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Network of networks: Time series clustering of AmeriFlux sites

  • David E Reed*
  • , Housen Chu*
  • , Brad G. Peter
  • , Jiquan Chen
  • , Michael Abraha
  • , Brian Amiro
  • , Ray G. Anderson
  • , M. Altaf Arain
  • , Paulo Arruda
  • , Greg A. Barron-Gafford
  • , Carl Bernacchi
  • , Daniel P. Beverly
  • , Sebastien C. Biraud
  • , T. Andrew Black
  • , Peter D. Blanken
  • , Gil Bohrer
  • , Rebecca Bowler
  • , David R. Bowling
  • , M. Syndonia Bret-Harte
  • , Mario Bretfeld
  • Nathaniel A. Brunsell, Stephen H. Bullock, Gerardo Celis, Xingyuan Chen, Aimee T. Classen, David R. Cook, Alejandro Cueva, Higo J. Dalmagro, Kenneth Davis, Ankur Desai, Alison J. Duff, Allison L. Dunn, David Durden, Colin W. Edgar, Eugenie Euskirchen, Rosvel Bracho, Brent Ewers, Lawrence B. Flanagan, Christopher Florian, Vanessa Foord, Inke Forbrich, Brandon R. Forsythe, John Frank, Jaime Garatuza-Payan, Sarah Goslee, Christopher Gough, Mark Green, Timothy Griffis, Manuel Helbig, Andrew C. Hill, Ross Hinkle, Jason Horne, Elyn Humphreys, Hiroki Ikawa, Go Iwahana, Rachhpal Jassal, Bruce Johnson, Mark Johnson, Steven A. Kannenberg, Eric Kelsey, John King, John F. Knowles, Sara Knox, Hideki Kobayashi, Thomas Kolb, Randy Kolka, Ken W. Krauss, Lars Kutzbach, Brian Lamb, Beverly Law, Sung-Ching Lee, Xuhui Lee, Heping Liu, Henry W. Loescher, Sparkle L. Malone, Roser Matamala, Marguerite Mauritz, Stefan Metzger, Gesa Meyer, Bhaskar Mitra, J. William Munger, Zoran Nesic, Asko Noormets, Thomas L. O'Halloran, Patrick T O'Keeffe, Steven F. Oberbauer, Walter Oechel, Patty Oikawa, Paulo C. Olivas, Andrew Ouimette, Gilberto Pastorello, Jorge F. Perez-Quezada, Claire Phillips, Gabriela Posse, Bo Qu, William L. Quinton, Michele L. Reba, Andrew D. Richardson, Valentin Picasso, Adrian V. Rocha, Julio C. Rodriguez, Roel Ruzol, Scott Saleska, Russell L. Scott, Adam P. Schreiner-McGraw, Edward A.G. Schuur, Maria Silveira, Oliver Sonnentag, David L. Spittlehouse, Ralf Staebler, Gregory Starr, Christina Staudhammer, Chris Still, Cove Sturtevant, Ryan C. Sullivan, Andy Suyker, David Trejo, Masahito Ueyama, Rodrigo Vargas, Brian Viner, Enrique R. Vivoni, Dong Wang, Eric J. Ward, Susanne Wiesner, Lisamarie Windham-Myers, David Yannick, Enrico A. Yepez, Terenzio Zenone, Junbin Zhao, Donatella Zona
*Corresponding author for this work
  • Yale University
  • Lawrence Berkeley National Laboratory
  • University of Arkansas
  • Michigan State University
  • Michigan State University
  • University of Manitoba
  • United States Department of Agriculture
  • McMaster University
  • Universidade Federal de Mato Grosso
  • University of Arizona
  • University of Indianapolis
  • University of British Columbia
  • University of Colorado Boulder
  • Ohio State University
  • British Columbia Ministry of Forests
  • University of Utah
  • University of Alaska Fairbanks
  • Kennesaw State University
  • University of Kansas
  • Centro de Investigacion Cientifica y de Educacion Superior de Ensenada
  • Pacific Northwest National Laboratory
  • University of Michigan, Ann Arbor
  • Argonne National Laboratory
  • El Colegio de la Frontera Sur
  • Universidade de Cuiabá
  • Pennsylvania State University
  • University of Wisconsin-Madison
  • Worcester State University
  • Battelle
  • University of Florida
  • University of Wyoming
  • University of Lethbridge
  • University of Toledo
  • US Forest Service
  • Instituto Technologico de Sonora
  • Virginia Commonwealth University
  • Case Western Reserve University
  • University of Minnesota Twin Cities
  • Dalhousie University
  • University of Central Florida
  • Carleton University
  • National Agriculture and Food Research Organization
  • University of Saskatchewan
  • West Virginia University
  • Plymouth State University
  • North Carolina State University
  • Montana State University
  • McGill University
  • Japan Agency for Marine-Earth Science and Technology
  • Northern Arizona University
  • Louisiana Universities Marine Consortium
  • University of Hamburg
  • Washington State University Pullman
  • Oregon State University
  • Max Planck Institute for Biogeochemistry
  • University of Texas at El Paso
  • Environment and Climate Change Canada
  • The James Hutton Institute
  • Harvard University
  • Texas A&M University
  • Clemson University
  • Florida International University
  • San Diego State University
  • California State University East Bay
  • University of Chile
  • USDA Agricultural Research Service
  • Centro de Investigación de Recursos Naturales
  • Université de Montréal
  • Wilfrid Laurier University
  • University of Notre Dame
  • Universidad de Sonora
  • University of Maine
  • University of Alabama
  • University of Nebraska-Lincoln
  • Osaka Metropolitan University
  • Arizona State University
  • Savannah River National Laboratory
  • NASA Goddard Space Flight Center
  • United States Geological Survey
  • National Research Council of Italy
  • Norwegian Institute of Bioeconomy Research

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Environmental observation networks, such as AmeriFlux, are foundational for monitoring ecosystem response to climate change, management practices, and natural disturbances; however, their effectiveness depends on their representativeness for the regions or continents. We proposed an empirical, time series approach to quantify the similarity of ecosystem fluxes across AmeriFlux sites. We extracted the diel and seasonal characteristics (i.e., amplitudes, phases) from carbon dioxide, water vapor, energy, and momentum fluxes, which reflect the effects of climate, plant phenology, and ecophysiology on the observations, and explored the potential aggregations of AmeriFlux sites through hierarchical clustering. While net radiation and temperature showed latitudinal clustering as expected, flux variables revealed a more uneven clustering with many small (number of sites < 5), unique groups and a few large (> 100) to intermediate (15–70) groups, highlighting the significant ecological regulations of ecosystem fluxes. Many identified unique groups were from under-sampled ecoregions and biome types of the International Geosphere-Biosphere Programme (IGBP), with distinct flux dynamics compared to the rest of the network. At the finer spatial scale, local topography, disturbance, management, edaphic, and hydrological regimes further enlarge the difference in flux dynamics within the groups. Nonetheless, our clustering approach is a data-driven method to interpret the AmeriFlux network, informing future cross-site syntheses, upscaling, and model-data benchmarking research. Finally, we highlighted the unique and underrepresented sites in the AmeriFlux network, which were found mainly in Hawaii and Latin America, mountains, and at under-sampled IGBP types (e.g., urban, open water), motivating the incorporation of new/unregistered sites from these groups.
Original languageEnglish
Article number110686
JournalAgricultural and Forest Meteorology
Volume372
Number of pages18
ISSN0168-1923
DOIs
Publication statusPublished - 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 13 - Climate Action
    SDG 13 Climate Action

Keywords

  • AmeriFlux network
  • Eddy covariance
  • Site uniqueness
  • Site clustering

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