Controls on groundwater response and runoff source area dynamics in a snowmelt-dominated montane catchment
Bibliographic record
Abstract
Abstract. The role of spatial variability in water inputs on runoff source area dynamics has generally not received as much research attention as topography and soils; however, the influence of topography and forest cover on snow surface energy exchanges can result in asynchronous snowmelt throughout a catchment complicating the space-time patterns of runoff generation. This study investigates temporal variation in the relative importance of spatial controls on the occurrence, timing, and persistence of shallow groundwater response utilizing a highly distributed monitoring network in a snowmelt-dominated montane catchment in western Canada. The study findings indicate that deep soil hydraulic conductivity is a first-order control on the distribution of sites that generate shallow groundwater response versus sites that experience only deep percolation. Upslope contributing area and slope gradient are first-order controls on the persistence of groundwater response during peak flow, recession flow, and low flow periods. Runoff source areas expand and contract throughout these periods according to an interplay between catchment wetness and the spatial patterns of topographic convergence. However, controls on the differential timing, intensity, and quantity of snowmelt and controls on vertical versus lateral flux partitioning in the soil overwhelm the influence of topographic convergence on runoff source area dynamics during early spring freshet periods. The study findings suggest that various topographic indices and topography-based rainfall runoff models are not necessarily applicable to modelling snowmelt runoff source area dynamics during all streamflow periods for snowmelt-dominated montane catchments.
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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 | 0.001 |
| 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".