Toward Robust Parameterizations in Ecosystem-Level Photosynthesis Models
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/159842 |
Resumo: | Funding Information: This work has received funding from the Youth Innovation Subject 2022 of National Space Science Center, Chinese Academy of Sciences, Sino-German (CSC-DAAD) Postdoc Scholarship Program, and from the European Space Agency (ESA) via the Earth System Data Lab (ESDL). This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, and USCCC. The FLUXNET eddy covariance data processing and harmonization was carried out by the ICOS Ecosystem Thematic Center, AmeriFlux Management Project and Fluxdata project of FLUXNET, with the support of CDIAC, and the OzFlux, ChinaFlux and AsiaFlux offices. Publisher Copyright: © 2023 The Authors. Journal of Advances in Modeling Earth Systems published by Wiley Periodicals LLC on behalf of American Geophysical Union. |
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Toward Robust Parameterizations in Ecosystem-Level Photosynthesis Modelsecosystem propertiesfeature importancehybrid modelparameter extrapolationparameterizationvegetation productivityGlobal and Planetary ChangeEnvironmental ChemistryEarth and Planetary Sciences(all)SDG 13 - Climate ActionFunding Information: This work has received funding from the Youth Innovation Subject 2022 of National Space Science Center, Chinese Academy of Sciences, Sino-German (CSC-DAAD) Postdoc Scholarship Program, and from the European Space Agency (ESA) via the Earth System Data Lab (ESDL). This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, and USCCC. The FLUXNET eddy covariance data processing and harmonization was carried out by the ICOS Ecosystem Thematic Center, AmeriFlux Management Project and Fluxdata project of FLUXNET, with the support of CDIAC, and the OzFlux, ChinaFlux and AsiaFlux offices. Publisher Copyright: © 2023 The Authors. Journal of Advances in Modeling Earth Systems published by Wiley Periodicals LLC on behalf of American Geophysical Union.In a model simulating dynamics of a system, parameters can represent system sensitivities and unresolved processes, therefore affecting model accuracy and uncertainty. Taking a light use efficiency (LUE) model as an example, which is a typical approach for estimating gross primary productivity (GPP), we propose a Simultaneous Parameter Inversion and Extrapolation approach (SPIE) to overcome issues stemming from plant-functional-type (PFT)-dependent parameterizations. SPIE refers to predicting model parameters using an artificial neural network based on collected variables, including PFT, climate types, bioclimatic variables, vegetation features, atmospheric nitrogen and phosphorus deposition, and soil properties. The neural network was optimized to minimize GPP errors and constrain LUE model sensitivity functions. We compared SPIE with 11 typical parameter extrapolating methods, including PFT- and climate-specific parameterizations, global and PFT-based parameter optimization, site-similarity, and regression approaches. All methods were assessed using Nash-Sutcliffe model efficiency (NSE), determination coefficient and normalized root mean squared error, and contrasted with site-specific calibrations. Ten-fold cross-validated results showed that SPIE had the best performance across sites, various temporal scales and assessing metrics. Taking site-level calibrations as a benchmark (NSE = 0.95), SPIE performed with an NSE of 0.68, while all the other investigated approaches showed negative NSE. The Shapley value, layer-wise relevance and partial dependence showed that vegetation features, bioclimatic variables, soil properties and some PFTs determine parameters. SPIE overcomes strong limitations observed in many standard parameterization methods. We argue that expanding SPIE to other models overcomes current limits and serves as an entry point to investigate the robustness and generalization of different models.DCEA - Departamento de Ciências e Engenharia do AmbienteRUNBao, ShanningAlonso, LazaroWang, SiyuanGensheimer, JohannesDe, RanitCarvalhais, Nuno2023-11-10T22:12:12Z2023-082023-08-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article21application/pdfhttp://hdl.handle.net/10362/159842eng1942-2466PURE: 75845646https://doi.org/10.1029/2022MS003464info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:42:22Zoai:run.unl.pt:10362/159842Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:57:44.949265Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Toward Robust Parameterizations in Ecosystem-Level Photosynthesis Models |
title |
Toward Robust Parameterizations in Ecosystem-Level Photosynthesis Models |
spellingShingle |
Toward Robust Parameterizations in Ecosystem-Level Photosynthesis Models Bao, Shanning ecosystem properties feature importance hybrid model parameter extrapolation parameterization vegetation productivity Global and Planetary Change Environmental Chemistry Earth and Planetary Sciences(all) SDG 13 - Climate Action |
title_short |
Toward Robust Parameterizations in Ecosystem-Level Photosynthesis Models |
title_full |
Toward Robust Parameterizations in Ecosystem-Level Photosynthesis Models |
title_fullStr |
Toward Robust Parameterizations in Ecosystem-Level Photosynthesis Models |
title_full_unstemmed |
Toward Robust Parameterizations in Ecosystem-Level Photosynthesis Models |
title_sort |
Toward Robust Parameterizations in Ecosystem-Level Photosynthesis Models |
author |
Bao, Shanning |
author_facet |
Bao, Shanning Alonso, Lazaro Wang, Siyuan Gensheimer, Johannes De, Ranit Carvalhais, Nuno |
author_role |
author |
author2 |
Alonso, Lazaro Wang, Siyuan Gensheimer, Johannes De, Ranit Carvalhais, Nuno |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
DCEA - Departamento de Ciências e Engenharia do Ambiente RUN |
dc.contributor.author.fl_str_mv |
Bao, Shanning Alonso, Lazaro Wang, Siyuan Gensheimer, Johannes De, Ranit Carvalhais, Nuno |
dc.subject.por.fl_str_mv |
ecosystem properties feature importance hybrid model parameter extrapolation parameterization vegetation productivity Global and Planetary Change Environmental Chemistry Earth and Planetary Sciences(all) SDG 13 - Climate Action |
topic |
ecosystem properties feature importance hybrid model parameter extrapolation parameterization vegetation productivity Global and Planetary Change Environmental Chemistry Earth and Planetary Sciences(all) SDG 13 - Climate Action |
description |
Funding Information: This work has received funding from the Youth Innovation Subject 2022 of National Space Science Center, Chinese Academy of Sciences, Sino-German (CSC-DAAD) Postdoc Scholarship Program, and from the European Space Agency (ESA) via the Earth System Data Lab (ESDL). This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, and USCCC. The FLUXNET eddy covariance data processing and harmonization was carried out by the ICOS Ecosystem Thematic Center, AmeriFlux Management Project and Fluxdata project of FLUXNET, with the support of CDIAC, and the OzFlux, ChinaFlux and AsiaFlux offices. Publisher Copyright: © 2023 The Authors. Journal of Advances in Modeling Earth Systems published by Wiley Periodicals LLC on behalf of American Geophysical Union. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-11-10T22:12:12Z 2023-08 2023-08-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/159842 |
url |
http://hdl.handle.net/10362/159842 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1942-2466 PURE: 75845646 https://doi.org/10.1029/2022MS003464 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
21 application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799138159550791680 |