Rainwater storage/infiltration function of rain gardens for management of urban storm runoff in Japan

Zhang, Linying; Oyake, Yui; Morimoto, Yukihiro; Niwa, Hideyuki; Shibata, Shozo

doi:10.1007/s11355-019-00391-w

Cited by 30 publications

(15 citation statements)

References 29 publications

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“…An equally important input condition involves the rain garden setting parameters. In this study, those suggested by Zhang et al [43] and the SWMM User's Manual [19] were used (Table 6). The design rain gardens are one meter in depth, incorporating a soil thickness of 700 mm and a storage thickness of 300 mm.…”

Section: Rain Garden Parametersmentioning

confidence: 99%

“…The study results showed that runoff volume reductions of 13.78, 13.72, 13.68, 13.66, 13.61 and 12.11% were achieved by adding rain garden facilities with different rainfall return periods in Nakagyo Ward. Zhang et al [43] also reported that more than 60% of runoff was controlled by two rain gardens at Kyoto Gakuen University (KGU). The runoff control rate observed in this study was significantly lower than that observed by Zhang et al [43] due to differences between the rain garden areas and rainfall conditions.…”

Section: Contaminant Reduction Rate With Six Rainfall Return Periodsmentioning

confidence: 99%

“…Zhang et al [43] also reported that more than 60% of runoff was controlled by two rain gardens at Kyoto Gakuen University (KGU). The runoff control rate observed in this study was significantly lower than that observed by Zhang et al [43] due to differences between the rain garden areas and rainfall conditions. In this study, the ratio of the rain garden facilities to the catchment area was 7.03%, which is much lower than the 91.25% of Zhang et al [43].…”

Section: Contaminant Reduction Rate With Six Rainfall Return Periodsmentioning

confidence: 99%

“…Another appealing aspect of rain gardens is flexibility relating to physical dimensions and incorporation into the natural landscape, resulting in a wide application for residential areas, parking lots, roads), parks and other places requiring urban runoff control and pollutant removal [35][36][37][38][39][40]. Japan has introduced small-watershed rain gardens in recent years on university campuses (Figure 3) and elsewhere [41][42][43][44], but none in Kyoto's Nakagyo Ward. There is a need to evaluate the effects of rain gardens on an urban catchment scale for promoting the green-movement approach of the rain garden and enhance ecosystem-related services to an urban area of Japan.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Rain Garden Effects for the Management of Urban Storm Runoff in Japan

Zhang

Shibata

2020

Sustainability

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Storm runoff is a growing concern against a background of increasing urban densification, land-use adaptation and climate change. In this study, a storm water management model was used to analyze the hydrological and water-quality effects of rain gardens (also known as bioretention cells) as nonpoint source control solutions in low-impact development (LID) practices for an urban catchment in the Nakagyo Ward area of Kyoto in Japan. The results of simulations with input involving Chicago hyetographs derived for different rainfall return periods (referred to as 3 a, 5 a, 10 a, 30 a, 50 a and 100 a) indicated the effectiveness of this arrangement, in particular for rainstorm 3 a, which exhibited the maximum contaminant reduction ratio (Total Suspended Solids (TSS) 15.50%, Chemical Oxygen Demand (COD) 16.17%, Total Nitrogen (TN) 17.34%, Total Phosphorus (TP) 19.07%) and a total runoff reduction volume of 46.56 × 106 L. With 5 a, the maximum number of flooding nodes was reduced to 87, demonstrating that rain gardens handle rainfall effectively over a five-year return period. There was a one-minute delay for 100 a, which again indicates that rain gardens support control of urban runoff and mitigate flooding. Such gardens were associated with reduced stormwater hazards and enhanced resistance to short-term rainstorms at the research site, and should be considered for urban planning in Kyoto and other cities all over the world.

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Section: Rain Garden Parametersmentioning

confidence: 99%

Section: Contaminant Reduction Rate With Six Rainfall Return Periodsmentioning

confidence: 99%

Section: Contaminant Reduction Rate With Six Rainfall Return Periodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of Rain Garden Effects for the Management of Urban Storm Runoff in Japan

Zhang

Shibata

2020

Sustainability

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“…The observed peak runoff time is also delayed by significant factor by rain gardens. Several authors have supported the overall effectiveness of rain gardens in reducing peak runoff when compared with the inflow hydrograph peak and control of urban runoff (Li et al 2016;Shuster et al 2017;Yuan et al 2017;Zhang et al 2019). Efficiency of rain garden depends upon several factors such as soil media, vegetation, runoff rate etc.…”

Section: Introductionmentioning

confidence: 99%

Rain garden infiltration rate modeling using gradient boosting machine and deep learning techniques

Kumar

Singh

2021

Water Science and Technology

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Rain garden are effective in reducing storm water runoff, whose efficiency depends upon several parameters such as soil type, vegetation and metrological factors. Evaluation of rain gardens has been done by various researchers. However, knowledge for sound design of rain gardens is still very limited, particularly the accurate modeling of infiltration rate and how much it differs from infiltration of natural ground surface. The present study uses experimentally observed infiltration rate of rain gardens with different types of vegetation (grass, candytuft, marigold and daisy with different plant densities) and flow conditions. After that, modeling has been done by the popular infiltration model i.e. Philip's model (which is valid for natural ground surface) and soft computing tools viz. Gradient Boosting Machine (GBM) and Deep Learning (DL). Results suggest a promising performance (in terms of CC, RMSE, MAE, MSE and NSE) by GBM and DL in comparison to the relation proposed by Philip's model (1957). Most of the values predicted by both GBM and DL are within scatter limits of ±5%, whereas the values by Philips model are within the range of ±25% error lines and even outside. GBM performs better than DL as the values of the correlation coefficients and Nash-Sutcliffe model efficiency (NSE) coefficient are the highest and the root mean square error is the lowest. The results of the study will be useful in selection of plant type and their density of the rain garden in the urban area.

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Reviewing the Performance of Nature-Based Solutions for Stormwater Management in Urban Areas

Orta-Ortiz

Geneletti

2021

Lecture Notes in Civil Engineering

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Rainwater storage/infiltration function of rain gardens for management of urban storm runoff in Japan

Cited by 30 publications

References 29 publications

Assessment of Rain Garden Effects for the Management of Urban Storm Runoff in Japan

Assessment of Rain Garden Effects for the Management of Urban Storm Runoff in Japan

Rain garden infiltration rate modeling using gradient boosting machine and deep learning techniques

Reviewing the Performance of Nature-Based Solutions for Stormwater Management in Urban Areas

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