Multidecadal estimation of hydrological contribution and glacier mass balance in the semi-arid Andes based on physically based modeling and geodetic mass balance
Bibliographic record
Abstract
Glaciers are of paramount importance in diverse environments, and due to the accelerated retreat experienced in recent decades, efforts have intensified to achieve a comprehensive understanding of key variables such as mass balance and glacial melting. However, the scarcity of data in regions that are difficult to access, such as the Andes Cordillera, hinders reliable glaciological studies of the historical period. This study examined the mass balance and melting dynamics of the Universidad Glacier, the largest in the semi-arid Andes, from 1955 to 2020, using the physically based Cold Regions Hydrological Model (CRHM). The model was calibrated with geodetic mass balance estimates available between 1955 and 2020 and evaluated against on-site observations available between 2012 and 2014. Change point analysis revealed three contrasting periods of mass balance evolution: significant mass loss for the periods 1955–1971 and 2006–2020 and near-equilibrium mass balance from 1971 to 2006. These loss and gain periods align with the negative phases of the Pacific Decadal Oscillation (PDO) and the positive ENSO (El Niño) events, respectively. Simulated runoff from glacier melt showed a positive trend of 8% per decade since 1971. Calibrated and uncalibrated versions of the model showed similar temporal variability, but cumulative mass balance differed significantly. The model calibrated from 1955 to 2020 had a minimal overestimation of 0.1% in mass loss and slightly improved the representation of the annual albedo. Relative to this best-performing model, the model calibrated with geodetic mass balance estimates from 2000 to 2020 overestimated mass loss by 25%, whereas the uncalibrated model overestimated mass balance by 62%. Physically based modeling with parameters adjusted based on field observations is adequate to reproduce the most salient features of MB interannual variability. However, long-term projections may diverge significantly, and albedo parameterizations, including its spatial and temporal evolution throughout a glacier surface, are an avenue for future research.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 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".