MétaCan
Menu
Back to cohort
Record W2560361211 · doi:10.14796/jwmm.c410

Modeling Stormwater Runoff from an Urban Park, Singapore Using PCSWWM

2016· article· en· W2560361211 on OpenAlexvenueno aff
Kim Irvine, Lloyd H.C. Chua

Bibliographic record

VenueJournal of Water Management Modeling · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsStormwaterSurface runoffEnvironmental planningStormwater managementGeographyUrban runoffUrban planningLand useEnvironmental scienceLow-impact developmentWater resource managementCivil engineeringEngineeringEcology

Abstract

fetched live from OpenAlex

The environmental and societal benefits of urban green space have long been recognized, but such land use often becomes a secondary consideration in urban drainage modeling, in part because it is more difficult to obtain site specific data for model calibration.Singapore has re-branded itself from garden city to city in a garden and as such parks, urban forests, and even agricultural areas are particularly important in sustaining the liveability of this highly urbanized city-state.The objective of this study was to quantify the rainfall-runoff processes for a park in Singapore and explore the efficacy of PCSWMM in estimating runoff from a nearly 100% pervious area.Admiralty Park consists of two sections, one being a traditional urban grassed area with trees, outdoor exercise areas, and promenades; and the other being a forested nature trail that includes a mangrove habitat opening to the Straits of Johor.Infiltration rates, using a double O-ring infiltrometer, were measured at five sites within the traditional grassed area and samples for textural analysis were collected at the same sites.This part of the park is serviced by a tile drain and an ISCO 2150 area-velocity meter was installed near the drain outlet, together with a tipping bucket rain gauge, to monitor rainfall and runoff between 2013-12-21 and 2014-05-04.The soils were 80% to 92% sand and classified mainly as loam sand or sandy loam.The measured maximum infiltration capacity (f 0 ) at Admiralty Park ranged between 140 mm/h and 850 mm/h, while for the fitted Horton infiltration equation, the estimated f 0 values tended to be lower than those measured and ranged between 80 mm/h and 690 mm/h.In total twelve storms were deemed to have a complete data record, with rainfall depths ranging between 9.6 mm and 99.4 mm and peak intensities between 42 mm/h and 144 mm/h.There was a strong correlation (0.872) between total storm event runoff volume and total rainfall depth, but weaker correlations between peak rainfall intensity and peak runoff rate (0.234) or peak rainfall intensity and total storm event runoff volume (0.382).In particular, two storms measured shortly after the 1 in 140 y drought exhibited relatively high peak runoff rates.PCSWMM was calibrated for ten of the twelve events and validated for two events.The Horton infiltration equation was used to operationalize PCSWMM and it was found that the rainfall derived inflow and infiltration (RDII) unit hydrograph approach also was required to help match the observed hydrograph shape.For the Horton infiltration equation, the calibrated values of f 0 ranged between 100 mm/h and 135 mm/h, while the values for f c , and α were the same for all events at 20 mm/h and 10/h respectively.The unit hydrograph values for T and K in the RDII calculations were the same for all events at 0.21 (h) and 1.2 respectively, while the value of R (proportion of the rainfall that is translated to RDII) ranged from 1% to 75%, but typically was around 40%.The calibration efforts prioritized matching peak over total event volume and for the ten events the Nash-Sutcliffe statistic was an excellent 0.98 for peak flow and 0.73 for event volume.Runoff estimates for the two validation events also matched measured flow quite well.PCSWMM appears to be well capable of modeling runoff from urban parks.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.248
Teacher spread0.222 · 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 designSimulation or modeling
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

Citations6
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Water Management ModelingSame topicFlood Risk Assessment and ManagementFrench-language works237,207