Toward a glacier retreat driven redistribution of water resources
Bibliographic record
Abstract
Assessments of glacier retreat impacts on water resources are often carried out using hydrological models calibrated using stream discharge time series. Because long-term discharge measurements are scarce in different regions of the world, models’ outcomes are analyzed assuming implicitly that stream discharge evolution projections at the outlet of a watershed affect the entire drainage area following a uniform pattern. In the present study, building on the learnings from the peak water analysis we performed in 2012, we explore the heterogeneity in Rio Santa sub-watersheds responses to deglaciation. The future of water resources at each watershed is projected by applying the peak water model with the latest glacier area estimations. The resulting map of the projected water availability across the Rio Santa watershed is then overlayed with previous works and literature-based water quality and demand maps.Results show that, while glaciers are losing their hydrological influence across the Cordillera Blanca, gaps open between water availability and demand for water at different levels of the watershed. Moreover, the dry season share of polluted sub-watersheds into the Rio Santa discharge increasing due to glacier retreat, water quality evolution will add up to the challenge of sharing an already scarce resource.Our study suggests that deglaciation in the tropical Andes affects populations and economic activities in a complex, disparate and evolutive way. Therefore, anticipating glaciers retreat redistribution of the water resources requires integrating hydrological, chemical, biological, economic, and sociological water resources aspects in locally grounded studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".