Modélisation hydrologique des sous-bassins\nversants de la Garonne à l'aide d'un modèle pluie-débit global
Bibliographic record
Abstract
Facing up growing water needs in a context of resources decrease will be a major challenge in the next years. The Garonne River basin at Lamagistère does not avoid this dilemma. The severe low flow periods of 2003, 2005 and 2006, underlined the possible conflicts around the distribution of water. Indeed, water is used for several purposes on the catchment: hydropower, irrigation, water supply, leisure and tourism. In this context, Cemagref and EDF R&D in partnership with the Water Agency Adour-Garonne answered a Call for Research proposal of the Ministry for Ecology. Called "Imagine 2030: Prospective in water resources: evolution of water availability, evolution of water use and consequences on the management of surface water Application to the Garonne basin", the project should provide a diagnosis on water\nresources availability under the hypothesis of climate change.\nTwo approaches of hydrological modelling are evaluated in the project: the EdF team works on a version of the spatialized conceptual hydrological CEQUEAU model developed by INRS-Eau in\nCanada (Charbonneau et al., 1977). The Cemagref team chose to use GR4J, an empirical lumped hydrological model (Perrin and al., 2003).\nThe work done in this training period is part of the first half of the project plan, and deals with the hydrological modelling of sub-catchments of the Garonne basin and sensitivity tests to the snow\nmodule, the input data and dam accounting. / Faire face à des besoins croissants en eau dans un contexte de raréfaction de la ressource est un défi majeur des prochaines années. Le bassin versant de la Garonne à Lamagistère n'échappe pas à ce\ndilemme. Les étiages sévères des années 2003, 2005 et 2006, ont souligné les potentiels conflits autour du partage de l'eau. Hydroélectricité, irrigation, alimentation en eau potable, loisirs, tourisme : les enjeux sur le bassin sont nombreux. Dans ce contexte, le Cemagref et EDF en partenariat avec l'Agence de l'Eau Adour-Garonne, ont répondu à un Appel à Proposition de Recherche du MEDD. Baptisé "Imagine 2030 : Prospectives Ressources en Eau : Evolution de la disponibilité en eau, évolution des usages et conséquences pour la gestion de la ressource superficielle Application au bassin de la Garonne", le projet doit permettre d'établir un diagnostic sur la pérennité de la ressource en eau sous l'hypothèse de changement climatique.\nDeux approches de modélisation hydrologique sont utilisées dans le cadre du projet : l'équipe d'EDF a travaillé sur une version dérivée du modèle hydrologique conceptuel spatialisé CEQUEAU développé par l'INRS-Eau du Canada (Charbonneau et al., 1977). L'équipe du Cemagref a choisi d'utiliser GR4J, un modèle hydrologique empirique global (Perrin et al., 2003). Les travaux réalisés durant ce stage de césure portent sur la première partie du projet, et concerne principalement la modélisation hydrologique de sous-bassins de la Garonne et des tests de sensibilité au module neige, aux données d'entrée et à la prise en compte des barrages.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".