Sea level rise and coastal flooding threaten affordable housing

To assess topography, we employ lidar-derived DEMs compiled and distributed by NOAA (NOAA 2015), supplemented with the USGS Northern Gulf of Mexico Topobathymetric DEM (USGS 2014) in Louisiana, and the USGS National Elevation Dataset (Gesch et al 2002) in the small fraction of land not covered by the preceding DEMs. These data have a continuous vertical resolution, and a horizontal resolution of about 5 m, except in parts of LA (3 m) and Norfolk, VA (1 m). We then recompute elevations relative to local mean higher high water (MHHW) levels at nearest neighbors in NOAA's VDatum grid (version 2.3.5; Parker et al 2003), measured in the National Tidal Datum Epoch (1983−2001).

Topography or levees isolate some low-lying areas from the ocean. To account for known protective features and to facilitate downstream computations, the DEM is further refined by raising individual grid cell heights in identified isolated regions. Designated pixel elevations are raised until they match the lowest water level connecting each cell to the ocean despite protective features. We use the following procedure.

We consider flood heights between 0–10 m above MHHW at quarter-meter intervals, denoting the i'th such height in this sequence by h_i. For each i, we generate a binary inundation surface S_i(lat, lon), equal to one where the DEM's elevation is less than h_i, and zero otherwise. For each grid cell below 10 m, we note the minimum value of i for which S_i(lat, lon) = 1, denoting this index by I(lat, lon).

We then incorporate levee data and use connected components analysis to remove isolated areas within each inundation surface, which produces new, connected binary surfaces denoted by $\widetilde{S_i}(lat, lon)$. Data from the Mid-term Levee Inventory (FEMA/USACE, acquired September 2013) is used to identify levees and other flood control structures. In Louisiana, we supplement this with data from Louisiana's Coastal Protection and Restoration Authority (Flood Protection GIS Database as of June 2015), and in Massachusetts, by Chris Watson at University of Massachusetts Boston, April 2014, based on MassGIS's Digital Orthophoto Topographic Breaklines, April 2003. We treat levees as impassible barriers, as these data lack information regarding levee strength or height. This could cause certain areas protected by weak levees to appear less vulnerable than they truly may be.

As before, for each grid cell below ∼ 10 m, we compute $\widetilde{I}(lat, lon)$, the smallest value of i in which $\widetilde{S_i}(lat, lon) = 1$. Where no such value of i exists (meaning the cell is isolated from the ocean up to a water height of more than 10 m), we reassign its elevation to 10 m—higher than any plausible combination of SLR and one year return level this century in the United States, thereby effectively removing it from further consideration. If $I(lat, lon) = \widetilde{I}(lat, lon)$, we assume this grid cell is not hydrologically isolated and do not modify its elevation. Otherwise, where $I(lat, lon)\lt \widetilde{I}(lat, lon)$, meaning a cell is hydrologically isolated up to a water height of at most $\sim h_{\widetilde{I}(lat, lon)}$, we reassign its elevation to $h_{\widetilde{I}(lat, lon)}$.

SLR is not geographically uniform. Because SLR is driven by global, regional, and local factors, the rise of local relative sea levels differs from the global mean. These factors include changes to temperature and salinity (i.e. steric processes), land-ice melt, changes in the Earth's rotation and gravitational field associated with water-mass redistribution (e.g. from land-ice melt; Mitrovica et al 2011), dynamic ocean processes (Levermann et al 2005), as well as glacial isostatic adjustment (GIA; Farrell and Clark 1976) and other drivers of vertical land motion. To localize SLR, we use probabilistic SLR projections from Kopp et al (2014)—hereafter denoted by K14—which account for these time- and geographically-varying components. The K14 projections are conditional on global carbon emissions scenarios, including Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5 (Van Vuuren et al 2011).

We use the formulation derived in Kulp and Strauss (2017) to construct P_annual(H ≥ h), the probability of the highest water height of the year exceeding h. This function is defined at each of 71 U.S. tide gauge stations with at least 30 years of hourly records, based on Tebaldi et al (2012), Supplementary Information (SI) table 1 (stacks.iop.org/ERL/15/124020/mmedia). The one year return levels for these stations are shown in figure 2. The station-distance sensitivity analysis presented in Kulp and Strauss (2017) suggests that the spatial density of these locations is sufficient for expected annual exposure analysis across the U.S. coastline.

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Given the (adjusted) elevation of a building's geolocation (see section 2.6), Elev(lat, lon), P_annual(H ≥ Elev(lat, lon)) reflects the annual probability of at least one flood risk event, in the absence of SLR. Making the assumption that the return level curves stay constant relative to sea level, and treating the year 2000 as the baseline case where SLR(2000) = 0, we incorporate a specific SLR projection to predict the flood event probability for any given year, y, $P_{annual}(H\geq Elev(lat, lon) | SLR(y) = x) = P_{annual}(H \geq Elev(lat, lon)-x).$

Since for each emissions scenario considered, K14 provides a set of probabilistic distributions with 10,000 Monte Carlo samples of relative sea-level change for each tide gauge, we denote each sample as the function SLR_(j)(y) for j ∈ [1, ..., 10000]. We can estimate the probability, unconditional on model sensitivity, as:

$$\begin{align}& P_{annual}(H\geq Elev(lat, lon)|Y = y)\approx\frac{1}{10000}\nonumber\\ & \times\sum^{10000}_{j = 1}\times\,\,P_{annual}(H\geq (Elev(lat, lon)-SLR_{(j)}(y)). \end{align} \tag{ 1 }$$

Making the simplifying assumption that the probability of a flood event on one day is independent of any other day, we can also estimate the daily probability of a flood event as:

$$\begin{align} & P_{daily}(H\geq Elev(lat, lon)|Y = y)\approx\nonumber\\ & \quad 1-(1-P_{annual}\times(H\geq Elev(lat, lon)|Y = y))^{1/365}. \end{align} \tag{ 2 }$$

The probability of annual flooding, $P_{annual}(H\geq Elev_k|Y = y)$, where Elev_k is the land elevation of building k, reflects the annual probability of at least one flood higher than the ground elevation of that individual building. Multiplying this probability with the number of housing units within the building (Units_k) represents the expected annual number of units exposed. Summing the values of this metric across all buildings within some administrative area (i.e. a particular city, state, etc) results in that area's total expected annual exposure of units. Although some units in an exposed building may not be directly flooded, access points (e.g. entrances, stairs) and amenities (e.g. electricity, water supply and sewage systems) may be affected.

Similarly, the product of the structure's daily flood event probability with Units_k results in the expected daily exposure of units. With the assumption of daily independence, we can estimate the total number of expected annual flood-risk events by multiplying expected daily exposure by 365.

2.5.1. Affordable housing stock: Subsidized

2.5.2. Affordable housing stock: Market-driven

2.5.3. General housing stock

We further refine the geographic representation of our affordable housing stock (subsidized and market-driven) and general housing stock datasets using Microsoft's U.S. Building Footprints database (https://github.com/Microsoft/USBuildingFootprints). Since points are poor representations of the areal extent of a building, building latitude and longitude locations are linked with the Building Footprints database and each point is assigned to the building footprint that contained it, or its nearest building footprint. If any part of a building is on land at a lower elevation than a given water height (according to the DEMs described in section 2.1), we considered the entire structure exposed, as well as all units within it, if applicable. This is a conservative measure, as not all buildings will necessarily suffer damage if water reaches the corner of a house, though those with basements or split levels still may.

Using mean sea levels for the year 2000 as a baseline for comparison with future threat (section 3.2), we found that 7,668 affordable housing units were recently at risk of flooding per year in the United States. Figure 3 illustrates the recent vulnerability among states. New Jersey has the highest number and percentage of its affordable housing stock exposed (1,640, ∼ 1%; figure 3.a,c; SI table 2). New York and Massachusetts are also within the top three states at risk in terms of the number of units exposed (1,574, and 1,530, respectively)—an order of magnitude more than the other coastal states (figure 3.a). Massachusetts, Maine, and the District of Columbia are noteworthy in that the percentage of the affordable housing stock exposed markedly exceeds that of the general housing stock.

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Looking at the number of flood-risk events per unit exposed shows another threat dimension (figure 3.b). Although California, for example, has about a third as many exposed units as New Jersey, it has roughly the same number of units exposed to flooding at least four times per year (358) as New Jersey (313; SI table 2). We chose at least four times per year because this corresponds to an average of at least once per quarter, although actual flood-risk events may be seasonally clustered. Along with New Jersey, Massachusetts, New York, and California, affordable housing units in Maryland are the most at risk of repetitive flooding, with an over 200 units exposed to at least four flood-risk events per year in each of these states. By contrast, units in Rhode Island, New Hampshire, and Oregon are some of the states least at risk to more than one flood event per year.

Cities as well as states vary dramatically in the vulnerability of their affordable housing to flood risk. Figure 4 shows the top 20 cities recently at risk of coastal flooding, in terms of the absolute number of units exposed (see SI table 3 for all cities). Threats are primarily clustered in smaller cities in California and in the northeastern United States. New York City has the largest number of units exposed per year (1,373), even though these units make up less than 1% of the city's supply of subsidized affordable housing (figure 4.a,c). The second most at-risk city in absolute terms is Atlantic City. Its significant number of units exposed per year (618) consists of more than 10% of the city's affordable housing stock. With a similar number of units exposed (609), Boston ranks third; more than half of its at-risk units face at least four flood-risk events per year.

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Five of the top-ranked cities have more than 200 units that face flood-risk at least four times per year, on average, including those in New York City; Boston; Foster City, CA; Revere, MA; and Crisfield, MD. Exposure may be overestimated in Foster City, CA, where new levees may not have been included in the Mid-term Levee Inventory. The percentage of the affordable housing stock exposed exceeded that of the general housing stock in nearly all of the top-ranked cities, with the greatest disparities in relative terms in Corte Madera and Suisun City, CA, and in Woodlawn, VA (figure 4.c).

To estimate future threat of coastal flooding to affordable housing, we focused on risks posed by 2050. This 30 year outlook reflects threats that could affect current residents. The projected threats could also affect private developers and government entities, as this time period spans the typical duration of loans and other financial instruments. Results presented here assume continued high carbon emissions (represented by RCP8.5); however, there is little difference in projected SLR across carbon emission scenarios by the mid-21st century (Kopp et al 2014). Results for 2100 and for other RCPs are listed in SI tables 2–4.

The mid-term change in risk is significant, with the aggregate number of affordable units exposed in the United States more than tripling by 2050 to 24,519 units. Table 2 shows the ranking of states in terms of units exposed per year in 2050. New Jersey remains the most vulnerable state, as measured by both the absolute and relative number of units exposed. In New Jersey, the number of units exposed approaches seven thousand per year, a four-fold increase from the year 2000, and equal to the aggregate number of units recently exposed across the country.

New York and Massachusetts remain within the top three states at risk in terms of the absolute and relative number of units exposed (figure 5.a,c). Pennsylvania, Florida, and South Carolina face the greatest percentage increase in the expected annual exposure from 2000 to 2050 (792%, 774%, and 669%, respectively; table 2). Across coastal states, a large majority of exposed affordable housing units are subsidized (72%; see SI table 4 for exposure by program). In 2050, the affordable housing stock is estimated to be markedly more exposed relative to the general housing stock in Massachusetts, New York, New Hampshire, Pennsylvania and the District of Columbia (figure 5.c).

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By 2050, most coastal states are estimated to have at least some affordable housing units exposed to flood risk events at least four times per year (table 2, figure 5.b). Nearly half of New Jersey's large stock of exposed affordable housing units could flood at least four times per year. Delaware, Washington, and South Carolina had zero affording housing units exposed to flooding at least four times per year in the year 2000, but approximately one hundred units exposed to such frequent flooding by 2050 (76, 103, and 119 units, respectively).

Table 3 shows the ranking of the top 20 cities in terms of annual number of units exposed by 2050. The top 20 cities account for 75% of the United State's aggregate expected annual exposure. These most vulnerable cities are highly concentrated along the northeastern corridor and in California. In some of these cities, with relatively smaller affordable housing stocks, over 90% of the stock is exposed (Crisfield, MD and Revere, MA).

New York City remains the most vulnerable city in absolute terms, with the number of units exposed exceeding 4,000 per year by 2050. However, these units represent less than 2% of the city's affordable housing stock and rich cities like New York generally have more resources to bolster protection than poorer ones. For example, New York City not only plans to increase its supply of affordable housing by 50% in 10 years, but has also revised its building design guidelines to address the projected impacts of climate change (NYC 2014, NYC 2019).

The rankings of cities include many smaller and less wealthy cities, where risk management efforts may be lower. Aside from New York City and Boston, all of the top-ranked cities have populations of ∼ 200 000 or less (m = 71 106, sd = 60 922; U.S. Census 2019). Four cities in New Jersey are of particular concern: Atlantic City, Camden, Penns Grove, and Salem. These top-ranked cities are some of the poorest in the country, with average median household income ($28,618) half of the national median, and a correspondingly high demand for affordable housing (U.S. Census 2018a). In addition, their proportion of people of color (81.2%) is double the national average (U.S. Census 2018a). In most of these New Jersey cities, about a third of the affordable housing stock is projected to be exposed, a 321% to 957% percentage increase in exposure from the year 2000 (table 3). This extensive exposure in multiple cities could put a major strain on the state and is particularly concerning since many affordable housing units in New Jersey are still being rehabilitated even seven years after Hurricane Sandy (e.g. Ortiz et al 2019).

The majority of the top-ranked cities face exposure to flooding at least four times per year, which could pose maintenance and public safety challenges. This risk highlights the importance of flood resilience measures to help residents and city managers cope with increasingly frequent flooding, which may be particularly challenging in the less wealthy top-ranked cities, such as Camden, New Jersey.

Flooding can wreak havoc on buildings and the residents who live in them. Even low levels of flooding can damage belongings, disrupt electrical equipment, contaminate water sources and septic systems, generate mold, and block roads (Moftakhari et al 2017, Sweet et al 2018). These impacts may increase maintenance costs, threaten public health, and cause profound disruptions to families already struggling to make ends meet. Because affordable housing units are frequently in poor repair to begin with, additional damage from flooding may be particularly challenging—and expensive—to remedy.

This study's findings demonstrate that if communities aim to preserve affordable housing stock in coastal areas, significant resiliency planning and investment is likely to be needed. Inaction could result in high risk for residents who may lack access to sufficient resources to prepare and recover from flooding impacts. As coastal flood risks to affordable housing units tend to be highly concentrated, flood protection measures in key cities and neighborhoods could help protect a large number of affordable housing residents. The number of expected annual flood-risk events for individual buildings (or aggregated within administrative areas) could be used to help identify hot spots of repetitive flooding, and where to invest in coastal protection or other adaptation measures for the greatest impact relative to cost. Over time, investment in these areas may pay off in terms of not only damage avoided, but also harm avoided to individuals and families in need.

As community resilience investments are made, complementary policies may be needed to protect against the displacement (and potential homelessness) of residents. Infrastructure improvements such as flood defenses can result in new amenities that can attract wealthier households and drive up property values and rents (e.g. Keenan et al 2018). The issue of improving the resilience of affordable housing, without compromising its affordability, is complex and increasingly being recognized in both public and private spheres. For example, it has become a focus of public-private partnership programs such as Energy Efficiency for All (EEFA 2019), which upgrades energy efficiency in multi-family affordable housing complexes, and the Urban Land Institute's Urban Resilience Program (Urban Land Institute 2018), which shares resilience information and strategies.

Such efforts are critically important to help avoid systemic effects which may deepen cycles of poverty. A reduction in affordable housing could have multiple downstream consequences for individuals and families (e.g. affecting equitable access to public transportation, healthcare, and other services) as well as for regional and local economies, which may lose part of their labor forces. The loss of affordable housing in coastal communities may also drive up housing costs in adjacent communities as competition for a dwindling supply of low-cost housing intensifies (e.g. Keenan et al 2018). Ultimately, increasing the overall supply of resilient affordable housing is critically needed to help ensure that communities can absorb the impacts of increased flooding among other climate-related hazards.