Urban Vulnerability to Climate Change: A Systems Dynamic Analysis January 12, 2009 Sharon L. Harlan, PhD firstname.lastname@example.org Wade Bannister, PhD email@example.com Project Overview • National Science Foundation grant no. GEO-08161608 • September 1, 2008 – February 29, 2012 • Coupled Human – Natural Systems research • Partnership with researchers at UC Riverside, NASA Johnson Space Center Image Analysis Laboratory, and ASU including: • School of Human Evolution and Social Change • Global Institute of Sustainability • School of Computing and Informatics • Department of Applied Biological Sciences Extreme Heat and Health: Increasing Global Concerns • Heat is the leading cause of death among weather-related fatalities in the US 1995-2004 (about 400 a year). • Heat waves occur in temperate cities as well as in desert cities all over the world. • Cities are warming faster than the rest of the planet due to the urban heat island effect. • Global climate models agree that the intensity, duration, and frequency of heat waves will increase significantly over the next century. Scientific Challenges: Research Objectives 1. Explain the heterogeneous spatial and temporal character of complex urban heat riskscapes as an emergent phenomenon of interacting processes that involve residential selection, changing landscapes, the urban heat island (UHI), and the forcing of global climate change. 2. Assess the current vulnerability of people in different neighborhood microclimates to chronic and episodic heat-related health hazards. 3. Build, implement, and visualize a spatial system dynamics model to test hypotheses about complex interactions between human manipulation of the environment and induced climate response and to forecast alternative future scenarios of heat-related health vulnerability. Impact Challenges: Educational Objectives 1. Develop innovative programs for teaching and learning about climate with active participation by low-income, minority residents in understanding and mitigating the impacts of extreme heat in their neighborhoods. 2. Make results accessible to the policy community in order to promote better decision-making on informed early-warning systems, heat-illness prevention, and community design and adaptation. Specific Scientific Challenge #2 Assess the current vulnerability of people in different neighborhood microclimates to chronic and episodic heat- related health hazards. Combine sophisticated health, climate, and environmental data with social theory and modeling tools to investigate human vulnerability to deadly heat exposure Research Questions • What impact does the development and intensification of urban heat islands have on human health and comfort? • Are certain population groups more vulnerable to the health effects of extreme temperature? • Why are people vulnerable to heat and what could mitigate the undesirable consequences? Neighborhood Conditions, 2003 Heat Wave (July 12-16), 5:00 pm Neighborhood Mean (sd) Mean (sd) Increase in HTCI Pct Summer 2003 Air Temp Human Thermal During HW Hours Comfort Index (HTCI) => 200 HTCI Historic Anglo Phoenix 40.2 (3.1) 186 (35) 28 4.2 104F North Desert Ranch 43.3 (2.7) 215 (37) 49 5.9 West Side Suburban 43.7 (2.7) 220 (37) 45 11.0 South Mountain 44.4 (2.6) 226 (35) 50 15.9 Preserve North Central 44.5 (3.1) 226 (44) 48 18.4 Apartments Historic Mexican 45.6 (2.9) 245 (37) 49 14.8 Phoenix New Tract Development 45.5 (3.1) 238 (44) 45 22.2 Black Canyon Freeway 47.7 (3.4) 259 (42) 62 19.8 118F Mean: All 44.4 (2.3) 227 (28) 47 14.0 Difference: highest - 7.5 73 44 18.0 lowest ANOVA F=22.98*** F=5.94*** Correlations of Residents’ Characteristics & HTCI in 8 Phoenix Neighborhoods • Exposure to heat stress is positively correlated with the percent of poor and ethnic minority residents. ( r = 0.64**, 0.69**) • Exposure is negatively correlated with place-specific ecological variables: vegetation abundance and open spaces. (r = -0.54*, -0.65**) • Exposure is negatively correlated with amount of social and material coping resources: social networks, air conditioning, quality of roofing materials, backyard swimming pools. (r = -0.85**, -0.71*, -0.36, -0.83***) Variable Temperatures in the Phoenix Metro Area Simulated Air Temperature, Weather Research and Forecast model Source: Darren Ruddell & Susanne Grossman-Clarke, 1km spatial resolution, July 17 2005 at 5 pm Arizona State University Risk and Exposure to Extreme Heat in Phoenix, AZ Hours of Exposure to Extreme Heat by Neighborhood July 15-19, 2005 Meehl and Tebaldi definition of heat event (2004) • 45°C (113°F) • Range: 0-24 hours out of 4 days (96 hr) • Mean: 12.65 • SD: 7.9 Source: Darren Ruddell & Susanne Grossman-Clarke, Arizona State University Role of AZHQ • Estimating and Predicting Heat-Related Health Outcomes by Neighborhood • AZHQ is a unique data resource • Complete data -- Identifying heat-related cases • Integrated data -- Identifying risk factors of patients • Working with other researchers to provide de-identified data for analyses • De-identification essential due to HIPAA privacy concerns • Provide summary and aggregate data files to be constructed in accordance with research design specifications • Provide data expertise to help find optimal data solutions for research questions Goals of CHIR’s Involvement • Discover health effects that are correlated with heat exposure • Provide objective statistical evidence of temperature/health relationship • Identify patient risk factors that may exacerbate effects of heat • Provide meaningful information to benefit the health of the community CHIR Work so far • In conjunction with researchers in the School of Human Evolution and Social Change • Mortality Data • Working to identify whether patterns can be detected among both heat and non-heat related deaths in relationship to climate conditions • In conjunction with CHiR’s Expert Physician Panel, identifying health conditions that may be correlated with high temperatures • “Expected” health events such as heat stroke, dehydration, etc • “Unexpected” health events which may be less known • Combining medical literature, clinical knowledge, and data discovery Next Steps • Establish relationship between extreme heat and specific adverse health outcomes • Develop models to better understand effects of patient risk factors • Better understand local geographical differences in health effects within urban heat islands Questions?
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