Improving global gross primary productivity estimation by fusing multi-source data products

Zhang, Yahai; Ye, Aizhong

doi:10.1016/j.heliyon.2022.e09153

Cited by 9 publications

(6 citation statements)

References 88 publications

(105 reference statements)

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“…We showed that the incorporation of CI reduced the discrepancy between the modeled GPP and that of two benchmarking datasets. This indicates the potential importance of CI, although does not imply that CI necessarily improves GPP estimation because we could get the correct answer with wrong reasons due to the uncertainty of the model itself, and the benchmarking datasets (and other global GPP datasets) do not necessarily represent the truth as large uncertainties remain especially in tropical regions (Xie et al, 2020 ; Zhang & Ye, 2022 ) as well. Therefore, further comprehensive validation of the benefit of incorporating CI for GPP magnitude, seasonal variation, long‐term interannual variations, and even related causal structures (Li, Zhu, et al, 2022 ; Runge et al, 2019 ; Yuan, Zhu, Li, et al, 2022 ; Yuan, Zhu, Riley, et al, 2022 ; Yuan et al, 2021 ) is needed when more reliable global GPP and CI data are available at the same spatial scale.…”

Section: Discussionmentioning

confidence: 99%

Vegetation clumping modulates global photosynthesis through adjusting canopy light environment

Hao

Zhu

et al. 2022

Global Change Biology

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The spatial dispersion of photoelements within a vegetation canopy, quantified by the clumping index (CI), directly regulates the within-canopy light environment and photosynthesis rate, but is not commonly implemented in terrestrial biosphere models to estimate the ecosystem carbon cycle. A few global CI products have been developed recently with remote sensing measurements, making it possible to examine the global impacts of CI. This study deployed CI in the radiative transfer scheme of the Community Land Model version 5 (CLM5) and used the revised CLM5 to quantitatively evaluate the extent to which CI can affect canopy absorbed radiation and gross primary production (GPP), and for the first time, considering the uncertainty and seasonal variation of CI with multiple remote sensing products. Compared to the results without considering the CI impact, the revised CLM5 estimated that sunlit canopy absorbed up to 9%-15% and 23%-34% less direct and diffuse radiation, respectively, while shaded canopy absorbed 3%-18% more diffuse radiation across different biome types. The CI impacts on canopy light conditions included changes in canopy light absorption, and sunlit-shaded leaf area fraction related to nitrogen distribution and thus the maximum rate of Rubisco carboxylase activity (V cmax ), which together decreased photosynthesis in sunlit canopy by 5.9-7.2 PgC year −1 while enhanced photosynthesis by 6.9-8.2 PgC year −1 in shaded canopy. With higher light use efficiency of shaded leaves, shaded canopy increased photosynthesis compensated and exceeded the lost

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Section: Discussionmentioning

confidence: 99%

Vegetation clumping modulates global photosynthesis through adjusting canopy light environment

Hao

Zhu

et al. 2022

Global Change Biology

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“…Because Bisley does not have established flux towers, we use different global data products to benchmark ELM-FATES at the monthly time scale; all the data sets have different spatial resolutions (Table S5 in Supporting Information S1). The comparison shows limited consistency between data sets (Figure 5), implying large uncertainties (Anav et al, 2015;Sriwongsitanon et al, 2020;Zhang & Ye, 2022). Rather than perform product comparison and generating weighted GPP (e.g., Zhang & Ye, 2022) and ET products, we use the arithmetic mean of different data products.…”

Section: Using the Global Data Sets To Benchmark Elm-fates At Bisleymentioning

confidence: 99%

“…The comparison shows limited consistency between data sets (Figure 5), implying large uncertainties (Anav et al, 2015;Sriwongsitanon et al, 2020;Zhang & Ye, 2022). Rather than perform product comparison and generating weighted GPP (e.g., Zhang & Ye, 2022) and ET products, we use the arithmetic mean of different data products. We show that the GPP and ET seasonality of ELM-FATES is in good agreement with that represented by the data products.…”

Section: Using the Global Data Sets To Benchmark Elm-fates At Bisleymentioning

confidence: 99%

Functionally Assembled Terrestrial Ecosystem Simulator (FATES) for Hurricane Disturbance and Recovery

Shi,

Keller,

Bomfim

et al. 2024

J Adv Model Earth Syst

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Tropical cyclones are an important cause of forest disturbance, and major storms caused severe structural damage and elevated tree mortality in coastal tropical forests. Model capabilities that can be used to understand post‐hurricane forest recovery are still limited. We use a vegetation demography model, the Functionally Assembled Terrestrial Ecosystem Simulator, coupled with the Energy Exascale Earth System Model Land Model (ELM‐FATES) to study the processes and the key factors regulating post‐hurricane forest recovery. We implemented hurricane‐induced forest damage, including defoliation, structural biomass reduction, and tree mortality, performed ensemble model simulations, and used random forest feature importance. For the simulation in the Luquillo Experimental Forest, Puerto Rico, we identified factors controlling the post‐hurricane forest recovery, and quantified the sensitivity of key model parameters to the post‐hurricane forest recovery. The results indicate a tendency for the Bisley forests to shift toward the light demanding plant functional type (PFT) when the pre‐hurricane biomass between the light demanding and shade tolerant PFTs is nearly equal and forests experience hurricane disturbance with mortality >60% for both the two PFTs. Under more realistic conditions where the shade tolerant PFT is initially dominant, mortality >80% is required for a shift toward dominance of the light demanding PFT at Bisley. Hurricane mortality and background mortality are the two major factors regulating post‐hurricane forest recovery in simulations. This research improves understanding of the ELM‐FATES model behavior associated with hurricane disturbance and provides guidance for dynamic vegetation model development in representing hurricane induced forest damage with varied intensities.

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“…Candidate predictors covering climatic and biophysical variables such as vegetation type, observed temperature, precipitation, and radiation, as well as absorbed photosynthetically active radiation (FAPAR) fractions from satellites, are then used to drive the global model to derive gridded GPP estimates (Beer et al, 2010;Jung et al, 2009Jung et al, , 2011. Its uncertainties are mainly the measurement uncertainty of the eddy covariance flux, the uncertainty of the global predictor variables, and the sampling bias driven by the inhomogeneous distribution of eddy covariance flux sites, which are numerous in temperate regions and few in tropical regions (Zhang & Ye, 2022). Some GPP estimates lack consideration of the cumulative effect of soil moisture (Jung et al, 2020;Piao et al, 2013;Yao et al, 2018).…”

Section: Drivers Of Uncertainty In Gppmentioning

confidence: 99%

“…Additional supporting information can be found online in the Supporting Information section at the end of this article. Zhang, Y., & Ye, A. (2022).…”

Section: Ack N Owled G M Entsmentioning

confidence: 99%

Uncertainty analysis of multiple terrestrial gross primary productivity products

Zhang

2022

Global Ecol Biogeogr

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Aim: Gross primary productivity (GPP) of vegetation is an important component of the carbon balance of terrestrial ecosystems. With the development of science and technology, there has been a proliferation of multiple GPP products, yet large uncertainties exist in the global GPP estimated by different GPP products. The objectives of this study were to assess the uncertainty of 45 GPP products in 4 different categories (machine learning, Fluxnet, light use efficiency, land surface model) and to perform an attribution analysis.

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Improving global gross primary productivity estimation by fusing multi-source data products

Cited by 9 publications

References 88 publications

Vegetation clumping modulates global photosynthesis through adjusting canopy light environment

Vegetation clumping modulates global photosynthesis through adjusting canopy light environment

Functionally Assembled Terrestrial Ecosystem Simulator (FATES) for Hurricane Disturbance and Recovery

Uncertainty analysis of multiple terrestrial gross primary productivity products

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