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- Beckebanze, Lutz, et al.
(författare)
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Lateral carbon export has low impact on the net ecosystem carbon balance of a polygonal tundra catchment
- 2022
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Ingår i: Biogeosciences. - : Copernicus Publications. - 1726-4170 .- 1726-4189. ; 19:16, s. 3863-3876
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Tidskriftsartikel (refereegranskat)abstract
- Permafrost-affected soils contain large quantities of soil organic carbon (SOC). Changes in the SOC pool of a particular ecosystem can be related to its net ecosystem carbon balance (NECB) in which the balance of carbon (C) influxes and effluxes is expressed. For polygonal tundra landscapes, accounts of ecosystem carbon balances in the literature are often solely based on estimates of vertical carbon fluxes. To fill this gap, we present data regarding the lateral export rates of dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC) from a polygonal tundra site in the north Siberian Lena River delta, Russia. We use water discharge observations in combination with concentration measurements of waterborne carbon to derive the lateral carbon fluxes from one growing season (2 June–8 September 2014 for DOC, 8 June–8 September 2014 for DIC). To put the lateral C fluxes into context, we furthermore present the surface–atmosphere eddy covariance fluxes of carbon dioxide (CO2) and methane (CH4) from this study site. The results show cumulative lateral DIC and DOC fluxes of 0.31–0.38 and 0.06–0.08 g m−2, respectively, during the 93 d observation period (8 June–8 September 2014). Vertical turbulent fluxes of CO2-C and CH4-C accumulated to −19.0 ± 1.2 and 1.0 ± 0.02 g m−2 in the same period. Thus, the lateral C export represented about 2 % of the net ecosystem exchange of (NEE) CO2. However, the relationship between lateral and surface–atmosphere fluxes changed over the observation period. At the beginning of the growing season (early June), the lateral C flux outpaced the surface-directed net vertical turbulent CO2 flux, causing the polygonal tundra landscape to be a net carbon source during this time of the year. Later in the growing season, the vertical turbulent CO2 flux dominated the NECB.
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| 2. |
- Yuan, Kunxiaojia, et al.
(författare)
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Causality guided machine learning model on wetland CH4 emissions across global wetlands
- 2022
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Ingår i: Agricultural and Forest Meteorology. - : Elsevier. - 0168-1923 .- 1873-2240. ; 324
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Tidskriftsartikel (refereegranskat)abstract
- Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub -seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH(4 )emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models.
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