scholarly journals AN ANALYSIS ON THE EFFICIENCY OF GREEN ROOF IN MANAGING URBAN STORMWATER RUNOFF

2021 ◽  
Vol 19 (17) ◽  
Author(s):  
Shazmin Shareena Ab. Azis ◽  
Muhammad Najib Mohamed Razali ◽  
Nurul Hana Adi Maimun ◽  
Nurul Syakima Mohd Yusoff ◽  
Mohd Shahril Abdul Rahman ◽  
...  
Keyword(s):  
Flash Flood ◽  
Meta Analysis ◽  
Green Roof ◽  
Drainage System ◽  
Green Roofs ◽  
Storm Water ◽  
Water Runoff ◽  
Extensive Green Roof ◽  
Storm Water Runoff ◽  
Urban Storm

Modernization has created new impervious urban landscape contributed to major catastrophe. Urban drainage system incapable to convey the excess rainwater resulting in flash flood due to heavy rainfall. The combination of green roof on building have tremendously proved to control stormwater efficiently. This study is conducted to review the efficiency of intensive and extensive green roof in reducing urban storm water runoff. This study identifies characteristic of green roof that contributes to lessening urban storm water runoff. Data was collected based on rigorous literature reviews and analyzed using meta-analysis. Overall, findings revealed intensive green roof performed better in reducing storm water runoff compared to extensive green roof. Green roof performance increases as the depth of substrate increased. Origanum and Sedum plants are both highly effective for intensive and extensive green roofs. The performance of green roof reduces as degree of roof slope increased.

scholarly journals Evaluating different machine learning methods to simulate runoff from extensive green roofs

2021 ◽  
Vol 25 (11) ◽  
pp. 5917-5935
Author(s):  
Elhadi Mohsen Hassan Abdalla ◽  
Vincent Pons ◽  
Virginia Stovin ◽  
Simon De-Ville ◽  
Elizabeth Fassman-Beck ◽  
...  
Keyword(s):  
Machine Learning ◽  
Green Roof ◽  
Green Roofs ◽  
Storm Water ◽  
Water Runoff ◽  
Learning Methods ◽  
Retention Model ◽  
Machine Learning Methods ◽  
Storm Water Runoff ◽  
Extensive Green Roofs

Abstract. Green roofs are increasingly popular measures to permanently reduce or delay storm-water runoff. The main objective of the study was to examine the potential of using machine learning (ML) to simulate runoff from green roofs to estimate their hydrological performance. Four machine learning methods, artificial neural network (ANN), M5 model tree, long short-term memory (LSTM) and k nearest neighbour (kNN), were applied to simulate storm-water runoff from 16 extensive green roofs located in four Norwegian cities across different climatic zones. The potential of these ML methods for estimating green roof retention was assessed by comparing their simulations with a proven conceptual retention model. Furthermore, the transferability of ML models between the different green roofs in the study was tested to investigate the potential of using ML models as a tool for planning and design purposes. The ML models yielded low volumetric errors that were comparable with the conceptual retention models, which indicates good performance in estimating annual retention. The ML models yielded satisfactory modelling results (NSE >0.5) in most of the roofs, which indicates an ability to estimate green roof detention. The variations in ML models' performance between the cities was larger than between the different configurations, which was attributed to the different climatic characteristics between the four cities. Transferred ML models between cities with similar rainfall events characteristics (Bergen–Sandnes, Trondheim–Oslo) could yield satisfactory modelling performance (Nash–Sutcliffe efficiency NSE >0.5 and percentage bias |PBIAS| <25 %) in most cases. However, we recommend the use of the conceptual retention model over the transferred ML models, to estimate the retention of new green roofs, as it gives more accurate volume estimates. Follow-up studies are needed to explore the potential of ML models in estimating detention from higher temporal resolution datasets.

A green roof experimental site in the Mediterranean climate: the storm water quality issue

2013 ◽  
Vol 68 (6) ◽  
pp. 1419-1424 ◽  
Author(s):  
Ilaria Gnecco ◽  
Anna Palla ◽  
Luca G. Lanza ◽  
Paolo La Barbera
Keyword(s):  
Atmospheric Deposition ◽  
Water Chemistry ◽  
Mediterranean Climate ◽  
Green Roof ◽  
Green Roofs ◽  
Storm Water ◽  
Water Runoff ◽  
Experimental Site ◽  
Storm Water Runoff ◽  
Runoff Quality

Since 2007, the University of Genoa has been carrying out a monitoring programme to investigate the hydrologic response of green roofs in the Mediterranean climate by installing a green roof experimental site. In order to assess the influence of green roofs on the storm water runoff quality, water chemistry data have been included in the monitoring programme since 2010, providing rainfall and outflow data. For atmospheric source, the bulk deposition is collected to evaluate the role of the overall atmospheric deposition in storm water runoff quality. For subsurface outflow, a maximum of 24 composite samples are taken on an event basis, thus aiming at a full characterization of the outflow hydrograph. Water chemistry data reveal that the pollutant loads associated with green roof outflow is low; in particular, solids and metal concentrations are lower than values generally observed in storm water runoff from traditional rooftops. The concentration values of chemical oxygen demand, total dissolved solids, Fe, Ca and K measured in the subsurface outflow are significantly higher than those observed in the bulk deposition (p &lt; 0.05). With respect to the atmospheric deposition, the green roof behaviour as a sink/source of pollutants is investigated based on both concentration and mass.

scholarly journals Novel advanced porous concrete in constructed wetlands: preparation, characterization and application in urban storm runoff treatment

2018 ◽  
Vol 78 (11) ◽  
pp. 2374-2382 ◽  
Author(s):  
Van Tai Tang ◽  
Kannan Pakshirajan
Keyword(s):  
Constructed Wetland ◽  
Oxygen Demand ◽  
Pollutant Removal ◽  
Storm Runoff ◽  
Storm Water ◽  
Water Runoff ◽  
Porous Concrete ◽  
Storm Water Runoff ◽  
Grass Growth ◽  
Urban Storm

Abstract Common porous concrete templates (CPCT) and advanced porous concrete templates (APCT) were employed in this study to construct wetlands for their applications in pollutant removal from storm runoff. The planting ability of the concrete was investigated by growing Festuca elata plants in them. Strength of the porous concrete (7.21 ± 0.19 Mpa) decreased by 1.8 and 4.9% over a period of six and 12 months, respectively, due to its immersion in lake water. The height and weight of Festuca elata grass growth on the porous concrete were observed to be 12.6–16.9 mm and 63.4–95.4 mg, respectively, after a duration of one month. Advanced porous concrete template based constructed wetland (APCT-CW) showed better removal of chemical oxygen demand (COD) (49.6%), total suspended solids (TSS) (58.9), NH3-N (52.4%), total nitrogen (TN) (47.7%) and total phosphorus (TP) (45.5%) in storm water, when compared with the common porous concrete template based constructed wetland (CPCT-CW) with 20.6, 29.8, 30.1, 35.4 and 26.9%, respectively. The removal of Pb, Ni, Zn by the CPCT-CW unit were 28.9, 33.3 and 42.3%, respectively, whereas these were 51.1, 62.5 and 53.8%, respectively, with the APCT-CW unit. These results demonstrate that the advanced porous concrete template in constructed wetland could be employed successfully for the removal of pollutants from urban storm water runoff.

scholarly journals Short-term forecasting of urban storm water runoff in real-time using extrapolated radar rainfall data

2013 ◽  
Vol 15 (3) ◽  
pp. 897-912 ◽  
Author(s):  
S. Thorndahl ◽  
M. R. Rasmussen
Keyword(s):  
Real Time ◽  
Water Levels ◽  
Rainfall Data ◽  
Storm Water ◽  
Water Runoff ◽  
Short Term ◽  
Radar Rainfall ◽  
Storm Water Runoff ◽  
Short Term Forecasting ◽  
Urban Storm

Model-based short-term forecasting of urban storm water runoff can be applied in real-time control of drainage systems in order to optimize system capacity during rain and minimize combined sewer overflows, improve wastewater treatment or activate alarms if local flooding is impending. A novel online system, which forecasts flows and water levels in real-time with inputs from extrapolated radar rainfall data, has been developed. The fully distributed urban drainage model includes auto-calibration using online in-sewer measurements which is seen to improve forecast skills significantly. The radar rainfall extrapolation (nowcast) limits the lead time of the system to 2 hours. In this paper, the model set-up is tested on a small urban catchment for a period of 1.5 years. The 50 largest events are presented.

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