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Enregistrement W4323363344 · doi:10.3389/feart.2023.1165061

Editorial: “Novel approaches for understanding groundwater dependent ecosystems in a changing environment”

2023· editorial· en· W4323363344 sur OpenAlexaffabout
Éric Rosa, Marie Larocque, Christine Hatch, Abraham E. Springer

Notice bibliographique

RevueFrontiers in Earth Science · 2023
Typeeditorial
Langueen
DomaineEnvironmental Science
ThématiqueHydrology and Watershed Management Studies
Établissements canadiensUniversité du Québec en Abitibi-TémiscamingueUniversité du Québec à Montréal
Organismes subventionnairesnon disponible
Mots-clésHydrosphereEnvironmental scienceGroundwaterEcosystemEarth scienceVolume (thermodynamics)BiosphereEnvironmental resource managementOceanographyGeologyEcologyBiologyGeotechnical engineering

Résumé

récupéré en direct d'OpenAlex

Groundwater represents one of the largest reservoirs of freshwater on the planet (Oki and Kanae, 2006) and is the source of drinking water for 2.5 billion people (Grönwall and Danert, 2020). From the earliest evidence of groundwater-related knowledge dating back to antiquity, with the construction of qanats in the Middle East, to the contemporary use of artificial intelligence in support of aquifer studies, hydrogeological science has expanded rapidly (Fetter, 2004a; b; Niu et al., 2014). Other related disciplines such as hydrology, ecology, land use planning, and climatology, have experienced comparable booms. Yet, one of the downfalls of the rapid growth and specialization of different scientific disciplines is that the scientific community sometimes becomes split into groups with complementary expertise. As a result, some challenges that should implicitly be addressed with holistic or multidisciplinary approaches have been neglected by the scientific community. This justifies pursuing the study of groundwater dependent ecosystems (GDEs). GDEs are ecosystems for which vegetation composition, structure, and functions are reliant on a given supply of groundwater (Kløve et al., 2011). GDE types are extremely varied and include springs, wetlands, and groundwater-fed terrestrial and aquatic environments (Bertrand et al., 2012). The study of GDEs is thus clearly positioned at the intersection of many disciplines (Figure 1). Consider the example of a spring located in a valley. The geological perspective is needed for evaluating how the architecture of rock formations and overburden deposits influence the geometry of aquifers. The hydrogeological perspective is critical for documenting the characteristics of exfiltration zones in groundwater flow systems (e.g., Lambert et al., 2022). The climate perspective is crucial to assess the inputs (precipitation) and outputs (evapotranspiration) of the flow systems (e.g., Rohde et al., 2021). The ecological perspective is central to identify the faunal and floral species in discharge areas and document their dependence on the (eco)hydrological, thermal, and geochemical conditions maintained by groundwater flows (e.g., Mitton and Allen, 2022). Ultimately, land-use planning and rehabilitation approaches will be critical to ensure the protection and rehabilitation of GDEs under increasing human pressures (e.g., Hatch and Ito, 2022; Watts et al., 2023). This Research Topic brings together five original contributions that address critical issues concerning GDEs from the different perspectives illustrated in Figure 1. The studied environmental contexts range from relatively arid California, USA (Rohde et al., 2021) to the boreal zone of the Province of Quebec, Canada (Lambert et al., 2022), the humid continental zone of the northeastern USA (Hatch and Ito, 2022; Watts et al., 2023) and the Lower Fraser Valley of British Columbia, Canada (Mitton and Allen, 2022). It is clear from the contributions collected here that assessing the sensitivity and resilience of GDEs to human pressures and climate change is of great importance. Each contribution stands out for deploying innovative approaches to the study of GDEs. For example, Lambert et al. (2022) focused on the evaluation of the hydrogeological balance of peatlands via numerical hydrogeological models based on field measurements. Their work shows that the hydrogeomorphic environment surrounding boreal peatlands is a major factor to consider when assessing their sensitivity to climate change. The work of Rohde et al. (2021) is further based on hydrogeological observations (groundwater levels) complemented by climate data and satellite images. The machine learning approach they developed allows for an efficient landscape-scale assessment of GDEs at risk. The authors highlight the increased impacts suffered by GDEs located in areas where sustainable water management is not implemented. The approach taken by Mitton and Allen (2022) stands out. Thanks to the coupled use of hydrological measurements, habitat monitoring and the assessment of benthic macroinvertebrate communities. Their findings highlight the need to better characterize benthic habitat heterogeneity within intermittent streams, particularly in areas of groundwater discharge, with a view to better understanding the resilience of such GDEs to human pressures and climate change. Hatch and Ito (2022) and Watts et al. (2023) bring GDE resilience to another level, by considering the possibility of restoring these ecosystems. Hatch and Ito (2022) assess groundwater flow within an "anthropogenic aquifer" once created for cranberry cultivation to provide the knowledge required to restore the "natural" water regime of the GDE. Watts et al. (2023) further advances this work and deploys innovative thermal remote sensing approaches to assess groundwater exfiltration to the impacted wetland in the pre- and post-restoration phases. Ultimately, the insights from these studies will provide the knowledge needed to optimize approaches to regenerate GDEs at human-impacted sites. Combining the studies presented in this Research Topic, not only contributes to disseminate innovative knowledge to better understand GDEs, but also to draw the attention of the scientific community to the anthropogenic and climatic pressures to which they are exposed. This is a step towards better protecting GDEs for the benefit of current and future generations of humans and of the breadth of fauna and flora they sustain.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,047
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,001
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,031
Tête enseignante GPT0,225
Écart entre enseignants0,194 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2023
Routes d'admission2
Résumé présentoui

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