Predicting glacio‐hydrologic change in the headwaters of the <scp>Z</scp>ongo <scp>R</scp>iver, <scp>C</scp>ordillera <scp>R</scp>eal, <scp>B</scp>olivia
Bibliographic record
Abstract
Abstract In many partially glacierized watersheds glacier recession driven by a warming climate could lead to complex patterns of streamflow response over time, often marked with rapid increases followed by sharp declines, depending on initial glacier ice cover and rate of climate change. Capturing such “phases” of hydrologic response is critical in regions where communities rely on glacier meltwater, particularly during low flows. In this paper, we investigate glacio‐hydrologic response in the headwaters of the Zongo River, Bolivia, under climate change using a distributed glacio‐hydrological model over the period of 1987–2100. Model predictions are evaluated through comparisons with satellite‐derived glacier extent estimates, glacier surface velocity, in situ glacier mass balance, surface energy flux, and stream discharge measurements. Historically (1987–2010) modeled glacier melt accounts for 27% of annual runoff, and 61% of dry season (JJA) runoff on average. During this period the relative glacier cover was observed to decline from 35 to 21% of the watershed. In the future, annual and dry season discharge is projected to decrease by 4% and 27% by midcentury and 25% and 57% by the end of the century, respectively, following the loss of 81% of the ice in the watershed. Modeled runoff patterns evolve through the interplay of positive and negative trends in glacier melt and increased evapotranspiration as the climate warms. Sensitivity analyses demonstrate that the selection of model surface energy balance parameters greatly influences the trajectory of hydrological change projected during the first half of the 21st century. These model results underscore the importance of coupled glacio‐hydrology modeling.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".