What Flood Maps Miss

As the physical risks from climate change grow, so does our reliance on accurate maps to assess them. In this episode, we dig into the limitations of publicly available flood maps and explore how data and modeling in the marketplace have evolved - often surpassing the capabilities of standard public tools. 

Host: Mike Disabato, MSCI ESG Research

Guest: Kate Towey, MSCI Climate Risk Center

Transcript

Mike Disabato (00:00):

Hello everyone. Welcome to Sustainability Now where we cover how the environment, our society and corporate governance effects and are affected by our economy. I'm your host Mike Disabato, and this week we discuss whether flood maps can actually assess the risks buildings have to flooding. Thanks as always for joining us. Stay tuned. There were serious floods in the US in July. The worst was seen in central Texas where a catastrophic flash flood claimed the lies of over 135 people and led to hard questions being asked of the Federal Emergency Management Agency or FEMA's response to the disaster. It really brought FEMA back to the forefront of the conversation in the us, the role it plays, the funding or lack thereof, it receives and the like. And while the public attention to funding or insufficient funding to FEMA focused on their role in disaster recovery, there's another very important function that FEMA plays for understanding where disasters like floods might strike.

(01:06):

Next. They create these maps that Americans can access on the internet, of course, and warn them as to where water might rise. And they also set the regulatory requirements for new buildings that are in the maps designated what are called floodplains. Those things are like better foundations or requiring the building to be elevated or getting appropriate flood insurance. When a building is in a floodplain according to fema, it has to do those things. It's a requirement. So the FEMA maps act like a master list for regulators showing where the risks may lie and what we should be doing to be prepared for the next flooding disaster. The trouble is those FEMA maps haven't been able to keep up with the times they're outdated in a way. They're not funded to the point where they need to be to actually capture the heightened physical risk we face in this warming world.

(01:59):

So in this episode, we're going to dig into those shortcomings. Not to pile on the criticisms of course, but to demonstrate how the data and modeling in the marketplace has evolved beyond what FEMA can provide. That's important not only because we do our own modeling, but because when you're investing a lot of money in a facility or ensuring your supply chain is secure to physical risks caused by climate change or wanting to protect your employees from physical risk events in the areas that you're operating, effective models are a requirement. So to explore this question, to explore what makes a model effective and why I have with me today, Katie Toi, my physical climate risk colleague who also has a PhD in coastal and inland hydro meteorological hazards. Now, whatever you think that means, what it really means is that she's very equipped to kind of take me through flood maps. So I wanted her to first tell me about the FEMA flood maps in general, just so we could all get acquainted with them.

Katie Towey (02:57):

Yeah, so FEMA produces flood hazard data, which allows you as a user to understand what your level of flood risk is. And this can be really beneficial because it's a publicly available data set that allows just about anyone to go and look up what their risk is to floods. And why it's so popular is because it's really the only such data set available to the public. So it has a wide array of applications and use cases.

Mike Disabato (03:28):

It's easy. I can look up my office, which is the MSCIs World headquarters. It's the seven World Trade Center in New York, and I can take a look at it. Here we go. Click, click, click, click, click. Okay. So what's on the screen right now is a FEMA map that is showing that our building is surrounded by areas of high flood risk, which are colored in orange. Now we aren't in those regulatory floodplains, but the seven world trade building is near the tip of Manhattan, which has the East River on one side and the Hudson River on the other. And what FEMA says is there's a risk that there could be some heavy flooding at some point, and that could be a potential hazard. But let's say I was going to invest a sizable amount on a building around those flood risk areas. Does this FEMA map give me the granularity required to make that long-term planning decision to prepare for possible damage to this fixed asset that I care so much about and that we rely on as a firm, this hypothetical firm I'm building, or probably more to the point for us, does it give investors an ability to really understand the sort of flood risks, the multitude of companies they invest in face if they're just typing in the locations of their companies into the search bar?

Katie Towey (04:37):

FEMA's flood data serves a purpose for the average person. So if you're curious, you own a home, you work in a building and you want to know what is my flood risk? You can go and look in their flood hazard maps and see what is my level of exposure to floods? And that can be useful for the average person. But for investors who are investing money in companies and relying on the underlying asset locations of their companies to perform and create revenue, they need more information about what the flood risk of those properties are. So if you're doing due diligence on a company building a new property or looking at the risk of companies and their underlying locations or related to understanding building codes for these properties in flood prone areas, you need a more robust modeling system. And there are three key features that really feed into having a robust flood model.

(05:38):

And so for investors, they really need to be aware of these. So the first aspect that you want to consider is having really robust location data. So it's not just a matter of looking at where is my location physically located, what are the coordinates, what is the latitude, longitude, but also relying on the elevation data that is mapping where that location physically is. So if you have a location that's on the top of a hill versus at the bottom of a hill, that can have a significant difference in the level of impact you would have from a flood. So resolving that will be really key to understanding first, what is your level of flood exposure? The second part is more broadly how your modeling floods. So this ties into that aspect of elevation and having the terrain accurately modeled, but also representing river channels and how the water flows through rivers and the drainage system.

(06:37):

So if you have intense precipitation falling, how well can sewer systems absorb that water? How well can the ground absorb water? All of these aspects are very complex in flood modeling. So you really need high resolution topnotch science to really understand how these aspects change. And then the third thing is really relying on the data that is feeding those predictions of flood risk. So how you determine flood risk. So FEMA for example, we'll look at what the 100 year return period of a flood is, and that's useful because it gives you a plausible but still rare type of event that would cause a significant amount of impact. But they also rely on historical data for calculating those 100 year return periods. And that can be an outdated metric because what a 100 year return period was 50 years ago is not what it is today, and nor will it be what it is in 50 years from now. Because as the world is warming, that also is going to have an impact on both the severity and frequency of floods.

Mike Disabato (07:51):

Okay, so those three key features, good location data, the accurate mapping of the natural features that prevent and cause floods. And lastly, how you score flood risk. The basic understanding of whether or not using current or historical data, these are all important. The first two are all about resolution. If I can sum them up. Resolution is the important thing to think about here. Resolution in modeling refers to the detail or granularity of data used in the flood models. High resolution models use smaller grid cells or spatial units, capturing more precise terrain, drainage networks and features like roads and levees. And if you have a high resolution dataset like we do, for example, you can have more accurate predictions of who and what gets impacted if a flood does indeed happen, enabling better planning, preparedness and medication strategies. Now, I think what's even more important in a way though, is that last one.

(08:46):

That's a massive one to me, the unreliability of historical data due to climate change. Now, FEMA is using historical data because that's what they have available to them right now, but you can't rely on it anymore. Climate change is altering precipitation patterns and sea levels flood probabilities must be recalculated models based solely on past trends, underestimate future risk. And so go on. You might say, I believe you in theory, and of course you're going to be touting the benefits of your own models, but theory is lost on me. I want you to give me a concrete example. You say that compares the FEMA model to your model with a real company. And let's see what really matters here. Well, my invisible and Ignatious listener that I just made up, your wish is my command. Let's talk about a Baxter, a company whose facility is in North Cove, North Carolina and produces 60% of the US' IV fluid and Neal Dialysis Solutions, which if you're unaware of what those are, they are dialysis systems for kidney disease patients. Basically what I'm trying to say is this is a facility where you really don't want to have something go wrong.

Katie Towey (09:54):

So Baxter International was a company that high profile event, right when Hurricane Helene happened last year, they had a manufacturing facility outside Asheville, North Carolina that was significantly impacted by floods. And at the time we saw several publications come out looking at, did the FEMA flood data accurately capture this location as being within their floodplain? And if you go and look at their FEMA's flood hazard data, you'll see that the location of the manufacturing facility in North Carolina does not directly come within the floodplain that's demarcated by the FEMA flood data. Now, when we look at where this asset location is and overlaid with our flood model, we do in fact see that this property has exposure to these flood events. And so what that means is that we can accurately see that, okay, yes, this location would be flagged as having very high exposure to flooding.

(11:00):

We actually see that with this particular location, they are amongst the most exposed locations to flooding out of all the locations that we cover in our data set. And so then the corresponding losses that would ensue from either asset damage or business operations would be significant as a result. And so we saw this materialize when this location had to shut down for four months before it fully resumed operations due to the damage that came about from the floods, and that had an impact on the stock price of Baxter as well as the sales of some of the goods that are produced at this manufacturing facility. And so what is really important then why our models can pick that up as opposed to fema, it comes back to all of the qualities that come with the flood models that we get from Fathom. And the two most important ones are having an accurate representation of where exactly this location is as well as resolving not just physically kind of the coordinates of where it's located, but also resolving the elevation of this point too.

(12:13):

Because you can imagine, especially with something like coastal flooding or river flooding and even flooding from heavy rains, resolving the resolution of the elevation of that facility is going to be important. And knowing, okay, if you're at the top of a hill or at the bottom of a hill, and that might change over a very short distance, that can have a significant impact on one, your level of exposure to floods and two, what the resulting damage could be as a result. And we saw that when we looked at all locations within our geospatial asset intelligence database. We found that if the elevation of these locations were overestimated by just 20 centimeters, there would be over a quarter of a reduction in flood exposure of these sites. So we go from over 35% of locations having some level of exposure to floods that would be reduced down to 9%. And the greater that elevation bias, the greater that increases, the more locations would erroneously show up as having no exposure to flooding.

Mike Disabato (13:16):

Okay, so there you go. And yes, it is easy to do this in hindsight where we look at a single building that was deeply affected by a storm that already happened and tell you why it was such a big deal when we already know it was such a big deal. But let's say you're an investor and you're not just looking at one building, you're looking at hundreds and thousands of buildings that may be in varying degrees of flood risks. Now the good thing is, is we're able to actually look at those. We're able to map all those facilities because we have millions of asset locations in our database and we even have the building types of those assets. Now that last point is vital. That's just not a boast there because remember my earlier example about the seven World Trade Center being in a risk of high flooding that really isn't that big of a deal for us.

(14:00):

It's an office building. I can work from home. Our major servers are offsite, but the Baxter facility that was a manufacturing facility that was producing life-saving materials, that being in a risky area is a much more material for a company than if an office building is in that same location. So what we can do is with that data, we can see whether or not Baxter's competitors face a similar set of physical climate risks. And if you look at all the similar manufacturing facilities to Baxter, you could see that North Cove, that facility was actually in a league of its own for as long as that facility has existed. It has an annual average loss, which is the expected loss from a flood to a company due to a facility being in a flood zone for a given year was three times higher than any of its competitors.

(14:51):

Now, of course, it also makes 60% of the IV fluid in the us so it's a strong player in the market, but protecting for that downside risk regardless of the company's heft is vital to understanding where your risk lies due to climate change as an investor. And the reason we're getting into this FEMA data situation wasn't just to showcase the granularity of our data, but it was also to note that Baxter was not required by regulation to enhance its flood protection for that facility because that facility was not in a FEMA floodplain. And as I said, US regulation around flood protection insurance is directly tied to those FEMA maps. If the FEMA map doesn't capture it due to it only looking at historical data, which Katie noted, investors may need to use other resources to better engage with companies to ensure they're prepping for physical risks due to climate change. And so what I wanted to know from Katie was is that's just how it is now. Are the publicly available sources that we've been able to use in the past not adequate to deal with the science experiment that we're all in right now with climate change because of how fast things are changing? Do we have to rely on these private models which sort of removes this public infrastructure availability for everyone that can't afford a private model?

Katie Towey (16:07):

I think 50 years ago when we had what was extreme weather or considered extreme weather 50 years ago, these types of events didn't have the same magnitude or same breadth of impact as they do today in one part because these events are just getting more intense, and that is a side effect of the additional warming that the planet is getting. The other side is where people are moving and where businesses are allocating. They want to set up shop in these areas that are more prone to floods or strong winds or extreme heat, and that has an impact as well. If people want to keep moving business operations to these areas too, then you're increasing your exposure just by being in these areas. And so it's kind of a twofold effect that you have increasing severity and frequency of these types of events, but also more buildings are getting allocated to these areas where floods are becoming more prevalent, where water shortages are prevalent, where wildfires are more prone. And that's something we have to be considerate about when companies are looking to build new buildings in areas or move assets or even just consider the assets that they have in place now, is it going to be okay to stay in these areas 30, 50 years from now? Or is the risk too high and it's worthwhile considering moving these assets to relocate to somewhere where the risks aren't as prevalent?

Mike Disabato (17:44):

I think it's a fair question, and one, maybe a lot of companies don't yet have the resources available for them to be able to ask, should we keep our building here? Are we at risk? Are the people that we employ at risk? Because FEMA is just one example of a model that needs updating, and it's the USS National model. It's the only way that you can get insurance through the National Flood Insurance Program, and it's vital for keeping not only companies but organizations without the sort of funding that a public company has abreast of their flood risk. If this risk is calculated on outdated data, for example, if there isn't enough funding to ensure that the maps are updated regularly, then you can have a situation where individuals and companies are left in the dark where they're unable to properly assess the looming risk faced by them due to extreme weather. And that's it for the week. I want to thank you so much for listening. I want to thank Katie for talking to me about the news with the sustainability twist. If you like what you heard, don't forget to rate them. Review us. That always pushes us up on podcast lists and subscribe if you want sustainability now in your inbox every week. Thanks again and talk to you soon.

Speaker 3 (19:08):

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