MétaCan
Menu
← Back to cohort
Record W3179022579 · doi:10.5194/egusphere-egu21-12564

Influence of morphometric parameters and meteorological conditions on ephemeral pool hydrology in the Canadian Shield forest 

2021· article· en· W3179022579 on OpenAlexaffabout
Marjolaine Roux, Marie Larocque, Philippe Nolet, Sylvain Gagné

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsInstitut du Savoir MontfortUniversité du Québec à Montréal
Fundersnot available
KeywordsEphemeral keyHydrology (agriculture)WetlandBedrockSnowmeltEnvironmental scienceSTREAMSSurface waterHabitatSpring (device)GroundwaterEcologySurface runoffGeologyGeomorphologyBiology

Abstract

fetched live from OpenAlex

Ephemeral pools are geographically isolated wetlands commonly found in temperate forests of northeastern North America. These wetlands are usually hydrologically isolated from the surface water network but in some conditions can be connected to local groundwater flow. They fill at maximal capacity following spring snowmelt and dry out during summer. They contribute to forest biodiversity by providing breeding habitats for amphibians during their spring and early summer period of hydrological activity. However, ephemeral pools are poorly understood and rarely studied because of their small dimensions and temporary hydrology. This work presents the final results of a five-year study aimed to acquire new knowledge on ephemeral pool hydrology to go beyond the anecdotical pool and to understand the conditions and processes that driving their hydrology. A large number of pools (39) located in the Canadian Shield forest were instrumented to monitor hourly water level variations in the pool and in the neighboring and underlying fractured bedrock aquifer. They were also described in extensive details for their geomorphological features and water levels over a period from one to five years (April 2016 to July 2020). The first rather surprising result from this work is that, although the pools are all located in bedrock depressions, they cover a wide range of morphologies. Their maximum sizes vary from 29 to 1866 m2 and their maximal volumes vary from 4 to 654 m3. Their maximum water depths are also highly contrasted, ranging from 0.14 m to 2.03 m. The pool depressions are overlain by mineral sediments (silt to fine sand with occurrences of coarse sand and gravel) of contrasted thicknesses (0 m to 1.70 m). An organic matter layer of highly varying thickness (0.12 m to 1.24 m) was observed at all sites above the mineral sediments. Despite these varied morphological conditions, all the pools have similar hydrological patterns throughout the year and these patterns are highly resilient to meteorological conditions. They dry out between the end of May and the end of July, rapid temporary refilling during important summer rainfall events, and partially refilling in autumn following more frequent rainfall events and lower evapotranspiration. The results show that surface water levels are maintained when the underlying sediments are saturated. Otherwise, the ephemeral pools lose water by infiltration to the underlying aquifer. Water level variations within the pools are positively and significantly correlated with net precipitation (P – PET). Hydroperiods vary between 28 days (2020) and 86 days (2017), reflecting the year-to-year meteorological variability. The mean hydroperiod is significantly correlated to spring rainfall (April to June), but also to the volume of water stored in the pool, and to the pool surface area. This study provides a unique and original dataset that contribute to better understand the hydrodynamics and resilience to anthropogenic (forestry) and natural (climate change) impacts of a wetland type that is rarely studied but provide crucial habitats for forest biodiversity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.241
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same topicHydrology and Watershed Management Studies→French-language works237,207→