Surface Water and Groundwater Contributions to Streamflow in the James Bay Lowland, Canada
Bibliographic record
Abstract
Climate and land-use changes are going to leave an indelible mark on the hydrology and globally significant peatlands of the Hudson Bay Lowlands (HBL), Canada. With forecasts for warmer and drier conditions over the next century, the relative contribution of water from surface and subsurface sources affecting both water quantity and quality will undoubtedly shift. Unfortunately, no empirical data exist for any streams or rivers of the HBL on the relative contributions of surface water and groundwater to streamflow, making assessment of future change difficult. Here we report the first data on sources of water to streams and rivers across a range of catchment sizes in the James Bay Lowland (JBL) ecoregion of the HBL. Solute chemistry was determined for a range of potential end members, end members were identified, and a chemical mixing model approach was used to determine the relative end-member contributions to streamflow across a range of catchment sizes (∼30–2000 km2). The relative contributions of bedrock-derived groundwater to streamflow increased with catchment area from <20 to >40% under dry conditions, and were ∼50% lower under wet conditions across all catchments. Runoff contributions from peatlands were relatively constant over space and time (53–67%), but the fraction of streamflow composed of rain and snowmelt varied dramatically between wet and dry periods, and among catchments. Given the importance of peatland-derived surface waters, future changes in precipitation and temperature could have significant implications for streamflow in the JBL, particularly during summer base-flow conditions. Moreover, the definition of reference catchments for baseline/impact monitoring must be carefully considered, given the potential for variation in hydrochemistry across physiographically similar catchments.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 teacher head, 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".