Automated Integration of Continental-Scale Observations in Near-Real Time for Simulation and Analysis of Biosphere–Atmosphere Interactions

Durden, David; Metzger, Stefan; Chu, Housen; Collier, Nathan; Davis, Kenneth J.; Desai, Ankur R.; Kumar, Jitendra; Wieder, William R.; Xu, Min; Hoffman, Forrest M.

doi:10.1007/978-3-030-63393-6_14

Cited by 3 publications

(4 citation statements)

References 84 publications

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“…NEON is a research network comprising 81 monitoring sites (47 terrestrial, 34 aquatic) that are collecting standardized, open data across the major ecosystems of the United States (Table S1 in the Supplement). NEON's data products are highly complementary to land models, providing highquality and standardized data for soil, vegetation, and atmosphere states and fluxes across vast spatiotemporal scales with high-throughput instrumented system data and spatially expansive remote sensing data (Hinckley et al, 2016;Balch et al, 2020;Durden et al, 2020). Each of the 47 NEON terrestrial sites includes an EC tower to determine the surfaceatmosphere exchange of momentum, heat, water, and CO 2 , alongside meteorology (precipitation, wind speed, humidity, temperature), atmospheric composition (water vapor and CO 2 concentrations and isotopic ratios), and soil sensor assemblies measuring depth-resolved soil temperature and moisture at several locations in the EC tower footprint (Metzger et al, 2019).…”

Section: Neon Datamentioning

confidence: 99%

“…Notably, single-point simulations can use EC measurements to facilitate more rapid model development and testing of ecological hypotheses (Bonan et al, 2012;Burns et al, 2018;Collier et al, 2018;Swenson et al, 2019;Wieder et al, 2017). An explosion of EC measurements and strong network coordination make these data easier to find (Beringer et al, 2022;Durden et al, 2020;Pastorello et al, 2020;Novick et al, 2018), but the need to perform additional data processing prior to use presents barriers to integrating ecological observations into land model development and evaluation. These barriers include gap filling associated meteorological data, assessing EC flux data quality, and persistent challenges in discovering and harmonizing complementary data -including information about vegetation and soils at EC tower sites.…”

Section: Introductionmentioning

confidence: 99%

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Overcoming barriers to enable convergence research by integrating ecological and climate sciences: the NCAR–NEON system Version 1

Lombardozzi,

Wieder,

Sobhani

et al. 2023

Geosci. Model Dev.

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Abstract. Global change research demands a convergence among academic disciplines to understand complex changes in Earth system function. Limitations related to data usability and computing infrastructure, however, present barriers to effective use of the research tools needed for this cross-disciplinary collaboration. To address these barriers, we created a computational platform that pairs meteorological data and site-level ecosystem characterizations from the National Ecological Observatory Network (NEON) with the Community Terrestrial System Model (CTSM) that is developed with university partners at the National Center for Atmospheric Research (NCAR). This NCAR–NEON system features a simplified user interface that facilitates access to and use of NEON observations and NCAR models. We present preliminary results that compare observed NEON fluxes with CTSM simulations and describe how the collaboration between NCAR and NEON that can be used by the global change research community improves both the data and model. Beyond datasets and computing, the NCAR–NEON system includes tutorials and visualization tools that facilitate interaction with observational and model datasets and further enable opportunities for teaching and research. By expanding access to data, models, and computing, cyberinfrastructure tools like the NCAR–NEON system will accelerate integration across ecology and climate science disciplines to advance understanding in Earth system science and global change.

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Section: Neon Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Overcoming barriers to enable convergence research by integrating ecological and climate sciences: the NCAR–NEON system Version 1

Lombardozzi,

Wieder,

Sobhani

et al. 2023

Geosci. Model Dev.

Self Cite

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“…In this context, studying the representativeness of the observed GPP from flux towers, and ways to match ground GPP and satellite-derived GPP is important in validating ecosystem models and remote sensing products. To summarize, the issue of spatial-temporal representativeness is of great significance in model-data benchmarking and remote sensing products' validation [5].…”

Section: Introductionmentioning

confidence: 99%

Spatial Representativeness of Gross Primary Productivity from Carbon Flux Sites in the Heihe River Basin, China

Zhang

Sun

2021

Remote Sensing

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Studying the spatial representativeness of carbon flux measurement data for typical land cover types can provide important information for benchmarking Earth system models and validating multiple-scale remote sensing products. In our study, daily gross primary productivity (GPP) was firstly derived from eddy covariance observation systems and seasonal variations in field GPP were analyzed at nine flux tower sites for typical land cover types in the Heihe River Basin, China. Then, the real-time footprint distance and climate footprint distance of the field GPP were obtained by using a footprint source area model. Lastly, multiple-scale GPP products were validated at footprint scale, and the impacts (measurement height, surface roughness and turbulent state of the atmosphere) on the footprint distance of field GPP were analyzed. The results of this paper demonstrated that climate footprint distances ranged from about 500 m to 1500 m for different land cover types in the Heihe River Basin. The accuracy was higher when validating MODIS GPP products at footprint scale (R2 = 0.56, RMSE = 3.07 g C m−2 d−1) than at field scale (R2 = 0.51, RMSE = 3.34 g C m−2 d−1), and the same situation occurred in the validation of high-resolution downscaled GPP (R2 = 0.85, RMSE = 1.34 g C m−2 d−1 when validated at footprint scale; R2 = 0.82, RMSE = 1.47 g C m−2 d−1 when validated at field scale). The results of this study provide information about the footprints of field GPP for typical land cover types in arid and semi-arid areas in Northwestern China, and reveal that precision may be higher when validating multiple-scale remote sensing GPP products at the footprint scale than at the field scale.

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“…As defined by Nappo et al (1982), the spatial representativeness of the site describes the extent to which a set of measurements taken in given spacetime domains reflect the actual conditions in different space-time domains. Although fluxes measured at eddy covariance sites can mostly reflect the integrated state of the regions represented by model or satellite-based grid cells, the space-time representativeness issue of flux data remains one of the main challenges for individual eddy covariance site (Chu et al, 2021;Durden et al, 2020).…”

mentioning

confidence: 99%

Quantifying the Spatial Representativeness of Carbon Flux Footprints of a Grassland Ecosystem in the Semi‐Arid Region

Gong

Wang

et al. 2023

JGR Atmospheres

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Anthropogenic carbon dioxide (CO 2 ) emissions have been increasing rapidly since the industrial revolution, causing global warming and environmental change. CO 2 emissions in arid regions are only 30% of those in humid regions, but the warming rate is 20%-40% higher than that of humid regions (Yao et al., 2020). Such a significant spatial heterogeneity in warming rate and CO 2 emissions causes arid regions to face greater climate hazards than humid regions (Huang et al., 2012(Huang et al., , 2019Yao et al., 2020), and increasingly frequent and severe droughts threaten the ability of dryland ecosystems to sequester carbon and maintain biodiversity (Yao et al., 2020). Therefore, it is

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Automated Integration of Continental-Scale Observations in Near-Real Time for Simulation and Analysis of Biosphere–Atmosphere Interactions

Cited by 3 publications

References 84 publications

Overcoming barriers to enable convergence research by integrating ecological and climate sciences: the NCAR–NEON system Version 1

Overcoming barriers to enable convergence research by integrating ecological and climate sciences: the NCAR–NEON system Version 1

Spatial Representativeness of Gross Primary Productivity from Carbon Flux Sites in the Heihe River Basin, China

Quantifying the Spatial Representativeness of Carbon Flux Footprints of a Grassland Ecosystem in the Semi‐Arid Region

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