WetCH<sub>4</sub>: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022
<p>Wetlands are the largest natural source of methane (CH<span class="inline-formula"><sub>4</sub></span>) emissions globally. Northern wetlands (<span class="inline-formula">>45</span>° N), accounting for 42 % of global wetland ar...
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-06-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/17/2507/2025/essd-17-2507-2025.pdf |
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| Summary: | <p>Wetlands are the largest natural source of methane (CH<span class="inline-formula"><sub>4</sub></span>) emissions globally. Northern wetlands (<span class="inline-formula">>45</span>° N), accounting for 42 % of global wetland area, are increasingly vulnerable to carbon loss, especially as CH<span class="inline-formula"><sub>4</sub></span> emissions may accelerate under intensified high-latitude warming. However, the magnitude and spatial patterns of high-latitude CH<span class="inline-formula"><sub>4</sub></span> emissions remain relatively uncertain. Here, we present estimates of daily CH<span class="inline-formula"><sub>4</sub></span> fluxes obtained using a new machine learning-based wetland CH<span class="inline-formula"><sub>4</sub></span> upscaling framework (WetCH<span class="inline-formula"><sub>4</sub></span>) that combines the most complete database of eddy-covariance (EC) observations available to date with satellite remote-sensing-informed observations of environmental conditions at 10 km resolution. The most important predictor<span id="page2508"/> variables included near-surface soil temperatures (top 40 cm), vegetation spectral reflectance, and soil moisture. Our results, modeled from 138 site years across 26 sites, had relatively strong predictive skill, with a mean <span class="inline-formula"><i>R</i><sup>2</sup></span> of 0.51 and 0.70 and a mean absolute error (MAE) of 30 and 27 nmol m<span class="inline-formula"><sup>−2</sup></span> s<span class="inline-formula"><sup>−1</sup></span> for daily and monthly fluxes, respectively. Based on the model results, we estimated an annual average of <span class="inline-formula">22.8±2.4</span> Tg CH<span class="inline-formula"><sub>4</sub></span> yr<span class="inline-formula"><sup>−1</sup></span> for the northern wetland region (2016–2022), and total budgets ranged from 15.7 to 51.6 Tg CH<span class="inline-formula"><sub>4</sub></span> yr<span class="inline-formula"><sup>−1</sup></span>, depending on wetland map extents. Although 88 % of the estimated CH<span class="inline-formula"><sub>4</sub></span> budget occurred during the May–October period, a considerable amount (<span class="inline-formula">2.6±0.3</span> Tg CH<span class="inline-formula"><sub>4</sub></span>) occurred during winter. Regionally, the Western Siberian wetlands accounted for a majority (51 %) of the interannual variation in domain CH<span class="inline-formula"><sub>4</sub></span> emissions. Overall, our results provide valuable new high-spatiotemporal-resolution information on the wetland emissions in the high-latitude carbon cycle. However, many key uncertainties remain, including those driven by wetland extent maps and soil moisture products and the incomplete spatial and temporal representativeness in the existing CH<span class="inline-formula"><sub>4</sub></span> flux database; e.g., only 23 % of the sites operate outside of summer months, and flux towers do not exist or are greatly limited in many wetland regions. These uncertainties will need to be addressed by the science community to remove the bottlenecks currently limiting progress in CH<span class="inline-formula"><sub>4</sub></span> detection and monitoring. The dataset can be found at <a href="https://doi.org/10.5281/zenodo.10802153">https://doi.org/10.5281/zenodo.10802153</a> (Ying et al., 2024).</p> |
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| ISSN: | 1866-3508 1866-3516 |