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Record W4220700251 · doi:10.5194/egusphere-egu22-13418

Stratified hydraulic conductivity testing of green infrastructure: A lysimeter bioretention cell study 

2022· preprint· en· W4220700251 on OpenAlexaboutno aff
Daniel Green, Alethea Goddard, Ross Stirling

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research Council
KeywordsBioretentionStormwaterSurface runoffGreen infrastructureHydraulic conductivityEnvironmental scienceLysimeterHydrology (agriculture)Infiltration (HVAC)Environmental engineeringSoil waterEngineeringSoil scienceGeographyEcologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Bioretention cells, also referred to as ‘rain gardens’, are Green Infrastructure features with a functional role of managing urban flood risk and relieving pressure on traditional grey infrastructure systems. These Sustainable Drainage Systems (SuDS) rely on the use of soil and vegetation to attenuate and discharge stormwater via infiltration into the ground or via underground outlets into sewer networks whilst filtering pollutants in urban runoff and providing value to public space. Soil makes up a large proportion of these systems and plays a key role in providing the storage capacity for retaining stormwater and determining outflow discharges. This role is typically characterised using laboratory or in-field surface assessments of saturated hydraulic conductivity (Ksat), which provide an empirical assessment of SuDS performance. Guidance suggests that SuDS substrates should have a Ksat that ensures that systems are able to collect and store runoff to provide water retention without becoming waterlogged before the next rainfall event. However, in-field evaluations are rarely conducted due to cost and testing rarely identifies variation with depth through the soil profile. This paper presents in-field Ksat testing from four-purpose built, vegetated bioretention cell lysimeters at the UKCRIC National Green Infrastructure Facility, Newcastle-upon-Tyne, UK, commissioned as part of the Engineering and Physical Sciences Research Council (EPSRC) project ‘Urban Green Design and Modelling of SuDS’ (EP/S005536/1). Ksat was measured using a Soil Moisture Equipment Corporation Guelph Constant Head Field Permeameter to obtain stratified Ksat values throughout the 750 mm deep soil profile of the lysimeters. Ksat was assessed in the context of four different vegetation treatments, including an unvegetated control lysimeter, an amenity grass covered lysimeter and two mono-cropped lysimeters planted with Iris sibirica and Deschampsia cespitosa. Results show that Ksat values are systematically variable through the soil column and are a function of confining pressure with soil depth and wash through processes. Trends in porosity with soil depth are shown to be comparable across all lysimeter planting styles with some subtle differences associated with vegetation planting. All lysimeters feature higher Ksat values at the near-surface (ranging from 160.2 – 648.0 mm/hr at 0 – 100 mm depth), thought to be due to weathering and wash-through processes associated with near-surface soil strata being exposed to prevalent weather conditions. Where larger vegetation is present, higher Ksat values are recorded, reflecting the presence of root-derived preferential flow pathways. The depth of elevated near-surface Ksat values reflects the rooting depth and structure of the plant species studied. The use of a single Ksat value does not adequately capture the spatially variable hydraulic properties of bioretention systems. The results presented herein also have implications for SuDS design and maintenance, suggesting that the hydraulic properties of these systems may change through time. Consequently, SuDS scheme planners and developers should conduct multiple assessments of Ksat through the soil profile to provide robust empirically-based model parameter values to ensure that systems are fit for purpose.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.051
GPT teacher head0.257
Teacher spread0.206 · 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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Citations1
Published2022
Admission routes1
Has abstractyes

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