Hydrologic controls of a wetland complex in British Columbia’s inland temperate rainforest
Bibliographic record
Abstract
Ancient Forest/Chun T’Oh Whudujut Provincial Park (AFP) is part of British Columbia’s (BC) inland temperate rainforest that receives high total annual precipitation amounts (>1000 mm) from orographic enhancement. AFP hosts a ~4 km2 complex of valley bottom wetlands which is supported by this precipitation, a good portion of which falls as snow. This study examines the hydrology of the wetland complex to determine the primary water sources and their influence on flow paths and recharge. Water levels and meteorological conditions were monitored for 2019 and 2020, and water samples were collected for isotopic data during the 2020 snow free period. Kriging of water level data revealed a northeastward nearly flat hydraulic gradient that shifted orientation during wet and dry periods through the summertime. Rainfall amounts were above average at 700.2 mm and 676.4 mm while snow water equivalent varied at 762.8 mm (below average) and 1082.8 mm (above average) for 2019 and 2020, respectively. The reduced snowpack of 2019 yielded lower water levels through the summertime when compared to those of 2020. End Member Mixing Analysis (EMMA) of stable water isotope data indicates that rainfall is not a significant enough recharge source to alter the wetland groundwater composition, while snowmelt is likely the dominant source of input for the groundwater. Cross correlation between rainfall and water level data indicates however that water levels do respond to rainfall events with lag times ranging from 13 – 30 hrs. This observation leads to the conclusion that rainfall serves to flush stored snowmelt generated water from the soils into the wetland complex, though not in a significant enough volume to replace snowmelt as the dominant soil water source. Since previous research has concluded that by 2050 as much as 50 % of the current snow contribution to total annual precipitation will be replaced by rainfall, this study indicates that this shift in precipitation regime may result in lower water levels in the wetland complex. Numerical modelling of the relationship between wetland water levels and precipitation phases and amounts would improve the understanding of the climate resiliency of valley bottom wetlands in the Robson Valley.
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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 | 0.001 | 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) | 0.002 | 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".