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Record W4401841589 · doi:10.1016/j.watres.2024.122333

Isotope-based source assessment of water flowing from storm sewer systems to a receiving river during dry weather periods

2024· article· en· W4401841589 on OpenAlexafffundabout
Sarah S. Ariano, Cody A. Ross, Claire Oswald

Bibliographic record

VenueWater Research · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsThe Scarborough HospitalToronto Metropolitan UniversityCanada Research ChairsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOutfallStormwaterEnvironmental scienceSanitary sewerHydrology (agriculture)BaseflowGroundwaterCombined sewerSurface runoffStreamflowHydrographUrban runoffStormDrainage basinEnvironmental engineeringGeologyGeographyMeteorologyEcology

Abstract

fetched live from OpenAlex

Urban stormwater management systems, particularly storm sewers, are critical for managing runoff in urban areas. These systems are designed to function during wet weather events; however, field-based observations of these systems suggest that they may also be active flow pathways in dry weather conditions, ultimately contributing to streamflow. Unlike dry weather flow in wastewater systems, storm sewer dry weather flow has not been thoroughly explored. This research used stable isotopes of oxygen and hydrogen in water to examine the sources of dry weather flow from storm sewers in a highly urban catchment. A stable isotope mixing model was applied at the outfalls of two stormwater catchments and the receiving Black Creek, located in Toronto, Canada. Findings suggest that during dry periods, storm sewers receive non-stormwater inputs from tap water, wastewater, and groundwater, along with some precipitation, and that these sources may constitute up to 19 % of Black Creek's flow at the watershed scale. Seasonal patterns in flow and water sources were observed for the Black Creek and outfalls. At one outfall, dry weather flow was predominantly from the water distribution system (i.e., tap water and/or wastewater) throughout spring, summer, and fall. In contrast, at the second outfall, groundwater dominated in spring and summer, and groundwater and water distribution were equally proportioned in fall. Black Creek baseflow comprises a dynamic mix of water sources that at times are similar to the sources observed at the stormwater outfalls. Considering these findings, future work should incorporate strategic sampling of additional outfalls, and multiple years of data collection to explore inter-annual variability in these processes and focus on replicating a similar study in other urban watersheds with different climates and/or water infrastructure design. The study findings highlight that our understanding of dry weather flow from storm sewers is relatively limited, emphasizing the need for further exploration of this phenomenon to inform urban hydrological modelling, water quality studies, and urban water 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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.003

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 head0.035
GPT teacher head0.309
Teacher spread0.274 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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

Citations11
Published2024
Admission routes3
Has abstractyes

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