Hydrological implications of the Chile Megadrought in high mountain basins and lessons for climate adaptation.
Bibliographic record
Abstract
La Niña years are historically associated with precipitation deficits in central Chile. However, since the onset of the so-called Chile Megadrought in 2010, the teleconnection between the El Niño-Southern Oscillation phases and the hydroclimate in central Chile has either weakened or disappeared. This study investigates the hydrological response of high mountain watersheds to La Niña (LN) and megadrought conditions (MD) in the Andes of central Chile (30°S – 35°S) through physically-based simulation of processes at the watershed scale. It is shown that during LN years, winters and summers are colder, but spring seasons are warmer, while in MD years the summers are warmer. In addition, the hydrologic response to LN and MD is distinct and amplified during MD in terms of flow deficit. Simulation results for five snow-dominated basins within the central Andes suggest lower efficiency in the transformation of precipitation to snowmelt flow (-3.7% and 1.6% with respect to the long-term average, for MD and LN, respectively), accompanied by higher evaporation (8.7% and 6.1%) and lower flow (-9.3% and -3.4%) relative to annual precipitation. Also, snow accumulation deficits at the end of winter propagate (-36.2% and -17.7%) with respect to the deficit of solid precipitation (-29.7% and -17.5%) and total precipitation (-26% and -19.3%), and during the MD the duration of snow is shorter compared to LN (-16.3 and -10.6 days). Thus, the key role played by snow processes and their variability in the hydrological response to droughts in central Chile is highlighted. The findings presented here are expected to inform ongoing discussion on adaptation strategies to climate change, as the observed climate during the megadrought (2010-?) is strikingly similar, on average, to GCM projections for this region toward the end of the 21st century.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".