Assessment of water resource vulnerability under changing climatic conditions in remote Arctic communities
Bibliographic record
Abstract
The influence of environmental change and increased resource demand on freshwater supplies in Arctic Canada emphasizes a critical need to anticipate future freshwater supply capabilities. A paucity of infrastructure and capacity has historically limited efforts to address water resource vulnerability for Arctic communities; therefore, we developed a locally relevant framework that quantifies liquid water volume under varying climate and demand scenarios. We incorporated vulnerability grading standards based on existing indices to assess the viability of single-source water reservoirs. Using municipal demand and meteorological data from the Arctic Canadian hamlets of Igloolik and Sanirajak, Nunavut, we forecasted end-of-winter reservoir volumes for 2022–2035 under a number of scenarios; baseline supply was assessed in relation to system capacity for summer recharge, anomalous seasonal events, and deviations in available ice-free days for recharge. Our assessment indicated that the respective reservoirs were highly responsive to air temperature and ice thickness. Cases of complete reservoir depletion (≤0 % available liquid water) were prevalent across simulated years, whereas normal reservoir conditions (30 ∼ 60 % available liquid reservoir water) were comparatively limited. We found that neither community had sufficient capacity within their current infrastructure for supplying freshwater over a typical planning horizon. Our analysis highlights the need for locally-specific planning related to freshwater supply to ensure sustainable capacity under a variable climate future. Together, our framework and models are an effective tool for assessing water resource vulnerability that can be linked to existing water resource indices to inform municipal planning and mitigation strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".