Environmental flow envelopes: quantifying ecosystem-threatening flow alterations
Notice bibliographique
Résumé
The benefits of harnessing rivers into human use should not come with a disproportionate expense on the Earth system. Especially, freshwater ecosystems suffer greatly from direct and indirect human impacts, such as excessive water withdrawals and climate change, which are expected to only increase in the near future. Here, we aim for quantifying the extent and degree of considerable flow alterations that threaten the well-being of freshwater ecosystems, across the world. At the global scale, the ecological status of river systems is often assessed using global hydrological models (GHMs) and hydrological environmental flow (EF) methods. These suffer from substantial uncertainties: 1) the GHMs parameterised with variable climate forcings may give highly dispersed discharge estimates and 2) individual hydrological EF methods capture ecosystem water needs poorly. We tackle these sources of uncertainty by introducing a novel methodology: environmental flow envelopes (EFEs). The EFE is an envelope of safe discharge variability between a lower and an upper bound, defined at the sub-basin scale in monthly time resolution. It is based on pre-industrial (1801-1860) discharge and a large ensemble of EF methods, GHMs, and climate forcings, using ISI-MIP2b data. Using the EFE, we can simultaneously assess the frequency and severity of ecosystem-threatening flow alterations. Comparing post-industrial (1976-2005) discharge to the EFEs, discharge in 32.7% of the total 3860 sub-basins, covering 28.4% of the global landmass, violates the EFE during more than 10% of all months across four GHMs. These violations are considered as severe threats to freshwater ecosystems. The most impacted regions include areas with high anthropogenic pressure, such as the Middle East, India, Eastern Asia, and Middle America. The violations clearly concentrate on the EFE lower bound during low or intermediate flow seasons. Discharge in 61.4% of sub-basins violates the EFE during more than 10% of low flow season months, average violation being 47.5% below the safe limit denoted by EFE lower bound. Indications of significantly increased flows by violations of the EFE upper bound are fewer and further apart, as well as lower bound violations during high flow season. Although fractional discharge allocations alone cannot fully capture the ecosystem water needs, this study is a step towards less uncertainty in global EF assessments. The introduced method provides a novel, globally robust way of estimating ecosystem water needs at the sub-basin scale. The results of this study underline the importance of the low flow season, during which EFE violations are the most prevalent. While only preliminary evidence of significantly increased flows emerges in relatively few areas, the EFE upper bound would benefit from further research. The EFE methodology can be used for exploring macro-regional areas where anthropogenic flow alteration threatens freshwater ecosystems the most. However, case-specific studies incorporating factors beyond quantitative flow only are required for practical implications.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».