What does it take to flood the <scp>P</scp>ampas?: Lessons from a decade of strong hydrological fluctuations
Bibliographic record
Abstract
Abstract While most landscapes respond to extreme rainfalls with increased surface water outflows, very flat and poorly drained ones have little capacity to do this and their most common responses include (i) increased water storage leading to rising water tables and floods, (ii) increased evaporative water losses, and, after reaching high levels of storage, (iii) increased liquid water outflows. The relative importance of these pathways was explored in the extensive plains of the Argentine Pampas, where two significant flood episodes (denoted FE1 and FE2) occurred in 2000–2003 and 2012–2013. In two of the most flood‐prone areas (Western and Lower Pampa, 60,000 km2 each), surface water cover reached 31 and 19% during FE1 in each subregion, while FE2 covered up to 22 and 10%, respectively. From the spatiotemporal heterogeneity of the flood events, we distinguished slow floods lasting several years when the water table is brought to the surface following sustained precipitation excesses in groundwater‐connected systems (Western Pampa), and “fast” floods triggered by surface water accumulation over the course of weeks to months, typical of poor surface‐groundwater connectivity (Lower Pampa) or when exceptionally strong rainfalls overwhelm infiltration capacity. Because of these different hydrological responses, precipitation and evapotranspiration were strongly linked in the Lower Pampa only, while the connection between water fluxes and storage was limited to the Western Pampa. In both regions, evapotranspirative losses were strongly linked to flooded conditions as a regulatory feedback, while liquid water outflows remained negligible.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".