The Minnesota Climate & Health Vulnerability Assessment Tool incorporates key sensitivity datasets (population health, environment and infrastructure) and exposure datasets (historic climate and projected climate) to support more strategic and equitable climate resilience planning in Minnesota. This pilot tool framework builds from the Minnesota Department of Health’s Minnesota Climate Change Vulnerability Assessment (2014) and Minnesota Climate and Health Profile (2015) to provide a current understanding of local population vulnerability to climate change within the context of local environmental and infrastructure factors. The pilot tool also introduces the ability to examine these sensitivity factors within the context of historic and projected climate change hazard exposures.
The current pilot focuses on flooding as a demonstration climate hazard, with the goal of scaling the tool to include analysis capabilities for other Minnesota climate hazards such as extreme heat, air quality, and vector-borne disease.
Population health data encompasses demographic and public health datasets that prioritize populations more at-risk to Minnesota’s climate hazards. Characteristics that can increase population vulnerability include, but is not limited to, factors like age, gender, income, and health status.
The majority of the data contained in these applications is demographic data. This is statistical information about a population such as age, income, race, and education. Most demographic data found in these applications comes directly from or is derived from the American Community Survey (ACS) 2011-2015 5-year estimates. Projected poplation data comes from the Minnesota State Demographic Center.
ACS data and GIS boundary files for the applications were downloaded from the National Historical Geographic Information System (NHGIS) (IPUMS NHGIS, University of Minnesota, www.nhgis.org).
The current scope of public health data in this application for counties in Minnesota is from the Minnesota Public Health Data Access Portal.
Environment & Infrastructure
The context of local environment and infrastructure can multiply or mitigate the impact of climate change. Environment and infrastructure factors to consider for local vulnerability could include factors like natural resources, land cover, tree cover, impervious surface, water infrastructure, private wells, and parks. Examining this context in conjunction with population health context can provide a better understanding of community vulnerability.
Environmental data currently available in this pilot tool include land cover information and impaired waters. Land cover information for counties, CTUs, and block groups is derived from the 2013 Minnesota Land Cover Classification and Impervious Surface Area by Landsat and Lidar. Impaired waterbodies data is from the 2016 Minnesota Impaired Waterbodies dataset as determined by MPCA's surface water quality assessment process.
The tools contain historical (1980 - 2016) and projected (2006 - 2099) climate data. Daily weather data for each county is calculated based on an average of all values that fall within its boundary on that particular day. County climate statistics are then calculated based on the daily county weather values. The tools also incorporate past storm event and weather phenomena data from the NOAA storm events database.
Historical climate data for each county is derived from the Daymet Daily Surface Weather Data on a 1-km Grid for North America, Version 3 dataset. Projected climate data for counties is derived from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset. The full NEX-GDDP dataset is comprised of downscaled climate projections from 21 models in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Data for these models are available for two of the four greenhouse gas emissions scenarios also known as Representative Concentration Pathways (RCPs). Projected climate data in the assessment tools are from a single model (BCC-CSM 1-1) in the NEX-GDDP dataset under RCP 4.5 and RCP 8.5 scenarios.
Currently, vulnerability indices are only supported in the Flood Vulnerability Assessment Tool. In this tool, sensitivity and exposure data are displayed as vulnerability indices. The sensitivity index should be interpreted as a community's resilience to flooding while the exposure index should be interpreted as a community's historic and future level of exposure to flooding. Higher values indicate a higher vulnerability. Sensitivity data include data about population, demographics, socioeconomic status, health conditions and several other relevant topics. Exposure data refers to historic and projected climate variables that play an important role in understanding past, present, and future flooding and precipitation.
The vulnerability indices are calculated based on the selected data. To calculate the vulnerability index, a percentile rank is calculated for each selected variable. A percentile rank is defined as the percentage of scores in its distribution that are less than or equal to it. The overall vulnerability index for each spatial unit (county, city, block group, etc.) is then calculated as the sum of the percentile ranks for the variables that are selected. This method follows the same procedure used by the ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) and CDC to calculate the Social Vulnerability Index (SVI). The only difference in this application is that the user is able to define which variables are incorporated in the vulnerability index. This method gives users insight into how including or excluding different variables affect the overall vulnerability index.
- Flanagan, B.E., Gregory, E.W., Edward, W., Hallisey, E.J., Heitgerd, J.L., Lewis, B. 2011. A Social Vulnerability Index for Disaster Management. Journal of Homeland Security and Emergency Management: Vol. 8: Iss. 1, Article 3. https://doi.org/10.2202/1547-7355.1792
- Thornton, P.E., M.M. Thornton, B.W. Mayer, Y. Wei, R. Devarakonda, R.S. Vose, and R.B. Cook. 2017. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 3. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1328
- Thrasher, B., Maurer, E. P., McKellar, C., & Duffy, P. B., 2012: Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrology and Earth System Sciences, 16(9), 3309-3314.
- Steven Manson, Jonathan Schroeder, David Van Riper, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 12.0 [Database]. Minneapolis: University of Minnesota. 2017. https://doi.org/10.18128/D050.V12.0