In a recent article published in the journal Sustainability, researchers introduced an innovative method that integrates building information modeling (BIM) with the personal computer version of the stormwater management model (PCSWMM). This approach aims to design and assess nature-based solutions (NbS) in urban areas more effectively.
BIM/PCSWMM workflow. In this study, certain specific NbS attributes (e.g., hydraulic conductivity) from the BIM design were entered manually in the PCSWMM pulldown dialogue tools such as the LID editor. Image Credit: https://www.mdpi.com/2071-1050/16/9/3694
Through a detailed case study, the researchers successfully demonstrated the application of their method by connecting an existing NbS development in Thailand with a theoretical new-build NbS for an adjacent property.
Additionally, the research demonstrated how BIM and PCSWMM could optimize the performance of NbS features such as green roofs, rain gardens, permeable pavement, and tree pits for sustainable stormwater management.
Background
BIM is a collaborative construction process that utilizes digital models to streamline building and infrastructure design, construction, and management. It improves project efficiency, quality, and environmental performance by centralizing information for all stakeholders. While widely adopted in architectural and engineering fields, its use in landscape architecture and NbS remains limited due to challenges like data interoperability, standardization, and multidisciplinary collaboration.
NbS is an emerging technique that leverages nature to help address societal challenges and offer multiple benefits, including climate resilience, biodiversity, water quality, and human well-being. It encompasses various green infrastructure types like wetlands, urban forests, green roofs, and rain gardens, enhancing urban environments and mitigating climate change impacts. NbS demands a holistic, integrative approach, considering natural systems' connectivity and functionality across scales and disciplines.
About the Research
In this paper, the authors developed an implementation and analytical framework to guide BIM and stormwater management modeling for NbS. They utilized PCSWMM, a dynamic hydrologic/hydraulic and water quality model employing the U.S. EPA SWMM5.1 computational engine, featuring a graphical user interface for data management and analysis.
PCSWMM can explicitly model NbS features such as rain gardens, green roofs, porous pavement, and tree pits, integrating with geographic information system (GIS) and computer-aided design (CAD) data. The study also utilized Autodesk InfraWorks and Civil three-dimensional (3D) BIM software tools facilitating the creation and visualization of 3D models of buildings and infrastructure, exchanging data with PCSWMM through the Industry Foundation Classes (IFCs) standard.
The researchers applied the BIM and PCSWMM models in a case study situated in Bangkok, Thailand, where urban sprawl and climate change present significant challenges for water management and environmental quality. The case study site was the PTT Metro Forest Park, an award-winning NbS development that transformed a former waste dumping site into an urban forest park with a natural waterscape.
The park features a storage/retention pond collecting and recirculating stormwater runoff while creating a habitat for wildlife. The study virtually placed a BIM school building on an empty lot adjacent to the park and simulated seven NbS scenarios with different combinations of green roofs, rain gardens, permeable pavement, and tree pits. These scenarios were evaluated under design storms with 2-year, 5-year, and 100-year return intervals, assessing the impact of the new-build NbS on the existing park pond.
Research Findings
The outcomes showed that the combination of permeable pavement, a rain garden, a retention pond, and a green roof emerged as the most effective NbS scenario in reducing the runoff volume and peak flow from the new-build site while minimizing the impact on the park pond. This scenario demonstrated a significant reduction in the runoff volume by 68 %, 64 %, and 60 % for the 2-year, 5-year, and 100-year storms, respectively, compared to the baseline scenario with no NbS.
Moreover, it lowered the peak flow by 77 %, 75 %, and 72 % for the same storms, respectively. Additionally, this scenario improved water quality by reducing pollutant loads, including total suspended solids, biochemical oxygen demand, and total nitrogen, by 67 %, 64 %, and 60 %, respectively, for the 100-year storm. The study also found that the existing park pond possessed sufficient storage capacity to prevent flooding across all scenarios and storms, emphasizing the importance of connectivity between NbS features for overall performance.
The innovative method facilitates collaboration and communication among different disciplines and stakeholders while providing a virtual representation and simulation of natural systems and processes. It supports planning and assessment of NbS across various scales and contexts, evaluating multiple benefits and trade-offs.
Furthermore, it can be extended to other types of NbS and green infrastructure, such as wetlands, urban forests, and green walls, and integrated with other models and tools like life cycle assessment, ecosystem services, and digital twins.
Conclusion
In summary, the novel approach effectively integrated BIM and stormwater management modeling for NbS. The study underscored the significance of considering the connectivity and functionality of natural systems and processes across diverse scales and disciplines.
Moving forward, the approach can be expanded and customized for different types of NbS and green infrastructure, integrating with other models and tools to support the adoption of NbS as a policy and practice for urban resilience and sustainability.
Journal Reference
Petschek, P.; Aung, A.P.P.; Suwanarit, A.; Irvine, K.N. Integration of Building Information Modeling and Stormwater Runoff Modeling: Enhancing Design Tools for Nature-Based Solutions in Sustainable Landscapes. Sustainability 2024, 16, 3694. https://doi.org/10.3390/su16093694, https://www.mdpi.com/2071-1050/16/9/3694.
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