Model Library

Personal Computer Storm Water Management Model (PCSWMM)

Model name: Personal Computer Storm Water Management Model (PCSWMM)

Developed by: Computational Hydraulics International (CHI) (Last update: 2024)

Model type: 1D/2D/quasi-2D, distributed, deterministic, process-based, hydrology-hydraulic, rainfall–runoff–subsurface runoff simulation model, single event or continuous simulation, urban catchment model

History: PCSWMM was developed based on the SWMM engine.

Computational requirements: 64-bit Windows (versions 11, 10, 8, 7, Vista, or XP (SP2)), Minimum 4 GB of RAM, At least 2 GB storage, Microsoft.NET 4.8 framework installed, Microsoft Visual C++ redistributable (x86) installed, Minimum horizontal and vertical screen resolutions of 1366x768 pixels (WXGA), Minimum screen resolution of 1600x900 pixels, Minimum of 1366x768 pixels for dual monitors.

Software requirements: GIS: optional.

Link to download model: Not open-source. (Cost).

Capabilities and Limitations:

Capabilities

  • This model allows for the simulation of surface runoff quantity and quality at hourly or daily intervals during single or continuous storm events (Paule-Mercado et al., 2022);
  • It is more comprehensive than SWMM (Paule-Mercado et al., 2022);
  • It has a modular and user-friendly design;
  • Native GIS support;
  • It considers rainfall-runoff simulation;
  • PCSWMM offers a comprehensive suite of tools for hydrologic and hydraulic modeling (Akhter et al., 2016).
  • The PCSWMM land use editor uses EMC and exponential functions to model wash-off and improve runoff water quality simulation.

Limitations

  • Not open-source;
  • PCSWMM models FIB fate and transport in watersheds but its application is limited to rapidly developing catchments with combined LID-BMPs (Paule-Mercado et al., 2022);
  • Low Impact Development (LID) component for modeling bioretention cells in PCSWMM cannot be adequately verified unless the mathematical representations of the surface and soil layers are made more realistic and dynamic (Zhang & Valeo, 2022);
  • It is primarily designed for urban catchment;
  • Urban stormwater modeling involves complexities such as the inherent intricacies of stormwater quality processes, challenges in data collection, and difficulties in model calibration (Hou et al., 2020; Obropta & Kardos, 2007).

Model Inputs and Outputs:

Inputs

Topography data, LULC data, Soil data, Meteorological data, Hydrological data, River systems, Sewer networks, System of nodes, Links and sub-catchment areas, Water quality data, Control structures Data, BMP data.

Outputs

  • Reports on time-series hydrological and water quality loadings in each sub-catchment.
  • Performance of build-up/wash-off parameters to evaluate effectiveness of best management practices (BMPs) and low impact development (LID) controls.

Examples:

References

Hou, X., Guo, H., Wang, F., Li, M., Xue, X., Liu, X., & Zeng, S. (2020). Is the sponge city construction sufficiently adaptable for the future stormwater management under climate change? Journal of Hydrology, 588, 125055. https://doi.org/10.1016/j.jhydrol.2020.125055

Paule-Mercado, M. C., Salim, I., Sajjad, R. U., Memon, S. A., Sukhbaatar, C., Lee, B.-Y., & Lee, C.-H. (2022). Quantifying the effects of land use change and aggregate stormwater management practices on fecal coliform dynamics in a temperate catchment. Science of The Total Environment, 838(Part 1), 155608. https://doi.org/10.1016/j.scitotenv.2022.155608

Objectives

This study aimed to: 1) provide inspiration for urban stormwater models by adopting multiple methods to express complex urban factors and to determine simulation parameters; 2) explore the responses of urban stormwater systems (urban flooding risk, as well as NPS and CSO pollution) to climate change; 3) verify if sponge city construction can mitigate the increasing risk of flooding and diffuse pollution at the city-scale under future climate change.

This study specifically aimed to: (1) measure the variability of pollutant (FIB and TSS) concentrations according to the hydrological stage of the runoff and dry period; (2) identify the relationship between pollutant concentration and environmental variables -%LULC composition, antecedent dry days (ADD), average rainfall intensity, runoff duration, runoff volume, and stormwater temperature; and (3) quantify the percentage reduction of aggregate LID-BMPs according to the land development phase.