Assessing curve number uncertainty for green roofs in a stochastic environment

W. S.C. Huang; L. W. You; Y. K. Tung; C. S. Yoo

doi:10.1088/1755-1315/191/1/012002

Assessing curve number uncertainty for green roofs in a stochastic environment

W. S.C. Huang, L. W. You, Y. K. Tung, C. S. Yoo

School of Civil, Environmental and Architectural Engineering

Research output: Contribution to journal › Conference article › peer-review

Abstract

Curve number (CN) is well-known by hydrologists for estimating rainfall induced runoff from a catchment. It can also be used as an indicator for measuring the impact of engineering or non-engineering measures on the runoff production in a catchment. In this study, a method is presented to quantify the uncertainty of CN for hydrologic performance of a green roof system. Latin hypercube sampling approach, coupled with the antithetic variate technique, is used to achieve efficient and accurate quantification of the uncertainty features of CN for a green roof system. Elements in green roofs subject to uncertainty considered are rainfall characteristics (i.e. amount and inter-event dry period), soil-plant-climate factors (i.e. field capacity, wilting point, interception, evapotranspiration rate), and model error in SCS I _a-S relation. Numerical study shows that model error in SCS I _a-S relation has the dominant effect on the uncertainty features of CN for green roof performance.

Original language	English
Article number	012002
Journal	IOP Conference Series: Earth and Environmental Science
Volume	191
Issue number	1
DOIs	https://doi.org/10.1088/1755-1315/191/1/012002
Publication status	Published - 2018 Nov 5
Event	4th International Conference on Water Resource and Environment, WRE 2018 - Kaohsiung City, Taiwan, Province of China Duration: 2018 Jul 17 → 2018 Jul 21

Bibliographical note

Funding Information:
The study is supported under the Joint Research Cooperative Program for the National Research Foundation of Korea (2016K2A9A1A06922023) and the Ministry of Science & Technology of Taiwan (MOST 105–2923-E-009-004-MY2).

Publisher Copyright:
© Published under licence by IOP Publishing Ltd.

ASJC Scopus subject areas

General Environmental Science
General Earth and Planetary Sciences

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1088/1755-1315/191/1/012002

Cite this

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title = "Assessing curve number uncertainty for green roofs in a stochastic environment",

abstract = "Curve number (CN) is well-known by hydrologists for estimating rainfall induced runoff from a catchment. It can also be used as an indicator for measuring the impact of engineering or non-engineering measures on the runoff production in a catchment. In this study, a method is presented to quantify the uncertainty of CN for hydrologic performance of a green roof system. Latin hypercube sampling approach, coupled with the antithetic variate technique, is used to achieve efficient and accurate quantification of the uncertainty features of CN for a green roof system. Elements in green roofs subject to uncertainty considered are rainfall characteristics (i.e. amount and inter-event dry period), soil-plant-climate factors (i.e. field capacity, wilting point, interception, evapotranspiration rate), and model error in SCS I a-S relation. Numerical study shows that model error in SCS I a-S relation has the dominant effect on the uncertainty features of CN for green roof performance.",

author = "Huang, {W. S.C.} and You, {L. W.} and Tung, {Y. K.} and Yoo, {C. S.}",

note = "Funding Information: The study is supported under the Joint Research Cooperative Program for the National Research Foundation of Korea (2016K2A9A1A06922023) and the Ministry of Science & Technology of Taiwan (MOST 105–2923-E-009-004-MY2). Publisher Copyright: {\textcopyright} Published under licence by IOP Publishing Ltd.; 4th International Conference on Water Resource and Environment, WRE 2018 ; Conference date: 17-07-2018 Through 21-07-2018",

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AU - Huang, W. S.C.

AU - You, L. W.

AU - Tung, Y. K.

AU - Yoo, C. S.

N1 - Funding Information: The study is supported under the Joint Research Cooperative Program for the National Research Foundation of Korea (2016K2A9A1A06922023) and the Ministry of Science & Technology of Taiwan (MOST 105–2923-E-009-004-MY2). Publisher Copyright: © Published under licence by IOP Publishing Ltd.

PY - 2018/11/5

Y1 - 2018/11/5

N2 - Curve number (CN) is well-known by hydrologists for estimating rainfall induced runoff from a catchment. It can also be used as an indicator for measuring the impact of engineering or non-engineering measures on the runoff production in a catchment. In this study, a method is presented to quantify the uncertainty of CN for hydrologic performance of a green roof system. Latin hypercube sampling approach, coupled with the antithetic variate technique, is used to achieve efficient and accurate quantification of the uncertainty features of CN for a green roof system. Elements in green roofs subject to uncertainty considered are rainfall characteristics (i.e. amount and inter-event dry period), soil-plant-climate factors (i.e. field capacity, wilting point, interception, evapotranspiration rate), and model error in SCS I a-S relation. Numerical study shows that model error in SCS I a-S relation has the dominant effect on the uncertainty features of CN for green roof performance.

AB - Curve number (CN) is well-known by hydrologists for estimating rainfall induced runoff from a catchment. It can also be used as an indicator for measuring the impact of engineering or non-engineering measures on the runoff production in a catchment. In this study, a method is presented to quantify the uncertainty of CN for hydrologic performance of a green roof system. Latin hypercube sampling approach, coupled with the antithetic variate technique, is used to achieve efficient and accurate quantification of the uncertainty features of CN for a green roof system. Elements in green roofs subject to uncertainty considered are rainfall characteristics (i.e. amount and inter-event dry period), soil-plant-climate factors (i.e. field capacity, wilting point, interception, evapotranspiration rate), and model error in SCS I a-S relation. Numerical study shows that model error in SCS I a-S relation has the dominant effect on the uncertainty features of CN for green roof performance.

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SN - 1755-1307

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JO - IOP Conference Series: Earth and Environmental Science

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Assessing curve number uncertainty for green roofs in a stochastic environment

Abstract

Bibliographical note

ASJC Scopus subject areas

UN SDGs

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