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Record W7162082732 · doi:10.82308/47619

Impacts of hydrological connectivity on the lateral movement of Water and Dissolved Organic Carbon in an Extracted Peatland

2025· dissertation· en· W7162082732 on OpenAlexaboutno aff
Nicolas Perciballi

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsPeatDissolved organic carbonHydrology (agriculture)Surface runoffDrainageInfiltration (HVAC)EvapotranspirationWater tableCarbon cycle

Abstract

fetched live from OpenAlex

Peat extraction practices in Canada significantly alter the hydrology of peatlands, leading to increased dissolved organic carbon (DOC) production and transport, influencing downstream carbon dynamics in artificial drainage networks. Despite these changes, the mechanisms controlling the lateral movement of water and associated DOC transport remain poorly understood. This study was conducted from May 2022 to July 2023, examining hydrology and carbon dynamics in an actively extracted peatland in Rivière-du-Loup, Quebec. Three distinct hydrological periods were identified based on the runoff from the peatland: Summer 2022, Fall 2022, and Winter-Spring 2023. These periods marked transitions in the hydrological network’s connectivity, with each phase having unique DOC mobilization from the peat matrix.During the summer of 2022, high evapotranspiration (AET) rates (~ 2 mm/day) reduced the water storage, and the fields became disconnected from the drainage network. Although DOC concentrations remained relatively stable at 74.98  1.41 mg/l, in the drainage channels. The supply of DOC during the summer 2022 period was transport-limited, even during a site-scaled disturbance that increased drainage efficiency (the 2022 resurfacing event). During Fall 2022, lower AET rates (~ 1 mm/day) and increased water storage led to runoff generation in response to rainfall. The drainage network became connected during precipitation, allowing DOC transport from the peat matrix to the channels. The DOC concentration was similar to that of the summer period, but now runoff occurred and as a consequence there was DOC export. During the spring melt of 2023, the peat fields and channel network became hydrologically connected, but the frozen peat surfaces limited infiltration and DOC mobilization. We conjecture, based on the lack of increase in soil water and water tables in the fields that overland flow occurred. Since there was less interaction with the water stored in the peat matrix, the channel DOC concentrations were lowest at 20.30  2.60 mg/l.A water balance for the extracted peatland was calculated using measured precipitation (P) = 953mm, runoff (R) = 357 mm, and a change in storage (S) = -53mm, and an estimate of AET = 581 mm. The residual of the water balance (i.e., sum of errors and unmeasured losses) was approximately -38 mm. The runoff was equivalent to 37 % of the system’s outputs, and the exported DOC was 16.03 g DOC/ m2 during the entire study period. The total gaseous carbon (from CO2 and CH4) lost from the drainage network surfaces across the entire study site was ~ 3.64  0.48 g C/m2 for the study period. The losses of carbon through runoff are larger than those lost by gaseous emissions from the drainage network surface; however, gas fluxes from the drainage network do not result in an insignificant loss of carbon.This study highlights the dynamic role of hydrological connectivity in controlling DOC transport in extracted peatlands. Understanding these mechanisms provides critical insights for refining carbon loss accounting and improving peatland management practices in Canada

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.961
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.231
Teacher spread0.222 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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