Toward Robust Parameterizations in Ecosystem-Level Photosynthesis Models

Detalhes bibliográficos
Autor(a) principal: Bao, Shanning
Data de Publicação: 2023
Outros Autores: Alonso, Lazaro, Wang, Siyuan, Gensheimer, Johannes, De, Ranit, Carvalhais, Nuno
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.
id RCAP_b95df374841ea3b92ddac7f89a096356
oai_identifier_str oai:run.unl.pt:10362/159842
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling 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
repository.mail.fl_str_mv
_version_ 1799138159550791680

Registros relacionados