A Framework for Quantifying Stormwater Control Measures’ Hydrologic Performance with Analytical Stochastic Models

Jiachang Wang, Jun Wang (), Shengle Cao, Chuanqi Li, Shouhong Zhang and Yiping Guo
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Jiachang Wang: Shandong University
Jun Wang: Shandong University
Shengle Cao: Shandong University
Chuanqi Li: Shandong University
Shouhong Zhang: Beijing Forestry University
Yiping Guo: McMaster University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 13, No 22, 5385-5402

Abstract: Abstract Previously developed analytical stochastic models (ASMs) have the limitations in that they can only be individually applied for specific end-of-pipe or low impact development facilities. This paper proposed a framework of ASMs that can be used for easily analyzing different types of storm water control measures (SCMs). The effective storage capacity for either storage-based or infiltration-based control measures was defined and formulated. Various inflow and outflow patterns of specific SCMs were also considered and analyzed. This framework also provides better insights into the similarities and differences among the model structures of different SCMs. Case studies with six climatically different locations in China and the U.S., different soil properties and six types of SCMs demonstrated the effects of various factors on runoff reduction ratios. Despite the limitations of the proposed framework such as the assumption of exponential distribution for rainfall event characteristics and the unavailability to perform single rainfall event simulation, it can still be used as a convenient toolkit to make fast and comprehensive decisions in selecting different SCMs for a specific runoff control target in design practices.

Keywords: Analytical stochastic model; Runoff control; Low impact development; End-of-pipe facilities; Stormwater management (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11269-024-03919-4

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