/download PDF

12. Use of change-point detection for friction–velocity threshold evaluation in eddy-covariance studies

Author: Barr, A.G., Richardson, A.D., Hollinger, D.Y., Papale, D., Arain, M.A., Black, T.A., Bohrer, G., Dragoni, D., Fischer, M.L., Gu, L., Law, B.E., Margolis, H.A., McCaughey, J.H., Munger, J.W., Oechel, W., and Schaeffer, K.
Published: 2013
Full Text: View/download PDF

13. Causality guided machine learning model on wetland CH4 emissions across global wetlands

Author: Yuan, K, Yuan, K, Zhu, Q, Li, F, Riley, WJ, Torn, M, Chu, H, McNicol, G, Chen, M, Knox, S, Delwiche, K, Wu, H, Baldocchi, D, Ma, H, Desai, AR, Chen, J, Sachs, T, Ueyama, M, Sonnentag, O, Helbig, M, Tuittila, ES, Jurasinski, G, Koebsch, F, Campbell, D, Schmid, HP, Lohila, A, Goeckede, M, Nilsson, MB, Friborg, T, Jansen, J, Zona, D, Euskirchen, E, Ward, EJ, Bohrer, G, Jin, Z, Liu, L, Iwata, H, Goodrich, J, Jackson, R, Yuan, K, Yuan, K, Zhu, Q, Li, F, Riley, WJ, Torn, M, Chu, H, McNicol, G, Chen, M, Knox, S, Delwiche, K, Wu, H, Baldocchi, D, Ma, H, Desai, AR, Chen, J, Sachs, T, Ueyama, M, Sonnentag, O, Helbig, M, Tuittila, ES, Jurasinski, G, Koebsch, F, Campbell, D, Schmid, HP, Lohila, A, Goeckede, M, Nilsson, MB, Friborg, T, Jansen, J, Zona, D, Euskirchen, E, Ward, EJ, Bohrer, G, Jin, Z, Liu, L, Iwata, H, Goodrich, J, and Jackson, R
Abstract: Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub-seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1°C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH4 emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models.
Published: 2022

14. Global estimation of evapotranspiration using a leaf area index-based surface energy and water balance model

Author: Yan, H., Wang, S.Q., Billesbach, D., Oechel, W., Zhang, J.H., Meyers, T., Martin, T.A., Matamala, R., Baldocchi, D., Bohrer, G., Dragoni, D., and Scott, R.
Published: 2012
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15. Tree hydrodynamic modelling of the soil-plant-atmosphere continuum using FETCH3

Author: Silva, M, Matheny, AM, Pauwels, VRN, Triadis, Dimetre, Missik, JE, Bohrer, G, and Daly, E
Subjects: Physics::Atmospheric and Oceanic Physics, Uncategorized
Abstract: Modelling the water transport along the soil-plant-atmosphere continuum is fundamental to estimating and predicting transpiration fluxes. A Finite-difference Ecosystem-scale Tree Crown Hydrodynamics model (FETCH3) for the water fluxes across the soil-plant-atmosphere continuum is presented here. The model combines the water transport pathways into one vertical dimension, and assumes that the water flow through the soil, roots, and above-ground xylem can be approximated as flow in porous media. This results in a system of three partial differential equations, resembling the Richardson-Richards equation, describing the transport of water through the plant system and with additional terms representing sinks and sources for the transfer of water from the soil to the roots and from the leaves to the atmosphere. The numerical scheme, developed in Python 3, was tested against exact analytical solutions for steady state and transient conditions using simplified but realistic model parameterizations. The model was also used to simulate a previously published case study, where observed transpiration rates were available, to evaluate model performance. With the same model setup as the published case study, FETCH3 results were in agreement with observations. Through a rigorous coupling of soil, root xylem, and stem xylem, FETCH3 can account for variable water capacitance, while conserving mass and the continuity of the water potential between these three layers. FETCH3 provides a ready-to-use open access numerical model for the simulation of water fluxes across the soil-plant-atmosphere continuum. Copyright
Published: 2022
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16. Movement Ecology of Migration in Turkey Vultures

Author: Mandel, J. T., Bildstein, K. L., Bohrer, G., and Winkler, D. W.
Published: 2008
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17. Global transpiration data from sap flow measurements: The SAPFLUXNET database

Author: Poyatos, R, Granda, V, Flo, V, Adams, MA, Adorján, B, Aguadé, D, Aidar, MPM, Allen, S, Alvarado-Barrientos, MS, Anderson-Teixeira, KJ, Aparecido, LM, Joly, CA, Kaplick, J, Kim, HS, Klemedtsson, L, Kropp, H, Lagergren, F, Lane, P, Lang, P, Lapenas, A, Lechuga, V, Migliavacca, M, Lee, M, Leuschner, C, Limousin, JM, Linares, JC, Linderson, ML, Lindroth, A, Llorens, P, López-Bernal, A, Loranty, MM, Lüttschwager, D, Mitchell, P, MacInnis-Ng, C, Maréchaux, I, Martin, TA, Matheny, A, McDowell, N, McMahon, S, Meir, P, Mészáros, I, Molder, M, Mölder, M, Montagnani, L, Moore, GW, Nakada, R, Niu, F, Nolan, R, Norby,R, Novick, K, Oberhuber, W, Obojes, N, Oishi, AC, Oliveira RS, Oren, R, Ourcival,JM, Paljakka, T, Perez-Priego, O, Peri, PL, Peters, RL, Pfautsch, S, Pockman, WT, Preisler, Y, Rascher, K, Robinson, G, Rocha, H, Rocheteau, A, Röll, A, Rosado, BHP, Rowland, L [, Rubtsov, A, Sabaté, S, Salmon, Y, Salomón, RL, Sánchez-Costa, E, Schäfer, K, Schuldt, B, Shashkin, A, Stahl, C, Stojanovic, M, Suárez, JC, Sun, G, Niu, FR, Szatniewska, J, Tatarinov, F, Tesar, M, Thomas, FM, Tor-ngern, P, Urban, J, Valladares, F, Van der Tol, C, Van Meerveld, Varlagin, A, Norby, R, Voigt, H, Warren, J, Werner, C, Werner, W, Wieser, G, Wingate, W, Wullschleger, S, Koong, Y, Zweifel, R, Arain, MA, Oliveira, RS, Ourcival, JM, Aranda, I, Roll, A, Asbjornsen, H, Baxter, R, Beamesderfer, E, Berry, ZC, Berveiller, D, Blakely, B, Boggs, J, Bohrer, G, Bolstad, PV, Bonal, D, Bracho, R, Brito, P, Brodeur, J, Casanoves, F, Chave, J, Chen, H, Cisneros, C, Clark, K, Cremonese, E, Dang, HZ, David, JS, David, TS, Delpierre, N, Desai, AR, Do, Frederic C., Dohnal, M, Domec, JC, Dzikiti, S, Edgar, C, Eichstaedt, R, El-Madany, TS, Elbers, J, Eller, CB, Euskirchen, ES, Ewers, B, Fonti, P, Forner, A, Forrester, DI, Freitas, HC, Galvagno, M, Garcia-Tejera, O, Ghimire, CP, Gimeno, TE, Grace, J, Granier, A, Griebel, A, Guangyu, Y, Gush, MB, Hanson, PJ, Hasselquist, NJ, Heinrich, I, Hernandez-Santana, V, Herrmann, V, Hölttä, T, Holwerda, F, Irvine, J, Ayutthaya, SIN, Jarvis, PG, Jochheim, H, Ministerio de Ciencia e Innovación (España), Agència de Gestió d'Ajuts Universitaris i de Recerca, Poyatos, R [0000-0003-0521-2523], Granda, V [0000-0002-0469-1991], Flo, V [0000-0003-1908-4577], Adams, MA [0000-0002-8154-0097], Adorjan, B [0000-0002-5482-2515], Aidar, MPM [0000-0001-5728-6749], Alvarado-Barrientos, MS [0000-0003-0098-0806], Anderson-Teixeira, KJ [0000-0001-7585-712X], Arain, MA [0000-0002-1433-5173], Aranda, I [0000-0001-9086-7940], Asbjornsen, H [000-0001-8126-3328], Oishi, AC [0000-0001-5064-4080], Oliveira RS [0000-0002-6392-2526], Oren, R [0000-0002-5654-1733], Ourcival,JM [0000-0002-3557-3496], Paljakka, T [0000-0002-3674-4904], Perez-Priego, O [0000-0002-3138-3177], Peri, PL [0000-0002-5398-4408], Peters, RL [0000-0002-7441-1297], Pfautsch, S [0000-0002-4390-4195], Pockman, WT [0000-0002-3286-0457], Baxter, R [0000-0002-7504-6797], Preisler, Y [0000-0001-5861-8362], Rocha, H [0000-0002-5981-4469], Röll, A [0000-0001-9457-4459], Rosado, BHP [0000-0002-8924-8672], Rowland, L [0000-0002-0774-3216], Rubtsov, A [0000-0002-9663-4344], Sabaté, S [0000-0003-1854-0761], Salmon, Y [0000-0003-4433-4021], Salomón, RL [0000-0003-2674-1731], Sánchez-Costa, E [0000-0001-6799-9611], Beamesderfer, E [0000-0002-7513-7349], Schäfer, K [0000-0001-9452-3619], Schuldt, B [0000-0003-4738-5289], Shashkin, A [0000-0001-9925-5019], Stahl, C [0000-0001-5411-1169], Stojanović, M [0000-0003-4918-8668], Sun, G [0000-0002-0159-1370], Szatniewska, J [0000-0003-3027-9965], Tatarinov, F [0000-0002-8338-6070], Tesar, M [0000-0002-5647-8498], Thomas, FM [0000-0001-6377-719X], Berveiller, D [0000-0001-7461-6420], Tor-ngern, P [0000-0001-7363-4926], Urban, J [0000-0003-1730-947X], Van der Tol, C [0000-0002-2484-8191], Van Meerveld [0000-0002-7547-3270], Varlagin, A [0000-0002-2549-5236], Werner, C [0000-0002-7676-9057], Wieser, G [0000-0002-7575-5657], Wingate, W [0000-0003-1921-1556], Wullschleger, S [0000-0002-9869-0446], Koong, Y [0000-0002-8630-3031], Blakely, B [0000-0001-9923-0794], Zweifel, R [0000-0001-9438-0582], Steppe, K [0000-0001-6252-0704], Mencuccini, M [0000-0003-0840-1477], Martínez-Vilalta, J [0000-0002-2332-7298], Boggs, J [0000-0003-4826-1843], Bohrer, G [0000-0002-9209-9540], Bolstad, P [0000-0002-1829-8859], Bonal, D [0000-0001-9602-8603], Bracho, R [0000-0002-8015-9796], Brodeur, J [0000-0001-7118-8360], Casanoves, F [0000-0001-8765-9382], Chave, J [0000-0002-7766-1347], Chen, H [0000-0001-7619-3425], Cisneros, C [0000-0001-9661-4581], Clark, K [0000-0003-0534-9677], Cremonese, E [0000-0002-6708-8532], Dang, H [0000-0003-4428-3834], David, J [0000-0001-6994-1085], David, T [0000-0003-1473-899X], Delpierre, D [0000-0003-0906-9402], Desai, AR [0000-0002-5226-6041], Do, FC [0000-0002-2852-627X], Dohnal, M [0000-0003-1769-4750], Domec, JC [0000-0003-0478-2559], Dzikiti, S [0000-0002-1892-4143], Edgar, C [0000-0002-7026-8358], Eichstaedt, R [0000-0002-4905-7994], El-Madany, T [0000-0002-0726-7141], Elbers, J [0000-0002-0631-3505], Eller, CB [0000-0002-7795-2574], Euskirchen, E [0000-0002-0848-4295], Ewers, B [0000-0001-6647-7475], Fonti, P [0000-0002-7070-3292], Forner, A [0000-0002-7123-6403], Forrester, D [0000-0003-2732-5692], Freitas, HC [0000-0002-4861-1164], Galvagno, M [0000-0002-0827-487X], Garcia-Tejera, O [0000-0001-7726-8118], Ghimire, CP [0000-0002-3715-6311], Gimeno, TE [0000-0002-1707-9291], Granier, A [0000-0002-4174-2487], Griebel, A [0000-0002-4476-8279], Guangyu, Y [0000-0003-3242-5348], Gush, MB [0000-0003-1328-9862], Hanson, PJ [0000-0001-7293-3561], Hasselquist, N [0000-0003-2777-0163], Heinrich, I [0000-0001-5800-6999], Hernandez-Santana, V [0000-0001-9018-8622], Herrmann, V [0000-0002-4519-481X], Hölttä, T [0000-0001-7677-7156], Holwerda, F [0000-0003-4125-1765], Ayutthaya, SIN [0000-0002-5354-1527], Jochheim,H [0000-0001-8047-4553], Joly, CA [0000-0002-7945-2805], Kim, HS [0000-0002-3440-6071], Klemedtsson, L [0000-0002-1122-0717], Kropp, H [0000-0002-4258-3393], Lagergren, F [0000-0002-0061-733X], Lane, P [0000-0001-6121-8386], Lapenas, A [0000-0002-2135-3636], Lechuga, V [0000-0003-3745-587X], Lee,M [0000-0002-9601-3863], Leuschner, C [0000-0002-5689-7932], Limousin, JM [0000-0002-2734-2495], Linares, JC [0000-0001-8375-6353], Linderson, ML [0000-0001-6578-6671], Lindroth, A [0000-0002-7669-784X], Llorens, P [0000-0003-4591-5303], López-Bernal, A [0000-0002-1034-4718], Loranty, MM[0000-0001-8851-7386], Macinnis-Ng, C [0000-0003-3935-9814], Marechaux, I [0000-0002-5401-0197], Martin, TA [0000-0002-7872-4194], Matheny, A [0000-0002-9532-7131], McDowell, N [0000-0002-2178-2254], Meir, P [0000-0002-2362-0398], Mészáros, I [0000-0001-8841-730X], Migliavacca, M [0000-0003-3546-8407], Mölder, M [0000-0001-6767-3195], Montagnani, L [0000-0003-2957-9071], Moore, GW [0000-0001-5190-5983], Nakada, R [0000-0002-3704-1784], Niu, F [0000-0003-3445-4011], Nolan, R [0000-0001-9277-5142], Norby,R [0000-0002-0238-9828], Novick, K [0000-0002-8431-0879], Oberhuber, W [0000-0002-5197-7044], and Obojes, N [0000-0002-6718-2756]
Subjects: Stomatal condua, Sapflow measurements, Heat-pulse method, Vapor- pressure deficit, Radial patterns, Water use strategies, Eddy covariance, Fluw density, Thermal dissipation
Abstract: Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land-atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80% of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50% of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56% of the datasets. Many datasets contain data for species that make up 90% or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely available from the Zenodo repository (10.5281/zenodo.3971689; Poyatos et al., 2020a). The "sapfluxnetr"R package-designed to access, visualize, and process SAPFLUXNET data-is available from CRAN. This research was supported by the Ministerio de Economía y Competitividad (grant no. CGL2014-55883-JIN), the Ministerio de Ciencia e Innovación (grant no. RTI2018-095297-J-I00), the Ministerio de Ciencia e Innovación (grant no. CAS16/00207), the Agència de Gestió d'Ajuts Universitaris i de Recerca (grant no. SGR1001), the Alexander von Humboldt-Stiftung (Humboldt Research Fellowship for Experienced Researchers (RP)), and the Institució Catalana de Recerca i Estudis Avançats (Academia Award (JMV)). Víctor Flo was supported by the doctoral fellowship FPU15/03939 (MECD, Spain).
Published: 2021

18. Microclimatic Effects of a Perched Peatland Forest Gap

Author: Green, A., primary, Bohrer, G., additional, and Petrone, R. M., additional
Published: 2021
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19. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data (vol 7, 225, 2020)

Author: Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Reichstein, M, Ribeca, A, van Ingen, C, Vuichard, N, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardo, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Brummer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D'Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrene, E, Dunn, A, Dusek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Grunwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hortnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janous, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, Lopez-Ballesteros, A, Lopez-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Luers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, U, Raz-Yaseef, N, Rebmann, C, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sanchez-Canete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlak, P, Serrano-Ortiz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Sigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, Papale, D, Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Reichstein, M, Ribeca, A, van Ingen, C, Vuichard, N, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardo, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Brummer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D'Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrene, E, Dunn, A, Dusek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Grunwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hortnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janous, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, Lopez-Ballesteros, A, Lopez-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Luers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, U, Raz-Yaseef, N, Rebmann, C, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sanchez-Canete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlak, P, Serrano-Ortiz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Sigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, and Papale, D
Abstract: A Correction to this paper has been published: https://doi.org/10.1038/s41597-021-00851-9.
Published: 2021

20. Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Author: Irvin, J, Irvin, J, Zhou, S, McNicol, G, Lu, F, Liu, V, Fluet-Chouinard, E, Ouyang, Z, Knox, SH, Lucas-Moffat, A, Trotta, C, Papale, D, Vitale, D, Mammarella, I, Alekseychik, P, Aurela, M, Avati, A, Baldocchi, D, Bansal, S, Bohrer, G, Campbell, DI, Chen, J, Chu, H, Dalmagro, HJ, Delwiche, KB, Desai, AR, Euskirchen, E, Feron, S, Goeckede, M, Heimann, M, Helbig, M, Helfter, C, Hemes, KS, Hirano, T, Iwata, H, Jurasinski, G, Kalhori, A, Kondrich, A, Lai, DY, Lohila, A, Malhotra, A, Merbold, L, Mitra, B, Ng, A, Nilsson, MB, Noormets, A, Peichl, M, Rey-Sanchez, AC, Richardson, AD, Runkle, BR, Schäfer, KV, Sonnentag, O, Stuart-Haëntjens, E, Sturtevant, C, Ueyama, M, Valach, AC, Vargas, R, Vourlitis, GL, Ward, EJ, Wong, GX, Zona, D, Alberto, MCR, Billesbach, DP, Celis, G, Dolman, H, Friborg, T, Fuchs, K, Gogo, S, Gondwe, MJ, Goodrich, JP, Gottschalk, P, Hörtnagl, L, Jacotot, A, Koebsch, F, Kasak, K, Maier, R, Morin, TH, Nemitz, E, Oechel, WC, Oikawa, PY, Ono, K, Sachs, T, Sakabe, A, Schuur, EA, Shortt, R, Sullivan, RC, Szutu, DJ, Tuittila, ES, Varlagin, A, Verfaillie, JG, Wille, C, Windham-Myers, L, Poulter, B, Jackson, RB, Irvin, J, Irvin, J, Zhou, S, McNicol, G, Lu, F, Liu, V, Fluet-Chouinard, E, Ouyang, Z, Knox, SH, Lucas-Moffat, A, Trotta, C, Papale, D, Vitale, D, Mammarella, I, Alekseychik, P, Aurela, M, Avati, A, Baldocchi, D, Bansal, S, Bohrer, G, Campbell, DI, Chen, J, Chu, H, Dalmagro, HJ, Delwiche, KB, Desai, AR, Euskirchen, E, Feron, S, Goeckede, M, Heimann, M, Helbig, M, Helfter, C, Hemes, KS, Hirano, T, Iwata, H, Jurasinski, G, Kalhori, A, Kondrich, A, Lai, DY, Lohila, A, Malhotra, A, Merbold, L, Mitra, B, Ng, A, Nilsson, MB, Noormets, A, Peichl, M, Rey-Sanchez, AC, Richardson, AD, Runkle, BR, Schäfer, KV, Sonnentag, O, Stuart-Haëntjens, E, Sturtevant, C, Ueyama, M, Valach, AC, Vargas, R, Vourlitis, GL, Ward, EJ, Wong, GX, Zona, D, Alberto, MCR, Billesbach, DP, Celis, G, Dolman, H, Friborg, T, Fuchs, K, Gogo, S, Gondwe, MJ, Goodrich, JP, Gottschalk, P, Hörtnagl, L, Jacotot, A, Koebsch, F, Kasak, K, Maier, R, Morin, TH, Nemitz, E, Oechel, WC, Oikawa, PY, Ono, K, Sachs, T, Sakabe, A, Schuur, EA, Shortt, R, Sullivan, RC, Szutu, DJ, Tuittila, ES, Varlagin, A, Verfaillie, JG, Wille, C, Windham-Myers, L, Poulter, B, and Jackson, RB
Abstract: Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an impro
Published: 2021

21. Global transpiration data from sap flow measurements: The SAPFLUXNET database

Author: Poyatos, R., Granda, V, Flo, V., Adorján, B., Adams, M. A., Aguadé, D., Aidar, M. P. M., Allen, S., Alvarado-Barrientos, M. S., Anderson-Teixeira, K. J., Aparecido, L. M., Altaf Arain, M., Aranda, I., Asbjornsen, H., Baxter, R., Beamesderfer, E., Berry, Z. C., Berveiller, D., Blakely, B., Boggs, J., Bohrer, G., Bolstad, P. V., Bonal, D., Bracho, R., Brito, P., Brodeur, J., Casanoves, F., Chave, J., Chen, H., Cisneros, C., Clark, K., Cremonese, E., Dang, H., David, J. S., David, T. S., Delpierre, N., Desai, A. R., Do, F. C., Dohnal, N., Domec, J. C., Dzikiti, S., Edgar, C., Eichstaedt, R., El-Madany, T. S., Elbers, J., Eller, C. B., Euskirchen, E. S., Ewers, B., Fonti, P., Forner, A., Forrester, D. I., Freitas, H. C., Galvagno, M., Garcia-Tejera, O., Ghimire, C. P., Gimeno, T. E., Grace, J., Granier, A., Griebel, A., Guangyu, Y., Gush, M. B., Hanson, P. J., Hasselquist, N. J., Heinrich, I., Hernandez-Santana, V., Herrmann, V, Hölttä, T., Holwerda, F., Irvine, J., Na Ayutthaya, S. Y., Jarvis, P. J., Jochheim, H., Joly, C. A., Kaplick, J., Kim, H. S., Klemedtsson, L., Kropp, H., Lagergren, F., Lane, P., Lang, P., Lapenas, A., Lechuga, V., Lee, M., Leuschner, C., Limousin, J. M., Linares, J. C., Linderson, M. L., Lindroth, A., Llorens, P., López-Bernal, A., Loranty, M. M., Lüttschwager, D., MacInnis-Ng, C., Maréchaux, I., Martin, T. A., Matheny, A., McDowell, N., McMahon, S., Meir, P., Mészáros, I., Migliavacca, M., Mitchell, P., Mölder, M., Montagnani, L., Moore, G. W., Nakada, R., Niu, F., Nolan, R.H., Norby, R., Novick, K., Oberhuber, W., Obojes, N., Oishi, A. C., Oliveira, R. S., Olen, R., Ourcival, J. M., Paljakka, T., Perez Priego, O., Peri, P. L., Peters, R. L., Pfautsch, S., Pockman, W. T., Preysler, I., Rascher, K., Robinson, G., Rocheteau, A., Rocha, H., Röll, A., Rosado, B. H. P., Rowland, L., Rubtsov, A. V., Sabaté, S., Salmón, Y., Salomon, R. L., Schäfer, K. V. R., Sánchez-Costa, E., Schuld, B., Shashkin, A., Stahl, C., Stojanovic, M., Suárez, J. C., Szatniewska, J, Sun, G., Tatarinov, F., TesaÅ, M, Thomas, F. M., Tor-Ngern, P., Urban, J., Valladares, F., Van Der Tol, C., Van Meerveld, I., Varlagin, A., Voigt, H., Warren, J., Werner, C., Poyatos, R., Granda, V, Flo, V., Adorján, B., Adams, M. A., Aguadé, D., Aidar, M. P. M., Allen, S., Alvarado-Barrientos, M. S., Anderson-Teixeira, K. J., Aparecido, L. M., Altaf Arain, M., Aranda, I., Asbjornsen, H., Baxter, R., Beamesderfer, E., Berry, Z. C., Berveiller, D., Blakely, B., Boggs, J., Bohrer, G., Bolstad, P. V., Bonal, D., Bracho, R., Brito, P., Brodeur, J., Casanoves, F., Chave, J., Chen, H., Cisneros, C., Clark, K., Cremonese, E., Dang, H., David, J. S., David, T. S., Delpierre, N., Desai, A. R., Do, F. C., Dohnal, N., Domec, J. C., Dzikiti, S., Edgar, C., Eichstaedt, R., El-Madany, T. S., Elbers, J., Eller, C. B., Euskirchen, E. S., Ewers, B., Fonti, P., Forner, A., Forrester, D. I., Freitas, H. C., Galvagno, M., Garcia-Tejera, O., Ghimire, C. P., Gimeno, T. E., Grace, J., Granier, A., Griebel, A., Guangyu, Y., Gush, M. B., Hanson, P. J., Hasselquist, N. J., Heinrich, I., Hernandez-Santana, V., Herrmann, V, Hölttä, T., Holwerda, F., Irvine, J., Na Ayutthaya, S. Y., Jarvis, P. J., Jochheim, H., Joly, C. A., Kaplick, J., Kim, H. S., Klemedtsson, L., Kropp, H., Lagergren, F., Lane, P., Lang, P., Lapenas, A., Lechuga, V., Lee, M., Leuschner, C., Limousin, J. M., Linares, J. C., Linderson, M. L., Lindroth, A., Llorens, P., López-Bernal, A., Loranty, M. M., Lüttschwager, D., MacInnis-Ng, C., Maréchaux, I., Martin, T. A., Matheny, A., McDowell, N., McMahon, S., Meir, P., Mészáros, I., Migliavacca, M., Mitchell, P., Mölder, M., Montagnani, L., Moore, G. W., Nakada, R., Niu, F., Nolan, R.H., Norby, R., Novick, K., Oberhuber, W., Obojes, N., Oishi, A. C., Oliveira, R. S., Olen, R., Ourcival, J. M., Paljakka, T., Perez Priego, O., Peri, P. L., Peters, R. L., Pfautsch, S., Pockman, W. T., Preysler, I., Rascher, K., Robinson, G., Rocheteau, A., Rocha, H., Röll, A., Rosado, B. H. P., Rowland, L., Rubtsov, A. V., Sabaté, S., Salmón, Y., Salomon, R. L., Schäfer, K. V. R., Sánchez-Costa, E., Schuld, B., Shashkin, A., Stahl, C., Stojanovic, M., Suárez, J. C., Szatniewska, J, Sun, G., Tatarinov, F., TesaÅ, M, Thomas, F. M., Tor-Ngern, P., Urban, J., Valladares, F., Van Der Tol, C., Van Meerveld, I., Varlagin, A., Voigt, H., Warren, J., and Werner, C.
Abstract: Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land-atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80% of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50% of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56% of the datasets. Many datasets contain data for species that make up 90% or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely a
Published: 2021

22. Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Author: Irvin, J., Zhou, S., McNicol, G., Lu, F., Liu, V., Fluet-Chouinard, E., Ouyang, Z., Knox, S.H., Lucas-Moffat, A., Trotta, C., Papale, D., Vitale, D., Mammarella, I., Alekseychik, P., Aurela, M., Avati, A., Baldocchi, D., Bansal, S., Bohrer, G., Campbell, D.I., Chen, J., Chu, H., Dalmagro, H.J., Delwiche, K.B., Desai, A.R., Euskirchen, E., Feron, S., Goeckede, M., Heimann, M., Helbig, M., Helfter, C., Hemes, K.S., Hirano, T., Iwata, H., Jurasinski, G., Kalhori, A., Kondrich, A., Lai, D.Y., Lohila, A., Malhotra, A., Merbold, L., Mitra, B., Ng, A., Nilsson, M.B., Noormets, A., Peichl, M., Rey-Sanchez, A.C., Richardson, A.D., Runkle, B.R., Schäfer, K.V., Sonnentag, O., Stuart-Haëntjens, E., Sturtevant, C., Ueyama, M., Valach, A.C., Vargas, R., Vourlitis, G.L., Ward, E.J., Wong, G.X., Zona, D., Alberto, M.C.R., Billesbach, D.P., Celis, G., Dolman, H., Friborg, T., Fuchs, K., Gogo, S., Gondwe, M.J., Goodrich, J.P., Gottschalk, P., Hörtnagl, L., Jacotot, A., Koebsch, F., Kasak, K., Maier, R., Morin, T.H., Nemitz, E., Oechel, W.C., Oikawa, P.Y., Ono, K., Sachs, T., Sakabe, A., Schuur, E.A., Shortt, R., Sullivan, R.C., Szutu, D.J., Tuittila, E.-S., Varlagin, A., Verfaillie, J.G., Wille, C., Windham-Myers, L., Poulter, B., Jackson, R.B., Irvin, J., Zhou, S., McNicol, G., Lu, F., Liu, V., Fluet-Chouinard, E., Ouyang, Z., Knox, S.H., Lucas-Moffat, A., Trotta, C., Papale, D., Vitale, D., Mammarella, I., Alekseychik, P., Aurela, M., Avati, A., Baldocchi, D., Bansal, S., Bohrer, G., Campbell, D.I., Chen, J., Chu, H., Dalmagro, H.J., Delwiche, K.B., Desai, A.R., Euskirchen, E., Feron, S., Goeckede, M., Heimann, M., Helbig, M., Helfter, C., Hemes, K.S., Hirano, T., Iwata, H., Jurasinski, G., Kalhori, A., Kondrich, A., Lai, D.Y., Lohila, A., Malhotra, A., Merbold, L., Mitra, B., Ng, A., Nilsson, M.B., Noormets, A., Peichl, M., Rey-Sanchez, A.C., Richardson, A.D., Runkle, B.R., Schäfer, K.V., Sonnentag, O., Stuart-Haëntjens, E., Sturtevant, C., Ueyama, M., Valach, A.C., Vargas, R., Vourlitis, G.L., Ward, E.J., Wong, G.X., Zona, D., Alberto, M.C.R., Billesbach, D.P., Celis, G., Dolman, H., Friborg, T., Fuchs, K., Gogo, S., Gondwe, M.J., Goodrich, J.P., Gottschalk, P., Hörtnagl, L., Jacotot, A., Koebsch, F., Kasak, K., Maier, R., Morin, T.H., Nemitz, E., Oechel, W.C., Oikawa, P.Y., Ono, K., Sachs, T., Sakabe, A., Schuur, E.A., Shortt, R., Sullivan, R.C., Szutu, D.J., Tuittila, E.-S., Varlagin, A., Verfaillie, J.G., Wille, C., Windham-Myers, L., Poulter, B., and Jackson, R.B.
Abstract: Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an impro
Published: 2021
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23. Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Author: Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Reichstein, M, Ribeca, A, van Ingen, C, Vuichard, N, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardö, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Brümmer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D’Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrêne, E, Dunn, A, Dušek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Grünwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hörtnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janouš, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, López-Ballesteros, A, López-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Lüers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, Ü, Raz-Yaseef, N, Rebmann, C, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sánchez-Cañete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlák, P, Serrano-Ortíz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Šigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, Papale, D, Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Reichstein, M, Ribeca, A, van Ingen, C, Vuichard, N, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardö, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Brümmer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D’Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrêne, E, Dunn, A, Dušek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Grünwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hörtnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janouš, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, López-Ballesteros, A, López-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Lüers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, Ü, Raz-Yaseef, N, Rebmann, C, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sánchez-Cañete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlák, P, Serrano-Ortíz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Šigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, and Papale, D
Abstract: The following authors were omitted from the original version of this Data Descriptor: Markus Reichstein and Nicolas Vuichard. Both contributed to the code development and N. Vuichard contributed to the processing of the ERA-Interim data downscaling. Furthermore, the contribution of the co-author Frank Tiedemann was re-evaluated relative to the colleague Corinna Rebmann, both working at the same sites, and based on this re-evaluation a substitution in the co-author list is implemented (with Rebmann replacing Tiedemann). Finally, two affiliations were listed incorrectly and are corrected here (entries 190 and 193). The author list and affiliations have been amended to address these omissions in both the HTML and PDF versions.
Published: 2021

24. A model of gas mixing into single-entrance tree cavities during wildland fires

Author: Bova, A.S., Bohrer, G., and Dickinson, M.B.
Subjects: Forest fires -- Management -- Canada, Environmental protection -- Methods, Environmental issue, Company business management, Earth sciences
Abstract: The level of protection to fauna provided by tree cavities during wildland fires is not well understood. Here we present a model for estimating the transport of combustion gases into cylindrical, single-entrance cavities during exposures caused by different wildland fire scenarios. In these shelters, the entrance occurs near the top of the cavity. This empirical model was developed from a suite of numerical experiments using the National Institute of Standards and Technology's Fire Dynamics Simulator, which spanned a range of entrance diameters, wind speeds, gas temperatures, and vertical angles of incidence. To evaluate the model's predictions, it was used to replicate, with great accuracy, a time series of carbon monoxide (CO) concentrations in a controlled experiment where a fabricated cylindrical cavity was exposed to combustion products. The time constant for cavity filling is proportional to the ratio of cavity volume to entrance area. Hot gases lead to significant stratification within the cavity during exposures. To demonstrate the model's potential use in predicting faunal exposures in the context of land management, we show that the model can be used to estimate dosage within red-cockaded woodpecker (Picoides borealis Vieillot, 1809) cavities without requiring temporally detailed, local measurements of wind speed and combustion product concentrations. Le degrede protection que procurent les cavites d'arbre pour la faune durant les feux de foret n'est pas bien connu. Nous presentons ici un modele pour l'estimation du deplacement des gaz de combustion dans des cavites cylindri-ques a une seule entree lors d'expositions correspondant a differents scenarios de feux de foret. Dans ces abris, l'entree se trouve pres du haut de la cavite. Ce modele empirique a ete developpe a partir d'une serie d'experiences numeriques reali-sees a l'aide du simulateur de dynamique du feu du National Institute of Standards and Technology, qui permet de tester une gamme de diametres d'entree, de vitesses du vent, de temperatures des gaz et d'angles verticaux d'incidence. Pour eva-luer les predictions du modele, le simulateur a ete utilise pour repeter, avec une grande exactitude, une serie temporelle de concentrations de monoxyde de carbone (CO) dans une experience en conditions controlees qui consistait a exposer une ca-vite cylindrique usinee e des produits de combustion. La constante de temps pour le remplissage de la cavite est proportion-nelle au ratio du volume de la cavite sur l'aire de l'entree. Les gaz chauds provoquent une importante stratification dans la cavite durant l'exposition. Pour demontrer la possibilite d'utiliser le modele pour predire les expositions de la faune dans un contexte d'amenagement du territoire, nous montrons que le modele peut etre utilise pour estimer les doses dans des cavites de pic a face blanche (Picoides borealis Vieillot, 1809) et ce, sans faire appel a des mesures detaillees dans le temps, a des mesures de vitesse locale du vent ou a des concentrations de produits de combustion. [Traduit par la Redaction], Introduction The level of protection afforded by animal shelters such as burrows, tree cavities, and caves during wildland fire events is of interest to wildland fire practitioners and wildlife managers, [...]
Published: 2011
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25. The handbook for standardized field and laboratory measurements in terrestrial climate change experiments and observational studies (ClimEx)

Author: Halbritter, A.H., De, Boeck, H.J., Eycott, A.E., Reinsch, S., Robinson, D.A., Vicca, S., Berauer, B., Christiansen, C.T., Estiarte, M., Grünzweig, J.M., Gya, R., Hansen, K., Jentsch, A., Lee, H., Linder, S., Marshall, J., Peñuelas, J., Kappel, Schmidt, I., Stuart-Haëntjens, E., Wilfahrt, P., Vandvik, V., Abrantes, N., Almagro, M., Althuizen, I.H.J., Barrio, I.C., Te, Beest, M., Beier, C., Beil, I., Carter, Berry, Z., Birkemoe, T., Bjerke, J.W., Blonder, B., Blume-Werry, G., Bohrer, G., Campos, I., Cernusak, L.A., Chojnicki, B.H., Cosby, B.J., Dickman, L.T., Djukic, I., Filella, I., Fuchslueger, L., Gargallo-Garriga, A., Gillespie, M.A.K., Goldsmith, G.R., Gough, C., Halliday, F.W., Hegland, S.J., Hoch, G., Holub, P., Jaroszynska, F., Johnson, D.M., Jones, S.B., Kardol, P., Keizer, J.J., Klem, K., Konestabo, H.S., Kreyling, J., Kröel-Dulay, G., Landhäusser, S.M., Larsen, K.S., Leblans, N., Lebron, I., Lehmann, M.M., Lembrechts, J.J., Lenz, A., Linstädter, A., Llusià, J., Macias-Fauria, M., Malyshev, A.V., Mänd, P., Marshall, M., Matheny, A.M., McDowell, N., Meier, I.C., Meinzer, F.C., Michaletz, S.T., Miller, M.L., Muffler, L., Oravec, M., Ostonen, I., Porcar Castell, Albert, Preece, C., Prentice, I.C., Radujkovic, D., Ravolainen, V., Ribbons, R., Ruppert, J.C., Sack, L., Sardans, J., Schindlbacher, A., Scoffoni, C., Sigurdsson, B.D., Smart, S., Smith, S.W., Soper, F., Speed, J.D.M., Sverdrup-Thygeson, A., Sydenham, M.A.K., Taghizadeh-Toosi, A., Telford, R.J., Tielbörger, K., Töpper, J.P., Urban, O., Van der, Ploeg, M., Van Langenhove, L., Vecerová, K., Ven, A., Verbruggen, E., Vik, U., Weigel, R., Wohlgemuth, T., Wood, L.K., Zinnert, J., Zurba, K., the, ClimMani, Working, Group, Halbritter, A.H., De, Boeck, H.J., Eycott, A.E., Reinsch, S., Robinson, D.A., Vicca, S., Berauer, B., Christiansen, C.T., Estiarte, M., Grünzweig, J.M., Gya, R., Hansen, K., Jentsch, A., Lee, H., Linder, S., Marshall, J., Peñuelas, J., Kappel, Schmidt, I., Stuart-Haëntjens, E., Wilfahrt, P., Vandvik, V., Abrantes, N., Almagro, M., Althuizen, I.H.J., Barrio, I.C., Te, Beest, M., Beier, C., Beil, I., Carter, Berry, Z., Birkemoe, T., Bjerke, J.W., Blonder, B., Blume-Werry, G., Bohrer, G., Campos, I., Cernusak, L.A., Chojnicki, B.H., Cosby, B.J., Dickman, L.T., Djukic, I., Filella, I., Fuchslueger, L., Gargallo-Garriga, A., Gillespie, M.A.K., Goldsmith, G.R., Gough, C., Halliday, F.W., Hegland, S.J., Hoch, G., Holub, P., Jaroszynska, F., Johnson, D.M., Jones, S.B., Kardol, P., Keizer, J.J., Klem, K., Konestabo, H.S., Kreyling, J., Kröel-Dulay, G., Landhäusser, S.M., Larsen, K.S., Leblans, N., Lebron, I., Lehmann, M.M., Lembrechts, J.J., Lenz, A., Linstädter, A., Llusià, J., Macias-Fauria, M., Malyshev, A.V., Mänd, P., Marshall, M., Matheny, A.M., McDowell, N., Meier, I.C., Meinzer, F.C., Michaletz, S.T., Miller, M.L., Muffler, L., Oravec, M., Ostonen, I., Porcar Castell, Albert, Preece, C., Prentice, I.C., Radujkovic, D., Ravolainen, V., Ribbons, R., Ruppert, J.C., Sack, L., Sardans, J., Schindlbacher, A., Scoffoni, C., Sigurdsson, B.D., Smart, S., Smith, S.W., Soper, F., Speed, J.D.M., Sverdrup-Thygeson, A., Sydenham, M.A.K., Taghizadeh-Toosi, A., Telford, R.J., Tielbörger, K., Töpper, J.P., Urban, O., Van der, Ploeg, M., Van Langenhove, L., Vecerová, K., Ven, A., Verbruggen, E., Vik, U., Weigel, R., Wohlgemuth, T., Wood, L.K., Zinnert, J., Zurba, K., and the, ClimMani, Working, Group
Abstract: Climate change is a world-wide threat to biodiversity and ecosystem structure, functioning and services. To understand the underlying drivers and mechanisms, and to predict the consequences for nature and people, we urgently need better understanding of the direction and magnitude of climate change impacts across the soil plant atmosphere continuum. An increasing number of climate change studies are creating new opportunities for meaningful and high-quality generalizations and improved process understanding. However, significant challenges exist related to data availability and/or compatibility across studies, compromising opportunities for data re-use, synthesis and upscaling. Many of these challenges relate to a lack of an established best practice for measuring key impacts and responses. This restrains our current understanding of complex processes and mechanisms in terrestrial ecosystems related to climate change. To overcome these challenges, we collected best-practice methods emerging from major ecological research networks and experiments, as synthesized by 115 experts from across a wide range of scientific disciplines. Our handbook contains guidance on the selection of response variables for different purposes, protocols for standardized measurements of 66 such response variables and advice on data management. Specifically, we recommend a minimum subset of variables that should be collected in all climate change studies to allow data re-use and synthesis, and give guidance on additional variables critical for different types of synthesis and upscaling. The goal of this community effort is to facilitate awareness of the importance and broader application of standardized methods to promote data re-use, availability, compatibility and transparency. We envision improved research practices that will increase returns on investments in individual research projects, facilitate second-order research outputs and create opportunities for collaboration across scientific
Published: 2020

26. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Author: Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Ribeca, A, van Ingen, C, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardo, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Bruemmer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D'Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrene, E, Dunn, A, Dusek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Gruenwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hoertnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janous, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, Lopez-Ballesteros, A, Lopez-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Lueers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, U, Raz-Yaseef, N, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sanchez-Canete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlak, P, Serrano-Ortiz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Sigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tiedemann, F, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, Papale, D, Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Ribeca, A, van Ingen, C, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardo, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Bruemmer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D'Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrene, E, Dunn, A, Dusek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Gruenwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hoertnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janous, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, Lopez-Ballesteros, A, Lopez-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Lueers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, U, Raz-Yaseef, N, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sanchez-Canete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlak, P, Serrano-Ortiz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Sigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tiedemann, F, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, and Papale, D
Abstract: 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.
Published: 2020

27. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Author: Pastorello, G. (Gilberto), Trotta, C. (Carlo), Canfora, E. (Eleonora), Chu, H. (Housen), Christianson, D. (Danielle), Cheah, Y.-W. (You-Wei), Poindexter, C. (Cristina), Chen, J. (Jiquan), Elbashandy, A. (Abdelrahman), Humphrey, M. (Marty), Isaac, P. (Peter), Polidori, D. (Diego), Ribeca, A. (Alessio), van Ingen, C. (Catharine), Zhang, L. (Leiming), Amiro, B. (Brian), Ammann, C. (Christof), Arain, M. A. (M. Altaf), Ardo, J. (Jonas), Arkebauer, T. (Timothy), Arndt, S. K. (Stefan K.), Arriga, N. (Nicola), Aubinet, M. (Marc), Aurela, M. (Mika), Baldocchi, D. (Dennis), Barr, A. (Alan), Beamesderfer, E. (Eric), Marchesini, L. B. (Luca Belelli), Bergeron, O. (Onil), Beringer, J. (Jason), Bernhofer, C. (Christian), Berveiller, D. (Daniel), Billesbach, D. (Dave), Black, T. A. (Thomas Andrew), Blanken, P. D. (Peter D.), Bohrer, G. (Gil), Boike, J. (Julia), Bolstad, P. V. (Paul V.), Bonal, D. (Damien), Bonnefond, J.-M. (Jean-Marc), Bowling, D. R. (David R.), Bracho, R. (Rosvel), Brodeur, J. (Jason), Bruemmer, C. (Christian), Buchmann, N. (Nina), Burban, B. (Benoit), Burns, S. P. (Sean P.), Buysse, P. (Pauline), Cale, P. (Peter), Cavagna, M. (Mauro), Cellier, P. (Pierre), Chen, S. (Shiping), Chini, I. (Isaac), Christensen, T. R. (Torben R.), Cleverly, J. (James), Collalti, A. (Alessio), Consalvo, C. (Claudia), Cook, B. D. (Bruce D.), Cook, D. (David), Coursolle, C. (Carole), Cremonese, E. (Edoardo), Curtis, P. S. (Peter S.), D'Andrea, E. (Ettore), da Rocha, H. (Humberto), Dai, X. (Xiaoqin), Davis, K. J. (Kenneth J.), De Cinti, B. (Bruno), de Grandcourt, A. (Agnes), De Ligne, A. (Anne), De Oliveira, R. C. (Raimundo C.), Delpierre, N. (Nicolas), Desai, A. R. (Ankur R.), Di Bella, C. M. (Carlos Marcelo), di Tommasi, P. (Paul), Dolman, H. (Han), Domingo, F. (Francisco), Dong, G. (Gang), Dore, S. (Sabina), Duce, P. (Pierpaolo), Dufrene, E. (Eric), Dunn, A. (Allison), Dusek, J. (Jiri), Eamus, D. (Derek), Eichelmann, U. (Uwe), ElKhidir, H. A. (Hatim Abdalla M.), Eugster, W. (Werner), Ewenz, C. M. (Cacilia M.), Ewers, B. (Brent), Famulari, D. (Daniela), Fares, S. (Silvano), Feigenwinter, I. (Iris), Feitz, A. (Andrew), Fensholt, R. (Rasmus), Filippa, G. (Gianluca), Fischer, M. (Marc), Frank, J. (John), Galvagno, M. (Marta), Gharun, M. (Mana), Gianelle, D. (Damiano), Gielen, B. (Bert), Gioli, B. (Beniamino), Gitelson, A. (Anatoly), Goded, I. (Ignacio), Goeckede, M. (Mathias), Goldstein, A. H. (Allen H.), Gough, C. M. (Christopher M.), Goulden, M. L. (Michael L.), Graf, A. (Alexander), Griebel, A. (Anne), Gruening, C. (Carsten), Gruenwald, T. (Thomas), Hammerle, A. (Albin), Han, S. (Shijie), Han, X. (Xingguo), Hansen, B. U. (Birger Ulf), Hanson, C. (Chad), Hatakka, J. (Juha), He, Y. (Yongtao), Hehn, M. (Markus), Heinesch, B. (Bernard), Hinko-Najera, N. (Nina), Hoertnagl, L. (Lukas), Hutley, L. (Lindsay), Ibrom, A. (Andreas), Ikawa, H. (Hiroki), Jackowicz-Korczynski, M. (Marcin), Janous, D. (Dalibor), Jans, W. (Wilma), Jassal, R. (Rachhpal), Jiang, S. (Shicheng), Kato, T. (Tomomichi), Khomik, M. (Myroslava), Klatt, J. (Janina), Knohl, A. (Alexander), Knox, S. (Sara), Kobayashi, H. (Hideki), Koerber, G. (Georgia), Kolle, O. (Olaf), Kosugi, Y. (Yoshiko), Kotani, A. (Ayumi), Kowalski, A. (Andrew), Kruijt, B. (Bart), Kurbatova, J. (Julia), Kutsch, W. L. (Werner L.), Kwon, H. (Hyojung), Launiainen, S. (Samuli), Laurila, T. (Tuomas), Law, B. (Bev), Leuning, R. (Ray), Li, Y. (Yingnian), Liddell, M. (Michael), Limousin, J.-M. (Jean-Marc), Lion, M. (Marryanna), Liska, A. J. (Adam J.), Lohila, A. (Annalea), Lopez-Ballesteros, A. (Ana), Lopez-Blanco, E. (Efren), Loubet, B. (Benjamin), Loustau, D. (Denis), Lucas-Moffat, A. (Antje), Lueers, J. (Johannes), Ma, S. (Siyan), Macfarlane, C. (Craig), Magliulo, V. (Vincenzo), Maier, R. (Regine), Mammarella, I. (Ivan), Manca, G. (Giovanni), Marcolla, B. (Barbara), Margolis, H. A. (Hank A.), Marras, S. (Serena), Massman, W. (William), Mastepanov, M. (Mikhail), Matamala, R. (Roser), Matthes, J. H. (Jaclyn Hatala), Mazzenga, F. (Francesco), McCaughey, H. (Harry), McHugh, I. (Ian), McMillan, A. M. (Andrew M. S.), Merbold, L. (Lutz), Meyer, W. (Wayne), Meyers, T. (Tilden), Miller, S. D. (Scott D.), Minerbi, S. (Stefano), Moderow, U. (Uta), Monson, R. K. (Russell K.), Montagnani, L. (Leonardo), Moore, C. E. (Caitlin E.), Moors, E. (Eddy), Moreaux, V. (Virginie), Moureaux, C. (Christine), Munger, J. W. (J. William), Nakai, T. (Taro), Neirynck, J. (Johan), Nesic, Z. (Zoran), Nicolini, G. (Giacomo), Noormets, A. (Asko), Northwood, M. (Matthew), Nosetto, M. (Marcelo), Nouvellon, Y. (Yann), Novick, K. (Kimberly), Oechel, W. (Walter), Olesen, J. E. (Jorgen Eivind), Ourcival, J.-M. (Jean-Marc), Papuga, S. A. (Shirley A.), Parmentier, F.-J. (Frans-Jan), Paul-Limoges, E. (Eugenie), Pavelka, M. (Marian), Peichl, M. (Matthias), Pendall, E. (Elise), Phillips, R. P. (Richard P.), Pilegaard, K. (Kim), Pirk, N. (Norbert), Posse, G. (Gabriela), Powell, T. (Thomas), Prasse, H. (Heiko), Prober, S. M. (Suzanne M.), Rambal, S. (Serge), Rannik, U. (Ullar), Raz-Yaseef, N. (Naama), Reed, D. (David), de Dios, V. R. (Victor Resco), Restrepo-Coupe, N. (Natalia), Reverter, B. R. (Borja R.), Roland, M. (Marilyn), Sabbatini, S. (Simone), Sachs, T. (Torsten), Saleska, S. R. (Scott R.), Sanchez-Canete, E. P. (Enrique P.), Sanchez-Mejia, Z. M. (Zulia M.), Schmid, H. P. (Hans Peter), Schmidt, M. (Marius), Schneider, K. (Karl), Schrader, F. (Frederik), Schroder, I. (Ivan), Scott, R. L. (Russell L.), Sedlak, P. (Pavel), Serrano-Ortiz, P. (Penelope), Shao, C. (Changliang), Shi, P. (Peili), Shironya, I. (Ivan), Siebicke, L. (Lukas), Sigut, L. (Ladislav), Silberstein, R. (Richard), Sirca, C. (Costantino), Spano, D. (Donatella), Steinbrecher, R. (Rainer), Stevens, R. M. (Robert M.), Sturtevant, C. (Cove), Suyker, A. (Andy), Tagesson, T. (Torbern), Takanashi, S. (Satoru), Tang, Y. (Yanhong), Tapper, N. (Nigel), Thom, J. (Jonathan), Tiedemann, F. (Frank), Tomassucci, M. (Michele), Tuovinen, J.-P. (Juha-Pekka), Urbanski, S. (Shawn), Valentini, R. (Riccardo), van der Molen, M. (Michiel), van Gorsel, E. (Eva), van Huissteden, K. (Ko), Varlagin, A. (Andrej), Verfaillie, J. (Joseph), Vesala, T. (Timo), Vincke, C. (Caroline), Vitale, D. (Domenico), Vygodskaya, N. (Natalia), Walker, J. P. (Jeffrey P.), Walter-Shea, E. (Elizabeth), Wang, H. (Huimin), Weber, R. (Robin), Westermann, S. (Sebastian), Wille, C. (Christian), Wofsy, S. (Steven), Wohlfahrt, G. (Georg), Wolf, S. (Sebastian), Woodgate, W. (William), Li, Y. (Yuelin), Zampedri, R. (Roberto), Zhang, J. (Junhui), Zhou, G. (Guoyi), Zona, D. (Donatella), Agarwal, D. (Deb), Biraud, S. (Sebastien), Torn, M. (Margaret), Papale, D. (Dario), Pastorello, G. (Gilberto), Trotta, C. (Carlo), Canfora, E. (Eleonora), Chu, H. (Housen), Christianson, D. (Danielle), Cheah, Y.-W. (You-Wei), Poindexter, C. (Cristina), Chen, J. (Jiquan), Elbashandy, A. (Abdelrahman), Humphrey, M. (Marty), Isaac, P. (Peter), Polidori, D. (Diego), Ribeca, A. (Alessio), van Ingen, C. (Catharine), Zhang, L. (Leiming), Amiro, B. (Brian), Ammann, C. (Christof), Arain, M. A. (M. Altaf), Ardo, J. (Jonas), Arkebauer, T. (Timothy), Arndt, S. K. (Stefan K.), Arriga, N. (Nicola), Aubinet, M. (Marc), Aurela, M. (Mika), Baldocchi, D. (Dennis), Barr, A. (Alan), Beamesderfer, E. (Eric), Marchesini, L. B. (Luca Belelli), Bergeron, O. (Onil), Beringer, J. (Jason), Bernhofer, C. (Christian), Berveiller, D. (Daniel), Billesbach, D. (Dave), Black, T. A. (Thomas Andrew), Blanken, P. D. (Peter D.), Bohrer, G. (Gil), Boike, J. (Julia), Bolstad, P. V. (Paul V.), Bonal, D. (Damien), Bonnefond, J.-M. (Jean-Marc), Bowling, D. R. (David R.), Bracho, R. (Rosvel), Brodeur, J. (Jason), Bruemmer, C. (Christian), Buchmann, N. (Nina), Burban, B. (Benoit), Burns, S. P. (Sean P.), Buysse, P. (Pauline), Cale, P. (Peter), Cavagna, M. (Mauro), Cellier, P. (Pierre), Chen, S. (Shiping), Chini, I. (Isaac), Christensen, T. R. (Torben R.), Cleverly, J. (James), Collalti, A. (Alessio), Consalvo, C. (Claudia), Cook, B. D. (Bruce D.), Cook, D. (David), Coursolle, C. (Carole), Cremonese, E. (Edoardo), Curtis, P. S. (Peter S.), D'Andrea, E. (Ettore), da Rocha, H. (Humberto), Dai, X. (Xiaoqin), Davis, K. J. (Kenneth J.), De Cinti, B. (Bruno), de Grandcourt, A. (Agnes), De Ligne, A. (Anne), De Oliveira, R. C. (Raimundo C.), Delpierre, N. (Nicolas), Desai, A. R. (Ankur R.), Di Bella, C. M. (Carlos Marcelo), di Tommasi, P. (Paul), Dolman, H. (Han), Domingo, F. (Francisco), Dong, G. (Gang), Dore, S. (Sabina), Duce, P. (Pierpaolo), Dufrene, E. (Eric), Dunn, A. (Allison), Dusek, J. (Jiri), Eamus, D. (Derek), Eichelmann, U. (Uwe), ElKhidir, H. A. (Hatim Abdalla M.), Eugster, W. (Werner), Ewenz, C. M. (Cacilia M.), Ewers, B. (Brent), Famulari, D. (Daniela), Fares, S. (Silvano), Feigenwinter, I. (Iris), Feitz, A. (Andrew), Fensholt, R. (Rasmus), Filippa, G. (Gianluca), Fischer, M. (Marc), Frank, J. (John), Galvagno, M. (Marta), Gharun, M. (Mana), Gianelle, D. (Damiano), Gielen, B. (Bert), Gioli, B. (Beniamino), Gitelson, A. (Anatoly), Goded, I. (Ignacio), Goeckede, M. (Mathias), Goldstein, A. H. (Allen H.), Gough, C. M. (Christopher M.), Goulden, M. L. (Michael L.), Graf, A. (Alexander), Griebel, A. (Anne), Gruening, C. (Carsten), Gruenwald, T. (Thomas), Hammerle, A. (Albin), Han, S. (Shijie), Han, X. (Xingguo), Hansen, B. U. (Birger Ulf), Hanson, C. (Chad), Hatakka, J. (Juha), He, Y. (Yongtao), Hehn, M. (Markus), Heinesch, B. (Bernard), Hinko-Najera, N. (Nina), Hoertnagl, L. (Lukas), Hutley, L. (Lindsay), Ibrom, A. (Andreas), Ikawa, H. (Hiroki), Jackowicz-Korczynski, M. (Marcin), Janous, D. (Dalibor), Jans, W. (Wilma), Jassal, R. (Rachhpal), Jiang, S. (Shicheng), Kato, T. (Tomomichi), Khomik, M. (Myroslava), Klatt, J. (Janina), Knohl, A. (Alexander), Knox, S. (Sara), Kobayashi, H. (Hideki), Koerber, G. (Georgia), Kolle, O. (Olaf), Kosugi, Y. (Yoshiko), Kotani, A. (Ayumi), Kowalski, A. (Andrew), Kruijt, B. (Bart), Kurbatova, J. (Julia), Kutsch, W. L. (Werner L.), Kwon, H. (Hyojung), Launiainen, S. (Samuli), Laurila, T. (Tuomas), Law, B. (Bev), Leuning, R. (Ray), Li, Y. (Yingnian), Liddell, M. (Michael), Limousin, J.-M. (Jean-Marc), Lion, M. (Marryanna), Liska, A. J. (Adam J.), Lohila, A. (Annalea), Lopez-Ballesteros, A. (Ana), Lopez-Blanco, E. (Efren), Loubet, B. (Benjamin), Loustau, D. (Denis), Lucas-Moffat, A. (Antje), Lueers, J. (Johannes), Ma, S. (Siyan), Macfarlane, C. (Craig), Magliulo, V. (Vincenzo), Maier, R. (Regine), Mammarella, I. (Ivan), Manca, G. (Giovanni), Marcolla, B. (Barbara), Margolis, H. A. (Hank A.), Marras, S. (Serena), Massman, W. (William), Mastepanov, M. (Mikhail), Matamala, R. (Roser), Matthes, J. H. (Jaclyn Hatala), Mazzenga, F. (Francesco), McCaughey, H. (Harry), McHugh, I. (Ian), McMillan, A. M. (Andrew M. S.), Merbold, L. (Lutz), Meyer, W. (Wayne), Meyers, T. (Tilden), Miller, S. D. (Scott D.), Minerbi, S. (Stefano), Moderow, U. (Uta), Monson, R. K. (Russell K.), Montagnani, L. (Leonardo), Moore, C. E. (Caitlin E.), Moors, E. (Eddy), Moreaux, V. (Virginie), Moureaux, C. (Christine), Munger, J. W. (J. William), Nakai, T. (Taro), Neirynck, J. (Johan), Nesic, Z. (Zoran), Nicolini, G. (Giacomo), Noormets, A. (Asko), Northwood, M. (Matthew), Nosetto, M. (Marcelo), Nouvellon, Y. (Yann), Novick, K. (Kimberly), Oechel, W. (Walter), Olesen, J. E. (Jorgen Eivind), Ourcival, J.-M. (Jean-Marc), Papuga, S. A. (Shirley A.), Parmentier, F.-J. (Frans-Jan), Paul-Limoges, E. (Eugenie), Pavelka, M. (Marian), Peichl, M. (Matthias), Pendall, E. (Elise), Phillips, R. P. (Richard P.), Pilegaard, K. (Kim), Pirk, N. (Norbert), Posse, G. (Gabriela), Powell, T. (Thomas), Prasse, H. (Heiko), Prober, S. M. (Suzanne M.), Rambal, S. (Serge), Rannik, U. (Ullar), Raz-Yaseef, N. (Naama), Reed, D. (David), de Dios, V. R. (Victor Resco), Restrepo-Coupe, N. (Natalia), Reverter, B. R. (Borja R.), Roland, M. (Marilyn), Sabbatini, S. (Simone), Sachs, T. (Torsten), Saleska, S. R. (Scott R.), Sanchez-Canete, E. P. (Enrique P.), Sanchez-Mejia, Z. M. (Zulia M.), Schmid, H. P. (Hans Peter), Schmidt, M. (Marius), Schneider, K. (Karl), Schrader, F. (Frederik), Schroder, I. (Ivan), Scott, R. L. (Russell L.), Sedlak, P. (Pavel), Serrano-Ortiz, P. (Penelope), Shao, C. (Changliang), Shi, P. (Peili), Shironya, I. (Ivan), Siebicke, L. (Lukas), Sigut, L. (Ladislav), Silberstein, R. (Richard), Sirca, C. (Costantino), Spano, D. (Donatella), Steinbrecher, R. (Rainer), Stevens, R. M. (Robert M.), Sturtevant, C. (Cove), Suyker, A. (Andy), Tagesson, T. (Torbern), Takanashi, S. (Satoru), Tang, Y. (Yanhong), Tapper, N. (Nigel), Thom, J. (Jonathan), Tiedemann, F. (Frank), Tomassucci, M. (Michele), Tuovinen, J.-P. (Juha-Pekka), Urbanski, S. (Shawn), Valentini, R. (Riccardo), van der Molen, M. (Michiel), van Gorsel, E. (Eva), van Huissteden, K. (Ko), Varlagin, A. (Andrej), Verfaillie, J. (Joseph), Vesala, T. (Timo), Vincke, C. (Caroline), Vitale, D. (Domenico), Vygodskaya, N. (Natalia), Walker, J. P. (Jeffrey P.), Walter-Shea, E. (Elizabeth), Wang, H. (Huimin), Weber, R. (Robin), Westermann, S. (Sebastian), Wille, C. (Christian), Wofsy, S. (Steven), Wohlfahrt, G. (Georg), Wolf, S. (Sebastian), Woodgate, W. (William), Li, Y. (Yuelin), Zampedri, R. (Roberto), Zhang, J. (Junhui), Zhou, G. (Guoyi), Zona, D. (Donatella), Agarwal, D. (Deb), Biraud, S. (Sebastien), Torn, M. (Margaret), and Papale, D. (Dario)
Abstract: 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.
Published: 2020

28. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data.

Author: Pastorello G, Trotta C, Canfora E, Chu H, Christianson D, Cheah Y-W, Poindexter C, Chen J, Elbashandy A, Humphrey M, Isaac P, Polidori D, Ribeca A, van Ingen C, Zhang L, Amiro B, Ammann C, Arain MA, Ardö J, Arkebauer T, Arndt SK, Arriga N, Aubinet M, Aurela M, Baldocchi D, Barr A, Beamesderfer E, Marchesini LB, Bergeron O, Beringer J, Bernhofer C, Berveiller D, Billesbach D, Black TA, Blanken PD, Bohrer G, Boike J, Bolstad PV, Bonal D, Bonnefond J-M, Bowling DR, Bracho R, Brodeur J, Brümmer C, Buchmann N, Burban B, Burns SP, Buysse P, Cale P, Cavagna M, Cellier P, Chen S, Chini I, Christensen TR, Cleverly J, Collalti A, Consalvo C, Cook BD, Cook D, Coursolle C, Cremonese E, Curtis PS, D'Andrea E, da Rocha H, Dai X, Davis KJ, De Cinti B, de Grandcourt A, De Ligne A, De Oliveira RC, Delpierre N, Desai AR, Di Bella CM, di Tommasi P, Dolman H, Domingo F, Dong G, Dore S, Duce P, Dufrêne E, Dunn A, Dušek J, Eamus D, Eichelmann U, ElKhidir HAM, Eugster W, Ewenz CM, Ewers B, Famulari D, Fares S, Feigenwinter I, Feitz A, Fensholt R, Filippa G, Fischer M, Frank J, Galvagno M, Gharun M, Gianelle D, Gielen B, Gioli B, Gitelson A, Goded I, Goeckede M, Goldstein AH, Gough CM, Goulden ML, Graf A, Griebel A, Gruening C, Grünwald T, Hammerle A, Han S, Han X, Hansen BU, Hanson C, Hatakka J, He Y, Hehn M, Heinesch B, Hinko-Najera N, Hörtnagl L, Hutley L, Ibrom A, Ikawa H, Jackowicz-Korczynski M, Janouš D, Jans W, Jassal R, Jiang S, Kato T, Khomik M, Klatt J, Knohl A, Knox S, Kobayashi H, Koerber G, Kolle O, Kosugi Y, Kotani A, Kowalski A, Kruijt B, Kurbatova J, Kutsch WL, Kwon H, Launiainen S, Laurila T, Law B, Leuning R, Li Y, Liddell M, Limousin J-M, Lion M, Liska AJ, Lohila A, López-Ballesteros A, López-Blanco E, Loubet B, Loustau D, Lucas-Moffat A, Lüers J, Ma S, Macfarlane C, Magliulo V, Maier R, Mammarella I, Manca G, Marcolla B, Margolis HA, Marras S, Massman W, Mastepanov M, Matamala R, Matthes JH, Mazzenga F, McCaughey H, McHugh I, McMillan AMS, Merbold L, Meyer W, Meyers T, Miller SD, Minerbi S, Moderow U, Monson RK, Montagnani L, Moore CE, Moors E, Moreaux V, Moureaux C, Munger JW, Nakai T, Neirynck J, Nesic Z, Nicolini G, Noormets A, Northwood M, Nosetto M, Nouvellon Y, Novick K, Oechel W, Olesen JE, Ourcival J-M, Papuga SA, Parmentier F-J, Paul-Limoges E, Pavelka M, Peichl M, Pendall E, Phillips RP, Pilegaard K, Pirk N, Posse G, Powell T, Prasse H, Prober SM, Rambal S, Rannik Ü, Raz-Yaseef N, Reed D, de Dios VR, Restrepo-Coupe N, Reverter BR, Roland M, Sabbatini S, Sachs T, Saleska SR, Sánchez-Cañete EP, Sanchez-Mejia ZM, Schmid HP, Schmidt M, Schneider K, Schrader F, Schroder I, Scott RL, Sedlák P, Serrano-Ortíz P, Shao C, Shi P, Shironya I, Siebicke L, Šigut L, Silberstein R, Sirca C, Spano D, Steinbrecher R, Stevens RM, Sturtevant C, Suyker A, Tagesson T, Takanashi S, Tang Y, Tapper N, Thom J, Tiedemann F, Tomassucci M, Tuovinen J-P, Urbanski S, Valentini R, van der Molen M, van Gorsel E, van Huissteden K, Varlagin A, Verfaillie J, Vesala T, Vincke C, Vitale D, Vygodskaya N, Walker JP, Walter-Shea E, Wang H, Weber R, Westermann S, Wille C, Wofsy S, Wohlfahrt G, Wolf S, Woodgate W, Zampedri R, Zhang J, Zhou G, Zona D, Agarwal D, Biraud S, Torn M, Papale D, Pastorello G, Trotta C, Canfora E, Chu H, Christianson D, Cheah Y-W, Poindexter C, Chen J, Elbashandy A, Humphrey M, Isaac P, Polidori D, Ribeca A, van Ingen C, Zhang L, Amiro B, Ammann C, Arain MA, Ardö J, Arkebauer T, Arndt SK, Arriga N, Aubinet M, Aurela M, Baldocchi D, Barr A, Beamesderfer E, Marchesini LB, Bergeron O, Beringer J, Bernhofer C, Berveiller D, Billesbach D, Black TA, Blanken PD, Bohrer G, Boike J, Bolstad PV, Bonal D, Bonnefond J-M, Bowling DR, Bracho R, Brodeur J, Brümmer C, Buchmann N, Burban B, Burns SP, Buysse P, Cale P, Cavagna M, Cellier P, Chen S, Chini I, Christensen TR, Cleverly J, Collalti A, Consalvo C, Cook BD, Cook D, Coursolle C, Cremonese E, Curtis PS, D'Andrea E, da Rocha H, Dai X, Davis KJ, De Cinti B, de Grandcourt A, De Ligne A, De Oliveira RC, Delpierre N, Desai AR, Di Bella CM, di Tommasi P, Dolman H, Domingo F, Dong G, Dore S, Duce P, Dufrêne E, Dunn A, Dušek J, Eamus D, Eichelmann U, ElKhidir HAM, Eugster W, Ewenz CM, Ewers B, Famulari D, Fares S, Feigenwinter I, Feitz A, Fensholt R, Filippa G, Fischer M, Frank J, Galvagno M, Gharun M, Gianelle D, Gielen B, Gioli B, Gitelson A, Goded I, Goeckede M, Goldstein AH, Gough CM, Goulden ML, Graf A, Griebel A, Gruening C, Grünwald T, Hammerle A, Han S, Han X, Hansen BU, Hanson C, Hatakka J, He Y, Hehn M, Heinesch B, Hinko-Najera N, Hörtnagl L, Hutley L, Ibrom A, Ikawa H, Jackowicz-Korczynski M, Janouš D, Jans W, Jassal R, Jiang S, Kato T, Khomik M, Klatt J, Knohl A, Knox S, Kobayashi H, Koerber G, Kolle O, Kosugi Y, Kotani A, Kowalski A, Kruijt B, Kurbatova J, Kutsch WL, Kwon H, Launiainen S, Laurila T, Law B, Leuning R, Li Y, Liddell M, Limousin J-M, Lion M, Liska AJ, Lohila A, López-Ballesteros A, López-Blanco E, Loubet B, Loustau D, Lucas-Moffat A, Lüers J, Ma S, Macfarlane C, Magliulo V, Maier R, Mammarella I, Manca G, Marcolla B, Margolis HA, Marras S, Massman W, Mastepanov M, Matamala R, Matthes JH, Mazzenga F, McCaughey H, McHugh I, McMillan AMS, Merbold L, Meyer W, Meyers T, Miller SD, Minerbi S, Moderow U, Monson RK, Montagnani L, Moore CE, Moors E, Moreaux V, Moureaux C, Munger JW, Nakai T, Neirynck J, Nesic Z, Nicolini G, Noormets A, Northwood M, Nosetto M, Nouvellon Y, Novick K, Oechel W, Olesen JE, Ourcival J-M, Papuga SA, Parmentier F-J, Paul-Limoges E, Pavelka M, Peichl M, Pendall E, Phillips RP, Pilegaard K, Pirk N, Posse G, Powell T, Prasse H, Prober SM, Rambal S, Rannik Ü, Raz-Yaseef N, Reed D, de Dios VR, Restrepo-Coupe N, Reverter BR, Roland M, Sabbatini S, Sachs T, Saleska SR, Sánchez-Cañete EP, Sanchez-Mejia ZM, Schmid HP, Schmidt M, Schneider K, Schrader F, Schroder I, Scott RL, Sedlák P, Serrano-Ortíz P, Shao C, Shi P, Shironya I, Siebicke L, Šigut L, Silberstein R, Sirca C, Spano D, Steinbrecher R, Stevens RM, Sturtevant C, Suyker A, Tagesson T, Takanashi S, Tang Y, Tapper N, Thom J, Tiedemann F, Tomassucci M, Tuovinen J-P, Urbanski S, Valentini R, van der Molen M, van Gorsel E, van Huissteden K, Varlagin A, Verfaillie J, Vesala T, Vincke C, Vitale D, Vygodskaya N, Walker JP, Walter-Shea E, Wang H, Weber R, Westermann S, Wille C, Wofsy S, Wohlfahrt G, Wolf S, Woodgate W, Zampedri R, Zhang J, Zhou G, Zona D, Agarwal D, Biraud S, Torn M, and Papale D
Abstract: 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.
Published: 2020

29. Microclimatic Effects of a Forest-to-Peatland Transition on Aerodynamic Resistance to Water Vapour Transfer in the Sub-humid Boreal Plains

Author: Green, A., primary, Bohrer, G., additional, and Petrone, R., additional
Published: 2020
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30. Determining total emissions and environmental drivers of methane flux in a Lake Erie estuarine marsh

Author: Rey-Sanchez, A.C., primary, Morin, T.H., additional, Stefanik, K.C., additional, Wrighton, K., additional, and Bohrer, G., additional
Published: 2018
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31. Carbon dioxide emissions from an oligotrophic temperate lake: An eddy covariance approach

Author: Morin, T.H., primary, Rey-Sánchez, A.C., additional, Vogel, C.S., additional, Matheny, A.M., additional, Kenny, W.T., additional, and Bohrer, G., additional
Published: 2018
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32. On the choice of the driving temperature for eddy-covariance carbon dioxide flux partitioning

Author: Lasslop, G., Migliavacca, M., Bohrer, G., Reichstein, M., Bahn, M., Ibrom, A., Jacobs, C., Kolari, P., Papale, D., Vesala, T., Wohlfahrt, G., Cescatti, A., Department of Physics, Ecosystem processes (INAR Forest Sciences), and Micrometeorology and biogeochemical cycles
Subjects: 0106 biological sciences, 10515, Biophysics - Biocybernetics, eddy-covariance measurement mathematical and computer techniques, 010504 meteorology & atmospheric sciences, lcsh:Life, 10060, Biochemistry studies - General, SCOTS PINE FOREST, 04500, Mathematical biology and statistical methods, soil respiration, disentangle photosynthetic flux, 01 natural sciences, CO2 EXCHANGE, SDG 13 - Climate Action, danish beech forest, 4112 Forestry, lcsh:QE1-996.5, deciduous forest, global convergence, air temperature, flux partitioning algorithm mathematical and computer techniques, GLOBAL CONVERGENCE, carbon dioxide 124-38-9, SOIL RESPIRATION, DANISH BEECH FOREST, soil temperature, net ecosystem exchange, SEASONAL-VARIATION, education, ENVIRONMENTAL CONTROLS, 114 Physical sciences, scots pine forest, environmental biology - General and methods [07502, Ecology], Models and Simulations, water-vapor exchange, lcsh:QH540-549.5, environmental controls, simple empirical model mathematical and computer techniques, 0105 earth and related environmental sciences, WIMEK, NET ECOSYSTEM EXCHANGE, co2 exchange, Computational Biology, 15. Life on land, DECIDUOUS FOREST, Climate Resilience, lcsh:Geology, lcsh:QH501-531, Klimaatbestendigheid, 13. Climate action, climate sensitivity, respiratory flux, lcsh:Ecology, WATER-VAPOR EXCHANGE, seasonal-variation, Environmental Sciences, 010606 plant biology & botany
Abstract: Networks that merge and harmonise eddy-covariance measurements from many different parts of the world have become an important observational resource for ecosystem science. Empirical algorithms have been developed which combine direct observations of the net ecosystem exchange of carbon dioxide with simple empirical models to disentangle photosynthetic (GPP) and respiratory fluxes (Reco). The increasing use of these estimates for the analysis of climate sensitivities, model evaluation and calibration demands a thorough understanding of assumptions in the analysis process and the resulting uncertainties of the partitioned fluxes. The semi-empirical models used in flux partitioning algorithms require temperature observations as input, but as respiration takes place in many parts of an ecosystem, it is unclear which temperature input – air, surface, bole, or soil at a specific depth – should be used. This choice is a source of uncertainty and potential biases. In this study, we analysed the correlation between different temperature observations and nighttime NEE (which equals nighttime respiration) across FLUXNET sites to understand the potential of the different temperature observations as input for the flux partitioning model. We found that the differences in the correlation between different temperature data streams and nighttime NEE are small and depend on the selection of sites. We investigated the effects of the choice of the temperature data by running two flux partitioning algorithms with air and soil temperature. We found the time lag (phase shift) between air and soil temperatures explains the differences in the GPP and Reco estimates when using either air or soil temperatures for flux partitioning. The impact of the source of temperature data on other derived ecosystem parameters was estimated, and the strongest impact was found for the temperature sensitivity. Overall, this study suggests that the choice between soil or air temperature must be made on site-by-site basis by analysing the correlation between temperature and nighttime NEE. We recommend using an ensemble of estimates based on different temperature observations to account for the uncertainty due to the choice of temperature and to assure the robustness of the temporal patterns of the derived variables.
Published: 2012

33. Environmental controls of energy and trace gas exchanges at the water-air interface: Global synthesis of eddy fluxes over inland waters

Author: Golub, M., Desai, A., Bohrer, G., Blanken, P., Deshmukh, C., Franz, D., Guérin, F., Heiskanen, J., Jammet, M., Jonsson, A., Karlsson, J., Koebsch, F., Liu, H., Lohila, A., Lundin, E., Mammarella, I., Rutgersson, A., Sachs, T., Serca, D., Spence, C., Strachan, I., Vesala, T., Weyhenmeyer, G., Xiao, W., and Glatzel, S.
Published: 2015

34. Biophysical Mechanistic Modelling Quantifies the Effects of Plant Traits on Fire Severity: Species, Not Surface Fuel Loads, Determine Flame Dimensions in Eucalypt Forests

Author: Bohrer, G, Zylstra, P, Bradstock, RA, Bedward, M, Penman, TD, Doherty, MD, Weber, RO, Gill, AM, Cary, GJ, Bohrer, G, Zylstra, P, Bradstock, RA, Bedward, M, Penman, TD, Doherty, MD, Weber, RO, Gill, AM, and Cary, GJ
Abstract: The influence of plant traits on forest fire behaviour has evolutionary, ecological and management implications, but is poorly understood and frequently discounted. We use a process model to quantify that influence and provide validation in a diverse range of eucalypt forests burnt under varying conditions. Measured height of consumption was compared to heights predicted using a surface fuel fire behaviour model, then key aspects of our model were sequentially added to this with and without species-specific information. Our fully specified model had a mean absolute error 3.8 times smaller than the otherwise identical surface fuel model (p < 0.01), and correctly predicted the height of larger (≥1 m) flames 12 times more often (p < 0.001). We conclude that the primary endogenous drivers of fire severity are the species of plants present rather than the surface fuel load, and demonstrate the accuracy and versatility of the model for quantifying this.
Published: 2016

35. Using satellite‐derived optical thickness to assess the influence of clouds on terrestrial carbon uptake

Author: Cheng, S. J., primary, Steiner, A. L., additional, Hollinger, D. Y., additional, Bohrer, G., additional, and Nadelhoffer, K. J., additional
Published: 2016
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36. Seasonal hysteresis of net ecosystem exchange in response to temperature change: patterns and causes

Author: Niu S., Luo Y., Fei S., Montagnani L., Bohrer G., Janssens I.A., Gielen B., Rambal S., Moors E., and Matteucci G.
Subjects: carbon cycle, climate change, day length, ecosystem respiration, gross ecosystem productivity, leaf area index, seasonal variation, temperature sensitivity
Abstract: Understanding how net ecosystem exchange (NEE) changes with temperature is central to the debate on climate change-carbon cycle feedbacks, but still remains unclear. Here, we used eddy covariance measurements of NEE from 20 FLUXNET sites (203 site-years of data) in mid- and high-latitude forests to investigate the temperature response of NEE. Years were divided into two half thermal years (increasing temperature in spring and decreasing temperature in autumn) using the maximum daily mean temperature. We observed a parabolic-like pattern of NEE in response to temperature change in both the spring and autumn half thermal years. However, at similar temperatures, NEE was considerably depressed during the decreasing temperature season as compared with the increasing temperature season, inducing a counter-clockwise hysteresis pattern in the NEEtemperature relation at most sites. The magnitude of this hysteresis was attributable mostly (68%) to gross primary production (GPP) differences but little (8%) to ecosystem respiration (ER) differences between the two half thermal years. The main environmental factors contributing to the hysteresis responses of NEE and GPP were daily accumulated radiation. Soil water content (SWC) also contributed to the hysteresis response of GPP but only at some sites. Shorter day length, lower light intensity, lower SWC and reduced photosynthetic capacity may all have contributed to the depressed GPP and net carbon uptake during the decreasing temperature seasons. The resultant hysteresis loop is an important indicator of the existence of limiting factors. As such, the role of radiation, LAI and SWC should be considered when modeling the dynamics of carbon cycling in response to temperature change.
Published: 2011

37. Redefinition and global estimation of basal ecosystem respiration rate

Author: Yuan, W., Luo, Y., Li, X., Liu, S., Yu, G., Zhou, T., Bahn, M., Black, A., Richardson, A.D., Desai, A.R., Cescatti, A., Marcolla, B., Jacobs, C., Chen, J., Aurela, M., Bernhofer, C., Gielen, B., Bohrer, G., Cook, D.R., Dragoni, D., Dunn, A.L., Gianelle, D., Grünwald, T., Ibrom, A., Leclerc, M.Y., Lindroth, A., Liu, H., Marchesini, L.B., Montagnani, L., Rodeghiero, M., Rodrigues, A., Starr, G., and Stoy, P.C.
Subjects: Ecosystem respiration, Gross primary production, Basal ecosystem respiration rate, Settore BIO/07 - ECOLOGIA, EC-LUE model, Eddy covariance
Published: 2011

38. Large-eddy simulations of surface roughness parameter sensitivity to canopy-structure characteristics

Author: Maurer, K. D., primary, Bohrer, G., additional, Kenny, W. T., additional, and Ivanov, V. Y., additional
Published: 2015
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39. Moderate forest disturbance as a stringent test for gap and big-leaf models

Author: Bond-Lamberty, B., primary, Fisk, J. P., additional, Holm, J. A., additional, Bailey, V., additional, Bohrer, G., additional, and Gough, C. M., additional
Published: 2015
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40. Modeling of particulate matter dispersion from a poultry facility using AERMOD

Author: Hadlocon, L.S., primary, Zhao, L.Y., additional, Bohrer, G., additional, Kenny, W., additional, Garrity, S.R., additional, Wang, J., additional, Wyslouzil, B., additional, and Upadhyay, J., additional
Published: 2015
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41. Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile data set

Author: Verma, M., Friedl, Mark A., Richardson, Andrew D., Kiely, Gérard, Cescatti, Alessandro, Law, B., Wohlfahrt, Georg, Gielen, Bert, Roupsard, Olivier, Moors, Eddy J., Toscano, Piero, Vaccari, Francesco P., Gianelle, Damiano, Bohrer, G., Varlagin, Andrej, Buchmann, Nina, Van Gorsel, Eva, Montagnani, Leonardo, Propastin, P., Verma, M., Friedl, Mark A., Richardson, Andrew D., Kiely, Gérard, Cescatti, Alessandro, Law, B., Wohlfahrt, Georg, Gielen, Bert, Roupsard, Olivier, Moors, Eddy J., Toscano, Piero, Vaccari, Francesco P., Gianelle, Damiano, Bohrer, G., Varlagin, Andrej, Buchmann, Nina, Van Gorsel, Eva, Montagnani, Leonardo, and Propastin, P.
Abstract: Gross primary productivity (GPP) is the largest and most variable component of the global terrestrial carbon cycle. Repeatable and accurate monitoring of terrestrial GPP is therefore critical for quantifying dynamics in regional-to-global carbon budgets. Remote sensing provides high frequency observations of terrestrial ecosystems and is widely used to monitor and model spatiotemporal variability in ecosystem properties and processes that affect terrestrial GPP. We used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and FLUXNET to assess how well four metrics derived from remotely sensed vegetation indices (hereafter referred to as proxies) and six remote sensing-based models capture spatial and temporal variations in annual GPP. Specifically, we used the FLUXNET La Thuile data set, which includes several times more sites (144) and site years (422) than previous studies have used. Our results show that remotely sensed proxies and modeled GPP are able to capture significant spatial variation in mean annual GPP in every biome except croplands, but that the percentage of explained variance differed substantially across biomes (10-80%). The ability of remotely sensed proxies and models to explain interannual variability in GPP was even more limited. Remotely sensed proxies explained 40-60% of interannual variance in annual GPP in moisture-limited biomes, including grasslands and shrublands. However, none of the models or remotely sensed proxies explained statistically significant amounts of interannual variation in GPP in croplands, evergreen needleleaf forests, or deciduous broadleaf forests. Robust and repeatable characterization of spatiotemporal variability in carbon budgets is critically important and the carbon cycle science community is increasingly relying on remotely sensing data. Our analyses highlight the power of remote sensing-based models, but also provide bounds on the uncertainties associated with these models. Uncertainty in flux to
Published: 2014

42. The Macroecology of Airborne Pollen in Australian and New Zealand Urban Areas

Author: Bohrer, G, Haberle, SG, Bowman, DMJS, Newnham, RM, Johnston, FH, Beggs, PJ, Buters, J, Campbell, B, Erbas, B, Godwin, I, Green, BJ, Huete, A, Jaggard, AK, Medek, D, Murray, F, Newbigin, E, Thibaudon, M, Vicendese, D, Williamson, GJ, Davies, JM, Bohrer, G, Haberle, SG, Bowman, DMJS, Newnham, RM, Johnston, FH, Beggs, PJ, Buters, J, Campbell, B, Erbas, B, Godwin, I, Green, BJ, Huete, A, Jaggard, AK, Medek, D, Murray, F, Newbigin, E, Thibaudon, M, Vicendese, D, Williamson, GJ, and Davies, JM
Abstract: The composition and relative abundance of airborne pollen in urban areas of Australia and New Zealand are strongly influenced by geographical location, climate and land use. There is mounting evidence that the diversity and quality of airborne pollen is substantially modified by climate change and land-use yet there are insufficient data to project the future nature of these changes. Our study highlights the need for long-term aerobiological monitoring in Australian and New Zealand urban areas in a systematic, standardised, and sustained way, and provides a framework for targeting the most clinically significant taxa in terms of abundance, allergenic effects and public health burden.
Published: 2014

43. Finite-Element Tree Crown Hydrodynamics model (FETCH) using porous media flow within branching elements - a new representation of tree hydrodynamics

Author: Bohrer, G., Mourad, H., Laursen, T. A., Drewry, D., Avissar, R., Poggi, Davide, Oren, R., and Katul, G. G.
Published: 2005

44. Large eddy simulations of surface roughness parameter sensitivity to canopy-structure characteristics

Author: Maurer, K. D., primary, Bohrer, G., additional, and Ivanov, V. Y., additional
Published: 2014
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45. Carbon dioxide fluxes of an urban tidal marsh in the Hudson-Raritan estuary

Author: Schäfer, K. V. R., primary, Tripathee, R., additional, Artigas, F., additional, Morin, T. H., additional, and Bohrer, G., additional
Published: 2014
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46. The seasonal and diurnal dynamics of methane flux at a created urban wetland

Author: Morin, T.H., primary, Bohrer, G., additional, Naor-Azrieli, L., additional, Mesi, S., additional, Kenny, W.T., additional, Mitsch, W.J., additional, and Schäfer, K.V.R., additional
Published: 2014
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47. Environmental drivers of methane fluxes from an urban temperate wetland park

Author: Morin, T. H., primary, Bohrer, G., additional, Frasson, R. P. d. M., additional, Naor‐Azreli, L., additional, Mesi, S., additional, Stefanik, K. C., additional, and Schäfer, K. V. R., additional
Published: 2014
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48. Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile data set

Author: Verma, M., primary, Friedl, M. A., additional, Richardson, A. D., additional, Kiely, G., additional, Cescatti, A., additional, Law, B. E., additional, Wohlfahrt, G., additional, Gielen, B., additional, Roupsard, O., additional, Moors, E. J., additional, Toscano, P., additional, Vaccari, F. P., additional, Gianelle, D., additional, Bohrer, G., additional, Varlagin, A., additional, Buchmann, N., additional, van Gorsel, E., additional, Montagnani, L., additional, and Propastin, P., additional
Published: 2014
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49. On the choice of the driving temperature for eddy-covariance carbon dioxide flux partitioning

Author: University of Helsinki, Department of Physics, Lasslop, G., Migliavacca, M., Bohrer, G., Reichstein, M., Bahn, M., Ibrom, A., Jacobs, C., Kolari, P., Papale, D., Vesala, T., Wohlfahrt, G., Cescatti, A., University of Helsinki, Department of Physics, Lasslop, G., Migliavacca, M., Bohrer, G., Reichstein, M., Bahn, M., Ibrom, A., Jacobs, C., Kolari, P., Papale, D., Vesala, T., Wohlfahrt, G., and Cescatti, A.
Published: 2012

50. Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile dataset

Author: Verma, M., primary, Friedl, M. A., additional, Richardson, A. D., additional, Kiely, G., additional, Cescatti, A., additional, Law, B. E., additional, Wohlfahrt, G., additional, Gielen, B., additional, Roupsard, O., additional, Moors, E. J., additional, Toscano, P., additional, Vaccari, F. P., additional, Gianelle, D., additional, Bohrer, G., additional, Varlagin, A., additional, Buchmann, N., additional, van Gorsel, E., additional, Montagnani, L., additional, and Propastin, P., additional
Published: 2013
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