Modeling Stormwater Runoff from an Urban Park, Singapore Using PCSWWM
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».