Evaluating the hydrological and hydrochemical responses of a High Arctic catchment during an exceptionally warm summer
Bibliographic record
Abstract
Abstract The Arctic has experienced substantial warming during the past century with models projecting continued warming accompanied by increases in summer precipitation for most regions. A key impact of increasing air surface temperatures is the deepening of the active layer, which is expected to alter hydrological processes and pathways. The aim of this study was to determine how one of the warmest and wettest summers in the past decade at a High Arctic watershed impacted water infiltration and storage in deeply thawed soil and solute concentrations in stream runoff during the thaw period. In June and July 2012 at the Cape Bounty Watershed Observatory, we combined active layer measurements with major ion concentrations and stable isotopes in surface waters to characterize the movement of different runoff sources: snowmelt, rainfall, and soil water. Results indicate that deep ground thaw enhanced the storage of infiltrated water following rainfall. Soil water from infiltrated rainfall flowed through the thawed transient layer and upper permafrost, which likely solubilized ions previously stored at depth. Subsequent rainfall events acted as a hydrological flushing mechanism, mobilizing solutes from the subsurface to the surface. This solute flushing substantially increased ion concentrations in stream runoff throughout mid to late July. Results further suggest the importance of rainfall and soil water as sources of runoff in a High Arctic catchment during mid to late summer as infiltrated snowmelt is drained from soil following baseflow. Although there was some evaporation of surface water, our study indicates that flushing from solute stores in the transient layer was the primary driver of increased ion concentrations in stream runoff and not evaporative concentration of surface water. With warmer and wetter summers projected for the Arctic, ion concentrations in runoff (especially in the late thaw season), will likely increase due to the deep storage and subsurface flow of infiltrated water and subsequent flushing of previously frozen solutes to the surface.
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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 | 0.001 |
| 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) | 0.001 | 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".