Snow is important too: disentangling the role of the cryosphere in the water cycle of a tropical Andean catchment
Bibliographic record
Abstract
<!--!introduction!--><b></b> The Peruvian Andes contains the largest mass of glaciers in the tropics and previous work identified glacier melt as a key contributor to sustaining dry season water resources. Snow, however, has been neglected to date, and modelling and hydro-chemical analyses have been unable to resolve the snow cover dynamics nor fully distinguish between the separate contributions of snow and glaciers to runoff. To provide these insights we run the fully distributed, hourly glacier-hydrological model TOPKAPI-ETH from 2008-2018 over the upper Rio Santa catchment in the Cordillera Blanca. The model parameters are derived from ground-based data and evaluated against independent snow cover and glacier mass balance estimates from remote sensing, alongside gauged runoff. Glacier melt is important in the dry season and in the Blanca (eastern) side of the catchment, where even the catchments with the smallest glacier-covered area benefit from dry season runoff. However, our results highlight the underappreciated importance of snow for discharge. Snowmelt is a strikingly consistent contributor to runoff temporally and spatially: its proportional contribution is largest at the beginning of the dry season and lowest at the beginning of the wet season. Off-glacier snowfall is significant in the wet season. However, this melts quickly, so that accumulation is limited to high elevations and the dry season snow-cover reduces to on-glacier areas. Snow cover durations are in the order of hours to days, contrasting with the seasonal snowpack typical of mid-latitude climates. Paradoxically ephemeral snow cover provides a reliable source of runoff in the tropical Andes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".