Simulation study on effect of permeable pavement on reducing flood risk of urban runoff

Zhu, Haoran; Yu, Mingming; Zhu, Junqing; Lü, Huanzhang; Cao, Ruibing

doi:10.1016/j.ijtst.2018.12.001

Cited by 80 publications

(37 citation statements)

References 3 publications

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“…Valinski and Chandler [18] showed that permeable pavement with asphalt mixture and Portland concrete as a surface presented filtering rates greater than native soils. It has been shown that permeable interlocking concrete pavement can reduce stormwater runoff [10,19,20] and can filter and decrease pollutants concentrations [15,[17][18][19][20][21][22][23][24][25][26]. Even the possibility of using a permeable pavement system to treat greywater for irrigation and toilet flushing has been studied [27]; it was found that permeable pavements can work as a treatment and storage unit in greywater reuse schemes, but they are inefficient in reducing total aerobic and total coliform bacteria.…”

Section: Reservoir Coursementioning

confidence: 99%

The Use of Permeable Interlocking Concrete Pavement to Filter Stormwater for Non-Potable Uses in Buildings

Ghisi

Belotto

Thives

2020

Water

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A reduction in potable water demand in buildings could be made by using non-potable water for certain uses, such as flushing toilets. This represents a sustainable strategy that results in potable water savings while also using an underutilised resource. This work assesses the use of permeable interlocking concrete pavement to filter stormwater that could be used for non-potable purposes in buildings. Two pavement model systems were tested. One of the model systems presents a filter course layer with coarse sand and the other model system has no filter course layer. In order to evaluate the filtering capacity, the model systems were exposed to rain events. The amount of water infiltrated through the layers was measured to represent the potential quantity available for use. Stormwater runoff samples were collected from a parking lot paved with impermeable interlocked blocks and then, these were tested in both model systems. Water samples were subjected to quality tests according to the parameters recommended by the Brazilian National Water Agency. The model system with no filter course showed filtering capacity higher (88.1%) than the one with a filter course layer (78.8%). The model system with a filter course layer was able to reduce fecal coliforms (54.7%), total suspended solids (62.5%), biochemical oxygen demand (78.8%), and total phosphorus concentrations (55.6%). Biochemical oxygen demand (42.4%) and total phosphorus concentrations (44.4%) increased in the model system with no filter course layer. In conclusion, one can state that the filter course layer used in permeable interlocking concrete pavement can contribute to decreasing pollutants and can improve stormwater quality. The use of permeable interlocking concrete pavement showed to be a potential alternative for filtering stormwater prior to subsequent treatment for non-potable uses in buildings.

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Section: Reservoir Coursementioning

confidence: 99%

The Use of Permeable Interlocking Concrete Pavement to Filter Stormwater for Non-Potable Uses in Buildings

Ghisi

Belotto

Thives

2020

Water

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“…Road areas' ground type was accepted as pervious/permeable pavement to minimizing flooding. Zhu et al 2018 made a simulation of the permeable pavement for decreasing the urban surface runoff coefficient and the flood peak flow, so the range of the runoff coefficient of different permeable pavement structures was recommended according to the simulation results: drainage surface = 0.70-0.85, semi-permeable pavements ≤ 0.45, and fully permeable pavement [40]. In this study, the runoff coefficient was assumed to be 0.70 for the pervious pavement.…”

Section: Scenario 2 (Sn2)mentioning

confidence: 99%

The Assessment of Urbanization Effect and Sustainable Drainage Solutions on Flood Hazard by GIS

Ertan

Çeli̇k

2021

Sustainability

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: Rapid and uncontrolled changes in land use patterns due to urbanization negatively affect urban rainfall-runoff processes and flood hazard. In this study, a method that included different sustainable drainage solutions, such as green infrastructure (GI) usage for flood hazard mitigation with various scenarios on a geographic information system (GIS) platform within a 1653 ha catchment of the Kağıthane Stream in İstanbul, Turkey is presented. Developed scenarios are as follows: scenario one (SN1) is the current situation; scenario two (SN2) used green roof application for buildings and a permeable surface for roads; scenario three (SN3) used only green roof application for buildings; scenario four (SN4) used a rainwater barrel for collecting roof water, a swale canal for collecting road water, and added additional structures to open areas to observe urbanization; scenario five (SN5) considered multiple GI implementations; and scenario six (SN6) considered full urbanization. The results indicate that greener infrastructure implementation provides benefits in reducing both the runoff coefficient and the peak flowrate, and the flood inundation area and number of structures affected by flood risk were decreased. The integrated evaluation system, which consisted of the geographic information system and the assessment of the 1D HEC-RAS hydrologic model, was applied to evaluate the GI usage and flood mitigation.

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“…Zhou et al [32] comprehensively considered the land use type, surface impermeability and drainage system to establish a SWMM model and estimated the flood volume and risk under urbanization and climate change. Zhu et al [33] simulated the influence of different pavement structures (drainage surface, permeable pavement and permeable road) on reducing surface runoff by constructing a SWMM model. Kim and Cho [34] employed SWMM and a 2D surface model to simulate the inundation area and range of the city under 320 different rainfall situations.…”

Section: Literature Reviewmentioning

confidence: 99%

Real-Time Prediction of the Water Accumulation Process of Urban Stormy Accumulation Points Based on Deep Learning

Zhou

Wang

2020

IEEE Access

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Influenced by climate change and urbanization, urban flood frequently occurs and represents a serious challenge for many cities. Therefore, it is necessary to generate refined predictions of urban floods, such as the prediction of water accumulation processes at water accumulation points, which is of great significance for supporting water-related managers to reduce flood losses. In this study, 16 combination schemes of rainfall sensitivity indicators were used to determine the optimal scheme for predicting the depth of accumulated water, and the gradient boosting decision tree (GBDT) algorithm in deep learning was used to build a prediction model of the accumulation process of urban stormy accumulation points. Among the 16 schemes, the relative error of scheme 1 is 15.39%, and the qualified rate is 92.86%. This scheme exhibits the highest accuracy for the prediction results of water accumulation depth. Given this finding, the GBDT algorithm was used to construct a regression prediction model of the water accumulation process based on the collected historical rainfall water accumulation data of 50 water accumulation points. The results demonstrated that the GBDT regression prediction model has a mean relative error of 19.77%, a qualified rate of 82.00%, and a peak average relative error of 5.48%, which verify the validity and applicability of the model for the real-time prediction of the process of water accumulation. INDEX TERMS Urban flood, Deep learning, Water accumulation, Real-time prediction.

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Simulation study on effect of permeable pavement on reducing flood risk of urban runoff

Cited by 80 publications

References 3 publications

The Use of Permeable Interlocking Concrete Pavement to Filter Stormwater for Non-Potable Uses in Buildings

The Use of Permeable Interlocking Concrete Pavement to Filter Stormwater for Non-Potable Uses in Buildings

The Assessment of Urbanization Effect and Sustainable Drainage Solutions on Flood Hazard by GIS

Real-Time Prediction of the Water Accumulation Process of Urban Stormy Accumulation Points Based on Deep Learning

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