Canopy temperature Tcan is a key driver of plant function that emerges as a result of interacting biotic and abiotic processes and properties. However, understanding controls on Tcan and forecasting canopy responses to weather extremes and climate change are difficult due to sparse measurements of Tcan at appropriate spatial and …
Journal: New Phytologist, Volume 230 (5): 1746-1753 (2021). DOI: https://doi.org/10.1111/nph.17321 Sites: US-Me2, US-Wrc
Salt marshes are large carbon reservoirs as part of blue carbon ecosystems. Unfortunately, there is limited information about the net ecosystem (NEE) and methane (CH4) exchange between salt marshes and the atmosphere to fully understand their carbon dynamics. We tested the influence of biophysical drivers by plant phenological phases …
Journal: Agricultural And Forest Meteorology, Volume 300: 108309 (2021). DOI: 10.1016/j.agrformet.2020.108309 Sites: US-StJ
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Journal: Journal Of Geophysical Research: Atmospheres, Volume 126 (7): (2021). DOI: 10.1029/2020JD033775 Sites: US-Bi1, US-Bi2, US-Myb, US-Tw1
Coastal settings variations are linked to composition, structural, and functional differences among mangrove ecotypes. Basin mangroves undergo larger flooding and salinity fluctuations, yet remain understudied, compared to other ecotypes. We evaluated the effect of flooding and air temperature (T a) on the surface energy balance …
Journal: Journal Of Geophysical Research: Biogeosciences, Volume 126 (2): e2020JG005811 (2021). DOI: https://doi.org/10.1029/2020JG005811 Sites: MX-PMm
Salt marshes constitute an important terrestrial-aquatic interface that remains underrepresented in Earth System Models due to constraining biophysical controls and spatially limited land cover. One promising approach to improve representativeness is the application of proximal remote sensing to generate phenological information, …
Journal: Agricultural And Forest Meteorology, Volume 307: 108481 (2021). DOI: 10.1016/j.agrformet.2021.108481 Sites: US-StJ
Measurements of the spatial heterogeneity of methane fluxes in wetlands are critical to better understand and predict methane emissions at the ecosystem scale. However, the within-wetland spatial heterogeneity of fluxes is rarely assessed. Here, we use a spatially balanced rapid chamber-based survey of methane at different ecohydrological …
Journal: Science Of The Total Environment, Volume 767: 144498 (2021). DOI: 10.1016/j.scitotenv.2020.144498 Sites: US-OWC
Three-dimensional (3D) vegetation canopy structure plays an important role in the way radiation interacts with the land surface. Accurately representing this process in Earth System models (ESMs) is crucial for the modeling of the global carbon, energy, and water cycles and hence future climate projections. Despite the importance …
Journal: Remote Sensing of Environment, Volume 261: (2021). DOI: https://doi.org/10.1016/j.rse.2021.112497 Sites: US-NR1, US-UMB
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Journal: Global Change Biology, Volume 27 (2): 359-375 (2021). DOI: 10.1111/gcb.15414 Sites: US-Ton, US-Var
Catchment‐scale response functions, such as transit time distribution (TTD) and evapotranspiration time distribution (ETTD), are considered fundamental descriptors of a catchment’s hydrologic and ecohydrologic responses to spatially and temporally varying precipitation inputs. Yet, estimating these functions is challenging, especially …
Journal: Hydrological Processes, Volume 35 (3): e14082 (2021). DOI: 10.1002/hyp.14082 Sites: US-MtB
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 …
Journal: Agricultural And Forest Meteorology, Volume 301-302: 108350 (2021). DOI: 10.1016/j.agrformet.2021.108350 Sites: CA-ARB, CA-ARF, CA-Ca1, CA-Ca2, CA-Ca3, CA-Cbo, CA-DBB, CA-ER1, CA-Gro, CA-Let, CA-Man, CA-MR3, CA-MR5, CA-Na1, CA-NS1, CA-NS2, CA-NS3, CA-NS4, CA-NS5, CA-NS6, CA-NS7, CA-Oas, CA-Obs, CA-Ojp, CA-Qc2, CA-Qcu, CA-Qfo, CA-SCC, CA-SF1, CA-SF2, CA-SF3, CA-SJ2, CA-SJ3, CA-TP1, CA-TP3, CA-TP4, CA-TPD, CA-WP1, US-A03, US-A10, US-A32, US-A74, US-ADR, US-AR1, US-AR2, US-ARb, US-ARc, US-ARM, US-Aud, US-Bar, US-Bi1, US-Bi2, US-Bkg, US-Blk, US-Blo, US-Bn1, US-Bn2, US-Bn3, US-Bo1, US-Bo2, US-Br3, US-CaV, US-Ced, US-CF1, US-CF2, US-CF3, US-CF4, US-ChR, US-Cop, US-CPk, US-CRT, US-Ctn, US-Dia, US-Dix, US-Dk1, US-Dk2, US-Dk3, US-EDN, US-Elm, US-EML, US-Fmf, US-FPe, US-FR2, US-FR3, US-Fuf, US-Fwf, US-GLE, US-GMF, US-Goo, US-Ha1, US-Ha2, US-Hn2, US-Hn3, US-Ho1, US-Ho2, US-Ho3, US-IB1, US-IB2, US-Ivo, US-KFS, US-KLS, US-Kon, US-KS1, US-KS2, US-KUT, US-Lin, US-Los, US-LPH, US-LWW, US-Me1, US-Me2, US-Me3, US-Me4, US-Me5, US-Me6, US-MMS, US-MOz, US-Mpj, US-MRf, US-MtB, US-Myb, US-NC1, US-NC2, US-NC3, US-NC4, US-Ne1, US-Ne2, US-Ne3, US-NGB, US-NR1, US-Oho, US-ORv, US-PHM, US-Pon, US-Prr, US-RC1, US-RC2, US-RC3, US-RC4, US-RC5, US-Rls, US-Rms, US-Ro1, US-Ro2, US-Ro5, US-Ro6, US-Rpf, US-Rws, US-SdH, US-Seg, US-Ses, US-SFP, US-Shd, US-Skr, US-Slt, US-Snd, US-Sne, US-Snf, US-SO2, US-SO3, US-SO4, US-SP1, US-SP2, US-SP3, US-SRC, US-SRG, US-SRM, US-Srr, US-Sta, US-StJ, US-Syv, US-Ton, US-Tw1, US-Tw2, US-Tw3, US-Tw4, US-Tw5, US-Twt, US-Uaf, US-UMB, US-UMd, US-Var, US-Vcm, US-Vcp, US-Vcs, US-WBW, US-WCr, US-Wdn, US-Wgr, US-Whs, US-Wi0, US-Wi1, US-Wi3, US-Wi4, US-Wi5, US-Wi6, US-Wi7, US-Wi8, US-Wi9, US-Wjs, US-Wkg, US-Wlr, US-Wpp, US-WPT, US-Wrc, US-xBR, US-xCP, US-xDL, US-xHA, US-xKA, US-xKZ, US-xRM, US-xSR, US-xWD