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Record W4408428170 · doi:10.5194/egusphere-egu25-13098

The Role of Littoral Vegetation and Open Water Greenhouse Gas Fluxes on the Carbon Budget of Urban Stormwater Ponds

2025· preprint· en· W4408428170 on OpenAlexaffabout
Fereidoun Rezanezhad, Stephanie Slowinski, Jovana Radosavljevic, Philippe Van Cappellen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsEnvironmental scienceGreenhouse gasHydrology (agriculture)Littoral zoneVegetation (pathology)Ecosystem respirationStormwaterSurface runoffEcosystemSurface waterCarbon dioxidePrimary productionEnvironmental engineeringEcology

Abstract

fetched live from OpenAlex

Stormwater ponds (SWPs) are a common stormwater management technology in new urban developments and have been suggested to be significant sources of the greenhouse gases (GHGs) carbon dioxide (CO2) and methane (CH4). However, they also sequester organic carbon and reduce the surface runoff of nutrients, hence, altering nutrient limitation patterns, trophic conditions, and GHG exchanges. Although numerous studies have focused on estimating open water GHG emissions in artificial ponds, there are limited studies that evaluate net carbon budgets of urban SWP systems comprehensively. In this study, we assessed the relative contributions of the littoral vegetation and open water GHG fluxes to the carbon budgets in two SWPs located in the City of Kitchener, Ontario, Canada. CO2 and CH4 fluxes were measured in the forebay and main basin of two SWPs draining catchments with two different catchment land use (residential versus industrial). Using vegetation and floating chambers, CO2 and CH4 fluxes were measured bi-weekly across all seasons, capturing Net Ecosystem Exchange (NEE), Ecosystem Respiration (ER), and Gross Ecosystem Production (GEP) from both bank and submerged vegetation, plus the diffusive and ebullitive fluxes from the open water surface. Additionally, key parameters, including photosynthetically active radiation (PAR), air and soil temperature, water pH, conductivity, and dissolved gas concentrations, were also measured. We observed significant differences in the fluxes between the littoral vegetation and open water surfaces. Carbon gas emissions from the open water surface were dominated by ebullitive CH₄ fluxes, with the open water acting as a net carbon source. Ebullition events occurred more frequently and with greater intensity in the forebay areas of the SWPs, contributing the most to open water carbon emissions. In contrast, carbon gas emissions from the vegetation were largely driven by photosynthesis and soil respiration, with the vegetated littoral zone functioning as a net CO2 sink. Different vegetation types exhibited varied responses to meteorological conditions, but all showed clear seasonal trends, with higher gas fluxes in summer due to increased biological activity, and minimal fluxes during the frozen season. Unlike vegetation, open water fluxes did not display a distinct seasonal trend; instead, they were primarily influenced by precipitation events and inflow runoff. The forebay of the industrial pond received higher carbon inputs from contaminated stormwater runoff, leading to greater sediment accumulation and elevated GHG fluxes, with frequent and high-intensity CH4 ebullition events being a notable feature. Our findings highlight the critical influence of land use, hydrological events, and seasonal cycles on the carbon balance of SWPs and their potential role in urban carbon cycling.

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)0.000
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 score0.511
Threshold uncertainty score0.984

Distilled classifier scores by category (both heads)

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

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 head0.222
Teacher spread0.211 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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