Coevolution of Hydrological Cycle Components under Climate Change: The Case of the Garonne River in France
Bibliographic record
Abstract
Climate change is suspected to impact water circulation within the hydrological cycle at catchment scale. A SWAT model approach to assess the evolution of the many hydrological components of the Garonne catchment (Southern France) is deployed in this study. Performance over the calibration period (2000–2010) are satisfactory, with Nash–Sutcliffe ranging from 0.55 to 0.94 or R2 from 0.86 to 0.98. Similar performance values are obtained in validation (1962–2000). Water cycle is first analyzed based on past observed climatic data (1962–2010) to understand its variations and geographical spread. Comparison is then conducted against the different trends obtained from a climate ensemble over 2010–2050. Results show a strong impact on green water, such as a reduction of the soil water content (SWC) and a substantial increase in evapotranspiration (ET) in winter. In summer, however, some part of the watershed faces lower ET fluxes because of a lack of SWC to answer the evapotranspiratory demand, highlighting possible future deficits of green water stocks. Blue water fluxes are found significantly decreasing during summer, when in winter, discharge in the higher part of the watershed is found increasing because of a lower snow stock associated to an increase of liquid precipitation, benefiting surface runoff.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".