Streamflow variations across the Andes (18°–55°S) during the instrumental era
Bibliographic record
Abstract
The rivers originating in the southern Andes (18°-55°S) support numerous ecosystems and a large number of human populations and socio-economic activities in the adjacent lowlands of Chile, Argentina and Bolivia. Here we show that ca. 75% of the total variance in the streamflow records from this extensive region can be explained by only eight spatially coherent patterns of variability. Five (three) of these Andean patterns exhibit extreme dry (wet) conditions in recent years, with strong interannual variations in northern Chile; long-term drying trends between 31° and 41°S; a transitional pattern in the central Patagonian Andes; and increasing trends in northwestern Argentina and southern Bolivia, the Fueguian Andes, and the eastern portion of the South Patagonian Icefield. Multivariate regression analyses show that large-scale indices of ENSO variability can predict 20% to 45% of annual runoff variability between 28° and 46°S. The influence of Antarctic and North Pacific indices becomes more relevant south of 43°S and in northwestern Argentina and southern Bolivia, respectively, but their overall skill as predictors of Andean streamflows is weak. The analyses provide relevant new information to improve understanding of the spatial coherence, the main temporal features, and the ocean-atmospheric forcings of surface runoff across the southern Andes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".