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Hu, Pu-Zhou; Tang, Jialiang; Fan, Jihui; Shu, Shumiao; Hu, Zhaoyong; Zhu, Bo

doi:10.1016/j.iswcr.2020.07.004

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…Thus, our results indicate that rainfall temporal distribution within the standard duration (24-hour) strongly affects runoff generation and, ultimately, the CN estimating. Hu et al (2020) obtained similar results at the watershed scale and found that accounting for rainfall intensity in the CN method improved runoff calculations. Chin (2021) demonstrated that rainfall distribution is essential in determining the appropriate CN value across the United States Curve number for runoff estimating in interlocking concrete pavement of America territory.…”

Section: /13mentioning

confidence: 60%

Curve number for runoff estimating in interlocking concrete pavement

Lucas

Jodas

Bertotto

et al. 2022

RBRH

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Curve Number (CN) values estimating from rainfall-runoff data is an attractive topic in hydrology. However, CN values are lacking for Interlocking Concrete Pavement (ICP) material, mainly when seated over bare soil (not over a permeable pavement structure). Here, we compute CN values for the ICP seated over clayey soil using measured rainfall and infiltration capacity data. We estimated runoff ( Q) using 32 events of 24-hour rainfall depth ( P 24) and an infiltration model, assuming a hortonian runoff process. To estimate the CN for each P 24 event, we used the rainfall-runoff incremental approach. Overall, we obtained CN values ranging from 52 to 63. The best CN values to estimate Q were equal to 52.2 ( R M S E = 9.09 mm and R 2 = 0.03) and 60.1 ( R M S E = 1.45 mm and R 2 = 0.97), considering natural- and rank-ordered P 24- Q data, respectively. Our results indicate that it is more suitable to use the initial abstraction ratio ( λ) equal to 0.20 for the ICP material. The findings provide a better understanding of the rainfall-runoff process in ICP and help improve the design of stormwater drainage systems.

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Section: /13mentioning

confidence: 60%