Assessing curve number uncertainty for green roofs in a stochastic environment

4th International Conference on Water Resource and Environment

地球科学;生态环境科学

Huang, W.S.C.^1 ; You, L.W.^1 ; Tung, Y.K.^1 ; Yoo, C.S.^2

Disaster Prevention and Water Environment Research Center, National Chiao-Tung University, Hsinchu, Taiwan^1

Department of Civil, Environmental and Architectural Engineering, Korea University, 145 Anam-ro, Seoungbuk-gu, Korea, Republic of^2

关键词: Accurate quantifications;    Antithetic variates;    Engineering measures;    Green roof systems;    Latin hypercube sampling;    Rainfall characteristics;    Runoff production;    Stochastic environment;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/191/1/012002/pdf
DOI  :  10.1088/1755-1315/191/1/012002

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

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.