Intra- to Multidecadal Variations of Snowpack and Streamflow Records in the Andes of Chile and Argentina between 30° and 37°S
Bibliographic record
Abstract
Abstract In a recent study the authors developed the first regionally averaged, transnational records of snowpack and streamflow for the Andes between 30° and 37°S using Chilean and Argentinean data. That study was mainly intended to evaluate the relationships between the interannual variations in the regional snowpack record and large-scale atmospheric variables and indices. Here the focus is on the main intra- to multidecadal variations in updated records of winter snowpack and mean annual river flows. River discharges show similar temporal variations on both sides of the Andes with extreme dry conditions concentrated between the mid-1940s and 1976/77 and extreme wet conditions peaking between the late 1970s and the 1980s. A regional streamflow composite (1906–2007) has a nonsignificant negative trend but significant regime shifts in 1945, when mean levels dropped 31%, and in 1977 when they increased 28%. These events coincide almost exactly with well-known shifts in the Pacific decadal oscillation (PDO). The analyses are preliminary but suggest a PDO influence on the low-frequency modes of hydroclimatic variability in the study area. Analyses of the magnitude of 5–20-yr moving windows in the regional streamflow composite indicate that the most significant concentration of high (low) discharges occurred between 1977 and 1987 (1954 and 1971). Snowpack series show a more heterogeneous pattern of variations on a local basis but when aggregated into a regional series (1951–2008) they share remarkable similarities with river flows. However, the snowpack composite has a stronger year-to-year variability, a slight positive trend, and no significant regime shifts.
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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 | 0.001 |
| 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".