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The impacts of flooding are increasing: damages are growing costlier and climate change is expected to exacerbate the risks and effects. How can we reduce costs—and save lives? In the latest episode of The Intersect podcast, Russ Jones and Mark Lorie explain how novel use of GIS data and analysis can help efforts to respond to—and plan for—floods.
For more on this topics, check out Episode 1: How Can We Improve Housing Resilience in the Face of Climate Change?
Read the Transcript
Eric Tischler: Hi, welcome to The Intersect. I'm Eric Tischler. Today I’m joined by Russ Jones and Mark Lorie. Russ is a specialist in geographic information systems, remote sensing, and cartography with more than 24 years of experience providing extensive mapping analysis and modeling support in the areas of natural resource damage assessments, ecology, sociology, economics, and climate change.
Mark is a water resources planner with a range of expertise in resource and strategic planning, policy and risk analysis, risk communication, and collaborative decision making methods. His work has addressed topics including water supply and drought, coastal and river flood risk, and ecosystem restoration in multiple regions across the U.S.
Mark Lorie: Thank you, it's great to be here.
Russ Jones: It's great to be here.
Eric: Mark, let's start with you. There’ve been a lot of major flood events in recent years, including one recently in Michigan. What can you tell us about the trends we're seeing?
Mark: Yeah, it's interesting. We have seen especially headline-grabbing events over the last five or seven years or something like that. I was looking up some numbers getting ready to talk with you today, and one of the ones that always amazes me is NOAA—National Oceanic and Atmospheric Administration—maintains a database of billion dollar disasters over history. And I don't know, there were a few hundred events in that database. The five biggest disasters in history were all coastal storms that were primarily flood events, and they all occurred in the last 15 years.
So we're definitely seeing more and more big damaging events in recent years than we used to in the past. The interesting thing about that is that the temptation is to kind of say, okay, "That's got to be climate change because climate change is running apace and it's in the news.” We think that the biggest driver of the events so far is that we tend to continue building houses and buildings in places that are prone to floods. In the long-term, we do expect those events to get more frequent and more severe, and that might become the bigger driver of those trends of the future. So those are kind of the bigger trends that I was thinking about as we were getting ready for this and thinking about what's driving these flood damages that we've been seeing.
Eric: One way or another, the flooding is increasing. You're saying we're going to see more of it. What can we be doing to prepare for mitigation, remediation? What are some tools that are available to us to help offset this?
Mark: I guess, boy, it's a complicated problem, right? So one, you have to understand as best we can how likely are big floods along different river bodies and coasts, right? So there's a physical element of how likely are the storms? How often do they come? And when they do come, where does the floodwater go over the landscape? Then you need to understand, well, where are people in buildings located within those areas that are likely to get inundated by flood? Where do we have houses? Where do we have apartment buildings? Where do we have critical infrastructure? Once you can marry those two up, and then obviously that kind of lends itself to a geospatial GIS analysis, then you can start to think about, well, what are our options for doing something about that?
In the past, we tended to rely on big structures to try to control where the floodwaters would go. I forget the exact number, but I think in the United States, we've spent quite a few tens of billions of dollars on infrastructure like big dams and levees that would prevent water from going in places where we don't want it to go. I think, increasingly, there's a recognition that, in addition to that, we have to do a few things. One, we have to better restore and maintain the infrastructure that exists. So you heard about that flood in Michigan, that was two weeks ago. That was caused by an old dam that failed due to extreme floods. We also have to get a little bit smarter in places that are very prone to floods so that when one does happen, the damages are a little bit less, maybe they're a little bit easier to repair and restore, fewer people are affected. And we're starting to see that increase just a little bit. It's gaining momentum because we realized that’s the more cost effective way to deal with this problem in certain areas.
Eric: So you mentioned geospatial GIS analysis, and it sounded like that'd be handy here. And it just so happens that Russ is on the call with us. Russ, do you want to talk to us about what GIS can bring to this?
Russ: Sure. So GIS is a technology that we can use for basic mapping or analysis or even modeling. And as we discussed, it could be used for a variety of disciplines, anything that you can think of that has a spatial component you can use GIS for. So, in terms of flooding and climate change, we've had, since I've been doing this, I've been working on climate change and flooding impacts for probably the last 20, 24 years. And the way that we do this is we're able to take projections from global climate models, for instance. And there are 40 different models from different agencies, that different countries around the world have produced to try to model all the physical interactions of the atmosphere and oceans and coupling that to look at, okay, well, how are things going to be changing in the future?
So when you heat up the air, it'll hold more water. When it's released, you get more extreme events, that type of thing. So we're able to use some of this global climate model data and pull that out into GIS and it's spatially explicit so that any given area on the earth, what are the models saying in terms of how the temperatures and precipitation and extreme precipitation patterns going to change? And then we can throw that onto the landscape, along with our known flood plains and the topography, and kind of get an idea where we might see increases or decreases in flooding through time.
And along with that, we also know where the infrastructure is. There's a lot of wonderful new data sets that show where all the housing footprints are. And so we can kind of get an idea as to what is the relative risk level for any particular area that you're in, both now and going into the future. And then what kinds of damages might we be expected to see. We can run that kind of quantification through our GIS system. So taking these flooded areas, overlaying that with our building footprints, and then Mark will take information on what are the building values? How many stories is it? What are the depth damage functions? So if you have a certain depth of inundation, what does that mean in terms of damages and how much is it going to cost to replace that? That type of thing. So, GIS is really well suited to looking at things like impacts from flooding and what are the potential adaptation options that we could use. In other words, you might want to say, well, let's model where it flooded and what kind of levee system could we put in that might prevent that in the future. So, before you even get down to the Army Corps of Engineers, you can do a lot of upfront work to look at relative risk and potential adaptation options that you might want to take.
Eric: Great. So Mark, you want to talk a little bit about that, how you could use that data or what kind of data should we be collecting that we aren't already collecting, either, or, both?
Mark: Yeah, so there's a number of things that would be great if we did a better job collecting data that would make that process that Russ described, it would give us better tools to do that. So, one thing as it turns out, we talked earlier about trends and flood damages, flood losses. It turns out we don't actually do a great job of tracking that. So for a lot of flood events, that tends to be a little bit of a back of the envelope kind of thing, usually by meteorologist, especially when they're small events. They might call the mayor, they might kind of call an insurance agency, "We think it was $7 million." So it would actually help a lot if we had more precise estimates of what those damages are.
We have done a very good accounting on a couple of big events, like for example, Hurricane Harvey in Houston, almost five years ago now, four years ago now, they did do a much more detailed kind of assessment of just what was damaged and why, and what was the total loss across different kinds of buildings in different kinds of sectors.
Other data that has been difficult, but as Russ mentioned, is getting much better literally by the day is... It's one thing to say, "Here's an estimate of where the water is going to go." And you've got some hydrology and hydraulic models, and you've got a model of what the landscape looks like, a digital elevation model. And you can say, "Okay, in this particular kind of storm, here's the parts of the landscape that are going to get wet due to this flood." Until the last couple of years, it's been very difficult to know exactly where are the buildings out there? Nobody was responsible for having an authoritative inventory of homes and apartment buildings and businesses, car dealerships and whatever else. We had very rough estimates, I guess I would say. Russ mentioned that, I don't know, probably the last couple of years that the building footprint data set was developed, Russ?
Russ: Yep, yep.
Mark: Yeah. So Microsoft, and I have no idea how they do this, but they have some sort of artificial intelligence process that processes satellite imagery and is able to identify buildings on the landscape. And then they'll put a little polygon, little square. That's a building, that's a building, that's a building. So now we have this data set of, I don't know, millions of buildings across North America or across the United States. So that tells us, I don't know, with 95 percent accuracy or better where every single building is. It doesn't tell us what kind of buildings they are, so it matters if you flood a warehouse on a Saturday night versus a nursing home on a Tuesday morning, right? Those two impacts are very, very different.
So, we've been working on some processes to take parcel tax databases from local counties and states, some real estate stuff, literally just using Zillow, although they make it a little hard to use some of that data, but we use some of it in ways that they allow, to be able to say, "Okay, this shape, this building footprint on the landscape, that's a Walgreen's. And this one over here is a multistory apartment building. And this one over here is a car dealership." So we're starting to do that.
And we've recently learned that some federal agencies have scaled up a process like that to try to come up with those attributes for all buildings. And that'll really help us understand what kinds of buildings are likely to be exposed, which of them are going to be associated with greater direct image, like just the building getting knocked over or business losses, right? If a car dealership or a restaurant or office building has to shut down for weeks or a month, that's a big loss to the people who work there. And so that, I think, is going to help us get more precise, better estimates of what's at risk so that we can make better decisions about how are we going to adapt to that. Some of those buildings, maybe we move them, maybe we say it doesn't make sense to have a nursing home in a place that's going to flood every 30 years down the road. Maybe it doesn't make sense to have a school located in a place like that, but maybe it's okay if we have a rec center or a boat marina or whatever. So we can start to get smarter about some of those decisions and making those changes.
Russ: And I would also say, along with that, we have lots of different socioeconomic data sets and demographic data sets that we're able to combine and look at areas that are high density or low income, where people might not have the resources to take preventive action. And we can also combine it with where are the evacuation routes? So if you have an area that might be vulnerable to flooding, what are the evacuation routes that are there? Might those be impacted by the flooding as well? And if they are impacted, what are the alternative routes? So these are some of the other things that you can use GIS for is to look at disruption. If you have an area that's cut off, how well, how much of a detour, how much time would that take? And then Mark can throw that in with the rest of the demographic information and socioeconomic information to say additional impacts that might be caused that are indirect. So very powerful set of tools that you can use in a myriad of ways. You just have to be creative on what you're doing.
Eric: Great. And clearly you guys are thinking about this creatively. Before you go, let me backtrack. So we've talked about how it can help plan for a response. Mark, you were alluding to planning, almost like civic planning, urban planning for buildings, etc. So, looking ahead, what are some things both of you would like to see local, state, federal government do to help better plan both for a response and then plan to sort of take into account the worsening of event as your municipality grows, as your state grows, as the country grows?
Russ: So, one of the things that I would like to see is, obviously in my discipline, more use of optimized location of services that may be needed. So for instance, you have the ability in GIS to say, "Okay, if we have say an emergency center, hospitals, or we have some evacuation center, where can we optimize the location to that to serve the most people that might be threatened or under threat in the future?” So you can calculate these service areas for any particular facility or be it emergency management or restoration centers or grocery stores or whatever it happens to be that you're looking for. You can say, what is the service area around this in terms of how long would it take people to drive their car here, or walk here, whatever mode of transit that you wanted to have, and then what are the areas that might be cut off from that? So if you did put a new facility in some location, you might want to add some additional roads or figure out additional ways of accessing that so that if you did have an event come in to take that out [roads], those people could still be able to get to that resource. So I think just smarter use of some of the geospatial technology for planning purposes and knowing what the upcoming threats might be. And Mark, you can probably add onto that.
Mark: Yeah. I mean, the thing that—and Russ, you and I are wrestling with this on a daily basis, it's one of the projects we're working on. When we do analyses that are just meant to provide the analysis, it's one thing to deal with uncertainty, but when you start getting down to a government person or a property owner or a community making decisions that uncertainty gets really, really important, and there's a ton of uncertainty in this stuff. And when we do these studies, there's uncertainty about how the storms are going to change 10, 20, a hundred years from now. There's uncertainty about where in the landscape the water is going to go. There's uncertainty about how much development is going to occur in any given location and therefore what's likely to be exposed. And it's hard for communities to make decisions with that degree of uncertainty.
We see it a lot in the face of climate change. I would like to see, in addition to sort of increasing the sophistication of our analysis and the way we use that analysis to make decisions, I'd like to see communities and government agencies choosing adaptation pathways that are a little bit more flexible. So, it used to be 50 years ago or 80 years ago, we thought whether or not to build a billion dollar levee was a relatively simple decision, do some analysis, and the benefits exceed the costs and, if there's enough money, you build it. But now it would be interesting to look at approaches that take into combination multiple features. Maybe you don't build a billion dollar levee, but you build a levee one-third that size that does protect some part of the landscape, but then you're also saying, "Okay, we're going to hold off on this new commercial development until we get better data about how storms are changing, or we're going to build them a levee in a modular way so that we've got the ability to add to it later in a more cost effective approach."
So a lot of these new kinds of approaches are getting, I would say, they're getting attention and they're getting tested. I mean, this is really at the forefront of what engineering academics are researching and trying to implement. And I would like to see more demand really for that from the community side, like just accepting the fact that they're going to have to approach things a little differently than they used to and using the data to kind of take that of incremental adaptive approach. I think that would be very helpful for flood risk and flood losses in the U.S. and around the world.
Eric: Great. Well, I love that both of you are thinking down the road in your respective fields and that you're working together daily. That's a nice intersect as we like to say here on The Intersect. On that note, thank you both for joining me.
Russ: Yeah. Thanks for having us.
Mark: Thank you, Eric. Happy to be here.
Eric: And thank you for joining us at The Intersect