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Record W4323363344 · doi:10.3389/feart.2023.1165061

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

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

Bibliographic record

VenueFrontiers in Earth Science · 2023
Typeeditorial
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsUniversité du Québec en Abitibi-TémiscamingueUniversité du Québec à Montréal
Fundersnot available
KeywordsHydrosphereEnvironmental scienceGroundwaterEcosystemEarth scienceVolume (thermodynamics)BiosphereEnvironmental resource managementOceanographyGeologyEcologyBiologyGeotechnical engineering

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.047
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.225
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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