Vulnerable basins for global prioritisation: Hotspots for social and ecological impacts from freshwater stress and freshwater storage loss
Notice bibliographique
Résumé
Society and ecosystems are deeply connected to, and through, hydrological processes. Significant research efforts have revealed the breadth of ways humans have become dominant drivers of the global water cycle, however less attention has been placed on how hydrological change will affect social and ecological systems at the global scale. Understanding both directions of this coupled social-ecological system are critical to achieving sustainable freshwater futures in complex, multi-objective decision making environments. Here, we identify the global hotspots for social and ecological impacts from freshwater stress and freshwater storage loss. We applied the concept of hotspot mapping, from the field of conservation biogeography, to integrated socio- and eco-hydrological considerations for the first time at the global scale. We identified 168 basins for global prioritisation that are most vulnerable to suffer social and ecological impacts from freshwater stress and storage loss. These basins encompass over 1.5 billion people, 17% of global food crop production, 13% of global gross domestic product, and hundreds of internationally significant wetlands (Ramsar sites). The impacts that can be realised in these basins include transgressed environmental flows, increased drought frequency, decreased ecological resilience, threatened water, economic, and food security through reduced freshwater availability, and increased risk of wells running dry which may exacerbate existing economic inequalities. Regions and nations home to hotspot basins include: Argentina, northeastern Brazil, southwestern USA, Northern, Eastern, and Southern Africa, the Middle East and Arabian Peninsula, the Caucasus, West Asia, northern India, Nepal, Pakistan, Southeast Asia, and northern China. The 168 hotspot basins present an initial set of regions to prioritise in global sustainability initiatives that link water, ecosystems, and society, such as the Sustainable Development Goals. Furthermore, the hotspots represent the multiple epicentres where management of trade-offs between social, economic, and ecological water uses is most crucial, and thus represent the regions where implementation of integrated water resources management (IWRM) practises becomes most critical. To this end, we compared IWRM implementation levels to our global vulnerability results. While no direct relationship was found between IWRM implementation and social-ecological vulnerability to freshwater stress and storage loss, we observed, among hotspot basins, that IWRM implementation is lower in transboundary basins than in non-transboundary basins, suggesting that greater multilateralism and cooperation are needed. We identified hotspot basins by integrating global socio-hydrological and eco-hydrological datasets, remote sensing observations of freshwater storage trends, freshwater use, and streamflow datasets into a basin-scale social-ecological vulnerability analysis. This presentation reports the findings from Huggins et al. (in press, Nature Communications), and the hotspot basin results are available online for use by policy and research communities. Freshwater stress and storage loss are only two of many important aspects of freshwater with broad social-ecological resilience implications. Developing a network of similar analyses based on other processes and attributes, such as intra- and inter-annual storage variability, and water quality considerations, will support a more comprehensive understanding of the social-ecological impacts of global hydrological change.
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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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».