Modelling the fill-and-spill dynamics and wildfire impacts on the hydrological connectivity of ephemeral wetlands in a rock barrens landscape
Bibliographic record
Abstract
Ontario’s rock barrens landscape consists of exposed bedrock ridges which host a mosaic of thin lichen- and moss- covered soil patches, forested valleys, beaver ponds, and depressional wetlands. Peat-filled ephemeral wetlands within bedrock depressions act as gatekeepers to hydrological connectivity between their small headwater catchments and the rest of the landscape downstream through strong fill-and-spill dynamics. We developed a water balance model, RHO, with inputs of precipitation and potential evapotranspiration (PET) to better understand the factors impacting water table (WT) and storage dynamics and in turn the hydrological connectivity of ephemeral wetlands. Field surveys were conducted at six wetlands to obtain and determine the variability in measurable site characteristics, in particular the wetland depression morphometry, to parameterize RHO. Three sites were used in a calibration and validation procedure where modelled WTs were compared to measured WT data from the snow-free seasons for each site to determine the best parameter values. We show that RHO is capable of predicting WT dynamics with inputs of precipitation and PET, when parameterized for specific sites. Wildfire disturbance is known to increase the run-off from hillslopes and remove surface organic soils through combustion. To predict the impacts of wildfire disturbance on ephemeral wetland hydrological connectivity, a generic model wetland depression was parameterized in RHO and used to predict the changes in hydrological connectivity under various wildfire scenarios and test the sensitivity of modelled connectedness to impacted parameters. Modelled results show that connectivity increases under all scenarios tested, and that changes to connectivity are primarily due to increases in run-in. Water balance models, like RHO, can be used to better understand the hydrological connectivity of wetlands in a rock barrens landscape. These models are useful in predicting impacts on the hydrological connectivity, and hydrological ecosystem services, from disturbances such as wildfire and can inform future field research experimental designs.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".