Probabilistic assessment of hydrologic retention performance of green roof considering aleatory and epistemic uncertainties

Lingwan You; Yeou Koung Tung; Chulsang Yoo

doi:10.2166/nh.2020.086

Probabilistic assessment of hydrologic retention performance of green roof considering aleatory and epistemic uncertainties

Lingwan You, Yeou Koung Tung, Chulsang Yoo

School of Civil, Environmental and Architectural Engineering

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

Green roofs (GRs) are well known for source control of runoff quantity in sustainable urban stormwater management. By considering the inherent randomness of rainfall characteristics, this study derives the probability distribution of rainfall retention ratio Rr and its statistical moments. The distribution function of Rr can be used to establish a unique relationship between target retention ratio Rr,T, achievable reliability AR, and substrate depth h for the aleatory-based probabilistic (AP) GR design. However, uncertainties of epistemic nature also exist in the AP GR model that makes AR uncertain. In the paper, the treatment of epistemic uncertainty in the AP GR model is presented and implemented for the uncertainty quantification of AR. It is shown that design without considering epistemic uncertainties by the AP GR model yields about 50% confidence of meeting Rr,T. A procedure is presented to determine the design substrate depth having the stipulated confidence to satisfy Rr,T and target achievable reliability ART.

Original language	English
Pages (from-to)	1377-1396
Number of pages	20
Journal	Hydrology Research
Volume	51
Issue number	6
DOIs	https://doi.org/10.2166/nh.2020.086
Publication status	Published - 2020 Dec 1

Bibliographical note

Funding Information:
The study is supported by the Joint Research under the National Research Foundation (Korea)–Ministry of Science & Technology (Taiwan) Cooperative Program (MOST 105-2923-E-009-004-MY2). All data used in this paper are properly cited and referred to in the reference list.

Funding Information:
The study is supported by the Joint Research under the National Research Foundation (Korea)-Ministry of Science & Technology (Taiwan) Cooperative Program (MOST 105-2923-E-009-004-MY2). All data used in this paper are properly cited and referred to in the reference list.

Publisher Copyright:
© 2020 The Authors.

Keywords

Green roof
Probabilistic-based design
Probability
Retention ratio
Uncertainty analysis

ASJC Scopus subject areas

Water Science and Technology

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10.2166/nh.2020.086

Cite this

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title = "Probabilistic assessment of hydrologic retention performance of green roof considering aleatory and epistemic uncertainties",

abstract = "Green roofs (GRs) are well known for source control of runoff quantity in sustainable urban stormwater management. By considering the inherent randomness of rainfall characteristics, this study derives the probability distribution of rainfall retention ratio Rr and its statistical moments. The distribution function of Rr can be used to establish a unique relationship between target retention ratio Rr,T, achievable reliability AR, and substrate depth h for the aleatory-based probabilistic (AP) GR design. However, uncertainties of epistemic nature also exist in the AP GR model that makes AR uncertain. In the paper, the treatment of epistemic uncertainty in the AP GR model is presented and implemented for the uncertainty quantification of AR. It is shown that design without considering epistemic uncertainties by the AP GR model yields about 50% confidence of meeting Rr,T. A procedure is presented to determine the design substrate depth having the stipulated confidence to satisfy Rr,T and target achievable reliability ART. ",

keywords = "Green roof, Probabilistic-based design, Probability, Retention ratio, Uncertainty analysis",

author = "Lingwan You and Tung, {Yeou Koung} and Chulsang Yoo",

note = "Funding Information: The study is supported by the Joint Research under the National Research Foundation (Korea)–Ministry of Science & Technology (Taiwan) Cooperative Program (MOST 105-2923-E-009-004-MY2). All data used in this paper are properly cited and referred to in the reference list. Funding Information: The study is supported by the Joint Research under the National Research Foundation (Korea)-Ministry of Science & Technology (Taiwan) Cooperative Program (MOST 105-2923-E-009-004-MY2). All data used in this paper are properly cited and referred to in the reference list. Publisher Copyright: {\textcopyright} 2020 The Authors.",

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AU - Yoo, Chulsang

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PY - 2020/12/1

Y1 - 2020/12/1

N2 - Green roofs (GRs) are well known for source control of runoff quantity in sustainable urban stormwater management. By considering the inherent randomness of rainfall characteristics, this study derives the probability distribution of rainfall retention ratio Rr and its statistical moments. The distribution function of Rr can be used to establish a unique relationship between target retention ratio Rr,T, achievable reliability AR, and substrate depth h for the aleatory-based probabilistic (AP) GR design. However, uncertainties of epistemic nature also exist in the AP GR model that makes AR uncertain. In the paper, the treatment of epistemic uncertainty in the AP GR model is presented and implemented for the uncertainty quantification of AR. It is shown that design without considering epistemic uncertainties by the AP GR model yields about 50% confidence of meeting Rr,T. A procedure is presented to determine the design substrate depth having the stipulated confidence to satisfy Rr,T and target achievable reliability ART.

AB - Green roofs (GRs) are well known for source control of runoff quantity in sustainable urban stormwater management. By considering the inherent randomness of rainfall characteristics, this study derives the probability distribution of rainfall retention ratio Rr and its statistical moments. The distribution function of Rr can be used to establish a unique relationship between target retention ratio Rr,T, achievable reliability AR, and substrate depth h for the aleatory-based probabilistic (AP) GR design. However, uncertainties of epistemic nature also exist in the AP GR model that makes AR uncertain. In the paper, the treatment of epistemic uncertainty in the AP GR model is presented and implemented for the uncertainty quantification of AR. It is shown that design without considering epistemic uncertainties by the AP GR model yields about 50% confidence of meeting Rr,T. A procedure is presented to determine the design substrate depth having the stipulated confidence to satisfy Rr,T and target achievable reliability ART.

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Probabilistic assessment of hydrologic retention performance of green roof considering aleatory and epistemic uncertainties

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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