Global groundwater archetypes: a new typology of groundwater interactions with social and ecological systems and an outlook for sustainable development
Notice bibliographique
Résumé
Groundwater resources do not exist in isolation but are deeply connected with social and ecological systems. As humans continue to modify the land surface, drive climate change, and place greater pressures on global freshwater resources, it is increasingly necessary to assess global groundwater resources through their relationships to these coupled systems. While several global classifications of physical groundwater systems exist, there is no data-driven global typology based on groundwater interactions with connected social and ecological systems. Though physical attributes remain hydrogeologically important, a more expansive systems-oriented classification is needed for policy development, applied research, and to develop the next generation of global hydrological models.We fill this gap by producing a spatially-explicit, moderate resolution (5 arcminute) global map of groundwater system archetypes based on groundwater interactions with social and ecological systems. These include interactions with streamflow, ecosystems, climate, agriculture, the economy, and water governance and management, all underpinned by existing global data. Archetypes, each with a unique set of interaction strengths and combinations, form a finite set of characteristic “fingerprints” that represent the dominant modes of interactions between groundwater and connected social and ecological systems. We find all WHYMAP large aquifer systems of the world are characterized by multiple social-ecological archetypes, suggesting that differentiated, context-appropriate approaches are necessary within large aquifers that are often assumed as uniform in global assessments and initiatives.We derive archetypes using multiple clustering algorithms and assign archetype membership based on majority agreement across clustering methods after cluster reclassification to create comparable maps. This multiple-method approach renders the archetypes more robust and less contingent on a single clustering algorithm while simultaneously enabling greater representation of archetype uncertainty.We additionally provide an outlook on sustainable development opportunities and challenges for each archetype. We summarize data sets that represent notable social-ecological outcomes related to the UN Sustainable Development Goals (SDGs), including: crop yield gaps (SDG 2), remotely sensed groundwater storage trends (SDG 6), economic inequality (SDG 10), human modification of terrestrial systems (SDG 15), and likelihood for hydropolitical interaction (SDG 16), among others. This work provides a number of useful contributions. First, the combination of archetyping (i.e., system characterization) and archetype-specific SDG outlook analysis provides a robust, data-driven overview of the role of groundwater in the global sustainability discourse. Secondly, the archetypes identify social-ecological system similarities across the globe, which may support interregional cooperation and networking, coordinated investment and interventions. Thirdly, as we harness the rapid growth in global data that document groundwater system interactions as the basis for our analysis, we simultaneously provide a synthesis and snapshot of the pertinent global data space. This snapshot can be used to identify the need for further data collection, especially on socio-economic interactions that remain underrepresented in global data. And finally, the archetypes raise awareness, build capacity, and shift mental models about the emerging perspective that it is necessary to conceptualize groundwater as a socially and ecologically connected resource.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,006 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».