Mortality as a key driver of the spatial distribution of aboveground biomass in Amazonian forest: results from a dynamic vegetation model

Delbart, Nicolas; Ciais, Philippe; Chave, Jérôme; Viovy, Nicolas; Malhi, Yadvinder; Toan, Thuy Le

doi:10.5194/bg-7-3027-2010

Cited by 73 publications

(82 citation statements)

References 41 publications

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“…4). Such relatively good agreement was also found in simulation runs from Delbart et al (2010) and Johnson et al (2016). Our results even yield relatively high similarity in observed and simulated spatial patterns of AGB at pixel scale (except LPJmL; c.f.…”

Section: How Well Do Model Simulations Represent Observed Biomass Patsupporting

confidence: 70%

A generic pixel-to-point comparison for simulated large-scale ecosystem properties and ground-based observations: an example from the Amazon region

Rammig¹,

Heinke²,

Hofhansl³

et al. 2018

Preprint

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Abstract.Comparing model output and observed data is an important step for assessing model performance and quality of simulation results. However, such comparisons are often hampered by differences in spatial scales between local point observations and large-scale simulations of grid-cells or pixels. In this study, we propose a generic approach for a pixel-topoint comparison that accounts for the uncertainty resulting from landscape variability and measurement errors in ecosystem 30 variables, and provide statistical measures. The basic concept of our approach is to determine the statistical properties of small-scale (within-pixel) variability and observational errors, and to use this information to correct for their effect when large-scale area averages (pixel) are compared to small-scale point estimates. We demonstrate our approach by comparing simulated values of aboveground biomass, woody productivity (woody net primary productivity, NPP) and residence time of woody biomass from four dynamic global vegetation models (DGVMs) with measured inventory data from permanent plots 35 in the Amazon rainforest, a region with the typical problem of low data availability, a scale mismatch and high model uncertainty. We find that the DGVMs under-and overestimate aboveground biomass by 25% and up to 60%, respectively.Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-182 Manuscript under review for journal Geosci. Model Dev. Discussion started: 27 July 2018 c Author(s) 2018. CC BY 4.0 License. 2Our comparison metrics provide a quantitative measure for model-data agreement and show moderate to good agreement with the region-wide spatial biomass pattern detected by plot observations. However, all four DGVMs overestimate woody productivity and underestimate residence time of woody biomass even when accounting for the large uncertainty range of the observational data. This is because DGVMs do not represent the relation between productivity and residence time of woody biomass correctly. Thus, the DGVMs may simulate the correct large-scale patterns of biomass but for the wrong 5 reasons. We conclude that more information about the underlying processes driving biomass distribution are necessary to improve DGVMs. Our approach provides robust statistical measures for any pixel-to-point comparison, which is applicable for evaluation of models and remote sensing products.

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Section: How Well Do Model Simulations Represent Observed Biomass Patsupporting

confidence: 70%

A generic pixel-to-point comparison for simulated large-scale ecosystem properties and ground-based observations: an example from the Amazon region

Rammig¹,

Heinke²,

Hofhansl³

et al. 2018

Preprint

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“…The fraction of carbon that is allocated daily to each carbon pool can be written as follows (Delbart et al, 2010):…”

Section: Net Primary Productivity and Carbon Allocationmentioning

confidence: 99%

Seasonal leaf dynamics for tropical evergreen forests in a process-based global ecosystem model

et al. 2012

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Abstract. The influence of seasonal phenology on canopy photosynthesis in tropical evergreen forests remains poorly understood, and its representation in global ecosystem models is highly simplified, typically with no seasonal variation of canopy leaf properties taken into account. Including seasonal variation in leaf age and photosynthetic capacity could improve the correspondence of global vegetation model outputs with the wet-dry season CO 2 patterns measured at flux tower sites in these forests. We introduced a leaf litterfall dynamics scheme in the global terrestrial ecosystem model ORCHIDEE based on seasonal variations in net primary production (NPP), resulting in higher leaf turnover in periods of high productivity. The modifications in the leaf litterfall scheme induce seasonal variation in leaf age distribution and photosynthetic capacity. We evaluated the results of the modification against seasonal patterns of three long-term in-situ leaf litterfall datasets of evergreen tropical forests in Panama, French Guiana and Brazil. In addition, we evaluated the impact of the model improvements on simulated latent heat (LE) and gross primary productivity (GPP) fluxes for the flux tower sites Guyaflux (French Guiana) and Tapajós (km 67, Brazil). The results show that the introduced seasonal leaf litterfall corresponds well with field inventory leaf litter data and times with its seasonality. Although the simulated litterfall improved substantially by the model modifications, the impact on the modelled fluxes remained limited. The seasonal pattern of GPP improved clearly for the Guyaflux site, but no significant improvement was obtained for the Tapajós site. The seasonal pattern of the modelled latent heat fluxes was hardly changed and remained consistent with the observed fluxes. We conclude that we introduced a realistic and generic litterfall dynamics scheme, but that other processes need to be improved in the model to achieve better simulations of GPP seasonal patterns for tropical evergreen forests.

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“…8b) are consistent with such a mechanism. While demographic feedbacks are not explicitly included in dynamic global vegetation models 10 , our results suggest that they could in fact influence the capacity of forests to gain biomass 28,29 , with transient rates of ecosystem net carbon accumulation highly sensitive to even small changes in carbon turnover times 10 .…”

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confidence: 99%

Long-term decline of the Amazon carbon sink

Brienen¹,

Phillips²,

Feldpausch³

et al. 2015

Nature

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830

882

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Atmospheric carbon dioxide records indicate that the land surface has acted as a strong global carbon sink over recent decades1, 2, with a substantial fraction of this sink probably located in the tropics3, particularly in the Amazon4. Nevertheless, it is unclear how the terrestrial carbon sink will evolve as climate and atmospheric composition continue to change. Here we analyse the historical evolution of the biomass dynamics of the Amazon rainforest over three decades using a distributed network of 321 plots. While this analysis confirms that Amazon forests have acted as a long-term net biomass sink, we find a long-term decreasing trend of carbon accumulation. Rates of net increase in above-ground biomass declined by one-third during the past decade compared to the 1990s. This is a consequence of growth rate increases levelling off recently, while biomass mortality persistently increased throughout, leading to a shortening of carbon residence times. Potential drivers for the mortality increase include greater climate variability, and feedbacks of faster growth on mortality, resulting in shortened tree longevity5. The observed decline of the Amazon sink diverges markedly from the recent increase in terrestrial carbon uptake at the global scale1, 2, and is contrary to expectations based on models. (Résumé d'auteur

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Mortality as a key driver of the spatial distribution of aboveground biomass in Amazonian forest: results from a dynamic vegetation model

Cited by 73 publications

References 41 publications

A generic pixel-to-point comparison for simulated large-scale ecosystem properties and ground-based observations: an example from the Amazon region

A generic pixel-to-point comparison for simulated large-scale ecosystem properties and ground-based observations: an example from the Amazon region

Seasonal leaf dynamics for tropical evergreen forests in a process-based global ecosystem model

Long-term decline of the Amazon carbon sink

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