A review of the current state of knowledge of proglacial hydrogeology in the Cordillera Blanca, Peru
Bibliographic record
Abstract
The rapidly melting glaciers of Peru are posing new risks to regional dry season water supplies, and this is evident in the Cordillera Blanca, the mountain range with the world's largest concentration of tropical glaciers. Permanent ice loss is causing reductions to dry season streamflow, which is coupled with shifting demands and control over water access and entitlements in the region. A full evaluation of hydrologic inputs is required to inform future water management in the relative absence of glaciers. Over the last decade, new studies have shown groundwater to be a significant component of the regional water budget during the dry season, and it cannot be ignored when accounting for water quality and quantity downstream of the Cordillera Blanca's alpine catchments. Distinctive common features of the Cordillera Blanca's proglacial catchments are sediment‐filled valleys that were once under proglacial lake conditions. The combination of lake sediments with other alpine depositional features results in storage and interseasonal release of groundwater that comprises up to 80% of the valley's streamflow during the driest months of the year. We summarize the emerging understanding of hydrogeologic processes in proglacial headwater systems of the region's principal river, the Rio Santa, and make suggestions for future research that will more clearly characterize the spatial distribution of stored groundwater within the mountain range. As glaciers continue to recede, differences in aquifer thickness and groundwater residence time between alpine catchments in the region will increasingly control dry season water availability at the local and basin scale. This article is categorized under: Science of Water > Hydrological Processes Science of Water > Water and Environmental Change Engineering Water > Planning Water
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".