Hydrological connectivity controls dissolved organic carbon exports in a peatland-dominated boreal catchment stream
Bibliographic record
Abstract
Abstract. Peatland-derived dissolved organic carbon (DOC) exports from boreal peatlands are variable during the ice-free season, depending on the peatland water table and the alternation of low and high flow in peat-draining streams. However, calculation of the specific DOC exports from a peatland can be challenging considering the multiple potential DOC sources within the catchment. A calculation approach based on the hydrological connectivity between the peat and the stream could help to solve this issue, an approach used in the present study. This study took place from June 2018 to October 2019 in a boreal catchment in north-eastern Canada, with 76.7 % of the catchment covered by ombrotrophic peatland. The objectives were (1) to establish relationships between DOC exports from a headwater stream and the peatland hydrology; (2) to quantify, at the catchment scale, the amount of DOC laterally exported to the draining stream; and (3) to define the patterns of DOC mobilization during high river flow events. At the peatland headwater stream outlet, the DOC concentrations were monitored at a high frequency (hourly) using a fluorescent dissolved organic matter (fDOM) sensor, a proxy for DOC concentrations. Hydrological variables, such as stream outlet discharge and the peatland water table depth (WTD), were continuously monitored for 2 years. Our results highlight the direct and delayed control of subsurface flow from peat to the stream and associated DOC exports. Rain events raised the peatland WTD, which increased the hydrological connectivity between the peatland and the stream. This led to increased stream discharge (Q) and a delayed DOC concentration increase, typical of lateral subsurface flow. The magnitude of the WTD increase played a crucial role in influencing the quantity of exported DOC. Based on the assumption that the peatland is the major contributor to DOC exports and other DOC sources were negligible during high-flow periods, we propose a new approach to calculate the specific DOC exports attributable to the peatland by distinguishing the surface used to the calculation between high-flow and low-flow periods. In 2018–2019, 92.6 % of DOC was exported during flood events, despite accounting for 59.1 % of the period. In 2019–2020, 93.8 % of DOC was exported during flood events, which represented 44.1 % of the period. Our analysis of individual flood events revealed three types of events and DOC mobilization patterns. The first type is characterized by high rainfall leading to an important WTD increase favouring the connection between the peatland and the stream, leading to high DOC exports. The second is characterized by a large WTD increase succeeding a previous event that had depleted DOC available to be transferred to the stream, leading to lower DOC exports. The third type corresponds to low rainfall events with an insufficient WTD increase to reconnect the peatland and the stream, leading to low DOC exports. Hence, DOC exports are sensitive to hydroclimatic conditions. Moreover, flood events, changes in rainfall regimes, the ice-free season duration and porewater temperatures may affect the exported DOC and, consequently, partially offset the carbon sequestration capacity of peatlands.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".