Computational modeling for climate change: Simulating and visualizing a resilient landscape architecture design approach. (June 2019)

• Record Type: Journal Article
• Authors: Ackerman, Aidan; Cave, Jonathan; Lin, Chien-Yu; Stillwell, Kyle
• Is Part Of: International journal of architectural computing. Volume 17:Number 2(2019)
• Page Start: 125
• Page End: 147
• Publication Date: 2019-06
• Subjects: Modeling -- simulation -- environment -- ecosystem -- landscape -- climate change -- sea level rise -- urban heat island; Architecture -- Data processing -- Periodicals; Architecture -- Informatique -- Périodiques; Virtual reality in architecture -- Periodicals; Computer-aided design -- Periodicals; Architecture -- Data processing; Periodicals; 720.2840285536
• DOI: 10.1177/1478077119849659
• Languages: English
• ISSNs: 1478-0771

Abstract

Coastlines are changing, wildfires are raging, cities are getting hotter, and spatial designers are charged with the task of designing to mitigate these unknowns. This research examines computational digital workflows to understand and alleviate the impacts of climate change on urban landscapes. The methodology includes two separate simulation and visualization workflows. The first workflow uses an animated particle fluid simulator in combination with geographic information systems data, Photoshop software, and three-dimensional modeling and animation software to simulate erosion and sedimentation patterns, coastal inundation, and sea level rise. The second workflow integrates building information modeling data, computational fluid dynamics simulators, and parameters from EnergyPlus and Landsat to produce typologies and strategies for mitigating urban heat island effects. The effectiveness of these workflows is demonstrated by inserting design prototypes into modeled environments to visualize their success or failure. The result of these efforts is a suite of workflows which have the potential to vastly improve the efficacy with which architects and landscape architects use existing data to address the urgency of climate change.