Stratified hydraulic conductivity testing of green infrastructure: A lysimeter bioretention cell study 
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".