View in EDS HTML Full Text PDF Full Text

Long-term Simulation of Gross Primary Productivity and its Impact Evaluation Based on a Mechanism and Data Co-Driven Model.

Saved in:
Bibliographic Details
Title: Long-term Simulation of Gross Primary Productivity and its Impact Evaluation Based on a Mechanism and Data Co-Driven Model.
Authors: Zhang, Xiaojing1 (AUTHOR), Yinglan, A.2 (AUTHOR), Wang, Guoqiang2,3 (AUTHOR) wanggq@bnu.edu.cn, Wang, Yuntao2 (AUTHOR), Shi, Min4 (AUTHOR), Yao, Jiping5 (AUTHOR), Fang, Qingqing6 (AUTHOR), Wang, Libo2 (AUTHOR), Ma, Guangwen7 (AUTHOR)
Source: Water Resources Management. Sep2025, Vol. 39 Issue 12, p6027-6052. 26p.
Subject Terms: *Carbon cycle, *Climate change, *Deep learning, *Biomass production, *Ecological models, *Data assimilation, *Restoration ecology, *Ecosystem dynamics
Abstract: Terrestrial Gross Primary Productivity (GPP) serves as a fundamental metric in carbon cycle investigations, offering insights into the health and resilience of terrestrial ecosystems. Long-term, high-precision GPP data facilitates comprehensive investigations into ecosystem dynamics and sustainability under the impacts of climate change and human activities. Here, we utilized the Noah-MP model, which simultaneously assimilates soil moisture data and Solar-Induced Chlorophyll Fluorescence (SIF) data, to simulate GPP. However, the estimation of long-term (1982–2020) GPP in semi-arid regions was constrained by the lack of SIF data prior to 2000. Therefore, this study synergistically integrated a mechanistic model (Noah-MP) and a data-driven model (Unet model) to derive long-term GPP. The synergy between these two approaches addressed their respective limitations, achieving faster computational speeds and higher simulation accuracy. Results demonstrated that data assimilation techniques effectively enhanced the simulation accuracy of the mechanistic model, and the Unet model incorporating attention mechanisms (Attention-Unet model) better reconstructed the spatiotemporal GPP data from 1982 to 2020. Based on the simulations, ecological restoration projects significantly improved regional GPP, exhibiting distinct seasonality and zonality. However, influenced by climate change, the carbon sequestration capacity of natural vegetation zones experienced a marked decline in 2008, while human activities exerted significant positive impacts on agricultural areas (e.g., Hetao Irrigation District), offsetting some climate change effects in these regions. Although ecological restoration projects enhanced vegetation carbon sequestration at annual scales, this upward trend did not steadily persist with the advancement of ecological engineering initiatives. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: enr
DbLabel: Energy & Power Source
An: 188452518
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Long-term Simulation of Gross Primary Productivity and its Impact Evaluation Based on a Mechanism and Data Co-Driven Model.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Xiaojing%22">Zhang, Xiaojing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yinglan%2C+A%2E%22">Yinglan, A.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Guoqiang%22">Wang, Guoqiang</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> wanggq@bnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Yuntao%22">Wang, Yuntao</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shi%2C+Min%22">Shi, Min</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yao%2C+Jiping%22">Yao, Jiping</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fang%2C+Qingqing%22">Fang, Qingqing</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Libo%22">Wang, Libo</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ma%2C+Guangwen%22">Ma, Guangwen</searchLink><relatesTo>7</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Water+Resources+Management%22">Water Resources Management</searchLink>. Sep2025, Vol. 39 Issue 12, p6027-6052. 26p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Carbon+cycle%22">Carbon cycle</searchLink><br />*<searchLink fieldCode="DE" term="%22Climate+change%22">Climate change</searchLink><br />*<searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Biomass+production%22">Biomass production</searchLink><br />*<searchLink fieldCode="DE" term="%22Ecological+models%22">Ecological models</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+assimilation%22">Data assimilation</searchLink><br />*<searchLink fieldCode="DE" term="%22Restoration+ecology%22">Restoration ecology</searchLink><br />*<searchLink fieldCode="DE" term="%22Ecosystem+dynamics%22">Ecosystem dynamics</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Terrestrial Gross Primary Productivity (GPP) serves as a fundamental metric in carbon cycle investigations, offering insights into the health and resilience of terrestrial ecosystems. Long-term, high-precision GPP data facilitates comprehensive investigations into ecosystem dynamics and sustainability under the impacts of climate change and human activities. Here, we utilized the Noah-MP model, which simultaneously assimilates soil moisture data and Solar-Induced Chlorophyll Fluorescence (SIF) data, to simulate GPP. However, the estimation of long-term (1982–2020) GPP in semi-arid regions was constrained by the lack of SIF data prior to 2000. Therefore, this study synergistically integrated a mechanistic model (Noah-MP) and a data-driven model (Unet model) to derive long-term GPP. The synergy between these two approaches addressed their respective limitations, achieving faster computational speeds and higher simulation accuracy. Results demonstrated that data assimilation techniques effectively enhanced the simulation accuracy of the mechanistic model, and the Unet model incorporating attention mechanisms (Attention-Unet model) better reconstructed the spatiotemporal GPP data from 1982 to 2020. Based on the simulations, ecological restoration projects significantly improved regional GPP, exhibiting distinct seasonality and zonality. However, influenced by climate change, the carbon sequestration capacity of natural vegetation zones experienced a marked decline in 2008, while human activities exerted significant positive impacts on agricultural areas (e.g., Hetao Irrigation District), offsetting some climate change effects in these regions. Although ecological restoration projects enhanced vegetation carbon sequestration at annual scales, this upward trend did not steadily persist with the advancement of ecological engineering initiatives. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=188452518
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s11269-025-04239-x
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 26
        StartPage: 6027
    Subjects:
      – SubjectFull: Carbon cycle
        Type: general
      – SubjectFull: Climate change
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Biomass production
        Type: general
      – SubjectFull: Ecological models
        Type: general
      – SubjectFull: Data assimilation
        Type: general
      – SubjectFull: Restoration ecology
        Type: general
      – SubjectFull: Ecosystem dynamics
        Type: general
    Titles:
      – TitleFull: Long-term Simulation of Gross Primary Productivity and its Impact Evaluation Based on a Mechanism and Data Co-Driven Model.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Zhang, Xiaojing
      – PersonEntity:
          Name:
            NameFull: Yinglan, A.
      – PersonEntity:
          Name:
            NameFull: Wang, Guoqiang
      – PersonEntity:
          Name:
            NameFull: Wang, Yuntao
      – PersonEntity:
          Name:
            NameFull: Shi, Min
      – PersonEntity:
          Name:
            NameFull: Yao, Jiping
      – PersonEntity:
          Name:
            NameFull: Fang, Qingqing
      – PersonEntity:
          Name:
            NameFull: Wang, Libo
      – PersonEntity:
          Name:
            NameFull: Ma, Guangwen
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 15
              M: 09
              Text: Sep2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 09204741
          Numbering:
            – Type: volume
              Value: 39
            – Type: issue
              Value: 12
          Titles:
            – TitleFull: Water Resources Management
              Type: main
ResultId 1