How NYC Flood Risk, Infrastructure, and Social Vulnerability Are Linked
FEATURE
By Alex Springer
Alex is a social scientist who recently completed his MSc in Environmental Technology at Imperial College London, conducting research key his thesis at the Urban Systems Lab. His work focuses on analyzing and mapping the interactions between economic and political policies, the environment, and climate change. Alex is particularly interested in how public policy and governance failures exacerbate urban vulnerability to climate change. Through his research, he aims to contribute to literature on compounded risk factors, informing policy and decision-making for climate change adaptation and mitigation in urban environments.
KEY TAKEAWAYS
Queens is especially vulnerable to critical infrastructure systems (CIS) failure caused by flooding. There is significant exposure to the risk of cascading failures from power failures that impact large parts of the water distribution network (WDN).
Out of New York City (NYC)’s five boroughs, Queens and the Bronx have the highest social vulnerability to flooding, while Manhattan and Staten Island have the lowest social vulnerability to flooding and the least vulnerable CIS.
In the Bronx and Brooklyn, CIS failures occur in neighborhoods with higher percentile ranks of low-income minority individuals.
In Queens, a significant overlap of social vulnerability to flooding and CIS vulnerability to flooding in Queens, suggests a spatial injustice in the ability of communities to adapt to and mitigate the effects of climate change.
Introduction: The Perfect Storm - NYC's Critical Infrastructure Faces Climate Change
NYC is one of the most densely populated cities in the U.S., and its geography makes it particularly prone to the effects of flooding. As these events become more frequent and severe, millions of residents could be affected, and the failure of critical infrastructure systems (CIS) could have cascading effects, spreading the impact beyond the initial flood zones. NYC’s diverse socio-economic landscape adds complexity to this problem, as specific neighborhoods are more susceptible to infrastructure failures and slower recovery periods. Understanding how these failures disproportionately affect vulnerable populations is critical for planning equitable disaster responses.
CIS are the backbone of NYC daily operations, supporting everything from its economy to public health and safety. However, the growing impacts of climate change, mainly the rising frequency and severity of storms, pose severe threats to these systems, primarily through flooding. Floodwaters have the potential to severely disrupt essential services, such as power, water, transportation, and healthcare, threatening both the city’s functionality and the well-being of its residents. To address this growing risk, it is necessary to assess the vulnerability of CIS to floodwaters, which could jeopardize the delivery of essential services.
Flooding not only poses a threat to critical infrastructure systems but also tends to have a greater impact on socially vulnerable populations within cities. These communities, already facing economic and social hardships, experience a heightened level of risk, reinforcing the spatial injustice that exists within the city. To address this, a multi-dimensional analysis that incorporates assessment of environmental, technical and social vulnerability to flooding can inform resiliency efforts. By modeling the city’s CIS and comparing it to socio-economic indicators of social vulnerability, we can provide a clearer picture of where flood risks intersect with social vulnerability. This approach can identify the neighborhoods and systems most at risk, informing targeted resilience strategies.
However, developing a report that encompasses infrastructure exposure, flooding risk, and social vulnerability is not straightforward. A report like this one has yet to be created and requires the combination of multiple forms of software, novel data analysis, and creative problem solving. In the following section, I will detail my process and the challenges I faced.
Methodology: From Pixels to Policy - Overcoming Technical Hurdles in Urban Resilience Mapping
The project, for me, began with the models of the water distribution network (WDN) of New York City. The WDN maps how water gets from its source to fulfill demand throughout NYC. It was created on EPANET, a water modeling software. EPANET is excellent if you simply want to create a map of any given WDN; however, I wanted to use that map for statistical analysis, which was anything but straightforward. EPANET does not work with other computer programs whatsoever, EPANET mixes with other software systems like oil and water. Not at all. Therefore, my first challenge was figuring out how to convert it into a format that could be uploaded to GIS - a popular mapping software - so I could begin the analysis of how WDN vulnerabilities impact social vulnerability. I had just started working at USL and was eager to impress and show my tech-savviness; however, EPANET had other plans. There are two options for exporting an EPANET map: one exports the entire model and lets you run it in different software systems, and the other allows you to export an image of the map. First, I tried exporting the entire model as I imagined it would be helpful to be able to run simulations in GIS of the WDN. My first attempt was a success. Or so I thought.
I successfully exported the model of the WDN into GIS; however, the model was not to scale with the rest of the map on GIS and was placed in the middle of the Atlantic Ocean, which was not helpful if I wanted to compare it to other indexes on social vulnerability, flooding, or infrastructure systems. To make matters worse, the model was quite complex and stored a lot of information. So, every time I uploaded the model to GIS, it took my computer 30-40 minutes to finish uploading the file. I tried everything I could imagine; I changed the coordinate system of the WDN model in GIS, changed the coordinate system of the entire map in the hope that it would move to match the model, I tried changing the coordinate system in EPANET, and so on and so forth. Nothing worked! And every time I tried something new, I had to wait half an hour for the file to re-upload on GIS.
My first week at USL went by, and I had nothing to show for it except an extensive knowledge of what didn’t work. So, I gave up on attempting to import the entire model into GIS and moved on to try and upload images of the WDN instead. I figured I could just run the simulations I needed in EPANET and upload the pictures of the outcomes of the solutions onto GIS. At the very least, I wouldn’t have to wait 30 minutes each time I tried something new, as the image files were much smaller.
However, the same problems I had encountered before persisted. The image of the WDN was still showing up in the Atlantic and was similar in size to a fish. Every time I reuploaded the map, I had to zoom in 1,000x just to get a glimpse of it. As I grew more and more frustrated, I consulted YouTube, ChatGPT, and Google, hoping someone had gone through this before.
Finally, after another week, something worked, and I could upload maps of the WDN in the correct location with the correct size. I wish I could give you an explanation of what I did. I know it had to do with the location settings in GIS. For the life of me, I haven’t been able to find the page in the settings I used to fix the problem. And trust me, I looked, a lot. Alas, the problem was solved, and the greatest hurdle of my project had been overcome.
With the map of the WDN safely and correctly uploaded to GIS, I could begin analyzing how weakness in the system overlapped with social vulnerability. I won’t go into much detail on the results (you’ll have to wait for the real project to be published), but long story short, Queen’s has a very vulnerable WDN infrastructure. Soon, I got the new power grid models in NYC from my supervisor, Ahmed, and I had all the data necessary to run my statistical analysis. At this point, naively, I thought I was in the final stretch. I was not.
The main problem I had once all the maps were uploaded onto GIS was they all displayed their information differently. The map of the WDN presented information with dots, the power grid displayed information with polygons, and there were over 30 different maps of social vulnerability displaying different characteristics for which I had to figure out how best to display (also shown with polygons, but crucially, differently sized polygons than the power grid’s polygons).
Figure 1: Map of areas vulnerable to power failure from flooding
Figure 2: Map of areas at risk of losing access to water
Figure 3: Index of social Vulnerability in NYC
I was confident I could overcome this next challenge. After all, at least everything I needed was in one place. Even more importantly, I knew the solution to this particular problem. So, I combined the information on infrastructure and social vulnerability into one map—an index on a scale of 1-5, where 5 was the value with multiple types of infrastructure vulnerability and high social vulnerability and where 1 represented areas without any vulnerability.
With that map, I had all the data I needed in one place and could export it to Excel to run my statistical analysis. However, when I uploaded the data, I expected roughly 2-3,000 rows of data. Instead, I was looking at over 200,000 rows of data. I knew I had done something wrong, but I had no idea what it was. Ultimately, I pressed one button incorrectly, and GIS squished my data together instead of combining them. So, while the map looked how I wanted it to look, the data had not been combined and I could not run any type of regression or analysis. I had missed one button, and I had to redo everything. I wish I could say that this was a rare occurrence throughout this project, but I would be lying. It is incredibly easy to mess up one thing and have to start over entirely.
Once I had reuploaded my data correctly to Excel, I ran a Pearson correlation, made pivot tables, and finished my project's data and analysis portion. I was not too fond of Excel before this project. I thought it was confusing and time-consuming; however, after I played around with it for a day or two, I began to enjoy using it. It is super helpful and pretty straightforward once you figure out how to write equations.
With the data visualized on GIS and analyzed in Excel, all left to do was write. I love writing, so this was my favorite part of the project; however, I was coming up on the deadline for my time at USL and had two weeks to write ~6,000 words, so it was a bit of a scramble at the end. However, I love the rush of being up against a deadline and having to do a lot of writing. It's the academic version of entering a 100-meter dash, except you're only competing against yourself.
Results: Unequal Ground - Revealing the Intersection of Infrastructure Risk and Social Vulnerability
The findings reveal that Queens and the Bronx are the most at risk, with cascading effects from power outages threatening to disrupt water distribution networks. These failures exacerbate existing social inequalities, making it harder for vulnerable communities to recover. While Manhattan and Staten Island exhibit lower levels of vulnerability, neighborhoods in the Bronx, Brooklyn, and Queens—home to many low-income and minority residents—face heightened risks. The study models the city's power and water systems, mapping their susceptibility to flood-related failures. By examining infrastructure layouts such as power substations and water pumps, I identify areas vulnerable to cascading failures. In Queens, for example, the failure of a single water pumping station could cut off water access to large portions of the borough. This geographic fragility highlights the disproportionate exposure of socially vulnerable populations to infrastructure failures.
Finally, my research has broader implications, offering insights for cities worldwide. The study emphasizes the need for targeted investments to build resilience in the most at-risk communities by drawing attention to the intersection of infrastructure risks and social vulnerability. Strengthening critical infrastructure in these areas can help mitigate the impacts of climate change, reduce inequalities, and foster a more equitable approach to disaster preparedness and urban sustainability. If you want to learn more about the results, and the implications for climate resilience in NYC, you’ll have to wait for the actual report to come out!
