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

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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.

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Data Availability

Data will be made available from the corresponding author upon reasonable request. Additional supplementary material is provided in the attached file.

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Acknowledgements

This work was supported by the Natural Science Foundation of Shandong Province (No. ZR2021QE001), the National Natural Science Foundation of China (No.52109025), the Future Plan for Young Scholars of Shandong University. The valuable suggestions made by the editors and three anonymous reviewers are gratefully appreciated.

Funding

This work was supported by the Natural Science Foundation of Shandong Province (No. ZR2021QE001, Jun Wang), the National Natural Science Foundation of China (No.52109025, Jun Wang), the Future Plan for Young Scholars of Shandong University (Jun Wang).

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Authors and Affiliations

  1. School of Civil Engineering, Shandong University, **an, 250061, Shandong Province, China

    Jiachang Wang, Jun Wang, Shengle Cao & Chuanqi Li

  2. School of Soil and Water Conservation, Bei**g Forestry University, Bei**g, 100083, China

    Shouhong Zhang

  3. Department of Civil Engineering, McMaster University, Hamilton, ON, L8S 4L7, Canada

    Yi** Guo

Authors
  1. Jiachang Wang
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  2. Jun Wang
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  3. Shengle Cao
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  4. Chuanqi Li
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  5. Shouhong Zhang
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  6. Yi** Guo
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Contributions

JW (Jiachang Wang): Methodology, Software, Investigation, Formal analysis, Data Curation, Visualization, Writing-Original Draft; JW (Jun Wang): Conceptualization, Methodology, Resources, Supervision, Writing-Original Draft, Writing-Review & Editing, Funding acquisition; SC: Validation, Resources, Supervision; CL: Investigation, Validation; SZ: Investigation, Validation; YG: Methodology, Writing—Review & Editing.

Corresponding author

Correspondence to Jun Wang.

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Highlights

• A hydrologic performance evaluation framework using analytical stochastic models is developed.

• The same storage capacity of different types of SCMs may result in different runoff reduction ratios.

• A fixed runoff reduction ratio target may not require the same storage capacity or dimensions for different SCMs.

• The effects of climate conditions, soil types and SCM types on runoff reduction ratio were investigated.

Supplementary Information

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Supplementary file1 (DOCX 6172 KB)

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Wang, J., Wang, J., Cao, S. et al. A Framework for Quantifying Stormwater Control Measures’ Hydrologic Performance with Analytical Stochastic Models. Water Resour Manage (2024). https://doi.org/10.1007/s11269-024-03919-4

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