Guiding green stormwater infrastructure planning through socio-ecological vulnerability: An integrated and spatially scalable prioritization framework

green stormwater infrastructure;socio-ecological vulnerability;planning support system;multi-criteria decision analysis;spatial analysis

Rivera, Samuel J.

关键词: green stormwater infrastructure;    socio-ecological vulnerability;    planning support system;    multi-criteria decision analysis;    spatial analysis;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/101571/RIVERA-DISSERTATION-2018.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Green stormwater infrastructure (GSI; e.g., rain gardens, bioswales, green roofs) is widely used as a climate change mitigation strategy for its potential to reduce stormwater management problems (e.g., poor water quality, increased streamflow velocities, and flood risk due to impervious surfaces), while providing other human and ecosystem benefits, such as urban heat island reduction. Despite the increased popularity of GSI, its implementation has significant challenges associated with stakeholder resistance, budget constraints, and lack of methods for integrated catchment-scale assessment of socio-ecological multifunctionality.Current approaches used for the spatial planning of GSI are often limited to a specific spatial scale (e.g., household, neighborhood) and are only intended for the evaluation of a specific objective (e.g., heat mitigation, flooding) by a particular stakeholder (e.g., homeowners, government agencies). As such, planning decisions are often based on limited information about where different types of GSI will be most effective and have failed to consider their potential benefits to the entire suite of socio-ecological systems and the risks associated with multiple hazards. These limitations have prevented the integration of regional/city assessments and neighborhood/site planning, which can lead to unsustainable solutions and stakeholder resistance to GSI installation. The central premise of this dissertation is to explore the use of vulnerability of socio-ecological systems as the driver for prioritizing locations and types of GSI installations in urban settings. Using commonly available data in cities, the concepts of “service-benefiting areas” and “service-needing areas” are used to first propose a new spatial analytical framework needed to better define and understand spatial relationships between GSI projects and the vulnerability of socio-economic, socio-ecological, and engineered systems to multiple hazards (i.e., flooding and urban heat island). The method allows rapid identification of the most vulnerable communities to potential hazard risks at the site scale (i.e., 10-30 meter raster cells) and quantification of risk mitigation potential of GSI at the appropriate spatial scale (site and catchment scale). Using screening rules associated with different design criteria and planning regulations, the method then identifies areas with the greatest suitability for GSI implementation. Lastly, a spatially scalable optimization approach is used to maximize the multifunctionality of GSI locations and types under multiple objectives (e.g., reducing flash flooding risk while increasing ecologic connectivity). The proposed framework uses a graph-based approach with a simplified distributed hydrologic model and mixed-integer linear programming to maximize the potential delivery of GSI benefits to the most vulnerable areas. This enables a better understanding of the impact that multiple stakeholder opinions could have on the prioritization of potential locations and types of GSI.Results from applying the proposed framework in multiple cities show that current methods used for quantifying socio-ecological vulnerability have failed to consider the appropriate scales at which GSI projects need to be planned and have often misestimated the spatial correlation of vulnerability. In particular, a new approach used to quantify social susceptibility is shown to be more robust to factors associated with data uncertainty and methodological decisions compared to previous methods. Furthermore, the use of a smaller spatial unit (i.e., census blocks) significantly reduces the impact of these factors on the spatial patterns of vulnerability. Comparing the results with actual GSI projects implemented in the city of Philadelphia, PA, shows that the lack of integrated methods for spatial planning of GSI projects has led to their siting in areas that do not maximize benefits for the most vulnerable communities (i.e., those most susceptible to suffer loss/damage during a hazard event and least likely to recover from the event). Using the presented framework to explore spatial synergies and tradeoffs among the socio-ecologic vulnerabilities, the most vulnerable areas were found to be significantly less likely to receive the potential GSI benefits. Additionally, "high priority” areas for GSI installations that are within one mile of current or planned GSI installations were identified. This suggests that a more integrated approach to the spatial planning of GSI could have avoided this problem. Lastly, results from applying the optimization framework to spatial prioritization of infiltration structures (e.g., rain gardens and ponds) and trees show the spatial synergies and tradeoffs that exist between these two types of GSI when different hazard mitigation goals are considered. Moreover, the results show that the consideration of vulnerability in the spatial planning of GSI has significant impacts to its spatial allocation, which could result in aggravating disparities in social justice if ignored. These results suggest that by using the proposed approach, city and regional organizations can reduce the cost and time associated with identifying suitable areas for GSI implementation, allow more informed design work, and improve social justice and community buy-in. However, the results of this study also suggest the need for more effective tools that enable better participatory and integrated assessment of GSI projects to promote social justice. Moreover, they suggest the need for more detailed distributed hydrologic and micro-climate models that enable a more accurate estimation of the impacts of implementing GSI to reduce vulnerability at multiple spatial scales.