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Record W4250149077 · doi:10.5194/bg-2017-150

Coupled eco-hydrology and biogeochemistry algorithms enable simulation of water table depth effects on boreal peatland net CO <sub>2</sub> exchange

2017· preprint· en· W4250149077 on OpenAlexaffabout
Symon Mezbahuddin, R. F. Grant, Lawrence B. Flanagan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversity of LethbridgeUniversity of Alberta
Fundersnot available
KeywordsPeatPrimary productionEnvironmental scienceBiogeochemistryEvapotranspirationWater tableBorealHydrology (agriculture)EcosystemSoil scienceEcologyGroundwaterGeologyBiology

Abstract

fetched live from OpenAlex

Abstract. Water table depth (WTD) effects on net ecosystem CO2 exchange of boreal peatlands are largely mediated by hydrological effects on peat biogeochemistry, and eco-physiology of peatland vegetation. Lack of representation of these effects in carbon models currently limits our predictive capacity for changes in boreal peatland carbon deposits under future drier and warmer climates. We therefore tested whether the effects of WTD variation on net ecosystem CO2 exchange of a Western Canadian boreal fen peatland could be modelled through a process-level coupling of a prognostic WTD dynamic, which arises from equilibrium between vertical and lateral water fluxes, with oxygen transport, which controls energy yields from microbial and root oxidation–reduction reactions, and vascular and non-vascular plant water relations in an ecosystem model ecosys. Ecosys successfully simulated a May–October WTD drawdown by ~ 0.25 m measured in the fen from 2004 to 2008, which was attributed to reduced precipitation relative to evapotranspiration, and reduced lateral recharge relative to discharge. This WTD drawdown hastened oxygen transport to microbial and root surfaces, enabling greater microbial and root energy yields, and peat and litter decomposition, which raised modelled ecosystem respiration (Re) by ~ 0.26 μmol CO2 m−2 s−1 per 0.1 m of WTD drawdown. It also augmented nutrient mineralization, and hence root nutrient availability and uptake, which resulted in improved leaf nutrient (nitrogen) status that facilitated carboxylation, and raised modelled vascular gross primary productivity (GPP) and plant growth. The increase in modelled vascular GPP exceeded declines in modelled non-vascular (moss) GPP due to greater shading from increased vascular plant growth, and moss drying from near surface peat desiccation, thereby causing a net increase in modelled growing season GPP by ~ 0.39 μmol CO2 m−2 s−1 per 0.1 m of WTD drawdown. Similar increases in GPP and Re left no significant WTD effects on modelled seasonal and interannual variations in net ecosystem productivity (NEP). These modelled trends were corroborated against eddy covariance hourly net CO2 fluxes (modelled vs. measured: R2 ~ 0.8, slopes ~ 1 ± 0.1, intercepts ~ 0.05 μmol m−2 s−1), and against other automated chamber, biometric, and laboratory measurements. Modelled drainage as an analog for climate change showed that this boreal peatland would switch from a large carbon sink (NEP ~ 160 g C m−2 yr−1) to carbon neutrality (NEP ~ 10 g C m−2 yr−1) should water table deepened by a further ~ 0.5 m. Therefore, representing the effects of interactions among hydrology, biogeochemistry and plant physiological ecology on ecosystem carbon, water, and nutrient cycling in global carbon models would improve our predictive capacity for changes in boreal peatland carbon sequestration under changing climates.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score0.402

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.237
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2017
Admission routes2
Has abstractyes

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