Regulation of peatland evaporation following wildfire; the complex control of soil tension under dynamic evaporation demand
Bibliographic record
Abstract
Abstract The capability of peatland ecosystems to regulate evapotranspiration (ET) following wildfire is a key control on the resilience of their globally important carbon stocks under future climatic conditions. Evaporation dominates post‐fire ET, with canopy and sub‐canopy removal restricting transpiration and increasing evaporation potential. Therefore, in order to project the hydrology and associated stability of peatlands to a diverse range of post‐fire weather conditions and future climates the regulation of evaporation must be accurately parameterised in peatland ecohydrological models. To achieve this, we measure the surface resistance (rs) to evaporation over the growing season one year post‐fire within four zones of a boreal peatland that burned to differing depths, relatingrsto near surface soil tensions. We show that the magnitude and temporal variability inrsvaries with burn severity. At the peatland scale,rsand near‐surface tension correlates non‐linearly. However, at the point scale no relationship was evident between temporal variations inrsand near‐surface tension across all burn severities; in part due to the limited fluctuation in near‐surface tensions and the precision ofrsmeasurements. Where automated measurements enabled averaging of errors, the relationship between near‐surface tension andrsswitched between periods of strong and weak correlation within a burned peat hummock. This relationship, when strong, deviated from that obtained under steady state laboratory conditions; increases inrswere more sensitive to fluctuations in near‐surface tension under dynamic field conditions. Calculating soil vapour densities directly from near‐surface tensions is shown to require calibration between peat types and provides little if any benefit beyond the derivation of empirical relationships betweenrsand measured soil tension. Thus, we demonstrate important spatiotemporal fluctuations in post‐firersthat will be key to regulating post‐fire peatland hydrology, but highlight the complex challenges in effectively parameterising this important underlying control of near‐surface tensions within hydrological simulations.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".