Quantifying groundwater-surface water interactions in a proglacial moraine using heat and solute tracers
Bibliographic record
Abstract
[1] Recent studies in mountain environments have indicated that groundwater represents a major component of the water balance of alpine streams and lakes. However, the scarcity of information on the hydraulic properties of geological materials in alpine environments presents a major obstacle to understanding the response of these watersheds to hydrological inputs and their future variability. The information is particularly limited for talus and proglacial moraine, where rugged topography prohibits the installation of groundwater monitoring wells. Observation of groundwater-surface water interaction provides a useful tool for studying groundwater in these challenging environments. Here we present a unique experiment using a tarn (i.e., pond on proglacial moraine) in a partially glaciated watershed in the Canadian Rockies as a surrogate for a groundwater monitoring well. A chloride dilution test and detailed energy balance monitoring were simultaneously conducted to quantify the groundwater-surface water interactions. The water balance of the tarn was dominated by groundwater inflow and outflow, ranging between 70 and 720 m3 d−1, while the volume of the water in the tarn fluctuated between 140 and 620 m3. Comparing the observed flow rates with a semianalytical solution of groundwater interactions with a flow-through pond, the hydraulic conductivity of the proglacial moraine is estimated to be in the order of 10−3 m s−1, which provides one of the very few measurements of large-scale hydraulic conductivity of proglacial moraine. The study demonstrates a useful application of mass and energy balance measurements in rugged environments and provides the essential information for advancing our understanding of alpine groundwater hydrology.
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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 | 0.001 | 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".