J11.6 Urban-Climate Adaptation Tool: Green Infrastructure Optimization

Thursday, 26 January 2017: 11:45 AM
Conference Center: Tahoma 2 (Washington State Convention Center )
Jack D. Fellows, ORNL, Oak Ridge, TN; and O. Omitaomu and E. Parish

Cities have an opportunity to become more resilient to future climate change and green through investments made in urban infrastructure today. However, most cities lack access to credible high-resolution climate change projection and other environmental information needed to assess and address potential vulnerabilities from future climate variability. Therefore, we present an integrated framework for developing an urban climate adaptation tool (Urban-CAT).  The initial focus of Urban-CAT is to optimize the placement of green infrastructure (e.g., green roofs, porous pavements, retention basins, etc.) to be better control stormwater runoff and lower the ambient urban temperature.  Urban-CAT consists of four modules. Firstly, it provides climate projections at different spatial resolutions for quantifying urban landscape. Secondly, this projected data is combined with socio-economic and other environmental data using leading and lagging indicators for assessing landscape vulnerability to climate extremes (e.g., urban flooding). Thirdly, a neighborhood scale modeling approach is presented for identifying candidate areas for adaptation strategies (e.g., green infrastructure as an adaptation strategy for urban flooding). Finally, all these capabilities are made available as a web-based tool to support decision-making and communication at the neighborhood and city levels.  This presentation will highlight some of the methods that drive each of the modules and demo some of the capabilities available to-date using the City of Knoxville, Tennessee as a case study.  Next steps on Urban-CAT is to additional capabilities to create a comprehensive climate adaptation tool, including energy, transportation, health, and other key urban services.
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner