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GP-CV-0IEPBT4CivilReady

SWMM green-infrastructure performance study

A completed EPA SWMM study comparing bioretention, permeable pavement, detention, and a combined strategy across five design storms and one synthetic continuous year.

SWMM green-infrastructure performance study project visual
GP-CV-0IEPBT4 · Civil
  • EPA SWMM 5.2.4
  • Python
  • NumPy
  • Pandas
  • Matplotlib
  • Jupyter

Project definition

Problem statement

Green infrastructure can reduce runoff at the source, while detention can delay and control discharge. Their effects change with rainfall severity and drainage-network capacity.

A useful comparison must measure runoff, peak outfall flow, flood volume, storage use, emergency overflow, and numerical continuity together.

Project objectives

  • Build one controlled 50-hectare urban drainage catchment in EPA SWMM.
  • Compare baseline, bioretention, permeable pavement, detention, and combined scenarios.
  • Test 2-year, 5-year, 10-year, 25-year, and 50-year design storms.
  • Run a deterministic synthetic continuous year with 8,760 hourly rainfall values.
  • Measure peak flow, outfall volume, flooding, infiltration, storage use, overflow, and continuity error.

Project structure

Project components

01

Rainfall generator

Creates five depth-preserving three-hour design storms and one reproducible synthetic continuous year.

02

SWMM model

Defines four subcatchments, pipes, outfalls, bioretention, permeable pavement, detention, controlled discharge, and emergency overflow.

03

Simulation study

Runs all thirty cases with the pinned official EPA SWMM 5.2.4 engine.

04

Result parser

Extracts runoff, outfall, flooding, storage, overflow, and continuity evidence from retained reports.

05

Analysis

Builds comparison tables, structured summaries, and eight labelled engineering figures.

Methodology

Project workflow

  1. 01
    Select rainfall

    Choose one design storm or the synthetic continuous year.

  2. 02
    Select scenario

    Choose baseline, bioretention, permeable pavement, detention, or combined controls.

  3. 03
    Generate model

    The workflow writes the complete SWMM input with the selected rainfall and control objects.

  4. 04
    Run SWMM

    The pinned engine performs hydrologic and dynamic-wave hydraulic simulation.

  5. 05
    Compare evidence

    Retained tables and figures show source reduction, peak control, flooding, storage saturation, and exceedance.

Demonstration scenario

The combined strategy lowers the 2-year peak from 6.110 to 2.212 cubic metres per second, removes modelled 5-year flooding, and reduces 10-year flooding by 63.9 percent. Storage becomes full by the 10-year event, so the student can explain why benefit falls during exceedance instead of presenting one universal reduction percentage.

Engineering

Tools and method

Tools
The project uses EPA SWMM 5.2.4, Python, NumPy, Pandas, Matplotlib, Jupyter for subject analysis, simulation, and results.
Hydrology
EPA SWMM models rainfall, depression storage, Green-Ampt infiltration, evaporation, and runoff.
Hydraulics
Dynamic-wave routing represents pipe capacity, backwater, surcharge, node flooding, storage, and overflow.
Green infrastructure
Bioretention and permeable pavement use documented SWMM LID layer and routing definitions.
Reproducibility
Docker builds the exact official SWMM source commit inside a digest-pinned Debian runtime.
Verification
Automated tests cover rainfall, model objects, report parsing, execution, study orchestration, and figures.

Testing

Evaluation

Evaluation measures

  • Peak outfall flow across five design storms
  • Outfall volume and runoff reduction
  • Node flood volume and flood reduction
  • Detention utilisation and emergency overflow
  • Continuous-year infiltration, outfall volume, and water balance
  • Runoff and routing continuity error for every case

Project boundaries

  • The catchment, rainfall, soil, network, and control sizes are declared teaching assumptions.
  • The model is not calibrated against monitored rainfall, flow, water level, or flooding.
  • The study does not provide surveyed geometry, local frequency analysis, construction details, cost, maintenance evidence, or regulatory approval.
  • Field use requires site data, calibration, local standards, and qualified civil engineering review.

Included

  1. 01Complete EPA SWMM model and Python study workflow
  2. 02Thirty completed simulation cases
  3. 03Five design storms and one synthetic continuous year
  4. 04CSV, JSON, and eight result figures
  5. 05Twenty-six automated tests with 98 percent combined coverage
  6. 06Complete project files, model, calculations, and analysis material in a private GitHub repository
  7. 0795-page project documentation in PDF and editable Word formats
  8. 0821-page setup and usage guide in PDF and editable Word formats
  9. 09Forty-five annotated references

Project record

No information is collected on this page.

Permanent project ID
GP-CV-0IEPBT4
Catalogued
21 Aug 2026
Completed
24 Aug 2026
Verified
24 Aug 2026
Demonstration
Included in repository

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.