Search

Your search keyword '"DIBAF, University of Tuscia, Via S.C. de Lellis, 01100 Viterbo, Italy"' showing total 2 results
Start Over Author "DIBAF, University of Tuscia, Via S.C. de Lellis, 01100 Viterbo, Italy" Topic 13. climate action
2 results on '"DIBAF, University of Tuscia, Via S.C. de Lellis, 01100 Viterbo, Italy"'

1. Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate

Author

Keith W. Oleson, Christian Beer, Mark R. Lomas, Dennis D. Baldocchi, Alessandro Cescatti, Anders Lindroth, Philippe Ciais, Dario Papale, F. Ian Woodward, Gordon B. Bonan, Christian Rödenbeck, Elmar Veenendaal, Enrico Tomelleri, Olivier Roupsard, Hank A. Margolis, M. Altaf Arain, Sebastiaan Luyssaert, Markus Reichstein, Nuno Carvalhais, Martin Jung, Nicolas Viovy, Gitta Lasslop, Alberte Bondeau, Christopher M. Williams, Systems Ecology, Department of Biogeochemical Integration [Jena], Max Planck Institute for Biogeochemistry (MPI-BGC), Max-Planck-Gesellschaft-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), ICOS-ATC (ICOS-ATC), 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), Faculdade de Ciências e Tecnologia = School of Science & Technology (FCT NOVA), Universidade Nova de Lisboa = NOVA University Lisbon (NOVA), Max-Planck-Gesellschaft, McMaster University [Hamilton, Ontario], Department of Environmental Science, Policy, and Management [Berkeley] (ESPM), University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), National Center for Atmospheric Research [Boulder] (NCAR), Potsdam Institute for Climate Impact Research (PIK), Climate Change Unit [Ispra], JRC Institute for Environment and Sustainability (IES), European Commission - Joint Research Centre [Ispra] (JRC)-European Commission - Joint Research Centre [Ispra] (JRC), Department of Earth and Ecosystem Sciences [Lund], Lund University [Lund], Department of Animal and Plant Sciences [Sheffield], University of Sheffield [Sheffield], Universiteit Antwerpen = University of Antwerpen [Antwerpen], Centre d'Etude de la Forêt (Faculté de foresterie, de géographie et de géomatique), Université Laval [Québec] (ULaval), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Centro Agronómico Tropical de Investigación y Enseñanza - Tropical Agricultural Research and Higher Education Center (CATIE), Nature Conservation and Plant Ecology Group, Wageningen University and Research [Wageningen] (WUR), Modélisation des Surfaces et Interfaces Continentales (MOSAIC), Graduate School of Geography, Clark University, DIBAF, University of Tuscia, Via S.C. de Lellis, 01100 Viterbo, Italy, 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)-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), University of California [Berkeley], University of California-University of California, and Universiteit Antwerpen [Antwerpen]

Subjects
0106 biological sciences, 010504 meteorology & atmospheric sciences, Climate, Flux, forêt tropicale, Atmospheric sciences, 7. Clean energy, 01 natural sciences, Trees, FluxNet, K01 - Foresterie - Considérations générales, SDG 13 - Climate Action, Photosynthesis, Savane, database, Multidisciplinary, Geography, Ecology, U10 - Informatique, mathématiques et statistiques, Temperature, Uncertainty, Biosphere, Vegetation, Plants, PE&RC, products, [SDE]Environmental Sciences, Plantenecologie en Natuurbeheer, P01 - Conservation de la nature et ressources foncières, ecosystems, Engineering sciences. Technology, Écosystème, biosphere, P40 - Météorologie et climatologie, Eddy covariance, Climatic Processes, Plant Ecology and Nature Conservation, 010603 evolutionary biology, Models, Biological, Oxygen Consumption, Artificial Intelligence, vegetation, fraction, Ecosystem, Precipitation, 0105 earth and related environmental sciences, Changement climatique, atmospheric co2, Models, Statistical, WIMEK, sulfide, Atmosphere, Primary production, Water, Modèle de simulation, 15. Life on land, Carbon Dioxide, Végétation, Plant Leaves, radiation, 13. Climate action, network, Neural Networks, Computer, Cycle du carbone, Dioxyde de carbone
Abstract

Terrestrial gross primary production (GPP) is the largest global CO2 °ux driving several ecosystem functions. We provide an observation- based estimate of this °ux at 123§8 PgCa¡1 using eddy covariance °ux data and various diagnostic models. Tropical forests and sa- vannahs account for 60%. GPP over 40% of the vegetated land is associated with precipitation. State-of-the-art process-oriented bio- sphere models used for climate predictions exhibit a large between- model variation of GPP's latitudinal patterns and show higher spa- tial correlations between GPP and precipitation, suggesting the ex- istence of missing processes or feedback mechanisms which attenu- ate the vegetation response to climate. Our estimates of spatially distributed GPP and its co-variation with climate can help improve coupled climate-carbon cycle process models., JRC.H.2-Air and Climate

Published
2010
Full Text
View/download PDF

2. Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks

Author

Gustau Camps-Valls, G. Tramontana, Trevor F. Keenan, Markus Reichstein, Dario Papale, Mirco Migliavacca, Jérôme Ogée, Martin Jung, Jochem Verrelst, Department for Innovation in Biological, Agro-Food and Forest Systems, University of Tuscia, Department of Biogeochemical Integration [Jena], Max Planck Institute for Biogeochemistry (MPI-BGC), Max-Planck-Gesellschaft-Max-Planck-Gesellschaft, Max-Planck-Gesellschaft, Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Universitat de València (UV), 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), Image Processing Laboratory (IPL), and DIBAF, University of Tuscia, Via S.C. de Lellis, 01100 Viterbo, Italy

Subjects
0106 biological sciences, ecosystem respiration, 010504 meteorology & atmospheric sciences, net ecosystem exchange, neural network, Eddy covariance, Climate change, Atmospheric sciences, Photosynthesis, 01 natural sciences, 7. Clean energy, Carbon Cycle, Atmosphere, Flux (metallurgy), FluxNet, Respiration, eddy covariance, Environmental Chemistry, Ecosystem, Primary Research Article, 0105 earth and related environmental sciences, General Environmental Science, Global and Planetary Change, Ecology, carbon dioxide fluxes partitioning, gross primary production (GPP), Carbon Dioxide, Biological Sciences, 15. Life on land, gross primary production, machine learning, 13. Climate action, [SDE]Environmental Sciences, Environmental science, Neural Networks, Computer, Seasons, ecosystem respiration (RECO), Environmental Sciences, 010606 plant biology & botany
Abstract

The eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO2 between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO2 release due to all respiration processes (RECO), and the gross carbon uptake by photosynthesis (GPP). These two gross CO2 fluxes are derived from EC measurements by applying partitioning methods that rely on physiologically based functional relationships with a limited number of environmental drivers. However, the partitioning methods applied in the global FLUXNET network of EC observations do not account for the multiple co‐acting factors that modulate GPP and RECO flux dynamics. To overcome this limitation, we developed a hybrid data‐driven approach based on combined neural networks (NNC‐part). NNC‐part incorporates process knowledge by introducing a photosynthetic response based on the light‐use efficiency (LUE) concept, and uses a comprehensive dataset of soil and micrometeorological variables as fluxes drivers. We applied the method to 36 sites from the FLUXNET2015 dataset and found a high consistency in the results with those derived from other standard partitioning methods for both GPP (R 2 > .94) and RECO (R 2 > .8). High consistency was also found for (a) the diurnal and seasonal patterns of fluxes and (b) the ecosystem functional responses. NNC‐part performed more realistic than the traditional methods for predicting additional patterns of gross CO2 fluxes, such as: (a) the GPP response to VPD, (b) direct effects of air temperature on GPP dynamics, (c) hysteresis in the diel cycle of gross CO2 fluxes, (d) the sensitivity of LUE to the diffuse to direct radiation ratio, and (e) the post rain respiration pulse after a long dry period. In conclusion, NNC‐part is a valid data‐driven approach to provide GPP and RECO estimates and complementary to the existing partitioning methods., In this paper, we present an innovative neural network (NNC‐part) for partitioning net ecosystem exchange of CO2, measured by eddy covariance over land ecosystems, into ecosystem respiration (RECO) and gross primary production (GPP). NNC‐part has a hybrid nature for assimilating the light‐use efficiency (LUE) concept. The GPP and RECO produced by NNC‐part resulted consistent with estimates provided in FLUXNET2015 and patterns of fluxes very realistic, being the method able to reproduce: the GPP and RECO response to meteorological forces; hysteresis in the diel cycle of fluxes; effect of diffuse to direct radiation ratio on LUE, respiration pulse.

Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results