Evaluation of the Impact of Climate Change on Runoff Generation in an Andean Glacier Watershed
Bibliographic record
Abstract
Excluding Antarctica and Greenland, 3.8% of the world’s glacier area is concentrated in Chile. The country has been strongly affected by the mega drought, which affects the south-central area and has produced an increase in dependence on water resources from snow and glacier melting in dry periods. Recent climate change has led to an elevation of the zero-degree isotherm, a decrease in solid-state precipitation amounts and an accelerated loss of glacier and snow storage in the Chilean Andes. This situation calls for a better understanding of future water discharge in Andean headwater catchments in order to improve water resources management in glacier-fed populated areas. The present study uses hydrological modeling to characterize the hydrological processes occurring in a glacio-nival watershed of the central Andes and to examine the impact of different climate change scenarios on discharge. The study site is the upper sub-watershed of the Tinguiririca River (area: 141 km2), of which nearly 20% is covered by Universidad Glacier. The semi-distributed Snowmelt Runoff Model + Glacier (SRM+G) was forced with local meteorological data to simulate catchment runoff. The model was calibrated on even years and validated on odd years during the 2008–2014 period and found to correctly reproduce daily runoff. The model was then forced with downscaled ensemble projected precipitation and temperature series under the RCP 4.5 and RCP 8.5 scenarios, and the glacier adjusted using a volume-area scaling relationship. The results obtained for 2050 indicate a decrease in mean annual discharge (MAD) of 18.1% for the lowest emission scenario and 43.3% for the most pessimistic emission scenario, while for 2100 the MAD decreases by 31.4 and 54.2%, respectively, for each emission scenario. Results show that decreasing precipitation lead to reduced rainfall and snowmelt contributions to discharge. Glacier melt thus partly buffers the drying climate trend, but our results show that the peak water occurs near 2040, after which glacier depletion leads to reducing discharge, threatening the long-term water resource availability in this region.
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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 | 0.000 | 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) | 0.001 | 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 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".