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Record W2608908776 · doi:10.5194/bg-14-5507-2017

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

2017· article· en· W2608908776 on OpenAlexafffundabout
Symon Mezbahuddin, R. F. Grant, Lawrence B. Flanagan

Bibliographic record

VenueBiogeosciences · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversity of LethbridgeUniversity of Alberta
FundersNatural Environment Research CouncilNatural Sciences and Engineering Research Council of CanadaSight Research UKCompute CanadaCanadian Foundation for Climate and Atmospheric SciencesWestern Canada Research GridUniversity of AlbertaBIOCAP Canada
KeywordsPeatBiogeochemistryPrimary productionEnvironmental scienceBorealWater tableHydrology (agriculture)Biogeochemical cycleEcosystemEcologyGeologyGroundwaterBiology

Abstract

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Abstract. Water table depth (WTD) effects on net ecosystem CO2 exchange of boreal peatlands are largely mediated by hydrological effects on peat biogeochemistry and the ecophysiology of peatland vegetation. The lack of representation of these effects in carbon models currently limits our predictive capacity for changes in boreal peatland carbon deposits under potential future drier and warmer climates. We examined whether a process-level coupling of a prognostic WTD with (1) oxygen transport, which controls energy yields from microbial and root oxidation–reduction reactions, and (2) vascular and nonvascular plant water relations could explain mechanisms that control variations in net CO2 exchange of a boreal fen under contrasting WTD conditions, i.e., shallow vs. deep WTD. Such coupling of eco-hydrology and biogeochemistry algorithms in a process-based ecosystem model, ecosys, was tested against net ecosystem CO2 exchange measurements in a western Canadian boreal fen peatland over a period of drier-weather-driven gradual WTD drawdown. A May–October WTD drawdown of ∼ 0.25 m from 2004 to 2009 hastened oxygen transport to microbial and root surfaces, enabling greater microbial and root energy yields and peat and litter decomposition, which raised modeled 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 modeled vascular gross primary productivity (GPP) and plant growth. The increase in modeled vascular GPP exceeded declines in modeled nonvascular (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 modeled 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 caused no significant WTD effects on modeled seasonal and interannual variations in net ecosystem productivity (NEP). These modeled trends were corroborated well by eddy covariance measured hourly net CO2 fluxes (modeled vs. measured: R2 ∼ 0.8, slopes ∼ 1 ± 0.1, intercepts ∼ 0.05 µmol m−2 s−1), hourly measured automated chamber net CO2 fluxes (modeled vs. measured: R2 ∼ 0.7, slopes ∼ 1 ± 0.1, intercepts ∼ 0.4 µmol m−2 s−1), and other biometric and laboratory measurements. Modeled drainage as an analog for WTD drawdown induced by climate-change-driven drying 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 the water table deepen by a further ∼ 0.5 m. This decline in projected NEP indicated that a further WTD drawdown at this fen would eventually lead to a decline in GPP due to water limitation. 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.235
Teacher spread0.225 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations10
Published2017
Admission routes3
Has abstractyes

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