Preprints
https://doi.org/10.5194/egusphere-2024-1082
https://doi.org/10.5194/egusphere-2024-1082
24 Apr 2024
 | 24 Apr 2024
Status: this preprint is open for discussion.

Constraining 2010–2020 Amazonian carbon flux estimates with satellite solar-induced fluorescence (SIF)

Archana Dayalu, Marikate Mountain, Bharat Rastogi, John B. Miller, and Luciana Gatti

Abstract. Amazonia’s Net Biome Exchange (NBE), the sum of biogenic and wildfire carbon fluxes, is a fundamental indicator of the state of its ecosystems. It also quantifies the magnitude and patterns of short- and long-term carbon dioxide sources and sinks but is poorly quantified and out of equilibrium (non-zero) due to both direct (deforestation) and indirect (climate-related) anthropogenic disturbance. Determining trends in Amazonia’s carbon balance, shifts in carbon exchange pathways of NBE, and timescales of ecosystem sensitivity to disturbance requires reliable biogenic flux models that adequately capture fluxes from diurnal to seasonal and annual timescales. Our study assimilates readily available observations and a derived solar-induced fluorescence (SIF) product to estimate hourly biogenic carbon dioxide (CO2) fluxes (here in units of mmol CO2 m-2 s-1) as Net Ecosystem Exchange (NEE), and its photosynthesis and respiration constituents, at 12 km resolution using four versions of the data-driven diagnostic Vegetation Photosynthesis and Respiration Model (VPRM). The VPRM versions are all calibrated with ground-based eddy flux data and vary based on whether (1) the photosynthesis term incorporates SIF (VPRM_SIF) or traditional surface reflectance (VPRM_TRA) and (2) the respiration term is modified beyond a simple linear air temperature dependence (VPRM_SIFg; VPRM_TRG). We compare the VPRM versions with each other and with hourly fluxes from the bottom-up mechanistic Simple Biosphere 4 (SiB4 v4.2) model. We also use NASA’s OCO-2 CO2 column observations to optimize the VPRM and SiB4 models during the 2016 wet season which occurred at the tail of the 2015/2016 severe El Niño. The wet season 2016 case study suggests that relative to SiB4 and the SIF-based VPRMs, the traditional VPRM versions can underestimate uptake by a factor of three. In addition, the VPRM_SIFg version better captures biogenic CO2 fluxes at hourly to seasonal scales than all other VPRM versions in both anomalously wet and anomalously dry conditions. We also find that the VPRM_SIFg model and the independent bottom-up mechanistic hourly SiB4 model converge in NEE, although there are differences in the partitioning of the photosynthesis and respiration components. We further note that VPRM_SIFg describes greater spatial heterogeneity in carbon exchange throughout the Amazon. Despite the paucity of OCO-2 CO2 column observations (XCO2) over the Amazon in the wet season, incorporating XCO2 into the models significantly reduces near-field model-measurement mismatch at aircraft vertical profiling locations. Finally, a qualitative analysis of the unoptimized biogenic models from 2010–2020 agrees with the wet season 2016 case study, where the traditional VPRM formulations significantly underestimate photosynthesis and respiration relative to VPRM-SIFg. Overall, the VPRM_SIFg biogenic flux model shows promise in its ability to capture Amazonian carbon fluxes across multiple timescale and moisture regimes, suggesting its suitability for larger studies evaluating interannual and seasonal carbon trends in fire as well as the biogenic components of the region’s NBE.

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Archana Dayalu, Marikate Mountain, Bharat Rastogi, John B. Miller, and Luciana Gatti

Status: open (until 07 Jun 2024)

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Archana Dayalu, Marikate Mountain, Bharat Rastogi, John B. Miller, and Luciana Gatti

Data sets

Supplementary Data for:Constraining 2010-2020 Amazonian carbon flux estimates with satellite solar-induced fluorescence (SIF) Archana Dayalu et al. https://doi.org/10.7910/DVN/PJ1EVC

Archana Dayalu, Marikate Mountain, Bharat Rastogi, John B. Miller, and Luciana Gatti

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Short summary
The Amazon is facing unprecedented disturbance. Determining trends in Amazonia’s carbon balance and its sensitivity to disturbance requires reliable vegetation models that adequately capture how its ecosystems exchange carbon with the atmosphere. Our work presents novel estimates of vegetation carbon exchange with the atmosphere across the Amazon, estimated using ground- and satellite-based ecosystem measurements. Our model agrees with independent aircraft observations from discrete locations.