New York City UREx Synthesis

NYC UREx City Team: Timon McPhearson, William Solecki, Rae Zimmerman

Research Fellows and Interns: Luis Ortiz, Claudia Tomateo, Daniel Sauter, Ahmed Mustafa

Existing SETS Conditions

New York City is the largest city in the USA with approximately 8.5 million people in 2016 (U.S. Census Bureau). It is built around a network of rivers, estuaries, and islands, and much of the Metropolitan region is less than 5 meters above sea level (Colle et al. 2008). Three hazards that have historically caused high impacts in NYC are heat waves, inland flooding, and coastal flooding (due to sea level rise, tropical storms, and hurricanes). NYC is experiencing changes in climate including higher temperatures, increasingly frequent heavy downpours, and a rising sea level (Rosenzweig and Solecki 2015). The New York City UREx team has conducted a review of climatic hazard events, which show that hazards have systematically affected the city, with heat waves as the deadliest events and hurricanes as the costliest (Depietri, Dahal, McPhearson 2018). We find that flooding and heat wave extreme events have regularly affected the city over its history with a trend toward increasing the mean number of hazards per decade. We suggest that, especially in urban areas of developed countries such as NYC, changes in built up infrastructure may be the primary drivers of risks of natural hazards. The team has developed research and products focusing on heat risk, flood risk, multi-hazard risk, nature-based solutions, and infrastructure connectivity.

Heat Risk in NYC: Infrastructure and Social Vulnerability

Extreme heat is one of the most hazardous weather events, impacting human health, energy use, and ecosystems. Our work on heat risk in New York City combines satellite imagery, land cover data, and ensembles of global climate models to translate climate from global to local scales. Our approach follows the work of Hamstead et al (2018) to map satellite image-derived land surface temperature (LST) to landscape features. To do this, we combine land cover data from high resolution aerial imagery and building topology. Landscape data is aggregated to the spatial resolution of the LST pixels, resulting in composite classes that have been shown to form independent groupings. Projections from an eight-member GCM ensemble are then used to scale the observed spatial differences throughout the city. This high resolution dataset allows us to estimate how summer temperature risk varies throughout NYC as a function of population indicators, as well as how these risks might change by the end of the century.

Extreme heat events are increasing in both magnitude and duration, including in NYC. The extent and density of infrastructures in NYC are among the highest in the U.S. and the potential consequences are also among the highest in terms of the number of people and economic activity that rely on that infrastructure. The threats to infrastructure primarily appear in connection with materials used in infrastructure, namely asphalt, concrete, and steel. NYC and its region have experienced heat effects on roadways and bridge surfaces in the form of melting of asphalt and buckling of concrete, and measures for such sensitivity have been explored (Zimmerman 2020, in review) .

The NYC UREx team has also considered heat risk in relationship to social vulnerability. Demographic variables found to predict heat-related mortality among elderly in NYC include poverty, poor housing conditions, lower rates of access to air-conditioning, impervious land cover, surface temperatures and seniors’ hypertension (Klein Rosenthal et al. 2014). Between 2000 and 2011, 447 patients were treated for heat illness and 154 died (CDCP 2013). Most counties that comprise NYC are predicted to experience higher than average mortality increases: Bronx County (Bronx) by 89 percent; Kings County (Brooklyn) by 86 percent; New York County (Manhattan) by 83 percent; Queens County (Queens) by 95 percent; and Richmond County (Staten Island) by 80.2 percent over the 1990s baseline. (Knowlton et al. 2007)

In 2017 the NYC Mayor’s Office released the Cool Neighborhoods plan, a US $100M initiative to combat heat and heat risk. The plan includes a mix of interventions from additional tree planting for cooling in high heat risk neighborhoods to “be a buddy” systems for building social cohesion and accountability during heat waves events, which are predicted to increase with climate change in the NYC region (Horton et al. 2015).

 

Flood Risk: Coastal and Inland Flooding

Inland Floods

In New York City, impervious surfaces cover 72% of the city’s area according to the NYC Department of Environmental Protection. Much of NYC’s infrastructure, especially in low-lying or poor drainage areas, cannot cope with more than 1.5 in. (or 38 mm) per hour of rainfall (Lane et al., 2013). Communities in low-lying areas with limited drainage capacity tend to experience sewer backups and street and basement flooding that can expose them to contaminated stormwater and wastewater. Combined sewer overflows are frequent in NYC and are a significant source of environmental pollution (Rosenzweig et al., 2006). Excessive rain washes away pollutants from the streets, which end up in the surrounding bodies of water. Exposure to contaminated water can have both short- and long term public health effects. Flooded basements and houses increase allergies, asthma and other respiratory illness from exposure to mold and fungus. However, flash floods in NYC are rarely life-threatening because of the local topography (Lane et al., 2013).

Research conducted by the UREx team, using historical data, has suggested that for the NYC region, daily precipitation extremes are increasing in autumn with less change in all other seasons (Frei et al., 2015; Huang et al., 2017, 2018). However, projections from downscaled global climate models (GCMs) provide less clear evidence for a shift in the intensity of flood caused by rain events in the Northeast United States (Knighton et al., 2017; Schoof and Robeson, 2016).

Coastal Flooding

Almost 33 square miles (about 85.5 km2 ) of NYC is within the equivalent of a 100-year floodplain. The most frequent coastal storms affecting NYC are nor’easters events, which can cause significant flooding (Colle et al., 2008) and are often associated with extended periods of high winds and high water (Orton et al., 2012; Rosenzweig et al., 2011). Extratropical cyclones followed by tropical cyclones tend to generate the greatest storm surge heights and flooding in NYC (Catalano and Broccoli, 2017; Smith et al., 2010; Towey et al., 2018), which can reach up to 5.12 m according to Lin et al. (2010). Extratropical storms account for 80 %– 85 % of total precipitation from December to May and 93 %– 100 % of extreme precipitation from November to May on the northeastern coast of the United States (Agel et al., 2015). Hurricanes affect NYC more infrequently.

However, the associated flooding is being exacerbated due to the increase of sea level and the increase in the intensity of the hazard itself (Kemp and Horton, 2013; Reed et al., 2015). Five major Category 3 hurricanes affected the New York area between 1851 and 2010, mostly in the month of September (Blake et al., 2011) and generally led to great damage (Rosenzweig et al., 2011). In 2012, Hurricane Sandy made landfall as a post-tropical cyclone in New Jersey with 70 kt maximum sustained winds, driving a catastrophic storm surge into the New Jersey and New York coastlines (Blake et al., 2013). In NYC the storm surge was 2.81 m and was the cause of most of the damage and losses (Kemp and Horton, 2013). Hurricane Sandy caused 43 deaths in NYC, and nearly half were adults aged 65 or older (Kinney et al., 2015). According to Lane et al. (2013), death was caused most frequently by drowning associated with the storm surge. Other deaths were caused by falling trees, falls, electrocution and other trauma. Further, Sandy caused at least USD 19 billion in economic losses to the city (NYC, 2013), left hundreds of thousands without power, some for many weeks (Lane et al., 2013). It was also found that power outages increase risk of death in NYC (Anderson and Bell, 2012). Five hospitals shut down due to Sandy, and three of them had to evacuate patients after the storm hit because of flood damage to critical equipment; power losses in these facilities further complicated evacuation operations (Lane et al., 2013). Nearly 70,000 buildings were damaged by the storm or destroyed by related fire especially in south Brooklyn, South Queens and Staten Island; the subway system was seriously affected; roads, railroads and airports were flooded; and the communication system was disrupted in many areas (NYC, 2013).

Multi-hazard Risk and Flood Risk Indicators

A URExSRN-wide project on integrated indicators covering SETs domains encompass many of the URExSRN cities under the Urban Flood Vulnerability Task Force, including NYC. The overall project is led by Heejun Chang and is currently underway.  A major objective is to identify the SETs characteristics within the 500 year floodplain as a reflection of flooding vulnerability. Although not all databases were incorporated for NYC, key databases were assembled for NYC at the Census Block Group level primarily from the U.S. Census American Community Survey and NYS Open Data in order to be consistently combined with other databases and cities. Most of the data is standardized in terms of per area of the census block group. For NYC, social indicators include population, population density, age, income/poverty, and property tax assessment. Technological indicators include critical infrastructure (wastewater treatment plants), road density, green infrastructure projects, and emergency centers. Ecological indicators include soil type, slope, and productivity.

The concept of cross-discipline indicators is increasing in importance in the context of climate change and extreme weather (Moss et al. 2019). This effort builds upon numerous other indicator documents referenced in the Moss et al. 2019 paper, many of which involve stakeholder engagement. New York City specific indicators that encompass infrastructure were identified and applied in the National Academies of Sciences, Engineering, and Medicine (2016) and Blake et al.(2019).

Nature-Based Solutions: Transitions to Green Infrastructure in a Large, Complex Metropolitan Area

The NYU UREx team has been looking at the use of green infrastructure being considered or in use in many cities for stormwater management. It is a popular transition from earlier infrastructure technologies, e.g., industrial and gray infrastructures, though it is often used together with those earlier technologies (Zimmerman, Grimm, Brawley-Chesworth 2020). A nationwide database of the American Society of Landscape Architects (ASLA), representing a convenience (non-random) sample of GI projects was evaluated with respect to the kinds of technologies and financing used for GI projects (Zimmerman, Brenner, Abella 2019). About three dozen of the cases (almost 10%) were in New York City, yet the financing insights gained elsewhere in the U.S. are potentially applicable to NYC as they are to other cities. NYS accounted for the largest number and highest cost of GI projects in the ASLA database relative to other states in the U.S. (see Figure 5). Green infrastructure finance is building upon conventional financing tools such as a variety of local bond mechanisms and state loan and grant programs administered by the NYS Environmental Facilities Corporation, some of which originate through federal funding (Zimmerman et al. 2019: 207-208); financing attached to gray infrastructure financing; mechanisms borrowed from other environmental areas such as climate, environmental impact, and catastrophe financing; tax exemptions; user fees; and private financing (Zimmerman, Brenner, Abella 2019: Table 1 and Appendix A – Table A1). The analysis of ASLA data found that  for those projects for which financing tools were reported, grants were the most commonly used mechanism, typically one mechanism and at most three mechanisms were used. Thus, a patchwork of financing opportunities exists to support GI projects.

Infrastructure Connectivity and Resilience: NYC’s Electric Power and Transportation Systems

The extent of connectivity of infrastructures is considered to be large and increasing especially in urban contexts. These interconnections have been the subject of considerable conceptual modeling and research (Zimmerman, et al. 2017 and references cited) and have drawn the attention of NYC climate studies (Blake et al. 2019; Zimmerman et al. 2019). Against that backdrop, NYC exemplifies such connectivity given the overall extent of its infrastructure, population density and the density of the built environment. The connection between electric power and transit is the focus of this URExSRN project. The New York Metropolitan area has the largest transit system or accounts for a large part of transit activity by many different measures. Likewise, its electric power system is also among the largest in the U.S. Heavy rail transit systems in NYC have increasingly moved toward the use of electric power to provide transit service, and as such when power outages occur, transit systems are inevitably affected.  The extent of reliance of the NYC transit system, the evolution of the dependency, and cases of the effects of electric power outages on transit services are identified and the means for adaptation to and mitigation of adverse consequences (Zimmerman 2020).

Land use/cover change (LUCC)

Land use/cover change (LUCC) may increase the risk of future extreme weather-related events. For example, changing previous lands into impervious urban uses will increase flooding frequency because of local changes in hydrological conditions, e.g., poorer water infiltration, and may increase the damage due to flood exposure caused by the increasing population and infrastructure within flood-prone zones. In the UREx SRN project, we model possible and desirable future LUC patterns up to 2080 using the Urban Systems Lab Cellular Automata (USL-CA) modeling environment. USL-CA is a spatially explicit LUCC modeling and simulation tool that can model all land use classes that have historical data, which is needed to calibrate/train the model.

Two LUC maps for 2001 and 2016 for NYC were extracted from USA National Land Cover Database (NLCD) which is a raster dataset at 30-m spatial resolution. The built-up density represents percent imperviousness which considers a cell with impervious surfaces to account for 20% to 49% as low-density, 50% to 79% as medium-density, and 80% to 100% as high-density. A series of factors were introduced as LUCC drivers. Elevation and slope (geophysical factors) were derived from USGS 30-meter Digital Elevation Models. Euclidean distances to highways and local roads (accessibility factors) that were extracted from USGS National Transportation Dataset (NTD). Population density and average household income (socioeconomic factors) were delivered from the American Community Survey (ACS) 2016. All the input data were generated or resampled to the 30-m spatial resolution. The temporal resolution, time-step, is one year.

Figure 6 shows the business-as-usual scenario which represents the extrapolation of past (2001-2016) LUCC trends into the future in terms of quantity and location of changes. The results indicate that NYC will experience a massive increase in the high-density urban areas at the expense of low-density urban, urban green and open spaces, and forests. Most of the new high-density development will occur in Brooklyn and Queens boroughs.

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