Using Data Science to Assess How Well US States are Responding to Flood Risks
By: Andrew Hong
Tags: Data Visualization, Data Science, Dashboard, Risk Management, Climate Change

Using Data Science to Assess How Well US States are Responding to Flood Risks

Photo by Jonathan Ford on Unsplash

With climate change and climate risks moving to the forefront of the conversation in almost every industry, it’s more important now than ever to understand where we are today and what trajectory we are heading on. In this article, I want to focus on flooding specifically in three parts:

What are the risks and causes of flooding and what are the impacts? What could an assessment framework look like, one that is generalized enough to be helpful for anyone? How can we fill the framework using data of any type from states along the U.S. coasts (East, West, Gulf)— news, state bills, municipal investments, geographic/sea data?

I’ll try not to get overly technical, to the point where you should be able to just skim the charts and bolded points below to get the main ideas of this article. I hope you have fun and learn something about climate science, risk frameworks, and/or data science!

Let’s Learn Some Basic Climate Science:

While they are not the sole reference on this topic, I like the way that the TCFD (Task Force for Climate Related Disclosures) categorizes physical risks from climate change:

Acute: increased severity of extreme weather events. This includes everything from wildfires to hurricanes.

Chronic: rising sea level, temperatures, heat and rain patterns.

These can be further split into direct and indirect effects on industries or businesses, for for flooding direct would be damage to property and indirect would be transport or supply chain interruption.

This analysis will be focused on direct impacts from acute (storm surges/floods) and chronic (sea level rise) climate risk.

Sea level rise is set to continue well past 2100 even in the most conservative scenarios due to the combination of glacier melting and thermal expansion. The East, West, and Gulf coasts are being affected unevenly but for similar global factors as well as local factors such as ice melt and sinking land. El Niño cycles can make this worse as well.

There are many types of flooding, some of the most common being storm surge, fluvial (river overflow), or backup from faulty drainage systems. These all have strong secondary effects like corrosion of infrastructure as well as changing the microbiome due to sewage or other waste that is brought up to the streets or underground areas. Sea level rise (especially during high-tide) is one of the main contributors of making any of these types of flooding more likely.

So what exactly do these two risks affect? C40 put out a great research report that shows the interdependence of urban infrastructure and how flooding damage to one or more categories creates much larger economic consequences.

Figure from https://www.c40.org/researches/understanding-infrastructure-interdependencies-in-cities (Image Not by Author)

It should go without saying that property damage and population displacement are significant effects as well on a personal or community level. Climate risks and impacts can quickly compound on top of each other, and they shouldn’t be viewed as just one-off or “black-swan” events.

Time to Have Some Fun Creating an Assessment Framework:

Let’s start by creating a crude chart to eventually place states in:

Image by Author

From this we can see that there are two values we need formulas for:

State Risk Severity = combination of Historical NFIP Claims, Inches of Sea Level Rise Since 1950, Annual Precipitation increase since 1950. Given both sea level rise and frequency/volume of rainfall makes flooding worse, they should have compounding risk effects. Non-coastal states will just have no sea level rise multiplier. NFIP claims are a way of saying “here’s where we are right now in terms of flood risk”. State Response Strength = combination of $ spent, properties protected, and # Flood Bills and Flood Projects. For this approach to work, we’ll need to check to make sure that the range of responses employed are relatively identical from state to state. This wouldn’t make sense if many states only pursue two or three types of action, and another cluster of states only pursue two or three completely different types of action.

At this point, you might be asking:

“Andrew, why are you trying to create a framework for something so complex that you probably need subject matter experts and a team of academics to help with?”

In my experience, a data science project that doesn’t have clear guidance on a large, ambiguous ask is destined to become a headache that fails to really move the needle for anyone — even if the visualizations and models from it look great. We can always improve this framework in the future with those smart people anyways.

Now for the Data Science:

Risk Severity Calculations

For this section we will be using 40 years of NFIP claims data, precipitation increase data from NOA National Climate Assessment 4 since 1958, and sea level rise from satellites, floating buoys off the coast, and tidal gauges since 1950. These three sources of data allow us to combine historical damage severity with historical climate trends to create a linear guess on how severe risk will get for a certain state in the future.

I recognize I have not included hurricanes in this analysis, but I feel that would overcomplicate the formula. However, I expect this to have already shown up in some capacity within the NFIP claims.

First let’s look at a choropleth map of total amount paid in claims from NFIP claims data, where I set $1m as the darkest shade on the heatmap. We can see the strong impact on the coasts, but also spread out through the East.

Sum of amountPaidOnContentsClaim by countyCode (Image by Author)

For the sake of our analysis, let’s see this again by state level and set a higher maximum. In reality, Texas has the highest at $3.9b — we’ll see this later:

Image by Author

Thankfully, someone else has already gone through and organized the hourly precipitation data from the NOA and applied the UN IPCC scenarios for forecasting them out. I’ll be using the more conservative RCP8.5 scenario for my model.

https://data.globalchange.gov/report/nca4/chapter/our-changing-climate/figure/heavy-precip (Image not by Author)

As mentioned earlier, sea level rise affects each coast a little differently with the East and Gulf coasts facing stronger near term impacts than the West coast. I’ll show all of these variables next to each state soon.

I recognize that doing this at a state level is quite the aggregation and if anyone wants to do more granular analysis, I would check out the First Street Foundation flood risk platform which has block level property exposure data. Their model already accounts for everything from protective actions to storm simulations on a 30 year outlook (to 2050). Here’s our NFIP claims, sea level rise, and precipitation metrics compared to their state level property risk:

Image by Author

Using their model as reference, it looks like sea level rise has the heaviest impact followed by precipitation increase. We have two choices now:

Create a linear regression (OLS) predicting historical claims data, then insert expected sea level rise and precipitation increase by 2100 under RCP8.5 to come to expected claims/risks by 2100 Create a simple model that is trying to just rank states, rather than predict some exact damage value. This also works better since we want this to be a generalized risk framework that can be fine tuned later based on industry or asset focus.

The FSF is already a much better example of #1 than anything I can create with two variables, so I will be going with approach #2 instead. My approach was to take the standard normal of my four variables (rain since 1958, rain by 2100, sea level since 1950, and NFIP historical claims) and then just add them together to get to a cumulative spread measure.

These are cumulative z-scores (Image by Author)

A quick caveat, there was missing data for Hawaii and Alaska under a few measures — so only sea level rise is reflected in my risk severity scores.

Response Strength Calculation

There are two types of responses a city can make. The first is adaptive, which encompasses anything that is helping to reduce overall emissions. The second is mitigation, which would be anything that directly protects against the effects of flooding or sea level rise (pumps, barriers, expanded drainage systems). The calculation in this article will be focused on adaptive responses only, as mitigation responses for most states has taken more of the form of announcements or target dates as of now instead of easily trackable concrete actions.

With this in mind, I went to National Conference of State Legislatures (NCSL) database of bills under the category of Environmental and Natural Resources — Flood Mitigation since 2015. This gave me 736 bills, originating from 47 states.

Image by Author

I usually like to use natural language processing on large sets of structured textual data to help figure out what the similarity/spread of keywords across a set of documents. It makes it easier to identify any categorical (in our case states or coasts) outliers or clusters. Here’s how it was done:

Technical Explanation: I performed a soft cosine similarity matrix on a word vectorization of the bill-texts and then reducing the dimensionality using a PCA and then t-SNE.

Non-technical Explanation: I created relationships in two dimensions (instead of 1000s where each word is an axis) between different bills by using a language model that “understands” the relationship between word meanings such as King and Queen and extrapolates that across all the words of two documents — so two bills that are more similar to each other will be plotted closer together than two bills that are very different.

It’s not perfect but we end up with the following distribution of bills, which we can then explore to figure out what is so different between bills at top left versus the bottom right of the chart.

See my Dash app here at floodrisk-us.herokuapp.com to explore the bills and more yourself. (Image by Author)

It’s a little hard to make sense of the clusters and spread from the bills alone since they do cover a wide variety of topics, but the bottom left is roughly about coastal protections and natural resources, and the top/top right covers insurance and real estate/property damage issues. What is quickly observable is that while the spread of topics is large, state’s generally have a similar distribution of bills across the space.

Now, I needed to quantify what states are doing so I turned to another source of mitigation action: FEMA hazard mitigation assistance projects. This open dataset showcased over 29,000 projects, of varying statuses and included projects from all 50 states + D.C.

Image by Author

Read here if you are interested in the details of project types, though they mostly mean exactly what you think they do.

Image by Author

It’s really interesting that acquisition of private real property is such a popular project type, meaning governments may be stockpiling real estate in flood zones that will one day be underwater (literally).

The dataset also tracks how much is spent in each project (funded in part by the state and in part by FEMA, usually 75% by the former) and the number of properties protected/impacted. All of this FEMA data is also explorable in my dashboard webpage.

Spending and Coverage for states seems to have some variance though generally positive corr. (Image by Author)

We now have all the parts we need to calculate response strength, but it helps to ask one more question before making our calculation:

“What exactly should cities by doing in response to increased flood risk and sea level rise?”

This is important because if a state categorized as a “Reasonably Strong Response”, it is only so when compared against other states in the US. If all US state responses are in reality weak compared to what should be done (or to other areas around the world), then this methodology fails in application. Frankly, I’m not qualified to answer this question on a very technical or political level — however based on the research we’ve already done and studies like this page, it seems like mitigation actions can include everything from street-level pumps to revamping the drainage system and water storage of a city. Some other common flood infrastructure improvements are putting up a levee, dam, channel, flood wall, tide gate, culvert, or pervious pavement. The graphic below showcases how green strategies work together from the sea (or river) inwards:

https://floodfactor.com/solutions (Image not by Author)

I acknowledge that there might also be a lot being done that isn’t seen in a bill or project yet, such as creation of working groups to improve communication and data sharing between various stakeholders. For example, Philadelphia created a working group that put together this actionable flow chart for other cities to put to use. Regardless, here are our four variables organized by state:

Image by Author

And using the same cumulative Z-score across four categories, to get to our response strength by state.

Image by Author

If you want to look at the FEMA projects or state bills to figure out where you might place them in a more subjective ranking, you can check out my interactive dashboard webpage here.

Final Framework

Putting the “risk severity” and “response strength” axes together, we finally get to our final quadrant plot:

Image by Author

If we use the diagonal line from the origin (slope of 1) as a base case, we can start to see which states have done more to respond across the risk severity spectrum. In this model, few states seem to be having a strong response given their risk severity.

Looking back at the variable by state charts, you’ll notice that risks as well as project spending/responses tend to increase quadratically at the top — not linearly. Therefore it may be better to use a quadratic curve as the base line instead, as the effects and costs don’t scale linearly with the risk severity.

Image by Author

As a final sanity check, I used First Street Foundation risk severity (% properties at risk by 2050) instead and re-plotted:

Image by Author

It seems my model is mostly more conservative, which makes sense if we remember that FSF model already takes into account physical flood protection mechanisms for each state in their at risk metric.

I’ve put an interactive scatter plot of my framework in the dashboard as well:

Example capture from dash app, text is a little easier to read and can explore data by region (Image by Author)

In my next article, I will be applying this framework to the US Real Estate and Flood Insurance market to see if we can find any holes in either the framework or the markets. We’ll look into the cyclical effects of floods decreasing property values, which impacts property taxes, and in turn the ability of a state to invest in adaptive infrastructure — leading to more flood damage. On top of that, we’ll observe if and how the insurance market (public and private) is pricing this risk in. It’ll be linked it here when it’s ready.

Concluding Thoughts: Can we “Open-Source” a risk framework?

I’ve always loved the open-source community in technology — I couldn’t have done most of my coding projects without them. While this article isn’t tailored toward finance or economics, it’s certainly the start of a framework I could see being used for someone’s investment or risk thesis. I would love to see those in the risk community start to practice open-source principles, as we need as many heads as we can jam together to cohesively tackle climate risks and push forward sustainable finance.

I welcome any and all contributions and criticisms, please let me know if you think a state should be placed somewhere else or if you think I need different axes or formulas! If you’re interested in the code (all python files) or need help with the data, please feel free to reach out to me. More research should definitely be done on the shape of the base line curve, as well as expanding to a global scale so that response strength is better calibrated.

All views expressed are my own, and are not representative of the views of anyone I am affiliated with. This article is not financial advice and should not be taken as such. I do not claim to own images that I have not marked as such.