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1. Adaptation and Response in Drylands (ARID): Community Insights for Scoping a NASA Terrestrial Ecology Field Campaign in Drylands

Author: Andrew F. Feldman, Sasha Reed, Cibele Amaral, Alicja Babst‐Kostecka, Flurin Babst, Joel Biederman, Charles Devine, Zheng Fu, Julia K. Green, Jessica Guo, Niall P. Hanan, Raymond Kokaly, Marcy Litvak, Natasha MacBean, David Moore, Dennis Ojima, Benjamin Poulter, Russell L. Scott, William K. Smith, Robert Swap, Compton J. Tucker, Lixin Wang, Jennifer Watts, Konrad Wessels, Fangyue Zhang, and Wen Zhang
Subjects: drylands, ARID, climate change, NASA, field campaign, adaptation, Environmental sciences, GE1-350, Ecology, QH540-549.5
Abstract: Abstract Dryland ecosystems cover 40% of our planet's land surface, support billions of people, and are responding rapidly to climate and land use change. These expansive systems also dominate core aspects of Earth's climate, storing and exchanging vast amounts of water, carbon, and energy with the atmosphere. Despite their indispensable ecosystem services and high vulnerability to change, drylands are one of the least understood ecosystem types, partly due to challenges studying their heterogeneous landscapes and misconceptions that drylands are unproductive “wastelands.” Consequently, inadequate understanding of dryland processes has resulted in poor model representation and forecasting capacity, hindering decision making for these at‐risk ecosystems. NASA satellite resources are increasingly available at the higher resolutions needed to enhance understanding of drylands' heterogeneous spatiotemporal dynamics. NASA's Terrestrial Ecology Program solicited proposals for scoping a multi‐year field campaign, of which Adaptation and Response in Drylands (ARID) was one of two scoping studies selected. A primary goal of the scoping study is to gather input from the scientific and data end‐user communities on dryland research gaps and data user needs. Here, we provide an overview of the ARID team's community engagement and how it has guided development of our framework. This includes an ARID kickoff meeting with over 300 participants held in October 2023 at the University of Arizona to gather input from data end‐users and scientists. We also summarize insights gained from hundreds of follow‐up activities, including from a tribal‐engagement focused workshop in New Mexico, conference town halls, intensive roundtables, and international engagements.
Published: 2024
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2. Using phenology to unravel differential soil water use and productivity in a semiarid savanna

Author: Blake Steiner, Russell L. Scott, Jia Hu, Natasha MacBean, Andrew Richardson, and David J. P. Moore
Subjects: eddy covariance, green chromatic coordinate (GCC), gross primary productivity, moderate‐resolution imaging spectroradiometer (MODIS), PhenoCam, phenology, Ecology, QH540-549.5
Abstract: Abstract Savannas are water‐limited ecosystems characterized by two dominant plant types: trees and an understory primarily made up grass. Different phenology and root structures of these plant types complicate how savanna primary productivity responds to changes in water availability. We tested the hypothesis that productivity in savannas is controlled by the temporal and vertical distribution of soil water content (SWC) and differences in growing season length of understory and tree plant functional types. To quantify the relationship between tree, understory, and savanna‐wide phenology and productivity, we used PhenoCam and satellite observations surrounding an eddy covariance tower at a semiarid savanna site in Arizona, USA. We distinguished between SWC across two different depth intervals (shallow, 30–100 cm). We found that tree greenness increased with SWC at both depths, while understory greenness was only sensitive to the shallower SWC measurements. Onset of ecosystem dormancy, estimated from satellite observations close to the eddy covariance tower, explained more variability in annual gross primary productivity (GPP) than in other phenometrics. Higher SWC led to an extended growing season, caused by delayed dormancy in trees, but the understory showed no evidence of delayed dormancy in wetter periods. We infer that the timing of ecosystem scale dormancy, driven by trees, is important in understanding changes in a savanna's GPP. These findings highlight the important effects of rainfall during the winter. These findings suggest that savanna GPP is conditional on different responses to moisture availability in each of the dominant vegetation components.
Published: 2024
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3. Improved dryland carbon flux predictions with explicit consideration of water-carbon coupling

Author: Mallory L. Barnes, Martha M. Farella, Russell L. Scott, David J. P. Moore, Guillermo E. Ponce-Campos, Joel A. Biederman, Natasha MacBean, Marcy E. Litvak, and David D. Breshears
Subjects: Geology, QE1-996.5, Environmental sciences, GE1-350
Abstract: Upscaling in situ carbon flux measurements using remotely sensed and meteorological observations in a machine learning algorithm leads to improved estimates of average uptake, and interannual variability in global drylands.
Published: 2021
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4. Response of soil carbon dioxide efflux to temporal repackaging of rainfall into fewer, larger events in a semiarid grassland

Author: Matthew C. Roby, Russell L. Scott, Joel A. Biederman, William K. Smith, and David J. P. Moore
Subjects: soil respiration, soil efflux, rainfall intensification, soil moisture, pulse, semiarid, Environmental sciences, GE1-350
Abstract: Changing rainfall patterns will alter soil water availability to plants and microbes and likely impact soil CO2 efflux (Fs) in semiarid ecosystems. However, our understanding of the response of Fs to compound changes in rainfall event size and frequency remains relatively limited. To address this knowledge gap, we examined how compound changes in rainfall size and frequency impact Fs in a semiarid grassland by deploying automated soil chambers at a rainfall manipulation experiment. All plots within the experiment received equal total summer growing season precipitation that was temporally repackaged into regular events of inversely varied size and frequency, with event sizes ranging from 5 to 50 mm and dry intervals ranging from 3.5 to 21 days. We found that repackaging rainfall into few/large events with long dry intervals decreased seasonal cumulative Fs. Repackaging influenced key aspects of pulses including mean, maximum, and antecedent (day before irrigation) values of soil moisture and Fs and their rate of decline during drying intervals. Soil moisture explained substantial variation in Fs (R2 > 0.84) for all treatments; however, the sensitivity of Fs to soil moisture decreased in the few/large regime compared to the reference and many/small regimes. Dynamics in plant phenology (quantified by plot greenness) and soil temperature interacted with soil moisture to influence the seasonal evolution of Fs pulses and cumulative efflux. Our findings demonstrate that soil moisture and vegetation responses to changes in rainfall size and frequency impact soil CO2 efflux pulses and seasonal emissions in semiarid grasslands. These results, coupled with the knowledge that CO2 efflux pulses play an outsized role in dryland carbon exchange, indicate the possibility of future climate-mediated shifts in the carbon cycling of semiarid ecosystems.
Published: 2022
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5. A Microbial‐Explicit Soil Organic Carbon Decomposition Model (MESDM): Development and Testing at a Semiarid Grassland Site

Author: Xia Zhang, Zhenghui Xie, Zhuguo Ma, Greg A. Barron‐Gafford, Russell L. Scott, and Guo‐Yue Niu
Subjects: soil heterotrophic respiration, soil organic carbon decomposition, land surface model, microbial‐explicit model, microbial dormancy, extracellular enzyme, Physical geography, GB3-5030, Oceanography, GC1-1581
Abstract: Abstract Explicit representations of microbial processes in soil organic carbon (SOC) decomposition models have received increasing attention, because soil heterotrophic respiration remains one of the greatest uncertainties in climate‐carbon feedbacks projected by Earth system models (ESMs). Microbial‐explicit models have been developed and applied in site‐ and global‐scale studies. These models, however, lack the ability to represent microbial respiration responses to drying‐wetting cycles, and few of them have been incorporated in land surface models (LSMs) and validated against field observations. In this study, we developed a multi‐layer, microbial‐explicit soil organic carbon decomposition model (MESDM), based on two main assumptions that (a) extracellular enzymes remain active at dry reaction microsites, and (b) microbes at wet microsites are active or potentially active, while microbes at the dry microsites are dormant, by dividing the soil volume into wet and dry zones. MESDM with O2 and CO2 gas transport models was coupled with Noah‐MP LSM and tested against half‐hourly field observations at a semiarid grassland site in the southwest US characterized by pulsed precipitation. The results show MESDM can reproduce the observed soil respiration pulses of various sizes in response to discrete precipitation events (Birch effect) and thus improve the simulation of net ecosystem exchange. Here, both microbial accessibility to accumulated dissolved organic carbon and reactivation of dormant microbes at the dry microsites upon rewetting are critical to reproducing the Birch effect. This study improves our understanding of and ability to simulate complex soil carbon dynamics that experience drying‐wetting cycle in climate‐carbon feedbacks.
Published: 2022
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6. Site Characteristics Mediate the Relationship Between Forest Productivity and Satellite Measured Solar Induced Fluorescence

Author: Theresia Yazbeck, Gil Bohrer, Pierre Gentine, Luping Ye, Nicola Arriga, Christian Bernhofer, Peter D. Blanken, Ankur R. Desai, David Durden, Alexander Knohl, Natalia Kowalska, Stefan Metzger, Meelis Mölder, Asko Noormets, Kim Novick, Russell L. Scott, Ladislav Šigut, Kamel Soudani, Masahito Ueyama, and Andrej Varlagin
Subjects: Gross Primary Production, Solar-Induced Chlorophyll Fluorescence, canopy conductance, canopy structure, photosynthesis, Forestry, SD1-669.5, Environmental sciences, GE1-350
Abstract: Solar-Induced Chlorophyll Fluorescence (SIF) can provide key information about the state of photosynthesis and offers the prospect of defining remote sensing-based estimation of Gross Primary Production (GPP). There is strong theoretical support for the link between SIF and GPP and this relationship has been empirically demonstrated using ground-based, airborne, and satellite-based SIF observations, as well as modeling. However, most evaluations have been based on monthly and annual scales, yet the GPP:SIF relations can be strongly influenced by both vegetation structure and physiology. At the monthly timescales, the structural response often dominates but short-term physiological variations can strongly impact the GPP:SIF relations. Here, we test how well SIF can predict the inter-daily variation of GPP during the growing season and under stress conditions, while taking into account the local effect of sites and abiotic conditions. We compare the accuracy of GPP predictions from SIF at different timescales (half-hourly, daily, and weekly), while evaluating effect of adding environmental variables to the relationship. We utilize observations for years 2018–2019 at 31 mid-latitudes, forested, eddy covariance (EC) flux sites in North America and Europe and use TROPOMI satellite data for SIF. Our results show that SIF is a good predictor of GPP, when accounting for inter-site variation, probably due to differences in canopy structure. Seasonally averaged leaf area index, fraction of absorbed photosynthetically active radiation (fPAR) and canopy conductance provide a predictor to the site-level effect. We show that fPAR is the main factor driving errors in the linear model at high temporal resolution. Adding water stress indicators, namely canopy conductance, to a multi-linear SIF-based GPP model provides the best improvement in the model precision at the three considered timescales, showing the importance of accounting for water stress in GPP predictions, independent of the SIF signal. SIF is a promising predictor for GPP among other remote sensing variables, but more focus should be placed on including canopy structure, and water stress effects in the relationship, especially when considering intra-seasonal, and inter- and intra-daily resolutions.
Published: 2021
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7. Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework

Author: Dawn M. Browning, Eric S. Russell, Guillermo E. Ponce-Campos, Nicole Kaplan, Andrew D. Richardson, Bijan Seyednasrollah, Sheri Spiegal, Nicanor Saliendra, Joseph G. Alfieri, John Baker, Carl Bernacchi, Brandon T. Bestelmeyer, David Bosch, Elizabeth H. Boughton, Raoul K. Boughton, Pat Clark, Gerald Flerchinger, Nuria Gomez-Casanovas, Sarah Goslee, Nick M. Haddad, David Hoover, Abdullah Jaradat, Marguerite Mauritz, Gregory W. McCarty, Gretchen R. Miller, John Sadler, Amartya Saha, Russell L. Scott, Andrew Suyker, Craig Tweedie, Jeffrey D. Wood, Xukai Zhang, and Shawn D. Taylor
Subjects: Agricultural management, Eddy covariance, GPP, Growing season length, Indicators, Long-Term Agroecosystem Research (LTAR) network, Ecology, QH540-549.5
Abstract: Effective measurement of seasonal variations in the timing and amount of production is critical to managing spatially heterogeneous agroecosystems in a changing climate. Although numerous technologies for such measurements are available, their relationships to one another at a continental extent are unknown. Using data collected from across the Long-Term Agroecosystem Research (LTAR) network and other networks, we investigated correlations among key metrics representing primary production, phenology, and carbon fluxes in croplands, grazing lands, and crop-grazing integrated systems across the continental U.S. Metrics we examined included gross primary productivity (GPP) estimated from eddy covariance (EC) towers and modelled from the Landsat satellite, Landsat NDVI, and vegetation greenness (Green Chromatic Coordinate, GCC) from tower-mounted PhenoCams for 2017 and 2018. Overall, our analysis compared production dynamics estimated from three independent ground and remote platforms using data for 34 agricultural sites constituting 51 site-years of co-located time series.Pairwise sensor comparisons across all four metrics revealed stronger correlation and lower root mean square error (RMSE) between end of season (EOS) dates (Pearson R ranged from 0.6 to 0.7 and RMSE from 32.5 to 67.8) than start of season (SOS) dates (0.46 to 0.69 and 40.4 to 66.2). Overall, moderate to high correlations between SOS and EOS metrics complemented one another except at some lower productivity grazing land sites where estimating SOS can be challenging. Growing season length estimates derived from 16-day satellite GPP (179.1 days) were significantly longer than those from PhenoCam GCC (70.4 days, padj
Published: 2021
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8. Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Author: Gilberto Pastorello, Carlo Trotta, Eleonora Canfora, Housen Chu, Danielle Christianson, You-Wei Cheah, Cristina Poindexter, Jiquan Chen, Abdelrahman Elbashandy, Marty Humphrey, Peter Isaac, Diego Polidori, Markus Reichstein, Alessio Ribeca, Catharine van Ingen, Nicolas Vuichard, Leiming Zhang, Brian Amiro, Christof Ammann, M. Altaf Arain, Jonas Ardö, Timothy Arkebauer, Stefan K. Arndt, Nicola Arriga, Marc Aubinet, Mika Aurela, Dennis Baldocchi, Alan Barr, Eric Beamesderfer, Luca Belelli Marchesini, Onil Bergeron, Jason Beringer, Christian Bernhofer, Daniel Berveiller, Dave Billesbach, Thomas Andrew Black, Peter D. Blanken, Gil Bohrer, Julia Boike, Paul V. Bolstad, Damien Bonal, Jean-Marc Bonnefond, David R. Bowling, Rosvel Bracho, Jason Brodeur, Christian Brümmer, Nina Buchmann, Benoit Burban, Sean P. Burns, Pauline Buysse, Peter Cale, Mauro Cavagna, Pierre Cellier, Shiping Chen, Isaac Chini, Torben R. Christensen, James Cleverly, Alessio Collalti, Claudia Consalvo, Bruce D. Cook, David Cook, Carole Coursolle, Edoardo Cremonese, Peter S. Curtis, Ettore D’Andrea, Humberto da Rocha, Xiaoqin Dai, Kenneth J. Davis, Bruno De Cinti, Agnes de Grandcourt, Anne De Ligne, Raimundo C. De Oliveira, Nicolas Delpierre, Ankur R. Desai, Carlos Marcelo Di Bella, Paul di Tommasi, Han Dolman, Francisco Domingo, Gang Dong, Sabina Dore, Pierpaolo Duce, Eric Dufrêne, Allison Dunn, Jiří Dušek, Derek Eamus, Uwe Eichelmann, Hatim Abdalla M. ElKhidir, Werner Eugster, Cacilia M. Ewenz, Brent Ewers, Daniela Famulari, Silvano Fares, Iris Feigenwinter, Andrew Feitz, Rasmus Fensholt, Gianluca Filippa, Marc Fischer, John Frank, Marta Galvagno, Mana Gharun, Damiano Gianelle, Bert Gielen, Beniamino Gioli, Anatoly Gitelson, Ignacio Goded, Mathias Goeckede, Allen H. Goldstein, Christopher M. Gough, Michael L. Goulden, Alexander Graf, Anne Griebel, Carsten Gruening, Thomas Grünwald, Albin Hammerle, Shijie Han, Xingguo Han, Birger Ulf Hansen, Chad Hanson, Juha Hatakka, Yongtao He, Markus Hehn, Bernard Heinesch, Nina Hinko-Najera, Lukas Hörtnagl, Lindsay Hutley, Andreas Ibrom, Hiroki Ikawa, Marcin Jackowicz-Korczynski, Dalibor Janouš, Wilma Jans, Rachhpal Jassal, Shicheng Jiang, Tomomichi Kato, Myroslava Khomik, Janina Klatt, Alexander Knohl, Sara Knox, Hideki Kobayashi, Georgia Koerber, Olaf Kolle, Yoshiko Kosugi, Ayumi Kotani, Andrew Kowalski, Bart Kruijt, Julia Kurbatova, Werner L. Kutsch, Hyojung Kwon, Samuli Launiainen, Tuomas Laurila, Bev Law, Ray Leuning, Yingnian Li, Michael Liddell, Jean-Marc Limousin, Marryanna Lion, Adam J. Liska, Annalea Lohila, Ana López-Ballesteros, Efrén López-Blanco, Benjamin Loubet, Denis Loustau, Antje Lucas-Moffat, Johannes Lüers, Siyan Ma, Craig Macfarlane, Vincenzo Magliulo, Regine Maier, Ivan Mammarella, Giovanni Manca, Barbara Marcolla, Hank A. Margolis, Serena Marras, William Massman, Mikhail Mastepanov, Roser Matamala, Jaclyn Hatala Matthes, Francesco Mazzenga, Harry McCaughey, Ian McHugh, Andrew M. S. McMillan, Lutz Merbold, Wayne Meyer, Tilden Meyers, Scott D. Miller, Stefano Minerbi, Uta Moderow, Russell K. Monson, Leonardo Montagnani, Caitlin E. Moore, Eddy Moors, Virginie Moreaux, Christine Moureaux, J. William Munger, Taro Nakai, Johan Neirynck, Zoran Nesic, Giacomo Nicolini, Asko Noormets, Matthew Northwood, Marcelo Nosetto, Yann Nouvellon, Kimberly Novick, Walter Oechel, Jørgen Eivind Olesen, Jean-Marc Ourcival, Shirley A. Papuga, Frans-Jan Parmentier, Eugenie Paul-Limoges, Marian Pavelka, Matthias Peichl, Elise Pendall, Richard P. Phillips, Kim Pilegaard, Norbert Pirk, Gabriela Posse, Thomas Powell, Heiko Prasse, Suzanne M. Prober, Serge Rambal, Üllar Rannik, Naama Raz-Yaseef, Corinna Rebmann, David Reed, Victor Resco de Dios, Natalia Restrepo-Coupe, Borja R. Reverter, Marilyn Roland, Simone Sabbatini, Torsten Sachs, Scott R. Saleska, Enrique P. Sánchez-Cañete, Zulia M. Sanchez-Mejia, Hans Peter Schmid, Marius Schmidt, Karl Schneider, Frederik Schrader, Ivan Schroder, Russell L. Scott, Pavel Sedlák, Penélope Serrano-Ortíz, Changliang Shao, Peili Shi, Ivan Shironya, Lukas Siebicke, Ladislav Šigut, Richard Silberstein, Costantino Sirca, Donatella Spano, Rainer Steinbrecher, Robert M. Stevens, Cove Sturtevant, Andy Suyker, Torbern Tagesson, Satoru Takanashi, Yanhong Tang, Nigel Tapper, Jonathan Thom, Michele Tomassucci, Juha-Pekka Tuovinen, Shawn Urbanski, Riccardo Valentini, Michiel van der Molen, Eva van Gorsel, Ko van Huissteden, Andrej Varlagin, Joseph Verfaillie, Timo Vesala, Caroline Vincke, Domenico Vitale, Natalia Vygodskaya, Jeffrey P. Walker, Elizabeth Walter-Shea, Huimin Wang, Robin Weber, Sebastian Westermann, Christian Wille, Steven Wofsy, Georg Wohlfahrt, Sebastian Wolf, William Woodgate, Yuelin Li, Roberto Zampedri, Junhui Zhang, Guoyi Zhou, Donatella Zona, Deb Agarwal, Sebastien Biraud, Margaret Torn, and Dario Papale
Subjects: Science
Abstract: A Correction to this paper has been published: https://doi.org/10.1038/s41597-021-00851-9.
Published: 2021
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9. Actual Evapotranspiration (Water Use) Assessment of the Colorado River Basin at the Landsat Resolution Using the Operational Simplified Surface Energy Balance Model

Author: Ramesh K. Singh, Gabriel B. Senay, Naga M. Velpuri, Stefanie Bohms, Russell L. Scott, and James P. Verdin
Subjects: evapotranspiration, energy balance, geospatial analysis, hydrologic cycle, irrigation, Landsat, remote sensing, water use, Science
Abstract: Accurately estimating consumptive water use in the Colorado River Basin (CRB) is important for assessing and managing limited water resources in the basin. Increasing water demand from various sectors may threaten long-term sustainability of the water supply in the arid southwestern United States. We have developed a first-ever basin-wide actual evapotranspiration (ETa) map of the CRB at the Landsat scale for water use assessment at the field level. We used the operational Simplified Surface Energy Balance (SSEBop) model for estimating ETa using 328 cloud-free Landsat images acquired during 2010. Our results show that cropland had the highest ETa among all land cover classes except for water. Validation using eddy covariance measured ETa showed that the SSEBop model nicely captured the variability in annual ETa with an overall R2 of 0.78 and a mean bias error of about 10%. Comparison with water balance-based ETa showed good agreement (R2 = 0.85) at the sub-basin level. Though there was good correlation (R2 = 0.79) between Moderate Resolution Imaging Spectroradiometer (MODIS)-based ETa (1 km spatial resolution) and Landsat-based ETa (30 m spatial resolution), the spatial distribution of MODIS-based ETa was not suitable for water use assessment at the field level. In contrast, Landsat-based ETa has good potential to be used at the field level for water management. With further validation using multiple years and sites, our methodology can be applied for regular production of ETa maps of larger areas such as the conterminous United States.
Published: 2013
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10. Estimating Riparian and Agricultural Actual Evapotranspiration by Reference Evapotranspiration and MODIS Enhanced Vegetation Index

Author: Russell L. Scott, Uyen Nguyen, Edward P. Glenn, Pamela L. Nagler, and Tanya Doody
Subjects: remote sensing, leaf area index, water balance, alfalfa, saltcedar, common reed, mesquite, Science
Abstract: Dryland river basins frequently support both irrigated agriculture and riparian vegetation and remote sensing methods are needed to monitor water use by both crops and natural vegetation in irrigation districts. We developed an algorithm for estimating actual evapotranspiration (ETa) based on the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectrometer (MODIS) sensor on the EOS-1 Terra satellite and locally-derived measurements of reference crop ET (ETo). The algorithm was calibrated with five years of ETa data from three eddy covariance flux towers set in riparian plant associations on the upper San Pedro River, Arizona, supplemented with ETa data for alfalfa and cotton from the literature. The algorithm was based on an equation of the form ETa = ETo [a(1 − e−bEVI) − c], where the term (1 − e−bEVI) is derived from the Beer-Lambert Law to express light absorption by a canopy, with EVI replacing leaf area index as an estimate of the density of light-absorbing units. The resulting algorithm capably predicted ETa across riparian plants and crops (r2 = 0.73). It was then tested against water balance data for five irrigation districts and flux tower data for two riparian zones for which season-long or multi-year ETa data were available. Predictions were within 10% of measured results in each case, with a non-significant (P = 0.89) difference between mean measured and modeled ETa of 5.4% over all validation sites. Validation and calibration data sets were combined to present a final predictive equation for application across crops and riparian plant associations for monitoring individual irrigation districts or for conducting global water use assessments of mixed agricultural and riparian biomes.
Published: 2013
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11. Vegetation productivity responds to sub‐annual climate conditions across semiarid biomes

Author: Mallory L. Barnes, M. Susan Moran, Russell L. Scott, Thomas E. Kolb, Guillermo E. Ponce‐Campos, David J. P. Moore, Morgan A. Ross, Bhaskar Mitra, and Sabina Dore
Subjects: aboveground net primary production, drought, eddy covariance, Enhanced Vegetation Index (EVI), forests, global change, Ecology, QH540-549.5
Abstract: Abstract In the southwest United States, the current prolonged warm drought is similar to the predicted future climate change scenarios for the region. This study aimed to determine patterns in vegetation response to the early 21st century drought across multiple biomes. We hypothesized that different biomes (forests, shrublands, and grasslands) would have different relative sensitivities to both climate drivers (precipitation and temperature) and legacy effects (previous‐year's productivity). We tested this hypothesis at eight Ameriflux sites in various Southwest biomes using NASA Moderate‐resolution Imaging Spectroradiometer Enhanced Vegetation Index (EVI) from 2001 to 2013. All sites experienced prolonged dry conditions during the study period. The impact of combined precipitation and temperature on Southwest ecosystems at both annual and sub‐annual timescales was tested using Standardized Precipitation Evapotranspiration Index (SPEI). All biomes studied had critical sub‐annual climate periods during which precipitation and temperature influenced production. In forests, annual peak greenness (EVImax) was best predicted by 9‐month SPEI calculated in July (i.e., January–July). In shrublands and grasslands, EVImax was best predicted by SPEI in July through September, with little effect of the previous year's EVImax. Daily gross ecosystem production (GEP) derived from flux tower data yielded further insights into the complex interplay between precipitation and temperature. In forests, GEP was driven by cool‐season precipitation and constrained by warm‐season maximum temperature. GEP in both shrublands and grasslands was driven by summer precipitation and constrained by high daily summer maximum temperatures. In grasslands, there was a negative relationship between temperature and GEP in July, but no relationship in August and September. Consideration of sub‐annual climate conditions and the inclusion of the effect of temperature on the water balance allowed us to generalize the functional responses of vegetation to predicted future climate conditions. We conclude that across biomes, drought conditions during critical sub‐annual climate periods could have a strong negative impact on vegetation production in the southwestern United States.
Published: 2016
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12. Exceptional heat and atmospheric dryness amplified losses of primary production during the 2020 U.S. Southwest hot drought

Author: Matthew P. Dannenberg, Dong Yan, Mallory L. Barnes, William K. Smith, Miriam R. Johnston, Russell L. Scott, Joel A. Biederman, John F. Knowles, Xian Wang, Tomer Duman, Marcy E. Litvak, John S. Kimball, A. Park Williams, and Yao Zhang
Published: 2022
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13. Remotely Sensed Soil Moisture Can Capture Dynamics Relevant to Plant Water Uptake

Author: Andrew F. Feldman, Daniel J. Short Gianotti, Jianzhi Dong, Ruzbeh Akbar, Wade T. Crow, Kaighin A. McColl, Alexandra G. Konings, Jesse B. Nipper, Shersingh Joseph Tumber-Dávila, Noel M. Holbrook, Fulton E. Rockwell, Russell L. Scott, Rolf Helmut Reichle, Abhishek Chatterjee, Joanna Joiner, Benjamin Poulter, and Dara Entekhabi
Subjects: Earth Resources and Remote Sensing
Abstract: A frequently expressed viewpoint across the Earth science community is that global soil moisture estimates from satellite L-band (1.4 GHz) measurements represent moisture only in a shallow surface layer (0–5 cm) and consequently are of limited value for studying global terrestrial ecosystems because plants use water from deeper rootzones. Using this argumentation, many observation-based land surface studies avoid satellite-observed soil moisture. Here, based on peer-reviewed literature across several fields, we argue that such a viewpoint is overly limiting for two reasons. First, microwave soil emission depth considerations and statistical considerations of vertically correlated soil moisture information together indicate that L-band measurements carry information about soil moisture extending below the commonly referenced 5 cm in many conditions. However, spatial variations of effective depths of representation remain uncertain. Second, in reviewing isotopic tracer field studies of plant water uptake, we find a prevalence of vegetation that primarily draws moisture from these upper soil layers. This is especially true for grasslands and croplands covering more than a third of global vegetated surfaces. Even some deeper-rooted species (i.e., shrubs and trees) preferentially or seasonally draw water from the upper soil layers. Therefore, L-band satellite soil moisture estimates are more relevant to global vegetation water uptake than commonly appreciated (i.e., relevant beyond only shallow soil processes like soil evaporation). Our commentary encourages the application of satellite soil moisture across a broader range of terrestrial hydrosphere and biosphere studies while urging more rigorous estimates of its effective depth of representation.
Published: 2023
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14. Assessment and Validation of AirMOSS P-Band Root-Zone Soil Moisture Products.

Author: Alireza Tabatabaeenejad, Richard H. Chen, Mariko S. Burgin, Xueyang Duan, Richard H. Cuenca, Michael H. Cosh, Russell L. Scott, and Mahta Moghaddam
Published: 2020
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15. Prosopis velutina Response to Aerial Herbicide Application

Author: Steven R. Archer, Adam T. Naito, Philip Heilman, Enrique R. Vivoni, and Russell L. Scott
Subjects: Ecology, Animal Science and Zoology, Management, Monitoring, Policy and Law, Nature and Landscape Conservation
Published: 2023

16. Satellite solar‐induced chlorophyll fluorescence tracks physiological drought stress development during 2020 southwest <scp>US</scp> drought

Author: Yao Zhang, Jianing Fang, William Kolby Smith, Xian Wang, Pierre Gentine, Russell L. Scott, Mirco Migliavacca, Sujong Jeong, Marcy Litvak, and Sha Zhou
Subjects: Global and Planetary Change, Ecology, Environmental Chemistry, General Environmental Science
Published: 2023

17. Integrating continuous atmospheric boundary layer and tower-based flux measurements to advance understanding of land-atmosphere interactions

Author: Manuel Helbig, Tobias Gerken, Eric R. Beamesderfer, Dennis D. Baldocchi, Tirtha Banerjee, Sébastien C. Biraud, William O.J. Brown, Nathaniel A. Brunsell, Elizabeth A Burakowski, Sean P. Burns, Brian J. Butterworth, Wai-Yin Stephen Chan, Kenneth J. Davis, Ankur R. Desai, Jose D. Fuentes, David Y. Hollinger, Natascha Kljun, Matthias Mauder, Kimberly A. Novick, John M. Perkins, David A. Rahn, Camilo Rey-Sanchez, Joseph A. Santanello, Russell L. Scott, Bijan Seyednasrollah, Paul C. Stoy, Ryan C. Sullivan, Jordi Vilà-Guerau de Arellano, Sonia Wharton, Chuixiang Yi, and Andrew D. Richardson
Subjects: Earth Resources And Remote Sensing, Meteorology And Climatology
Abstract: The atmospheric boundary layer mediates the exchange of energy, matter, and momentum between the land surface and the free troposphere, integrating a range of physical, chemical, and biological processes and is defined as the lowest layer of the atmosphere (ranging from a few meters to 3 km). In this review, we investigate how continuous, automated observations of the atmospheric boundary layer can enhance the scientific value of co-located eddy covariance measurements of land-atmosphere fluxes of carbon, water, and energy, as are being made at FLUXNET sites worldwide. We highlight four key opportunities to integrate tower-based flux measurements with continuous, long-term atmospheric boundary layer measurements: (1) to interpret surface flux and atmospheric boundary layer exchange dynamics and feedbacks at flux tower sites, (2) to support flux footprint modelling, the interpretation of surface fluxes in heterogeneous and mountainous terrain, and quality control of eddy covariance flux measurements, (3) to support regional-scale modeling and upscaling of surface fluxes to continental scales, and (4) to quantify land-atmosphere coupling and validate its representation in Earth system models. Adding a suite of atmospheric boundary layer measurements to eddy covariance flux tower sites, and supporting the sharing of these data to tower networks, would allow the Earth science community to address new emerging research questions, better interpret ongoing flux tower measurements, and would present novel opportunities for collaborations between FLUXNET scientists and atmospheric and remote sensing scientists.
Published: 2021
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18. The SMAP Level 4 Carbon Product for Monitoring Ecosystem Land-Atmosphere CO2 Exchange.

Author: Lucas A. Jones, John S. Kimball, Rolf Reichle, Nima Madani, Joe Glassy, Joe V. Ardizzone, Andreas Colliander, James Cleverly, Ankur R. Desai, Derek Eamus, Eugenie S. Euskirchen, Lindsay B. Hutley, Craig Macfarlane, and Russell L. Scott
Published: 2017
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19. Synergistic Use of SMAP and OCO-2 Data in Assessing the Responses of Ecosystem Productivity to the 2018 U.S. Drought

Author: Xing Li, Jingfeng Xiao, John S Kimball, Rolf H Reichle, Russell L Scott, Marcy E Litvak, Gil Bohrer, and Christian Frankenberg
Subjects: Earth Resources And Remote Sensing
Abstract: Soil moisture and gross primary productivity (GPP) estimates from the Soil Moisture Active Passive (SMAP) and solar-induced chlorophyll fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2) provide new opportunities for understanding the relationship between soil moisture and terrestrial photosynthesis over large regions. Here we explored the potential of the synergistic use of SMAP and OCO-2 based data for monitoring the responses of ecosystem productivity to drought. We used complementary observational information on root-zone soil moisture and GPP (9 km) from SMAP and fine-resolution SIF (0.05°; GOSIF) derived from OCO-2 SIF soundings. We compared the spatial pattern and temporal evolution of anomalies of these variables over the conterminous U.S. during the 2018 drought, and examined to what extent they could characterize the drought-induced variations of flux tower GPP and crop yield data. Our results showed that SMAP GPP and GOSIF could well capture the spatial extent and dynamics of drought indicated by the U.S. Drought Monitor maps and the SMAP root-zone soil moisture deficit. Over the U.S. Southwest, monthly anomalies of soil moisture showed significant positive correlations with those of SMAP GPP (R2=0.44, p<0.001) and GOSIF (R2=0.76, p<0.001), demonstrating strong water availability constraints on plant productivity across dryland ecosystems. We further found that SMAP GPP and GOSIF captured the impact of drought on tower GPP and crop yield. Our results suggest that synergistic use of SMAP and OCO-2 data products can reveal the drought evolution and its impact on ecosystem productivity and carbon uptake at multiple spatial and temporal scales, and demonstrate the value of SMAP and OCO-2 for studying ecosystem function, carbon cycling, and climate change.
Published: 2020
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20. Implementing Dynamic Root Optimization in Noah‐MP for Simulating Phreatophytic Root Water Uptake

Author: Ping Wang, Guo‐Yue Niu, Yuan‐Hao Fang, Run‐Jian Wu, Jing‐Jie Yu, Guo‐Fu Yuan, Sergey P. Pozdniakov, and Russell L. Scott
Published: 2018
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21. Albedo-Induced Global Warming Potential Following Disturbances in Global Temperate and Boreal Forests

Author: qingsong Zhu, Jiquan Chen, Charles P.-A. Bourque, Oliver Sonnentag, Thomas L. O'Halloran, Leonardo Montagnani, Russell L. Scott, Jeremy Forsythe, Bo Song, Huimin Zou, Meihui Duan, and Xianglan Li
Published: 2023

22. Upscaling dryland carbon and water fluxes with artificial neural networks of optical, thermal, and microwave satellite remote sensing

Author: Matthew P. Dannenberg, Mallory L. Barnes, William K. Smith, Miriam R. Johnston, Susan K. Meerdink, Xian Wang, Russell L. Scott, and Joel A. Biederman
Subjects: Ecology, Evolution, Behavior and Systematics, Earth-Surface Processes
Abstract: Earth's drylands are home to more than two billion people, provide key ecosystem services, and exert a large influence on the trends and variability in Earth's carbon cycle. However, modeling dryland carbon and water fluxes with remote sensing suffers from unique challenges not typically encountered in mesic systems, particularly in capturing soil moisture stress. Here, we develop and evaluate an approach for the joint modeling of dryland gross primary production (GPP), net ecosystem exchange (NEE), and evapotranspiration (ET) in the western United States (US) using a suite of AmeriFlux eddy covariance sites spanning major functional types and aridity regimes. We use artificial neural networks (ANNs) to predict dryland ecosystem fluxes by fusing optical vegetation indices, multitemporal thermal observations, and microwave soil moisture and temperature retrievals from the Soil Moisture Active Passive (SMAP) sensor. Our new dryland ANN (DrylANNd) carbon and water flux model explains more than 70 % of monthly variance in GPP and ET, improving upon existing MODIS GPP and ET estimates at most dryland eddy covariance sites. DrylANNd predictions of NEE were considerably worse than its predictions of GPP and ET likely because soil and plant respiratory processes are largely invisible to satellite sensors. Optical vegetation indices, particularly the normalized difference vegetation index (NDVI) and near-infrared reflectance of vegetation (NIRv), were generally the most important variables contributing to model skill. However, daytime and nighttime land surface temperatures and SMAP soil moisture and soil temperature also contributed to model skill, with SMAP especially improving model predictions of shrubland, grassland, and savanna fluxes and land surface temperatures improving predictions in evergreen needleleaf forests. Our results show that a combination of optical vegetation indices and thermal infrared and microwave observations can substantially improve estimates of carbon and water fluxes in drylands, potentially providing the means to better monitor vegetation function and ecosystem services in these important regions that are undergoing rapid hydroclimatic change.
Published: 2022

23. Supplementary material to 'Upscaling dryland carbon and water fluxes with artificial neural networks of optical, thermal, and microwave satellite remote sensing'

Author: Matthew P. Dannenberg, Mallory L. Barnes, William K. Smith, Miriam R. Johnston, Susan K. Meerdink, Xian Wang, Russell L. Scott, and Joel A. Biederman
Published: 2022

24. Indicators of water use efficiency across diverse agroecosystems and spatiotemporal scales

Author: David L. Hoover, Lori J. Abendroth, Dawn M. Browning, Amartya Saha, Keirith Snyder, Pradeep Wagle, Lindsey Witthaus, Claire Baffaut, Joel A. Biederman, David D. Bosch, Rosvel Bracho, Dennis Busch, Patrick Clark, Patrick Ellsworth, Philip A. Fay, Gerald Flerchinger, Sean Kearney, Lucia Levers, Nicanor Saliendra, Marty Schmer, Harry Schomberg, and Russell L. Scott
Subjects: Environmental Engineering, Environmental Chemistry, Pollution, Waste Management and Disposal
Abstract: Understanding the relationship between water and production within and across agroecosystems is essential for addressing several agricultural challenges of the 21st century: providing food, fuel, and fiber to a growing human population, reducing the environmental impacts of agricultural production, and adapting food systems to climate change. Of all human activities, agriculture has the highest demand for water globally. Therefore, increasing water use efficiency (WUE), or producing 'more crop per drop', has been a long-term goal of agricultural management, engineering, and crop breeding. WUE is a widely used term applied across a diverse array of spatial scales, spanning from the leaf to the globe, and over temporal scales ranging from seconds to months to years. The measurement, interpretation, and complexity of WUE varies enormously across these spatial and temporal scales, challenging comparisons within and across diverse agroecosystems. The goals of this review are to evaluate common indicators of WUE in agricultural production and assess tradeoffs when applying these indicators within and across agroecosystems amidst a changing climate. We examine three questions: (1) what are the uses and limitations of common WUE indicators, (2) how can WUE indicators be applied within and across agroecosystems, and (3) how can WUE indicators help adapt agriculture to climate change? Addressing these agricultural challenges will require land managers, producers, policy makers, researchers, and consumers to evaluate costs and benefits of practices and innovations of water use in agricultural production. Clearly defining and interpreting WUE in the most scale-appropriate way is crucial for advancing agroecosystem sustainability.
Published: 2022

25. Disentangling the Relative Drivers of Seasonal Evapotranspiration Across a Continental‐Scale Aridity Gradient

Author: Adam M. Young, Mark A. Friedl, Kimberly Novick, Russell L. Scott, Minkyu Moon, Steve Frolking, Xiaolu Li, Carlos M. Carrillo, and Andrew D. Richardson
Subjects: Atmospheric Science, Ecology, Paleontology, Soil Science, Forestry, Aquatic Science, Water Science and Technology
Published: 2022

26. Bark beetle impacts on forest evapotranspiration and its partitioning

Author: John F. Knowles, Nels R. Bjarke, Andrew M. Badger, Max Berkelhammer, Joel A. Biederman, Peter D. Blanken, Mario Bretfeld, Sean P. Burns, Brent E. Ewers, John M. Frank, Jeffrey A. Hicke, Leanne Lestak, Ben Livneh, David E. Reed, Russell L. Scott, and Noah P. Molotch
Subjects: Environmental Engineering, Environmental Chemistry, Pollution, Waste Management and Disposal
Published: 2023

27. Warm spring reduced carbon cycle impact of the 2012 US summer drought

Author: Sebastian Wolf, Trevor F. Keenan, Joshua B. Fisher, Dennis D. Baldocchi, Ankur R. Desai, Andrew D. Richardson, Russell L. Scott, Beverly E. Law, Marcy E. Litvak, Nathaniel A. Brunsell, Wouter Peters, and Ingrid T. van der Laan-Luijkx
Published: 2016
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28. Actual Evapotranspiration (Water Use) Assessment of the Colorado River Basin at the Landsat Resolution Using the Operational Simplified Surface Energy Balance Model.

Author: Ramesh K. Singh, Gabriel B. Senay, Naga M. Velpuri, Stefanie Bohms, Russell L. Scott, and James P. Verdin
Published: 2014
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29. Estimating Riparian and Agricultural Actual Evapotranspiration by Reference Evapotranspiration and MODIS Enhanced Vegetation Index.

Author: Pamela L. Nagler, Edward P. Glenn, Uyen Nguyen, Russell L. Scott, and Tanya Doody
Published: 2013
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30. Testing water fluxes and storage from two hydrology configurations within the ORCHIDEE land surface model across US semi-arid sites

Author: Thomas Kolb, Natasha MacBean, N. Vuichard, Sabina Dore, Catherine Ottlé, Marcy E. Litvak, Joel A. Biederman, Russell L. Scott, Agnès Ducharne, David J. P. Moore, Department of Geography, Indiana University, Southwest Research Center, United States Department of Agriculture, Tucson, Arizona (USDA), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Modélisation des Surfaces et Interfaces Continentales (MOSAIC), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Milieux Environnementaux, Transferts et Interactions dans les hydrosystèmes et les Sols (METIS), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), 5School of Forestry, Northern Arizona University, Flagstaff, AZ 86011, USA, Hydrofocus, Inc., Davis, CA 95618, USA, Department of Biology, University of New Mexico, Albuquerque, NM 87131, USA, School of Natural Resources and the Environment, University of Arizona, Tucson, AZ 85721, USA, Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), University of Arizona, USDA-ARS : Agricultural Research Service, DEPARTMENT OF BIOLOGY UNIVERSITY OF NEW MEXICO ALBUQUERQUE USA, Partenaires IRSTEA, and Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)
Subjects: 010504 meteorology & atmospheric sciences, 0208 environmental biotechnology, 02 engineering and technology, lcsh:Technology, 01 natural sciences, lcsh:TD1-1066, Hydrology (agriculture), Evapotranspiration, Precipitation, lcsh:Environmental technology. Sanitary engineering, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, Water content, lcsh:Environmental sciences, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, General Environmental Science, Transpiration, lcsh:GE1-350, Hydrology, lcsh:T, lcsh:Geography. Anthropology. Recreation, Vegetation, 15. Life on land, Snow, 6. Clean water, 020801 environmental engineering, lcsh:G, 13. Climate action, [SDE]Environmental Sciences, General Earth and Planetary Sciences, Environmental science, Surface runoff
Abstract: Plant activity in semi-arid ecosystems is largely controlled by pulses of precipitation, making them particularly vulnerable to increased aridity that is expected with climate change. Simple bucket-model hydrology schemes in land surface models (LSMs) have had limited ability in accurately capturing semi-arid water stores and fluxes. Recent, more complex, LSM hydrology models have not been widely evaluated against semi-arid ecosystem in situ data. We hypothesize that the failure of older LSM versions to represent evapotranspiration, ET, in arid lands is because simple bucket models do not capture realistic fluctuations in upper-layer soil moisture. We therefore predict that including a discretized soil hydrology scheme based on a mechanistic description of moisture diffusion will result in an improvement in model ET when compared to data because the temporal variability of upper-layer soil moisture content better corresponds to that of precipitation inputs. To test this prediction, we compared ORCHIDEE LSM simulations from (1) a simple conceptual 2-layer bucket scheme with fixed hydraulic parameters and (2) an 11-layer discretized mechanistic scheme of moisture diffusion in unsaturated soil based on Richards equations, against daily and monthly soil moisture and ET observations, together with data-derived estimates of transpiration / evapotranspiration, T∕ET, ratios, from six semi-arid grass, shrub, and forest sites in the south-western USA. The 11-layer scheme also has modified calculations of surface runoff, water limitation, and resistance to bare soil evaporation, E, to be compatible with the more complex hydrology configuration. To diagnose remaining discrepancies in the 11-layer model, we tested two further configurations: (i) the addition of a term that captures bare soil evaporation resistance to dry soil; and (ii) reduced bare soil fractional vegetation cover. We found that the more mechanistic 11-layer model results in a better representation of the daily and monthly ET observations. We show that, as predicted, this is because of improved simulation of soil moisture in the upper layers of soil (top ∼ 10 cm). Some discrepancies between observed and modelled soil moisture and ET may allow us to prioritize future model development and the collection of additional data. Biases in winter and spring soil moisture at the forest sites could be explained by inaccurate soil moisture data during periods of soil freezing and/or underestimated snow forcing data. Although ET is generally well captured by the 11-layer model, modelled T∕ET ratios were generally lower than estimated values across all sites, particularly during the monsoon season. Adding a soil resistance term generally decreased simulated bare soil evaporation, E, and increased soil moisture content, thus increasing transpiration, T, and reducing the negative bias between modelled and estimated monsoon T∕ET ratios. This negative bias could also be accounted for at the low-elevation sites by decreasing the model bare soil fraction, thus increasing the amount of transpiring leaf area. However, adding the bare soil resistance term and decreasing the bare soil fraction both degraded the model fit to ET observations. Furthermore, remaining discrepancies in the timing of the transition from minimum T∕ET ratios during the hot, dry May–June period to high values at the start of the monsoon in July–August may also point towards incorrect modelling of leaf phenology and vegetation growth in response to monsoon rains. We conclude that a discretized soil hydrology scheme and associated developments improve estimates of ET by allowing the modelled upper-layer soil moisture to more closely match the pulse precipitation dynamics of these semi-arid ecosystems; however, the partitioning of T from E is not solved by this modification alone.
Published: 2020

31. Streamflow Response to Wildfire Differs With Season and Elevation in Adjacent Headwaters of the Lower Colorado River Basin

Author: Joel A. Biederman, Marcos D. Robles, Russell L. Scott, and John F. Knowles
Subjects: Water Science and Technology
Published: 2022

32. Canopy Temperature Is Regulated by Ecosystem Structural Traits and Captures the Ecohydrologic Dynamics of a Semiarid Mixed Conifer Forest Site

Author: Mostafa Javadian, William K. Smith, Kangsan Lee, John F. Knowles, Russell L. Scott, Joshua B. Fisher, David J. P. Moore, Willem J. D. Leeuwen, Greg Barron‐Gafford, and Ali Behrangi
Subjects: Atmospheric Science, Ecology, Paleontology, Soil Science, Forestry, Aquatic Science, Water Science and Technology
Published: 2022

33. Assessment and Validation of AirMOSS P-Band Root-Zone Soil Moisture Products

Author: Russell L. Scott, Richard H. Cuenca, Alireza Tabatabaeenejad, Michael H. Cosh, Richard H. Chen, Mahta Moghaddam, Xueyang Duan, and Mariko Burgin
Subjects: Synthetic aperture radar, geography, geography.geographical_feature_category, Biome, Taiga, 0211 other engineering and technologies, Soil classification, 02 engineering and technology, Atmospheric model, Atmospheric sciences, law.invention, Shrubland, law, General Earth and Planetary Sciences, Environmental science, Electrical and Electronic Engineering, Radar, Water content, 021101 geological & geomatics engineering
Abstract: The Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) P-band synthetic aperture radar (SAR) was flown more than 1200 h from August 2012 to September 2015, covering regions of 2500 km2 spread over nine major biomes in North America. The flights, as a part of the NASA AirMOSS Earth Venture Suborbital 1 (EVS-1) mission, collected radar data used to map root-zone soil moisture (RZSM) at 3-arcsec resolution. We previously reported the baseline retrieval algorithm and demonstrated its performance for a semiarid shrubland (Walnut Gulch, AZ, USA); we represented the RZSM profile as a continuous quadratic function and solved a radar scattering nonlinear optimization problem to obtain the unknown polynomial coefficients. In this article, we expand the retrievals to other AirMOSS sites that, in addition to the semiarid shrubland, include grassland and crops (MOISST, OK, USA), woody savanna (Tonzi Ranch, CA, USA), temperate conifer forest (Metolius, OR, USA), and boreal forest (Saskatchewan, Canada). Due to a wide range of land covers, soil types, and soil moisture regimes, we parameterize the forward model and constrain the inverse algorithm for each site separately. We present the full set of retrievals for these sites, validating the results against in situ observations. Error sources and strategies to minimize their effects are discussed. The concept of sensing depth is introduced. We find that the retrieval errors are smallest for the top 25 cm of soil with a root-mean-square error (RMSE) of less than 0.05 m3/m3. The RMSE remains around 0.06 m3/m3 even for depths reaching 45 cm, which is the typical sensing depth for the sites considered. These AirMOSS RZSM products (known as Level-2/3 RZSM, or L2/3-RZSM, products) are the first of their kind in that it is the first time RZSM has been retrieved directly from remote sensing observation.
Published: 2020

34. Confronting the water potential information gap

Author: Kimberly A. Novick, Darren L. Ficklin, Dennis Baldocchi, Kenneth J. Davis, Teamrat A. Ghezzehei, Alexandra G. Konings, Natasha MacBean, Nina Raoult, Russell L. Scott, Yuning Shi, Benjamin N. Sulman, Jeffrey D. Wood, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), and Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: [SDU]Sciences of the Universe [physics], General Earth and Planetary Sciences, Meteorology & Atmospheric Sciences
Abstract: International audience; Water potential directly controls the function of leaves, roots and microbes, and gradients in water potential drive water flows throughout the soil-plant-atmosphere continuum. Notwithstanding its clear relevance for many ecosystem processes, soil water potential is rarely measured in situ, and plant water potential observations are generally discrete, sparse, and not yet aggregated into accessible databases. These gaps limit our conceptual understanding of biophysical responses to moisture stress and inject large uncertainty into hydrologic and land-surface models. Here, we outline the conceptual and predictive gains that could be made with more continuous and discoverable observations of water potential in soils and plants. We discuss improvements to sensor technologies that facilitate in situ characterization of water potential, as well as strategies for building new networks that aggregate water potential data across sites. We end by highlighting novel opportunities for linking more representative site-level observations of water potential to remotely sensed proxies. Together, these considerations offer a road map for clearer links between ecohydrological processes and the water potential gradients that have the `potential' to substantially reduce conceptual and modelling uncertainties.
Published: 2022

35. Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework

Author: Sarah C. Goslee, Russell L. Scott, Gerald N. Flerchinger, David D. Bosch, Shawn D. Taylor, David L. Hoover, Nicole Kaplan, Elizabeth H. Boughton, Amartya Saha, Nuria Gomez-Casanovas, Jeffrey D. Wood, Gretchen R. Miller, Joseph G. Alfieri, Brandon T. Bestelmeyer, Andrew E. Suyker, Nicanor Z. Saliendra, Andrew D. Richardson, Nick M. Haddad, Eric S. Russell, Dawn M. Browning, Pat Clark, Guillermo E. Ponce-Campos, Raoul K. Boughton, Gregory W. McCarty, Sheri Spiegal, Marguerite Mauritz, Xukai Zhang, Craig E. Tweedie, Carl J. Bernacchi, John M. Baker, John Sadler, Abdullah A. Jaradat, and Bijan Seyednasrollah
Subjects: Agroecosystem, Growing season length, Ecology, Phenology, Eddy covariance, General Decision Sciences, Growing season, Vegetation, Atmospheric sciences, Normalized Difference Vegetation Index, Pearson product-moment correlation coefficient, Agricultural management, symbols.namesake, Long-Term Agroecosystem Research (LTAR) network, Productivity (ecology), symbols, Environmental science, Indicators, GPP, Ecology, Evolution, Behavior and Systematics, QH540-549.5
Abstract: Effective measurement of seasonal variations in the timing and amount of production is critical to managing spatially heterogeneous agroecosystems in a changing climate. Although numerous technologies for such measurements are available, their relationships to one another at a continental extent are unknown. Using data collected from across the Long-Term Agroecosystem Research (LTAR) network and other networks, we investigated correlations among key metrics representing primary production, phenology, and carbon fluxes in croplands, grazing lands, and crop-grazing integrated systems across the continental U.S. Metrics we examined included gross primary productivity (GPP) estimated from eddy covariance (EC) towers and modelled from the Landsat satellite, Landsat NDVI, and vegetation greenness (Green Chromatic Coordinate, GCC) from tower-mounted PhenoCams for 2017 and 2018. Overall, our analysis compared production dynamics estimated from three independent ground and remote platforms using data for 34 agricultural sites constituting 51 site-years of co-located time series.Pairwise sensor comparisons across all four metrics revealed stronger correlation and lower root mean square error (RMSE) between end of season (EOS) dates (Pearson R ranged from 0.6 to 0.7 and RMSE from 32.5 to 67.8) than start of season (SOS) dates (0.46 to 0.69 and 40.4 to 66.2). Overall, moderate to high correlations between SOS and EOS metrics complemented one another except at some lower productivity grazing land sites where estimating SOS can be challenging. Growing season length estimates derived from 16-day satellite GPP (179.1 days) were significantly longer than those from PhenoCam GCC (70.4 days, padj
Published: 2021

36. Unraveling the effects of management and climate on carbon fluxes of U.S. croplands using the USDA Long-Term Agroecosystem (LTAR) network

Author: D. Menefee, Russell L. Scott, M. Abraha, J.G. Alfieri, J. Baker, Dawn M. Browning, Jiquan Chen, Jeff Gonet, J.M.F. Johnson, G.R. Miller, Rachel Nifong, Phil Robertson, E.S. Russell, N. Saliendra, Adam P. Schreiner-Mcgraw, A. Suyker, P. Wagle, Chris Wente, P.M. White, and Doug Smith
Subjects: Atmospheric Science, Global and Planetary Change, Forestry, Agronomy and Crop Science
Published: 2022

37. The three major axes of terrestrial ecosystem function

Author: Georg Wohlfahrt, Sebastian Wolf, David Martini, Alexander Knohl, Micol Rossini, Dennis D. Baldocchi, Nuno Carvalhais, Talie Musavi, Tarek S. El-Madany, Ulisse Gomarasca, Ankur R. Desai, Matthias Forkel, Dario Papale, Marcos Fernández-Martínez, Jens Kattge, Jacob A. Nelson, Michael J. Liddell, Peter B. Reich, Guido Kraemer, Christopher M. Gough, Miguel D. Mahecha, Mathias Göckede, Hiroki Ikawa, Elise Pendall, Ivan A. Janssens, Rune Christiansen, Jiquan Chen, Craig Macfarlane, Alessandro Cescatti, Andreas Ibrom, Leonardo Montagnani, Marta Galvagno, T. Andrew Black, Michael Bahn, Sönke Zaehle, Giorgio Matteucci, Dan Yakir, Russell L. Scott, Silvia Caldararu, Jürgen Knauer, Markus Reichstein, Ulrich Weber, Richard P. Phillips, Cinzia Panigada, Mirco Migliavacca, Ian J. Wright, Jonas Peters, Beverly E. Law, Josep Peñuelas, Martha M. Farella, Arnaud Carrara, Eyal Rotenberg, Ivan Mammarella, Gianluca Filippa, Xuanlong Ma, Hideki Kobayashi, Jamie Cleverly, Oscar Perez-Priego, Edoardo Cremonese, Peter D. Blanken, Karen Anderson, Martin Jung, Clément Stahl, Daniel E. Pabon-Moreno, Damien Bonal, Nina Buchmann, Trevor F. Keenan, Institute for Atmospheric and Earth System Research (INAR), Micrometeorology and biogeochemical cycles, Migliavacca, M, Musavi, T, Mahecha, M, Nelson, J, Knauer, J, Baldocchi, D, Perez-Priego, O, Christiansen, R, Peters, J, Anderson, K, Bahn, M, Black, T, Blanken, P, Bonal, D, Buchmann, N, Caldararu, S, Carrara, A, Carvalhais, N, Cescatti, A, Chen, J, Cleverly, J, Cremonese, E, Desai, A, El-Madany, T, Farella, M, Fernandez-Martinez, M, Filippa, G, Forkel, M, Galvagno, M, Gomarasca, U, Gough, C, Gockede, M, Ibrom, A, Ikawa, H, Janssens, I, Jung, M, Kattge, J, Keenan, T, Knohl, A, Kobayashi, H, Kraemer, G, Law, B, Liddell, M, Ma, X, Mammarella, I, Martini, D, Macfarlane, C, Matteucci, G, Montagnani, L, Pabon-Moreno, D, Panigada, C, Papale, D, Pendall, E, Penuelas, J, Phillips, R, Reich, P, Rossini, M, Rotenberg, E, Scott, R, Stahl, C, Weber, U, Wohlfahrt, G, Wolf, S, Wright, I, Yakir, D, Zaehle, S, and Reichstein, M
Subjects: 010504 meteorology & atmospheric sciences, Range (biology), General Science & Technology, Climate, Ecosystem ecology, Biome, Datasets as Topic, 01 natural sciences, 114 Physical sciences, Article, Carbon Cycle, 03 medical and health sciences, Water Cycle, SDG 13 - Climate Action, Ecosystem, Biology, 030304 developmental biology, 0105 earth and related environmental sciences, SDG 15 - Life on Land, 0303 health sciences, Principal Component Analysis, Multidisciplinary, Ecology, Humidity, ecosystem ecology, Vegetation, 15. Life on land, Carbon Dioxide, Plants, Arid, Productivity (ecology), Biogeography, 13. Climate action, Ecosystem function, terrestrial biomes, productivity, vegetation structure, water-use efficiency, carbon-use efficiency, Environmental science, Terrestrial ecosystem, Engineering sciences. Technology
Abstract: The leaf economics spectrum1,2 and the global spectrum of plant forms and functions3 revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species2. Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities4. However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability4,5. Here we derive a set of ecosystem functions6 from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems7,8., Three key axes of variation of ecosystem functional changes and their underlying causes are identified from a dataset of surface gas exchange measurements across major terrestrial biomes and climate zones.
Published: 2021

38. Dynamic global vegetation models underestimate net CO2 flux mean and interannual variability in dryland ecosystems

Author: Marcy E. Litvak, Tilden P. Meyers, Anthony P. Walker, Praveena Krishnan, Julia E. M. S. Nabel, Vivek K. Arora, Julia Pongratz, Stephen Sitch, Danica Lombardozzi, Natasha MacBean, Vladislav Bastrikov, Thomas Kolb, Sönke Zaehle, Philippe Peylin, David J. P. Moore, Joel A. Biederman, Russell L. Scott, Daniel S. Goll, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Modélisation des Surfaces et Interfaces Continentales (MOSAIC), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0106 biological sciences, 010504 meteorology & atmospheric sciences, global carbon cycle, [SDE.MCG]Environmental Sciences/Global Changes, Atmospheric sciences, 010603 evolutionary biology, 01 natural sciences, Carbon cycle, inter-annual variability, medicine, Ecosystem, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, General Environmental Science, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Renewable Energy, Sustainability and the Environment, Public Health, Environmental and Occupational Health, Co2 flux, Drylands, 15. Life on land, 13. Climate action, dynamic global vegetation models, Environmental science, medicine.symptom, Vegetation (pathology)
Abstract: Despite their sparse vegetation, dryland regions exert a huge influence over global biogeochemical cycles because they cover more than 40% of the world surface (Schimel 2010 Science 327 418–9). It is thought that drylands dominate the inter-annual variability (IAV) and long-term trend in the global carbon (C) cycle (Poulter et al 2014 Nature 509 600–3, Ahlstrom et al 2015 Science 348 895–9, Zhang et al 2018 Glob. Change Biol. 24 3954–68). Projections of the global land C sink therefore rely on accurate representation of dryland C cycle processes; however, the dynamic global vegetation models (DGVMs) used in future projections have rarely been evaluated against dryland C flux data. Here, we carried out an evaluation of 14 DGVMs (TRENDY v7) against net ecosystem exchange (NEE) data from 12 dryland flux sites in the southwestern US encompassing a range of ecosystem types (forests, shrub- and grasslands). We find that all the models underestimate both mean annual C uptake/release as well as the magnitude of NEE IAV, suggesting that improvements in representing dryland regions may improve global C cycle projections. Across all models, the sensitivity and timing of ecosystem C uptake to plant available moisture was at fault. Spring biases in gross primary production (GPP) dominate the underestimate of mean annual NEE, whereas models’ lack of GPP response to water availability in both spring and summer monsoon are responsible for inability to capture NEE IAV. Errors in GPP moisture sensitivity at high elevation forested sites were more prominent during the spring, while errors at the low elevation shrub and grass-dominated sites were more important during the monsoon. We propose a range of hypotheses for why model GPP does not respond sufficiently to changing water availability that can serve as a guide for future dryland DGVM developments. Our analysis suggests that improvements in modeling C cycle processes across more than a quarter of the Earth’s land surface could be achieved by addressing the moisture sensitivity of dryland C uptake.
Published: 2021

39. Evapotranspiration Estimates Derived Using Multi-Platform Remote Sensing in a Semiarid Region.

Author: Kyle R. Knipper, Terri Hogue, Russell L. Scott, and Kristie Franz
Published: 2017
Full Text: View/download PDF

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

Author: Eddy Moors, Uwe Eichelmann, Christian Brümmer, Stefano Minerbi, Barbara Marcolla, Gil Bohrer, Leonardo Montagnani, Üllar Rannik, Han Dolman, Janina Klatt, Samuli Launiainen, Elizabeth A. Walter-Shea, Nina Buchmann, Hank A. Margolis, Beniamino Gioli, Peter S. Curtis, Margaret S. Torn, Gabriela Posse, Luca Belelli Marchesini, Gianluca Filippa, Kenneth J. Davis, Leiming Zhang, Alexander Graf, Ray Leuning, Andrew Feitz, Simone Sabbatini, Harry McCaughey, Werner Eugster, Juha Pekka Tuovinen, Timothy J. Arkebauer, N. N. Vygodskaya, Adam J. Liska, Rosvel Bracho, Sebastian Wolf, Marc Aubinet, Jiří Dušek, Eugénie Paul-Limoges, Christof Ammann, Daniel Berveiller, Zoran Nesic, Giacomo Nicolini, Jaclyn Hatala Matthes, Russell L. Scott, David E. Reed, Frans-Jan W. Parmentier, Changliang Shao, Penélope Serrano-Ortiz, Yingnian Li, Jason Beringer, Marc Fischer, Deb Agarwal, Rasmus Fensholt, Russell K. Monson, Agnès de Grandcourt, Stefan K. Arndt, Timo Vesala, Uta Moderow, Joseph Verfaillie, Mika Aurela, Bev Law, Nina Hinko-Najera, Taro Nakai, Richard P. Phillips, Lindsay B. Hutley, Benjamin Loubet, Michele Tomassucci, Ayumi Kotani, Hans Peter Schmid, Raimundo Cosme de Oliveira, Anatoly A. Gitelson, Domenico Vitale, Regine Maier, Caitlin E. Moore, Xiaoqin Dai, Damien Bonal, John M. Frank, Yuelin Li, Christopher M. Gough, Shijie Han, Shirley A. Papuga, Edoardo Cremonese, Shawn Urbanski, Sébastien C. Biraud, Scott D. Miller, Mana Gharun, Annalea Lohila, Ian McHugh, Giovanni Manca, Bert Gielen, Wayne S. Meyer, Pierpaolo Duce, Bruce D. Cook, Carsten Gruening, Hiroki Ikawa, B.R. Reverter, Marian Pavelka, Andrew M. S. McMillan, Gang Dong, Isaac Chini, Kimberly A. Novick, Dalibor Janouš, Anne De Ligne, E. Beamesderfer, Marty Humphrey, Virginie Moreaux, Christian Wille, Markus Hehn, Hideki Kobayashi, Allen H. Goldstein, Walter C. Oechel, Richard Silberstein, Francisco Domingo, Francesco Mazzenga, Elise Pendall, Juha Hatakka, Lutz Merbold, Xingguo Han, Daniela Famulari, Carlo Trotta, Naama Raz-Yaseef, Dario Papale, Jean Marc Ourcival, Benoit Burban, Pavel Sedlák, Diego Polidori, Asko Noormets, Huimin Wang, Birger Ulf Hansen, Thomas Grünwald, Caroline Vincke, Robert M. Stevens, Carole Coursolle, D. P. Billesbach, Karl Schneider, Guoyi Zhou, Marcin Jackowicz-Korczynski, Paul V. Bolstad, Iris Feigenwinter, Shiping Chen, Julia Boike, Ivan Schroder, D. S. Christianson, Junhui Zhang, Pierre Cellier, Catharine van Ingen, Andrej Varlagin, A. Ribeca, Claudia Consalvo, Derek Eamus, Jason Brodeur, Alan G. Barr, Denis Loustau, Andreas Ibrom, Ankur R. Desai, Andrew E. Suyker, Efrén López-Blanco, Peter Cale, Nicola Arriga, William J. Massman, Abdelrahman Elbashandy, Yoshiko Kosugi, Pauline Buysse, Cove Sturtevant, T. A. Black, Housen Chu, David R. Bowling, Sabina Dore, Albin Hammerle, Tilden P. Meyers, M. Altaf Arain, Hatim Abdalla M. ElKhidir, Ignacio Goded, Roberto Zampedri, Alessio Collalti, Torsten Sachs, Tuomas Laurila, Cristina Poindexter, E. Canfora, Alexander Knohl, Donatella Spano, Silvano Fares, Scott R. Saleska, Michiel K. van der Molen, Suzanne M. Prober, Marryanna Lion, Steven C. Wofsy, Michael L. Goulden, Matthew Northwood, Antje Lucas-Moffat, Christine Moureaux, Jean-Marc Limousin, Sara H. Knox, Damiano Gianelle, Olaf Kolle, Jørgen E. Olesen, Mikhail Mastepanov, Bernard Heinesch, Christian Bernhofer, Peter D. Blanken, Hyojung Kwon, Georg Wohlfahrt, Peili Shi, Yann Nouvellon, Allison L. Dunn, Onil Bergeron, Mauro Cavagna, Heiko Prasse, Natalia Restrepo-Coupe, Yanhong Tang, Donatella Zona, Andrew S. Kowalski, Eric Dufrêne, Kim Pilegaard, Serena Marras, Yongtao He, Brent E. Ewers, Siyan Ma, Jean Marc Bonnefond, Jonas Ardö, Ko van Huissteden, Roser Matamala, Robin Weber, Nigel J. Tapper, Humberto Ribeiro da Rocha, Eva van Gorsel, Torbern Tagesson, Frederik Schrader, Frank Tiedemann, Myroslava Khomik, Torben R. Christensen, Jonathan E. Thom, James Cleverly, Víctor Resco de Dios, Ivan Shironya, Jeffrey P. Walker, You Wei Cheah, Ana López-Ballesteros, Georgia R. Koerber, J. William Munger, Shicheng Jiang, Johannes Lüers, Bruno De Cinti, Gilberto Pastorello, David R. Cook, Werner L. Kutsch, Paul Di Tommasi, Nicolas Delpierre, Peter Isaac, Carlos Marcelo Di Bella, Jiquan Chen, Craig Macfarlane, Dennis D. Baldocchi, William Woodgate, Riccardo Valentini, Marilyn Roland, Ladislav Šigut, Tomomichi Kato, Sebastian Westermann, Ivan Mammarella, Bart Kruijt, Marta Galvagno, Marius Schmidt, Serge Rambal, J. Kurbatova, Sean P. Burns, Ettore D'Andrea, Chad Hanson, Vincenzo Magliulo, Anne Griebel, Brian D. Amiro, M. Goeckede, Enrique P. Sánchez-Cañete, Thomas L. Powell, Marcelo D. Nosetto, Cacilia Ewenz, Michael J. Liddell, Satoru Takanashi, Lukas Hörtnagl, Zulia Mayari Sanchez-Mejia, W.W.P. Jans, N. Pirk, Johan Neirynck, Rainer Steinbrecher, Lukas Siebicke, Matthias Peichl, Rachhpal S. Jassal, Costantino Sirca, Earth and Climate, Earth Sciences, Institute for Atmospheric and Earth System Research (INAR), INAR Physics, Micrometeorology and biogeochemical cycles, Viikki Plant Science Centre (ViPS), Ecosystem processes (INAR Forest Sciences), Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Università degli studi della Tuscia [Viterbo], California State University [Sacramento], Michigan State University System, University of Virginia, Max Planck Institute for Biogeochemistry (MPI-BGC), Max-Planck-Gesellschaft, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Chinese Academy of Sciences [Beijing] (CAS), University of Manitoba [Winnipeg], Agroscope, McMaster University [Hamilton, Ontario], Lund University [Lund], University of Nebraska–Lincoln, University of Nebraska System, University of Melbourne, University of Antwerp (UA), Université de Liège, Finnish Meteorological Institute (FMI), University of California [Berkeley] (UC Berkeley), University of California (UC), University of Saskatchewan [Saskatoon] (U of S), Peoples Friendship University of Russia [RUDN University] (RUDN), The University of Western Australia (UWA), Technische Universität Dresden = Dresden University of Technology (TU Dresden), Ecologie Systématique et Evolution (ESE), AgroParisTech-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), University of British Columbia (UBC), University of Colorado [Colorado Springs] (UCCS), Ohio State University [Columbus] (OSU), Humboldt University Of Berlin, University of Minnesota System, SILVA (SILVA), AgroParisTech-Université de Lorraine (UL)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Interactions Sol Plante Atmosphère (UMR ISPA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of Utah, University of Central Florida [Orlando] (UCF), Thunen Institute of Climate-Smart Agriculture, Department of Environmental Systems Science [ETH Zürich] (D-USYS), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Ecologie des forêts de Guyane (UMR ECOFOG), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Université de Guyane (UG)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Fondazione Edmund Mach - Edmund Mach Foundation [Italie] (FEM), Aarhus University [Aarhus], University of Technology Sydney (UTS), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), NASA Goddard Space Flight Center (GSFC), Argonne National Laboratory [Lemont] (ANL), Université Laval [Québec] (ULaval), Universidade de São Paulo = University of São Paulo (USP), Pennsylvania State University (Penn State), Penn State System, Ecologie fonctionnelle et biogéochimie des sols et des agro-écosystèmes (UMR Eco&Sols), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), University of Wisconsin-Madison, Vrije Universiteit Amsterdam [Amsterdam] (VU), Centro de Investigaciones Biológicas (CSIC), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Shanxi University (SXU), Worcester State University [Worcester], Czech Academy of Sciences [Prague] (CAS), Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Université Paul-Valéry - Montpellier 3 (UPVM)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), UCL - SST/ELI/ELIE - Environmental Sciences, GILBERTO PASTORELLO, Lawrence Berkeley National Laboratory, THOMAS ANDREW BLACK, University of British Columbia, PETER D. BLANKEN, University of Colorado, GIL BOHRER, Ohio State University, JULIA BOIKE, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research / Humboldt-Universität zu Berlin, PAUL V. BOLSTAD, University of Minnesota, JEAN-MARC BONNEFOND, ISPA Bordeaux Sciences Agro, DAVID R. BOWLING, University of Utah, ROSVEL BRACHO, University of Florida, JASON BRODEUR, McMaster University, CHRISTIAN BRÜMMER, Thünen Institute of Climate-Smart Agriculture, NINA BUCHMANN, ETH Zurich, BENOIT BURBAN, INRAE UMR ECOFOG, AGNES DE GRANDCOURT, UMR Eco&Sols, CIRAD, ANNE DE LIGNE, University of Liege, RAIMUNDO COSME DE OLIVEIRA JUNIOR, CPATU, HAN DOLMAN, Universiteit Amsterdam, FRANCISCO DOMINGO, CSIC, GANG DONG, Shanxi University, SABINA DORE, HydroFocus, PIERPAOLO DUCE, National Research Council of Italy, MARTA GALVAGNO, Environmental Protection Agency of Aosta Valley, MANA GHARUN, ETH Zurich, DAMIANO GIANELLE, Fondazione Edmund Mach, MARCIN JACKOWICZ-KORCZYNSKI, Lund University / Aarhus University, DALIBOR JANOUS, Global Change Research Institute of the Czech Academy of Sciences, WILMA JANS, Wageningen University and Research, RACHHPAL JASSAL, University of British Columbia, SHICHENG JIANG, Northeast Normal University, ANA LÓPEZ-BALLESTEROS, Trinity College Dublin, EFRÉN LÓPEZ-BLANCO, Aarhus University, BENJAMIN LOUBET, Université Paris-Saclay, DENIS LOUSTAU, ISPA - INRA, JOHANNES LÜERS, University of Bayreuth, JOHAN NEIRYNCK, Research Institute for Nature and Forest, ZORAN NESIC, University of British Columbia, GIACOMO NICOLINI, University of Tuscia / CMCC, ASKO NOORMETS, Texas A&M University, MATTHEW NORTHWOOD, Charles Darwin University, KIMBERLY NOVICK, Indiana University Bloomington, MARILYN ROLAND, University of Antwerp, SIMONE SABBATINI, University of Tuscia, TORSTEN SACHS, GFZ German Research Centre for Geosciences, SCOTT R. SALESKA, University of Arizona, ENRIQUE P. SÁNCHEZ-CAÑETE, University of Granada / CEAMA-IISTA, ZULIA M. SANCHEZ-MEJIA, Instituto Tecnológico de Sonora, RAINER STEINBRECHER, Karlsruhe Institute of Technology, ROBERT M. STEVENS, Sentek Pty Ltd, COVE STURTEVANT, National Ecological Observatory Network Program, ANDY SUYKER, University of Nebraska-Lincoln, TORBERN TAGESSON, Lund University / University of Copenhagen, SATORU TAKANASHI, Forestry and Forest Products Research Institute, DOMENICO VITALE, University of Tuscia / CMCC, NATALIA VYGODSKAYA, Russian Academy of Sciences, JEFFREY P. WALKER, Monash University, ELIZABETH WALTER-SHEA, University of Nebraska-Lincoln, HUIMIN WANG, Chinese Academy of Sciences, ROBIN WEBER, University of California Berkeley, SEBASTIAN WESTERMANN, Instituto Nacional de Tecnologia Agropecuaria (INTA), CHRISTIAN WILLE, GFZ German Research Centre for Geosciences, STEVEN WOFSY, Harvard University, GEORG WOHLFAHRT, University of Innsbruck, SEBASTIAN WOLF, ETH Zurich, WILLIAM WOODGATE, CSIRO Land and Water, YUELIN LI, Chinese Academy of Sciences, DONATELLA ZONA, San Diego State University / University of Sheffield, DEB AGARWAL, Lawrence Berkeley National Laboratory, SEBASTIEN BIRAUD, Lawrence Berkeley National Laboratory, MARGARET TORN, Lawrence Berkeley National Laboratory, DARIO PAPALE, University of Tuscia / CMCC., ALLISON DUNN, Worcester State University, JIRÍ DUSEK, Global Change Research Institute of the Czech Academy of Sciences, DEREK EAMUS, University of Technology Sydney, UWE EICHELMANN, Technische Universität Dresden, HOUSEN CHU, Lawrence Berkeley National Laboratory, DANIELLE CHRISTIANSON, Lawrence Berkeley National Laboratory, YOU-WEI CHEAH, Lawrence Berkeley National Laboratory, CRISTINA POINDEXTER, California State University, JIQUAN CHEN, Michigan State University, ABDELRAHMAN ELBASHANDY, Lawrence Berkeley National Laboratory, MARTY HUMPHREY, University of Virginia, PETER ISAAC, TERN Ecosystrem Processes, DIEGO POLIDORI, University of Tuscia / CMCC, ALESSIO RIBECA, University of Tuscia / CMCC, CATHARINE VAN INGEN, Lawrence Berkeley National Laboratory, LEIMINGZ HANG, Chinese Academy of Sciences, BRIAN AMIRO, University of Manitoba, CHRISTOF AMMANN, Agroscope Research Institute, M. ALTAF ARAIN, McMaster University, JONAS ARDÖ, Lund University, TIMOTHY ARKEBAUER, University of Nebraska-Lincoln, STEFAN K. ARNDT, The University of Melbourne, NICOLA ARRIGA, University of Antwerp / Joint Research Centre, MARC AUBINET, University of Liege, MIKA AURELA, Finnish Meteorological Institute, DENNIS BALDOCCHI, University of California Berkeley, ALAN BARR, University of Saskatchewan / Environment and Climate Change Canada, DAMIEN BONAL, Université de Lorraine, SEAN P. BURNS, University of Colorado / National Center for Atmospheric Research, PAULINE BUYSSE, Université Paris-Saclay, PETER CALE, Australian Landscape Trust, MAURO CAVAGNA, Fondazione Edmund Mach, PIERRE CELLIER, Université Paris-Saclay, SHIPING CHEN, Chinese Academy of Sciences, ISAAC CHINI, Fondazione Edmund Mach, TORBEN R . CHRISTENSEN, Aarhus University, JAMES CLEVERLY, University of Technology Sydney, ALESSIO COLLALTI, University of Tuscia / National Research Council of Italy, CLAUDIA CONSALVO, University of Tuscia / National Research Council of Italy, BRUCE D. COOK, NASA Goddard Space Flight Center, DAVID COOK, Argonne National Laboratory, CAROLE COURSOLLE, Natural Resources Canada / Université Laval, EDOARDO CREMONESE, Climate Change Unit, PETER S. CURTIS, Ohio State University, ETTORE DANDREA, National Research Council of Italy, HUMBERTO DA ROCHA, USP, XIAOQIN DAI, Chinese Academy of Sciences, KENNETH J. DAVIS, The Pennsylvania State University, BRUNO DE CINTI, National Research Council of Italy, NICOLAS DELPIERRE, Université Paris-Saclay, ANKUR R . DESAI, University of Wisconsin-Madison, CARLOS MARCELO DI BELLA, Facultad de Agronomía, UBA, Buenos Aires., PAUL DI TOMMASI, National Research Council of Italy, ERIC DUFRÊNE, Université Paris-Saclay, MARIUS SCHMIDT, Agrosphere (IBG3), HATIM ABDALLA M. ELKHIDIR, ElObeid Research Station, WERNER EUGSTER, ETH Zurich, CACILIA M. EWENZ, TERN Ecosystem Processes Central Node, BRENT EWERS, University of Wyoming, DANIELA FAMULARI, National Research Council of Italy, SILVANO FARES, National Research Council of Italy / Research Centre for Forestry and Wood, IRIS FEIGENWINTER, ETH Zurich, ANDREW FEITZ, Geoscience Australia, RASMUS FENSHOLT, University of Copenhagen, GIANLUCA FILIPPA, Environmental Protection Agency of Aosta Valley, MARC FISCHER, Lawrence Berkeley National Laboratory, JOHN FRANK, USDA Forest Service, BERT GIELEN, University of Antwerp, BENIAMINO GIOLI, National Research Council of Italy, ANATOLY GITELSON, University of Nebraska-Lincoln, IGNACIO BALLARIN GODED, Joint Research Centre, MATHIAS GOECKEDE, University of Nebraska-Lincoln, ALLEN H. GOLDSTEIN, University of California Berkeley, CHRISTOPHER M. GOUGH, Virginia Commonwealth University, MICHAEL L. GOULDEN, University of California, ALEXANDER GRAF, Forschungszentrum Jülich, ANNE GRIEBEL, The University of Melbourne, CARSTEN GRUENING, Joint Research Centre, THOMAS GRÜNWALD, Technische Universität Dresden, ALBIN HAMMERLE, University of Innsbruck, SHIJIE HAN, Henan University / Chinese Academy of Sciences, XINGGUO HAN, Chinese Academy of Sciences, BIRGER ULF HANSEN, University of Copenhagen, CHAD HANSON, Oregon State University, JUHA HATAKKA, Finnish Meteorological Institute, YONGTAO HE, Chinese Academy of Sciences / University of Chinese Academy of Sciences, MARKUS HEHN, Technische Universität Dresden, BERNARD HEINESCH, University of Liege, NINA HINKO-NAJERA, The University of Melbourne, LUKAS HÖRTNAGL, ETH Zurich, LINDSAY HUTLEY, Charles Darwin University, ANDREAS IBROM, Technical University of Denmark, HIROKI IKAWA, National Agriculture and Food Research Organization, TOMOMICHI KATO, Hokkaido University, MYROSLAVA KHOMIK, McMaster University / Geography and Environmental Management, JANINA KLATT, Karlsruhe Institute of Technology, ALEXANDER KNOHL, University of Goettingen, SARA KNOX, The University of British Columbia, HIDEKI KOBAYASHI, Institute of Arctic Climate and Environment Research, GEORGIA KOERBER, University of Adelaide, OLAF KOLLE, Max Planck Institute for Biogeochemistry, YOSHIKO KOSUGI, Kyoto University, AYUMI KOTANI, Nagoya University, ANDREW KOWALSKI, University of Granada, BART KRUIJT, Wageningen University, JULIA KURBATOVA, Russian Academy of Sciences, WERNER L. KUTSCH, ICOS ERIC, HYOJUNG KWON, Oregon State University, SAMULI LAUNIAINEN, Natural Resources Institute Finland, TUOMAS LAURILA, Finnish Meteorological Institute, BEV LAW, Oregon State University, RAY LEUNING, In memoriam, YINGNIAN LI, Chinese Academy of Sciences, MICHAEL LIDDELL, James Cook University, JEAN-MARC LIMOUSIN, Univ Montpellier, KARL SCHNEIDER, University of Cologne, MARRYANNA LION, Forest Research Institute Malaysia, ADAM J. LISKA, University of Nebraska-Lincoln, ANNALEA LOHILA, Finnish Meteorological Institute / University of Helsinki, ANTJE LUCAS-MOFFAT, Thünen Institute of Climate-Smart Agriculture / Centre for Agrometeorological Research, SIYAN MA, University of California Berkeley, CRAIG MACFARLANE, CSIRO Land and Water, VINCENZO MAGLIULO, National Research Council of Italy, REGINE MAIER, ETH Zurich, IVAN MAMMARELLA, University of Helsinki, GIOVANNI MANCA, Joint Research Centre, BARBARA MARCOLLA, Fondazione Edmund Mach, HANK A . MARGOLIS, Université Laval, SERENA MARRAS, CMCC / University of Sassari, WILLIAM MASSMAN, USDA Forest Service, MIKHAIL MASTEPANOV, Aarhus University / University of Oulu, ROSER MATAMALA, Argonne National Laboratory, JACLYN HATALA MATTHES, Wellesley College, FRANCESCO MAZZENGA, National Research Council of Italy, HARRY MCCAUGHEY, Queen’s University, IAN MCHUGH, The University of Melbourne, ANDREW M. S. MCMILLAN, Environmental Analytics NZ, LUTZ MERBOLD, International Livestock Research Institute, WAYNE MEYER, University of Adelaide, TILDEN MEYERS, NOAA/OAR/Air Resources Laboratory, SCOTT D. MILLER, State University of New York at Albany, STEFANO MINERBI, Forest Department of South Tyrol, UTA MODEROW, Technische Universität Dresden, RUSSELL K. MONSON, University of Arizona, LEONARDO MONTAGNANI, Forest Department of South Tyrol / Free University of Bolzano, CAITLIN E. MOORE, University of Illinois at Urbana-Champaign, EDDY MOORS, IHE Delft / VU Amsterdam, VIRGINIE MOREAUX, ISPA / University Grenoble Alpes, CHRISTINE MOUREAUX, University of Liege, J. WILLIAM MUNGER, Harvard University, TARO NAKAI, National Taiwan University / University of Alaska Fairbanks, MARCELO NOSETTO, Instituto de Matemática Aplicada San Luis / UNER, YANN NOUVELLON, Univ Montpellier-CIRAD-INRA-IRD-Montpellier SupAgro, WALTER OECHEL, San Diego State University / University of Exeter, JORGEN EIVIND OLESEN, Aarhus University, JEAN-MARC OURCIVAL, Univ Montpellier, SHIRLEY A. PAPUGA, Wayne State University, FRANS-JAN PARMENTIER, Lund University / University of Oslo, EUGENIE PAUL-LIMOGES, University of Zurich, MARIAN PAVELKA, Global Change Research Institute of the Czech Academy of Sciences, MATTHIAS PEICHL, Swedish University of Agricultural Sciences, ELISE PENDALL, Western Sydney University, RICHARD P. PHILLIPS, Indiana University Bloomington, KIM PILEGAARD, Technical University of Denmark, NORBERT PIRK, Lund University / CSIRO Land and Water, GABRIELA POSSE, Instituto Nacional de Tecnologia Agropecuaria (INTA), THOMAS POWELL, Lawrence Berkeley National Laboratory, HEIKO PRASSE, Technische Universität Dresden, SUZANNE M. PROBER, CSIRO Land and Water, SERGE RAMBAL, Univ Montpellier, ÜLLAR RANNIK, University of Helsinki, DAVID REED, Michigan State University, VICTOR RESCO DE DIOS, Western Sydney University / Southwest University of Science and Technology, NATALIA RESTREPO-COUPE, University of Arizona, BORJA R. REVERTER, Universidade Federal da Paraiba, HANS PETER SCHMID, Karlsruhe Institute of Technology, FREDERIK SCHRADER, Federal Research Institute of Rural Areas, IVAN SCHRODER, Geoscience Australia, RUSSELL L. SCOTT, Southwest Watershed Research Center, PAVEL SEDLÁK, Global Change Research Institute of the Czech Academy of Sciences / Institute of Atmospheric Physics of the Czech Academy of Sciences, PENÉLOPE SERRANO-ORTÍZ, CEAMA-IISTA / University of Granada, CHANGLIANG SHAO, Chinese Academy of Agricultural Sciences, PEILI SHI, Chinese Academy of Sciences, IVAN SHIRONYA, Russian Academy of Sciences, LUKAS SIEBICKE, Bioclimatology, University of Goettingen, LADISLAV SIGUT, Global Change Research Institute of the Czech Academy of Sciences, RICHARD SILBERSTEIN, University of Western Australia / Edith Cowan University, COSTANTINO SIRCA, CMCC / University of Sassari, DONATELLA SPANO, CMCC / University of Sassari, YANHONG TANG, Peking University, NIGEL TAPPER, Monash University, JONATHAN THOM, University of Wisconsin-Madison, FRANK TIEDEMANN, University of Goettingen, MICHELE TOMASSUCCI, University of Tuscia / Terrasystem srl, JUHA-PEKKA TUOVINEN, Finnish Meteorological Institute, SHAWN URBANSKI, Rocky Mountain Research Station, RICCARDO VALENTINI, University of Tuscia / CMCC, MICHIEL VAN DER MOLEN, Wageningen University, EVA VAN GORSEL, Australian National University Canberra, KO VAN HUISSTEDEN, Vrije Universiteit Amsterdam, ANDREJ VARLAGIN, Russian Academy of Sciences, JOSEPH VERFAILLIE, University of California Berkeley, TIMO VESALA, University of Helsinki, CAROLINE VINCKE, Chinese Academy of Sciences, ROBERTO ZAMPEDRI, Fondazione Edmund Mach, JUNHUI ZHANG, Chinese Academy of Sciences, GUOYI ZHOU, Nanjing University of Information Science & Technology, NAAMA RAZ-YASEEF, Lawrence Berkeley National Laboratory, ERIC BEAMESDERFER, McMaster University, CARLO TROTTA, University of Tuscia, ELEONORA CANFORA, University of Tuscia / CMCC, LUCA BELELLI MARCHESINI, Fondazione Edmund Mach / RUDN University, ONIL BERGERON, Ministère du Développement durable de l’Environnement et de la Lutte contre les changements climatiques, JASON BERINGER, University of Western Australia, CHRISTIAN BERNHOFER, Technische Universität Dresden, DANIEL BERVEILLER, Université Paris-Saclay, and DAVE BILLESBACH, University of Nebraska-Lincoln
Subjects: Meteorologie en Luchtkwaliteit, Data Descriptor, 010504 meteorology & atmospheric sciences, Settore AGR/05 - ASSESTAMENTO FORESTALE E SELVICOLTURA, dataset provides ecosystem, UNCERTAINTY, Eddy covariance, Observation météorologique, 01 natural sciences, ecosystem-scale data, lcsh:Science, SITES, Energy, Respiration, Statistics, Uncertainty, Carbon cycle, Biological measurements, Terrestrial biome, RESPIRATION, gapfilling, [SDE]Environmental Sciences, Assimilation, Anhídrid carbònic, ddc:500, Net ecosystem exchange, Écosystème, STORAGE, Information Systems, Statistics and Probability, ecosystem approaches [EN], Meteorology and Air Quality, ASSIMILATION, Library and Information Sciences, Education, collection [EN], Donnée climatique, Data collection, Water, 15. Life on land, Earth system science, Climate Resilience, Klimaatbestendigheid, lcsh:Q, processing, Climate sciences, Ecophysiology, Storage, Oceanography, Hydrology, Water Resources, 010501 environmental sciences, CARBON-DIOXIDE, ENERGY-BALANCE CLOSURE, ddc:550, Échange d'énergie, FLUXNET2015, Biosphere, Energy balance closure, fluxnet, Computer Science Applications, Collecte de données, Energia, P01 - Conservation de la nature et ressources foncières, Statistics, Probability and Uncertainty, INTERANNUAL VARIABILITY, Eddy Covariance, SDG 6 - Clean Water and Sanitation, Engineering sciences. Technology, Sensoriamento Remoto, FLUX, 1171 Geosciences, Consistency (database systems), eau, Life Science, Time series, Remote sensing studies, Measurement device, 0105 earth and related environmental sciences, Remote sensing, Ecosystem respiration and photosynthetic, WIMEK, NET ECOSYSTEM EXCHANGE, Pipeline (software), Environmental sciences, Metadata, Earth sciences, Carbon dioxide, 13. Climate action, Environmental science, Probability and Uncertainty, Water Systems and Global Change, Dioxyde de carbone
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-flled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the frst time in this paper. In addition, 206 of these sites are for the frst 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., European Union (EU), United States Department of Energy (DOE)
Published: 2020

41. Convergent Hydraulic Redistribution and Groundwater Access Supported Facilitative Dependency Between Trees and Grasses in a Semi‐Arid Environment

Author: E. Lee, R. L. Minor, Russell L. Scott, Newton Tran, Greg A. Barron-Gafford, John F. Knowles, and Praveen Kumar
Subjects: Dependency (UML), Environmental science, Hydraulic redistribution, Water resource management, Arid, Groundwater, Water use, Water Science and Technology
Published: 2021

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

Author: Zutao Ouyang, Sébastien C. Biraud, Xiangzhong Luo, Hideki Kobayashi, Masahito Ueyama, Marcy E. Litvak, Andrew E. Suyker, Walter C. Oechel, Gregory Starr, Peter D. Blanken, Andrew D. Richardson, Prajaya Prajapati, Asko Noormets, Steven F. Oberbauer, Russell L. Scott, Sara H. Knox, Eric S. Russell, John F. Knowles, Ankur R. Desai, Sigrid Dengel, John A. Gamon, Rodrigo Vargas, Elise Pendall, Shirley A. Papuga, Hiroki Iwata, Margaret S. Torn, Ralf M. Staebler, Jiquan Chen, David Durden, Timothy J. Arkebauer, Jitendra Kumar, Heping Liu, Gil Bohrer, Tomer Duman, Paul C. Stoy, William L. Quinton, Inke Forbrich, Nathaniel A. Brunsell, Ryan C. Sullivan, Rosvel Bracho, Elyn Humphreys, Kimberly A. Novick, Jeffrey D. Wood, Christopher M. Gough, Hiroki Ikawa, Xuhui Lee, Carl J. Bernacchi, Yang Ju, Silvano Fares, W. Stephen Chan, Oliver Sonnentag, Housen Chu, T. Andrew Black, P. Y. Oikawa, David Y. Hollinger, Timothy J. Griffis, Donatella Zona, Xingyuan Chen, Stefan Metzger, D. P. Billesbach, Thomas Kolb, Manuel Helbig, Dennis D. Baldocchi, Beverly E. Law, Shannon E. Brown, M. Altaf Arain, John H. Prueger, Kenneth L. Clark, Ellen Stuart-Haëntjens, and J. William Munger
Subjects: 0106 biological sciences, Atmospheric Science, Land cover, 010504 meteorology & atmospheric sciences, Eddy covariance, Atmospheric sciences, 01 natural sciences, Footprint, Meteorology & Atmospheric Sciences, Landsat EVI, Flux footprint, Spatial analysis, 0105 earth and related environmental sciences, Global and Planetary Change, Agricultural and Veterinary Sciences, Sensor location bias, Forestry, Spatial representativeness, Enhanced vegetation index, Vegetation, Wind direction, Biological Sciences, Model-data benchmarking, Footprint Flussi carbonio, Earth Sciences, Environmental science, Eddy Covariance, Agronomy and Crop Science, 010606 plant biology & botany
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

43. Intensification of the North American Monsoon Rainfall as Observed From a Long‐Term High‐Density Gauge Network

Author: Menberu B. Meles, Eleonora M. C. Demaria, Russell L. Scott, Pieter Hazenberg, Mary H. Nichols, and David C. Goodrich
Subjects: Geophysics, Climatology, North American Monsoon, General Earth and Planetary Sciences, High density, Environmental science, Gauge (firearms), Term (time)
Published: 2019

44. Understanding the relationship between vegetation greenness and productivity across dryland ecosystems through the integration of PhenoCam, satellite, and eddy covariance data

Author: Russell L. Scott, David J. P. Moore, Dong Yan, William K. Smith, and Joel A. Biederman
Subjects: 010504 meteorology & atmospheric sciences, 0208 environmental biotechnology, Eddy covariance, Soil Science, Geology, 02 engineering and technology, Vegetation, Land cover, Atmospheric sciences, 01 natural sciences, Gross primary productivity, 020801 environmental engineering, Productivity (ecology), Environmental science, Ecosystem, Satellite, Computers in Earth Sciences, Scale (map), 0105 earth and related environmental sciences, Remote sensing
Abstract: Drylands account for approximately 40% of the global land surface and play a dominant role in the trend and variability of terrestrial carbon uptake and storage. Gross ecosystem photosynthesis – termed gross primary productivity (GPP) – is a critical driver of terrestrial carbon uptake and remains challenging to be observed directly. Currently, vegetation indices that largely capture changes in greenness are the most commonly used datasets in satellite-based GPP modeling. However, there remains significant uncertainty in the spatiotemporal relationship between greenness indices and GPP, especially for relatively heterogeneous dryland ecosystems. In this paper, we compared vegetation greenness indices from PhenoCam and satellite (Landsat and MODIS) observations against GPP estimates from the eddy covariance technique, across three representative ecosystem types of the southwestern United States. We systematically evaluated the changes in the relationship between vegetation greenness indices and GPP: i) across spatial scales of canopy-level, 30-meter, and 500-meter resolution; and ii) across temporal scale of daily, 8-day, 16-day, and monthly resolution. We found that greenness-GPP relationships were independent of spatial scales as long as land cover type and composition remained relatively constant. We also found that the greenness-GPP relationships became stronger as the time interval increased, with the strongest relationships observed at the monthly resolution. We posit that the greenness-GPP relationship breaks down at short timescales because greenness changes more slowly than plant physiological function, which responds rapidly to changes in key biophysical drivers. These findings provide insights into the potential for and limitations of modeling GPP using remotely sensed greenness indices across dryland ecosystem types.
Published: 2019

45. Evaluating Soil Resistance Formulations in Thermal‐Based Two‐Source Energy Balance (TSEB) Model: Implications for Heterogeneous Semiarid and Arid Regions

Author: Monica Garcia, William P. Kustas, Russell L. Scott, Martha C. Anderson, Chunlin Huang, Yan Li, Francisco Domingo, Hector Nieto, Erfan Haghighi, Ministerio de Ciencia e Innovación (España), Swiss National Science Foundation, Chinese Academy of Sciences, Producció Vegetal, and Ús Eficient de l'Aigua en Agricultura
Subjects: Research program, 010504 meteorology & atmospheric sciences, 0208 environmental biotechnology, Soil resistance, Foundation (engineering), Energy balance, 02 engineering and technology, 15. Life on land, 01 natural sciences, Chinese academy of sciences, Arid, 020801 environmental engineering, Surface energy flux, 13. Climate action, Environmental science, Christian ministry, Water resource management, 0105 earth and related environmental sciences, Water Science and Technology
Abstract: Relatively small fluctuations in the surface energy balance and evapotranspiration in semiarid and arid regions can be indicative of significant changes to ecosystem health. Therefore, it is imperative to have approaches for monitoring surface fluxes in these regions. The remote sensing-based two-source energy balance (TSEB) model is a suitable method for flux estimation over sparsely vegetated semiarid and arid landscapes since it explicitly considers surface energy flux contributions from soil and vegetation. However, previous studies indicate that TSEB generally underestimates sensible heat flux (H) and hence overestimates latent heat flux (LE) or evapotranspiration for these regions unless soil resistance coefficients are modified based on additional ground information. In this study, TSEB is applied over semiarid and arid regions on three continents using the original soil resistance formulation with modified coefficients and a recently developed physically based soil resistance formulation. Model sensitivity analysis demonstrates the high sensitivity of TSEB with original soil resistance formulation to soil resistance coefficients, while TSEB with the new soil resistance formulation has relatively low sensitivity to uncertainties in all coefficients. The performance of TSEB using different soil resistance formulations is evaluated by comparing modeled H against eddy covariance measurements in six semiarid and arid study sites and ranking the error statistics. Our results indicate that incorporating the new soil resistance formulation into TSEB would enhance its utility in flux estimation over heterogeneous landscapes by obviating its reliance on semiempirical coefficients and thus provide more robust fluxes over sparsely vegetated regions without model calibration and/or parameter tuning., Spanish Ministry of Science and Innovation. Grant Number: CGL2016‐78075‐P DINCOS. Grant Number: CGL2016‐78075‐P Swiss National Science Foundation. Grant Number: P2EZP2‐165244 Strategic Priority Research Program of Chinese Academy of Sciences. Grant Number: XDA19040504
Published: 2019

46. Critical Zone Water Balance Over 13 Years in a Semiarid Savanna

Author: Joel A. Biederman and Russell L. Scott
Subjects: Hydrology, Water balance, Hydrology (agriculture), Evapotranspiration, Ecohydrology, Soil water, Environmental science, Groundwater recharge, Surface runoff, Water content, Water Science and Technology
Abstract: Quantifying how much and when precipitation (P) becomes runoff (R), evapotranspiration (ET), and drainage (D) in the critical zone is key to understanding how climate and land use impact hydrology. We quantify water balance dynamics of a semiarid savanna with a summer/winter rainfall pattern with 13 years of water fluxes and soil moisture. We find multiyear P is partitioned 96% to ET and 7% to R, while D is negligible. While weather regulates ET over diurnal timescales, soil water inputs control seasonal to annual ET amounts. Here we establish that seasonally-integrated ET is a better metric of ecosystem-available water than time-averaged soil moisture or precipitation, some of which is lost to runoff. Contrary to expectations, we find significant, episodic carryover of soil moisture from one growing season to the next. Abundant late-summer P can supply ET in the subsequent spring, even after multi-month dry periods. However, over an annual cycle nearly all soil moisture inputs are used by ET. Likewise, drainage beyond the monitored root zone occurs within a season, but this is counteracted by subsequent ET extraction of deep moisture over the year. Thus, very little groundwater recharge (net drainage) occurs at this site, though there is considerable uncertainty in estimation of this small flux as the residual of much larger ones. These comprehensive, long-term measurements support expectations about the over-riding importance of ET in the dryland critical zone water balance and reveal a surprising degree of inter-seasonal water storage.
Published: 2019

47. Long‐term research catchments to investigate shrub encroachment in the Sonoran and Chihuahuan deserts: Santa Rita and Jornada experimental ranges

Author: Luis A. Méndez-Barroso, Enrique R. Vivoni, Zachary T. Keller, Ryan C. Templeton, Adam P. Schreiner-McGraw, Debra P. C. Peters, Agustin Robles-Morua, E. R. Perez-Ruiz, Steven R. Archer, Russell L. Scott, Nolie P. Templeton, Eric A. Escoto, and Cody A. Anderson
Subjects: Hydrology, Water balance, ved/biology, Hydrological modelling, Ecohydrology, ved/biology.organism_classification_rank.species, Environmental science, Shrub, Water Science and Technology, Term (time)
Published: 2021

48. Hydraulic redistribution buffers climate variability and regulates grass‐tree interactions in a semiarid riparian savanna

Author: Greg A. Barron-Gafford, R. L. Minor, Newton Tran, John F. Knowles, Russell L. Scott, Erik P. Hamerlynck, P. Murphy, E. Lee, Praveen Kumar, Enrique P. Sánchez-Cañete, and L. Sutter
Subjects: Hydrology, Tree (data structure), geography, geography.geographical_feature_category, Ecology, Environmental science, Hydraulic redistribution, Aquatic Science, Ecology, Evolution, Behavior and Systematics, Earth-Surface Processes, Riparian zone
Published: 2021

49. Integrating continuous atmospheric boundary layer and tower-based flux measurements to advance understanding of land-atmosphere interactions

Author: Kenneth J. Davis, Elizabeth A. Burakowski, Russell L. Scott, David A. Rahn, Sean P. Burns, Paul C. Stoy, Manuel Helbig, Tirtha Banerjee, E. Beamesderfer, W. Stephen Chan, Dennis D. Baldocchi, Jordi Vilà-Guerau de Arellano, Sonia Wharton, David Y. Hollinger, Natascha Kljun, Sébastien C. Biraud, Nathaniel A. Brunsell, Jose D. Fuentes, John M. Perkins, Camilo Rey-Sanchez, Brian J. Butterworth, Bijan Seyednasrollah, Kimberly A. Novick, Matthias Mauder, William O. J. Brown, Ankur R. Desai, Ryan C. Sullivan, Andrew D. Richardson, Chuixiang Yi, Joseph A. Santanello, and T. Gerken
Subjects: 0106 biological sciences, Atmospheric Science, 010504 meteorology & atmospheric sciences, Planetary boundary layer, Eddy covariance, Atmospheric sciences, 01 natural sciences, Atmosphere, Troposphere, Flux (metallurgy), Meteorology, FluxNet, Meteorology & Atmospheric Sciences, Atmospheric inversion, Flux footprint, Meteorologie, Physics::Atmospheric and Oceanic Physics, 0105 earth and related environmental sciences, Land-atmosphere, Atmospheric inversion models, Global and Planetary Change, WIMEK, Agricultural and Veterinary Sciences, Forestry, Biological Sciences, Micrometeorology, Remote sensing, Boundary layer, Earth Sciences, Environmental science, Agronomy and Crop Science, 010606 plant biology & botany
Abstract: The atmospheric boundary layer mediates the exchange of energy, matter, and momentum between the land surface and the free troposphere, integrating a range of physical, chemical, and biological processes and is defined as the lowest layer of the atmosphere (ranging from a few meters to 3 km). In this review, we investigate how continuous, automated observations of the atmospheric boundary layer can enhance the scientific value of co-located eddy covariance measurements of land-atmosphere fluxes of carbon, water, and energy, as are being made at FLUXNET sites worldwide. We highlight four key opportunities to integrate tower-based flux measurements with continuous, long-term atmospheric boundary layer measurements: (1) to interpret surface flux and atmospheric boundary layer exchange dynamics and feedbacks at flux tower sites, (2) to support flux footprint modelling, the interpretation of surface fluxes in heterogeneous and mountainous terrain, and quality control of eddy covariance flux measurements, (3) to support regional-scale modeling and upscaling of surface fluxes to continental scales, and (4) to quantify land-atmosphere coupling and validate its representation in Earth system models. Adding a suite of atmospheric boundary layer measurements to eddy covariance flux tower sites, and supporting the sharing of these data to tower networks, would allow the Earth science community to address new emerging research questions, better interpret ongoing flux tower measurements, and would present novel opportunities for collaborations between FLUXNET scientists and atmospheric and remote sensing scientists.
Published: 2021

50. COSORE: A community database for continuous soil respiration and other soil‐atmosphere greenhouse gas flux data

Author: Dennis D. Baldocchi, Kadmiel Maseyk, Yuji Kominami, Nadine K. Ruehr, Patrick M. Crill, John E. Drake, Mioko Ataka, Anya M. Hopple, Haiming Kan, Samaneh Ashraf, Matthew Saunders, Zhuo Pang, Daphne Szutu, Stephanie C. Pennington, Whendee L. Silver, Scott T. Miller, Cecilio Oyonarte, David A. Lipson, Naishen Liang, Masahito Ueyama, Thomas Wutzler, Michael L. Goulden, Järvi Järveoja, Jiye Zeng, Wu Sun, Debjani Sihi, Takashi Hirano, Nina Buchmann, Amir AghaKouchak, Peter S. Curtis, Ruth K. Varner, Greg Winston, Munemasa Teramoto, Mark G. Tjoelker, Susan E. Trumbore, Kathleen Savage, Omar Gutiérrez del Arroyo, Asko Noormets, Mats Nilsson, Catriona A. Macdonald, Carolyn Monika Görres, M. Altaf Arain, Alexandre A. Renchon, Joseph Verfaillie, James W. Raich, Masahiro Takagi, Jason P. Kaye, Quan Zhang, Hamidreza Norouzi, Ulli Seibt, Melanie A. Mayes, Jinsong Wang, Juan J. Armesto, Marion Schrumpf, Tianshan Zha, Mirco Migliavacca, Chelcy Ford Miniat, Jin-Sheng He, Enrique P. Sánchez-Cañete, Michael Gavazzi, Tarek S. El-Madany, T. A. Black, H. Hughes, Elise Pendall, Christopher M. Gough, Jillian W. Gregg, Guofang Miao, Junliang Zou, Avni Malhotra, Russell L. Scott, D. S. Christianson, Marguerite Mauritz, Steve McNulty, Juying Wu, Jinshi Jian, K. C. Mathes, Tana E. Wood, Rodrigo Vargas, Jennifer Goedhart Nietz, Christoph S. Vogel, Claire L. Phillips, Mariah S. Carbone, Kentaro Takagi, Shih-Chieh Chang, Jorge F. Perez-Quezada, Richard P. Phillips, Hassan Anjileli, Eric A. Davidson, Ankur R. Desai, Christine S. O’Connell, Matthias Peichl, Bruce Osborne, Ben Bond-Lamberty, and Rachhpal S. Jassal
Subjects: 0106 biological sciences, 010504 meteorology & atmospheric sciences, Nitrous Oxide, Climate change, open data, computer.software_genre, Greenhouse gas, 010603 evolutionary biology, 01 natural sciences, Database design, soil respiration, Soil respiration, Greenhouse Gases, Soil, 11. Sustainability, greenhouse gases, open science, ddc:550, Environmental Chemistry, Biology, Ecosystem, 0105 earth and related environmental sciences, General Environmental Science, Global and Planetary Change, Database, Ecology, Atmosphere, carbon dioxide, methane, Respiration, Reproducibility of Results, 15. Life on land, Biological Sciences, Climate Action, Earth system science, Ancillary data, Chemistry, Earth sciences, Technical Advance, 13. Climate action, Soil water, Environmental science, Ecosystem respiration, computer, Environmental Sciences
Abstract: Globally, soils store two to three times as much carbon as currently resides in the atmosphere, and it is critical to understand how soil greenhouse gas (GHG) emissions and uptake will respond to ongoing climate change. In particular, the soil‐to‐atmosphere CO2 flux, commonly though imprecisely termed soil respiration (R S), is one of the largest carbon fluxes in the Earth system. An increasing number of high‐frequency R S measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open‐source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long‐term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data. The design emphasizes the importance of reproducibility, scientific transparency and open access to data. While being oriented towards continuously measured R S, the database design accommodates other soil‐atmosphere measurements (e.g. ecosystem respiration, chamber‐measured net ecosystem exchange, methane fluxes) as well as experimental treatments (heterotrophic only, etc.). We give brief examples of the types of analyses possible using this new community resource and describe its accompanying R software package., Here we describe the lightweight, open source COSORE (COntinuous SOil REspiration) database and software. COSORE focuses on automated, continuous and long‐term greenhouse gas flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation.
Published: 2020

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