the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methane fluxes from tropical wetlands of the Orinoco River Basin and their regional implications
Abstract. The Llanos del Orinoco, a vast tropical savanna floodplain in northern South America, plays a significant yet understudied role in the global methane (CH4) budget. This study synthesizes existing data to evaluate CH4 emissions from the region, highlighting the interplay between natural processes and anthropogenic influences. Top-down and bottom-up estimates for 2018 reveal annual CH4 emissions ranging from 3.27 ± 0.71 to 5.31 ± 2.50, with wetlands contributing 41–70 % of total fluxes. Seasonal variability follows precipitation patterns, with greater emissions occurring during the rainy season (April–October). However, discrepancies between global biogeochemical models and sparse field measurements underscore significant uncertainties, exacerbated by inconsistencies in inundation mapping and outdated local data. Anthropogenic activities, including oil extraction, livestock farming, and expanding rice cultivation, further modulate CH4 fluxes, though their impacts remain poorly quantified. Historical trends show declining precipitation and increasing temperatures, with models predicting more extreme weather events, potentially reducing wetland extent but favouring CH4 release during remaining inundated periods. Critical research gaps, including the need for updated field measurements, improved inundation mapping, and a better understanding of neglected habitats like peatlands and seasonal wetlands, are discussed. Addressing these gaps is essential for refining global and regional CH4 budgets and developing mitigation strategies. This work calls for integrated monitoring efforts to reconcile model disparities, assess land-use impacts, and predict responses to climate change, ensuring accurate representation of the Llanos del Orinoco in regional and global carbon cycle models.
- Preprint
(4725 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 28 Jun 2026)
- RC1: 'Comment on egusphere-2026-2080', Anonymous Referee #1, 22 May 2026 reply
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 458 | 101 | 23 | 582 | 28 | 27 |
- HTML: 458
- PDF: 101
- XML: 23
- Total: 582
- BibTeX: 28
- EndNote: 27
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
Methane emissions from the tropics play a significant role in the global methane cycle. While the Llanos del Orinoco represents a vast tropical savanna floodplain in northern South America, the sources, sinks, and processes driving regional methane dynamics remain understudied.
In this manuscript, the authors integrate top-down atmospheric inversion estimates (CAMS) and bottom-up process-based modeling (WetCHARTs, GCP-CH₄), together with a systematic compilation of sparse field measurements, to quantify regional CH₄ emissions and identify key uncertainties.
Overall, the manuscript is valuable and has the potential to advance our understanding of methane cycling and its uncertainty sources in the tropical Llanos del Orinoco region. I would recommend this manuscript for publication after addressing the following comments, which mainly relate to methodological clarification, uncertainties, and interpretation of results.
Main comments
“Databases associated with lakes and wetlands were analyzed at 1 km resolution”: Could the authors clarify the temporal resolution of each dataset used? In addition, more recent datasets are now available and could improve the analysis or could be discussed:
GLWD v2: Lehner et al. (2024). Mapping the world’s inland surface waters: an update to the Global Lakes and Wetlands Database (GLWD v2). Earth System Science Data.
GIEMS-MethaneCentric: Bernard et al. (2024). The GIEMS-MethaneCentric database: a dynamic and comprehensive global product of methane-emitting aquatic areas. Earth System Science Data.
2. Bottom-up and top-down models used: 1) It appears that only one top-down product (CAMS) was used. How are uncertainties across different top-down inversion systems accounted for? 2) For bottom-up estimates, the analysis seems to focus primarily on wetland CH₄ models. How are uncertainties from other sectors (e.g., lakes, rivers, livestock, oil and gas, soil uptake) incorporated? 3) For the GCP-CH₄ dataset, which specific models, and sectors were used in the analysis?
Minor comments