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Doktorsavhandling2025Öppen tillgång

Methane emissions from high latitude peatlands : controls of their spatio-temporal dynamics across a mire complex

Noumonvi, Koffi Dodji

Sammanfattning

Northern peatlands are significant natural sources of atmospheric methane (CH4), yet high uncertainties remain in estimates of global CH4 budgets. A key source of this uncertainty is a limited understanding of spatial variations at the mesoscale of peatland complexes. Current estimates typically rely on single-site measurements at the local scale. This thesis utilized the Kulbäcksliden Research Infrastructure (KRI) in northern Sweden, comprising four eddy covariance tower stations across a peatland complex, with the overall aim to investigate and predict mesoscale variations in CH4 fluxes (FCH4). The research first documented the infrastructure's unique configuration with replicated ecosystem-scale measurements and detailed mapping of vegetation (Paper I), then developed the Hummock-Hollow-Lawn (HuHoLa) model for peatland microtopography classification (Paper II). Using data from the KRI, spatial patterns in FCH4 across the mire complex and their environmental controls were investigated (Paper III), followed by the development of data-driven approaches for upscaling single-site measurements (Paper IV). Key findings demonstrated that replicated ecosystem-scale measurements are essential for understanding spatial heterogeneity in peatland processes and developing upscaling approaches. The HuHoLa model advanced peatland surface characterization beyond traditional binary classifications by identifying three distinct microform classes and providing proxies for mapping water table depth and soil temperature (Ts). Spatial variability in FCH4 within the mire complex matched that typically observed among geographically distant mire systems, with C:N ratio setting the baseline for spatial variations while Ts and plant productivity controlled temporal dynamics. The developed upscaling approaches reduced uncertainty in mire complex FCH4 estimates by up to 50% compared to simple single-site extrapolation. These advances provide new understanding and tools for reducing uncertainties in global CH4 budget estimates, crucial for predicting peatland-climate feedbacks under changing environmental conditions.

Nyckelord

methane flux; eddy covariance; peatland; microtopography; upscaling; machine learning; C:N ratio; footprint analysis; research infrastructure

Publicerad i

Acta Universitatis Agriculturae Sueciae
2025, nummer: 2025:13
Utgivare: Swedish University of Agricultural Sciences

SLU författare

UKÄ forskningsämne

Ekologi
Markvetenskap

Publikationens identifierare

  • DOI: https://doi.org/10.54612/a.7rle58mqf6
  • ISBN: 978-91-8046-448-2
  • eISBN: 978-91-8046-498-7

Permanent länk till denna sida (URI)

https://res.slu.se/id/publ/132953