A Coupled Urban Spatial Simulation and Stormwater Runoff Models and its Implications for Physical Design: The Case of Chicago




The goal of this proposed research is to develop a framework that bridges the gap between scientific knowledge (hydrologic engineering) that quantitatively simulates stormwater runoff and the design practices that visually implicate the built environment. We claim that these gaps should be filled by cross-disciplinary approaches in a bid to guide urban systems toward more resilient outcomes. To do this, we will utilize an existing coupled model system that closely couples the Gridded Surface Subsurface Hydrologic Analysis (GSSHA) with the Land-use Evaluation and Impact Assessment Model (LEAM). The modeling system has been developed by the LEAM laboratory to spatially and quantifiably forecast runoff in Chicago in response to various growth scenarios. Second, we will examine the relationship between runoff and design factors to identify the salient factors of influence that are most applicable to landscape design practices. Specifically, the combination of a boosted regression tree and piecewise linear regression will be used to identify the rank importance of design factors and establish their thresholds. Third, we will test the framework in practice by infusing the modeling system in a cross-disciplinary design studio environment by infusing the spatial simulation results and statistical relationship outcomes with the design process. The proposed research will promote cross-disciplinary research in areas ranging from hydrologic/land-use modeling to urban/landscape design. Ultimately, it will lead the design discipline toward analytic approaches for resilience issues. Lastly, we expect the results of our proposed research to benefit the wellbeing of communities in Illinois by potentially decreasing runoff vulnerability.

Project Impact:

This project argues that designing for resilience in contemporary landscape architecture should be a cross-disciplinary endeavor that is driven by an understanding of urban system science. The shift in focus from a designer-controlled process as a normative exercise to landscape design within the context of a multitude of exigent circumstances requires designers to understand the mechanisms underlying complex systems. Despite the rush to establish design or planning guidelines to cope with emerging resilience issues, however, the integration of urban system science with design creation is still an arduous task. To this aim, we need to have complex (but accessible) analytic approaches that investigate urban dynamics and that produce information that landscape architects find useful, practical, and understandable. This project explores ways to promote a science based design process that is actively incorporated with quantifiable information, dynamic modeling systems, and real-world applications. Our results show that location choices of land development are affected by not only typical socioeconomic variables but also hydrological variables and that those relationships appear to be nonlinear. The coupled GSSHA-LEAM system enables the exploration of bi-directional effects between LUC and runoff by iteratively coupling modeling inputs and outputs at a similar spatial scale (30m × 30m). This project ultimately contributes to efforts to move toward more robust and resilient regional planning and applications that take into account changing environmental and social conditions. It supports the need for cell-based forward-looking dynamic modeling to better understand potential sociohydrological interactions and strategically establish a resilient built environment.


Kwak, Y., Deal, B., & Mosey, G. (2021). Landscape Design toward Urban Resilience: Bridging Science and Physical Design Coupling Sociohydrological Modeling and Design Process. Sustainability, 13(9), 4666; https://doi.org/10.3390/su13094666.

Kwak, Y., & Deal, B. (2021). Resilient planning optimization through spatially explicit, Bi-directional sociohydrological modeling. Journal of Environmental Management, 300, 113742. https://doi.org/10.1016/J.JENVMAN.2021.113742

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