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Record W4402788760 · doi:10.1016/j.jhydrol.2024.132048

Constraining seasonal and spatial ambient and urban groundwater contributions to streamflow across a mixed land-use regional-scale Precambrian Shield watershed

2024· article· en· W4402788760 on OpenAlexafffundabout
Kimberly Montgomery, April L. James, Merrin L. Macrae, Arghavan Tafvizi, Rebecca G. Snider, Pradeep Goel, Helen Zhang, Huaxia Yao, Mark P. Wachowiak

Bibliographic record

VenueJournal of Hydrology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsMinistry of EnvironmentMinistry of the Environment, Conservation and ParksUniversity of WaterlooNipissing University
FundersNatural Sciences and Engineering Research Council of CanadaMinistère de l’Environnement, de la Protection de la nature et des Parcs
KeywordsWatershedGroundwaterStreamflowHydrology (agriculture)PrecambrianShieldScale (ratio)Environmental scienceLand useSpatial ecologySpatial variabilityGeologyPhysical geographyGeographyDrainage basinCartographyEcology

Abstract

fetched live from OpenAlex

• Ambient and urban groundwater expressed in streamflow for a Precambrian Shield basin. • Elevated Chloride in cold region basin helps identify urban groundwater in streamflow. • Summer urban groundwater can rival ambient groundwater contributions to streamflow. • Fall tracer-based models show less groundwater compared to graphical separation. Groundwater-surface water (GW-SW) interactions are complex phenomena that vary naturally over space and time and are being influenced by environmental change. The Whitson River watershed is a mixed land-use Precambrian shield watershed located in Northeastern Ontario, Canada in which groundwater is a source of municipal water supply and in which groundwater and surface waters are at risk of potential impacts from urban runoff, ongoing municipal drainage projects, periodic flooding, and climate change. This study used watershed-scale synoptic surveys of stable isotopes (δ 18 O and δ 2 H) and geochemistry, and corresponding mixing-model approaches (two-component and three-component) to quantify the seasonal and spatial variation in surface water, ambient, and urban groundwater contributions to streamflow. Multiple mixing models were generated based on isotope and geochemical source water identification, and the performance of these models was compared. Mixing-model analyses showed that groundwater contributes a critical, sustaining source to streamflow (> 50 % or more) during summer baseflow conditions, dropping to ∼ 15 % in fall when connection to shield lakes and wetlands dominate. Three-component mixing models showed similar results to the two-component models, refining groundwater contributions into ambient and urban groundwater sources. Estimated urban groundwater contributed 30 % or more to summer baseflow, dropping to ∼ 4 % in the fall. Baseflow estimates derived from graphical hydrograph separation were similar to mixing model groundwater estimates in the summer but suggest this approach overestimates groundwater contributions during the fall. These results demonstrate the complexity of source water contributions to streamflow and the challenges in their determination in mixed-land-use Precambrian Shield landscapes where communities are largely located. Assessment of source water contributions to streamflow across the Whitson River sub-watershed has generated a region-specific conceptualization (e.g. urban versus ambient groundwater) of a basin that is experiencing increased urban development, providing information that will be of value for long term management.

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 score0.318
Threshold uncertainty score0.632

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.251
Teacher spread0.240 · 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

Citations3
Published2024
Admission routes3
Has abstractyes

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