And will the pursuit of equity hold back death and destruction from the deadly Climate Change?
A new paper was published in Environmental Research Letters this week: Modes of climate mobility under sea level rise.
It is a bizarre attempt to do granular modeling of the response to hypothesized catastrophic sea level rise with an array of socio-economic indicators. It is a dazzling combination of alarmist modeling of sea level, geospatial flood modeling, “equitable” social justice policies, and economic modeling.
Exposure to sea-level rise (SLR) and flooding will make some areas uninhabitable, and the increased demand for housing in safer areas may cause displacement through economic pressures. Anticipating such direct and indirect impacts of SLR is important for equitable adaptation policies. Here we build upon recent advances in flood exposure modeling and social vulnerability assessment to demonstrate a framework for estimating the direct and indirect impacts of SLR on mobility. Using two spatially distributed indicators of vulnerability and exposure, four specific modes of climate mobility are characterized: (1) minimally exposed to SLR (Stable), (2) directly exposed to SLR with capacity to relocate (Migrating), (3) indirectly exposed to SLR through economic pressures (Displaced), and (4) directly exposed to SLR without capacity to relocate (Trapped). We explore these dynamics within Miami-Dade County, USA, a metropolitan region with substantial social inequality and SLR exposure. Social vulnerability is estimated by cluster analysis using 13 social indicators at the census tract scale. Exposure is estimated under increasing SLR using a 1.5 m resolution compound flood hazard model accounting for inundation from high tides and rising groundwater and flooding from extreme precipitation and storm surge. Social vulnerability and exposure are intersected at the scale of residential buildings where exposed population is estimated by dasymetric methods. Under 1 m SLR, 56% of residents in areas of low flood hazard may experience displacement, whereas 26% of the population risks being trapped (19%) in or migrating (7%) from areas of high flood hazard, and concerns of depopulation and fiscal stress increase within at least 9 municipalities where 50% or more of their total population is exposed to flooding. As SLR increases from 1 to 2 m, the dominant flood driver shifts from precipitation to inundation, with population exposed to inundation rising from 2.8% to 54.7%. Understanding shifting geographies of flood risks and the potential for different modes of climate mobility can enable adaptation planning across household-to-regional scales.
with population exposed to inundation rising from 2.8% to 54.7%. It’s good to know that they can predict the exposure to within tenths of a percent 80 years from now. It’s not like there’s going to be any construction or changes to the neighborhoods in eight decades, so we’re good.
The paper tries to check every social justice box with subjective grievance and political categories masquerading as data parameters. I’m surprised it wasn’t rejected by the reviewers it neglected data about the transgender community.
Thirteen different indicators were drawn from U.S. American Community Survey data (2015–2019; US Census Bureau 2020) and U.S. Housing and Urban Development Comprehensive Housing Affordability Strategy data (2013–2017; CHAS 2019) to support cluster analysis. We selected indicators based on relevance to the MDC context incorporating correlation analyses (figure SM 1), determinations of estimate reliability, and sensitivity testing (table 1). Indicators are ranked in descending order by relative vulnerability and further grouped into three sets based on similarities: low, moderate, and high social vulnerability. Neighborhoods with higher proportions of people or households under any given indicator are generally understood to have higher social vulnerability with two exceptions. First, in MDC, higher proportions of foreign-born persons do not necessarily indicate higher vulnerability (unless this proportion is coupled with higher proportions of limited English speakers), and second, higher median household incomes have an inverse relationship with social vulnerability. See supplemental methods 1.1 and 1.2 for details.
Table 1. Indicators and associated metrics in the analysis of social vulnerability. For Miami-Dade County (MDC), Florida, United States, profiles of social vulnerability, especially as relevant to sea-level rise risks, are assessed and constructed across census tracts based on these indicators and associated metrics.
INDICATOR METRIC RELATION TO SOCIAL VULNERABILITY CITATIONS SENIORS (AGE 65+) Percent of individuals aged 65 and older living alone Elderly populations are generally considered more socially vulnerable, although higher proportions of affluent elderly populations, who may have lower social vulnerability, live along coastal areas in Florida Morrow 1997; Wang and Yarnal 2012 BLACK POPULATION (BLACK POP) Percent Black population In MDC, high proportions of Black residents in an area are indicative of the legacy of Jim Crow policies, discriminatory redlining policies, and ongoing racial segregation of neighborhoods Connolly 2014; UM Office of Civic and Community Engagement 2016 FOREIGN BORN (FOREIGN B) Percent of individuals born outside United States In MDC, 53.7% of persons are foreign born, and social vulnerability may be unevenly distributed across different nationalities Montgomery and Chakraborty 2015; U.S. Census Bureau (2020b) LIMITED ENGLISH (LIMITED ENG) Percent of limited English-speaking households (all languages) In MDC, high proportions of limited English speakers can signal linguistic isolation challenging access to public services, information, and economic opportunities Boyd 2009; Xiang et al 2021 NO HIGH SCHOOL DIPLOMA a (HS DIPLOMA) Percent population age 18+ without a HS diploma Education levels are tied to income and poverty, which can shape social vulnerability Morrow 1997; Flanagan et al 2011; Rufat et al 2015 LIMITED MOBILITY (VEHICLE) Percent households with workers aged 16 and over and with no vehicles available High levels of this indicator could indicate vulnerability to extreme events or events where immediate mobility is needed to avoid hazards Morrow 1997; Flanagan et al 2011; Bullard and Wright 2012 POVERTY LEVEL (POV LEVEL) Percent population living below the poverty level In MDC, 15.7% of residents live in poverty; census tracts with median incomes below the poverty level are considered more vulnerable Rufat et al 2015; U.S. Census Bureau 2020c RENTER (RENTER) Percent renter occupied households Renters generally face lower economic loss from flooding but higher rates of displacement and job loss; disaster relief programs tend to favor property owners Kamel 2012; Rufat et al 2015 HOUSING BURDEN a , b (RENTER CB) Percent of renter occupied households that contribute more than 30% of income to housing costs Cost burdened renters pay more than 30% of their income towards housing costs, thereby reducing disposable income and increasing social/financial vulnerability Greiner et al 2017 PUBLIC BENEFITS (SNAP) Percent households receiving SNAP benefits Higher proportions of households receiving Supplemental Nutrition Assistance Program (SNAP) benefits indicate food insecurity and social/financial vulnerability Dilly et al (2001); Fitzpatrick et al (2021) UNEMPLOYMENT (UNEMPLOYED) Percent unemployed workers aged 16 and older In Florida, the average monthly, seasonally adjusted unemployment rate from 2015–2019 was 4.2%. Census tracts with unemployment rates higher than 4.2% may be considered more socially/financially vulnerable U.S. Bureau of Labor Statistics (2021) NO HEALTH a INSURANCE (UNINSURED) Percent population without health insurance (in and not in labor force) Ponding conditions from flooding may impact health due to waterborne diseases or effects of dampness (e.g., mold). Greater social vulnerability in areas with lower insured rates Bloetscher et al 2016 MEDIAN INCOME (INCOME) Household median income MDC median household income is $51 347. Census tracts with lower median income may be considered more socially/financially vulnerable U.S. Census Bureau 2020a
a Reflects a pooled estimate or more than one metric within indicator. b Housing Burden is the only indicator derived from U.S. Housing and Urban Development Comprehensive Housing Affordability Strategy data (2013–2017; CHAS 2019). All other indicators were derived from U.S. American Community Survey 2019 5 year data (2015–2019; U.S. Census Bureau 2020a).
Oh look, a conceptual model of exposure and vulnerability.
Those people in the upper right quadrant are TRAPPED. There’s literally nothing they can do to move over a 40 to 80 year period. PERMANENT VICTIMS OF THE DEADLY CLIMATE CHANGE.
And all this assumes a departure from this:
Here’s a longer record from Key West, a nearby area less affected by subsidence as well. BTW, the word subsidence does not appear once in the paper above.
NOAA’s own catastrophic predictions look suspect even in their own presentation. The paper noted above leans primarily on the yellow worst case prediction or even worse predictions from activist sites such as Climate Central. But to make to make it look reasonable, NOAA has cut off the long term record, starting in 1960, aka chartsmanship.
Here’s a more visually accurate presentation, with a longer record, of NOAA’s worst case scenario for Key West.
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