Surface and groundwater dynamics in the sedimentary plains of the Western Pampas (Argentina)
Bibliographic record
Abstract
Abstract Sedimentary plains with extremely flat topography, such as the Pampas in Argentina, often display flooding–drought cycles. Changes in water table depth and surface water coverage affect natural and cultivated vegetation, wildlife, and people. Here, we describe groundwater dynamics and water‐body expansion in a 10‐year flooding cycle in the valuable agricultural lands of Western Pampas. We analysed water‐table depth, surface water coverage, and rainfall from 1996 to 2005 covering ∼28 000 km2. We characterized the dynamics of water storage based on groundwater observations and remote sensing estimates of the coverage (LANDSAT) and elevation (ENVISAT) of water bodies as well as water storage anomalies captured by the gravity recovery and climate experiment (GRACE). Surface water coverage fluctuated from 3 to 28% and groundwater levels displayed a ∼2·5 m change. Regional water storage raised by ∼800 mm with 63% of this water accretion accounted by groundwater. Ground and surface water dynamics were closely coupled but this link differed between lowlands and highlands and depending on whether the system was at the gaining or retraction stage. This asymmetrical behaviour was likely caused by shifts in regional connectivity. Regional surface + groundwater storage compared well with water storage anomalies obtained from GRACE, suggesting that this tool may represent a methodological shortcut to estimate water storage changes. The tight connection between ground and surface water, and the relatively slow process of cumulative water accretion and coalescence of water bodies that precedes flood events offer the opportunity of developing warning systems that could help land managers to adapt to climate changes. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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 | 0.000 | 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 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".