TY - JOUR
T1 - A simplified model for analyzing rainwater retention performance and irrigation management of green roofs with an inclusion of water storage layer
AU - Wang, Jun
AU - Mei, Guoxiong
AU - Garg, Ankit
AU - Chen, Deqiang
AU - Liu, Ning
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2023/1/15
Y1 - 2023/1/15
N2 - Rainwater retention and water content in green roofs are primarily influenced by structural configurations (i.e., soil layer, vegetation layer, and water storage layer) and climatic factors (i.e., rainfall and evapotranspiration (ET)). Based on the principle of water balance, this study proposes a conceptual model for simulating water flow in green roofs with water storage layers. Three green roof model experiments were conducted from August 1st, 2020 to July 31st, 2021 for calibrating and verifying the conceptual model. The proposed model was solved iteratively using a newly developed program in Visual Basic. The results showed that the conceptual model can capture the dynamic variations in the rainwater retention and water content of green roofs well. The average Nash-Sutcliffe efficiency coefficient is 0.65 and the average error is 6%. The annual rainwater retention capacity (RRC) of green roofs in the perennial rainy climate model was on average 28% higher than that in the seasonal rainy climate model. At the expense of water stress, high ET plants significantly increased the annual RRC of green roofs at a low level. As the water storage layer depth increased from zero to 150 mm, the annual RRC of green roofs increased by 41%, and the water stress decreased by 49%. Compared with an increase in water holding capacity and soil depth, the response of the annual RRC and water stress of green roofs for increasing water storage layer depth is much greater. As per climate of Southern China region, the water storage layer depth of 100 mm is found to obtain optimal rainwater retention and irrigation management in green roof with similar soil thickness (100 mm).
AB - Rainwater retention and water content in green roofs are primarily influenced by structural configurations (i.e., soil layer, vegetation layer, and water storage layer) and climatic factors (i.e., rainfall and evapotranspiration (ET)). Based on the principle of water balance, this study proposes a conceptual model for simulating water flow in green roofs with water storage layers. Three green roof model experiments were conducted from August 1st, 2020 to July 31st, 2021 for calibrating and verifying the conceptual model. The proposed model was solved iteratively using a newly developed program in Visual Basic. The results showed that the conceptual model can capture the dynamic variations in the rainwater retention and water content of green roofs well. The average Nash-Sutcliffe efficiency coefficient is 0.65 and the average error is 6%. The annual rainwater retention capacity (RRC) of green roofs in the perennial rainy climate model was on average 28% higher than that in the seasonal rainy climate model. At the expense of water stress, high ET plants significantly increased the annual RRC of green roofs at a low level. As the water storage layer depth increased from zero to 150 mm, the annual RRC of green roofs increased by 41%, and the water stress decreased by 49%. Compared with an increase in water holding capacity and soil depth, the response of the annual RRC and water stress of green roofs for increasing water storage layer depth is much greater. As per climate of Southern China region, the water storage layer depth of 100 mm is found to obtain optimal rainwater retention and irrigation management in green roof with similar soil thickness (100 mm).
KW - Conceptual model
KW - Green roofs
KW - Irrigation management
KW - Urban runoff
KW - Water resources management
UR - http://www.scopus.com/inward/record.url?scp=85142177372&partnerID=8YFLogxK
U2 - 10.1016/j.jenvman.2022.116740
DO - 10.1016/j.jenvman.2022.116740
M3 - Article
C2 - 36413952
AN - SCOPUS:85142177372
SN - 0301-4797
VL - 326
JO - Journal of Environmental Management
JF - Journal of Environmental Management
M1 - 116740
ER -