Quantifying Changing Groundwater Exfiltration at a High Arctic Site due to Climate Change
Bibliographic record
Abstract
ABSTRACT Increasing Arctic warming rates drive significant environmental change, including permafrost thaw and new groundwater pathway development, thereby increasing groundwater vulnerability to contaminant transport at the thousands of unremediated sites in the circumpolar north. As a first step in assessing hydrogeological controls of Arctic contaminant transport, this study uses numerical modelling to disentangle the impacts of increasing precipitation and air temperature on groundwater flow within the active layer at a high arctic site (63°30′ N). The study uses the numerical model SUTRA 4.0 to simulate groundwater flow and energy transport, including dynamic freeze–thaw processes, across an Arctic hillslope under current and future air temperatures due to climate warming. The model domain represents a two‐dimensional hillslope terminating in a lake. Two layers implemented in the model represent unconsolidated glacial till and underlying crystalline bedrock. Four simulation cases are examined based on downscaled CMIP5 projections under the high‐emissions “business as usual” scenario: Baseline Conditions (1981–2010), Near‐Projections (2011–2040), Mid‐Projections (2041–2070), and Far Projections (2071–2100). Climate projections indicate increasing mean annual air temperatures, reducing annual air temperature amplitude, and increasing precipitation. Further, model results show that groundwater flow dynamics are primarily influenced by the coupling of both increased mean annual temperatures and precipitation, with the consequent deepening and prolonged thawing of the active layer allowing for increased groundwater exfiltration to the lake. Sensitivity analysis identifies overburden permeability, overburden residual liquid freezing temperature, and model base temperature as significant parameters that affect model outcomes. Finally, a variable transmissivity assessment provides new insight into active layer groundwater flows.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".