Rhodium-SWMM: An open-source tool for green infrastructure placement under deep uncertainty
Section snippets
Software availability
- •
Name of Software: Rhodium-SWMM
- •
Developer: Nastaran Tebyanian, Department of Architecture, The Pennsylvania State University, University Park, PA, USA. With contributions from George Rossick, Zetier Inc., State College, PA, USA.
- •
Year first available: 2022
- •
Hardware required: 1 GB RAM
- •
Software required: Docker
- •
Availability: https://github.com/NastaranT/rhodium-swmm
- •
Cost: Free
- •
Program language: Python
- •
Supported Systems: Linux, Windows, Mac (x86 machines)
- •
Program size: 4.3 MB
- •
License: Rhodium-SWMM can be
Rhodium-SWMM
Rhodium-SWMM is an open-source python library for robust green infrastructure planning under deep uncertainty. Developed in the Linux environment, it connects two open-source tools: the Stormwater Management Model (SWMM) by the US Environmental Protection Agency (EPA) (Rossman, 2015) and Rhodium, a Python library for MORDM (Hadjimichael et al., 2020).
SWMM is “a dynamic rainfall-runoff simulation model used for the design and analysis of urban drainage systems” (Rossman, 2015). Although many
Example green infrastructure placement problem
This section illustrates the software using an example green infrastructure planning problem. We used a sample EPA stormwater management model (Site_Drainage_Model.inp) included with the installation of the SWMM 5.1 desktop version (US EPA, 2014) (see model details in Table A3 and Fig. A1, Appendix A). It models runoff quantity and quality in a 39-acre residential subdivision. The site contains seven sub-catchments ranging from 0.80 to 2.75 ha in area, 2.0%–3.1% in slope, and 0.0%–95.0% in
Conclusions
SWMM is frequently used worldwide for urban drainage system design and green infrastructure modeling. Rhodium-SWMM provides a generalizable, flexible open-source interface for taking any SWMM input file and setting up a multi-objective optimization problem with the ability to define a wide range of SWMM input parameters as uncertainties or levers. In doing so, Rhodium-SWMM enables decision support that informs GI robustness and multi-functionality and facilitates uncertainty analysis and
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This study was supported by The RAND Frederick S. Pardee Center for Longer Range Global Policy and the Future Human Condition, Mid-Atlantic Regional Integrated Sciences and Assessments (MARISA), Penn State Initiative for Resilient Community (PSIRC), Penn State Center for Climate Risk Management, Hamer Center for Community Design, and the Thayer School of Engineering at Dartmouth College. We thank George Rossick for his invaluable contribution to software development and usability, Sitara
References (60)
- et al.
City DRAIN © – an open source approach for simulation of integrated urban drainage systems
Environ. Model. Software
(2007) - et al.
Including uncertainty in valuing blue and green infrastructure for stormwater management
Ecosyst. Serv.
(2018) - et al.
Economic valuation of green and blue nature in cities: a meta-analysis
Ecol. Econ.
(2020) - et al.
Multi-objective optimization of low impact development stormwater controls
J. Hydrol.
(2018) - et al.
Sustainable Urban Drainage System (SUDS) modeling supporting decision-making: a systematic quantitative review
Sci. Total Environ.
(2022) - et al.
An open source framework for many-objective robust decision making
Environ. Model. Software
(2015) - et al.
Uncertainties in landscape analysis and ecosystem service assessment
J. Environ. Manag.
(2013) - et al.
Many objective robust decision making for complex environmental systems undergoing change
Environ. Model. Software
(2013) - et al.
Multi-objective optimization for green-grey infrastructures in response to external uncertainties
Sci. Total Environ.
(2021) - et al.
Incorporating receiving waters responses into the framework of spatial optimization of LID-BMPs in plain river network region
Water Res.
(2022)
Green infrastructure and public policies: an international review of green roofs and green walls incentives
Land Use Pol.
A fast and robust simulation-optimization methodology for stormwater quality management
J. Hydrol.
Integrating socioecological indexes in intelligent optimization of green-grey coupled infrastructures
Resour. Conserv. Recycl.
OSTRICH-SWMM: a new multi-objective optimization tool for green infrastructure planning with SWMM
Environ. Model. Software
Reconceptualizing green infrastructure for climate change adaptation: barriers to adoption and drivers for uptake by spatial planners
Landsc. Urban Plann.
Integrated assessments of green infrastructure for flood mitigation to support robust decision-making for sponge city construction in an urbanized watershed
Sci. Total Environ.
Rapid assessment of the cost-effectiveness of low impact development for CSO control
Landsc. Urban Plann.
Multi-objective decision-making for green infrastructure planning (LID-BMPs) in urban storm water management under uncertainty
J. Hydrol.
Marginal-cost-based greedy strategy (MCGS): fast and reliable optimization of low impact development (LID) layout
Sci. Total Environ.
Optimal adaptation pathway for sustainable low impact development planning under deep uncertainty of climate change: a greedy strategy
J. Environ. Manag.
Many-objective robust decision making for water allocation under climate change
Sci. Total Environ.
Optimizing surface and contributing areas of bioretention cells for stormwater runoff quality and quantity management
J. Environ. Manag.
A comprehensive review of spatial allocation of LID-BMP-GI practices: strategies and optimization tools
Sci. Total Environ.
An integrated environmental assessment of green and gray infrastructure strategies for robust decision making
Environ. Sci. Technol.
Optimizing green-gray infrastructure for non-point source pollution control under future uncertainties
Int. J. Environ. Res. Publ. Health
Robust Stormwater Management in the Pittsburgh Region: A Pilot Study
Many objective robust decision‐making model for agriculture decisions (MORDMAgro)
Int. Trans. Oper. Res.
The impact of uncertainty factors on optimal sizing and costs of low-impact development: a case study from beijing, China
Water Resour. Manag.
Cited by (2)
A Coupled Parameter Automation Calibration Module for Urban Stormwater Modelling
2024, Water (Switzerland)Research on system simulation approach under deep uncertainty
2024, Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice