Data Chat: Dr. Tamlin Pavelsky

The Surface Water and Ocean Topography (SWOT) mission provides critical data for understanding Earth's water cycle, including data about freshwater systems that have not been available from any previous hydrology-related missions.
Joseph M. Smith
Dr. Tamlin Pavelsky is the freshwater science lead for the SWOT mission. Credit: University of North Carolina at Chapel Hill.

The Surface Water and Ocean Topography (SWOT) mission is producing NASA's first global survey of Earth's surface water. Given that Earth's warming climate is likely to alter the movement and accessibility of the planet's lakes, rivers, and reservoirs—resulting in significant societal impact—this information is both timely and necessary. Without an adequate inventory of these freshwater resources and the volume of water they store, it will not be possible to assess the effects of environmental change on agriculture, industry, and other sectors critical to livelihoods of people around the globe.

Fortunately, SWOT is up to the task. With its state-of-the-art "radar interferometry" technology, SWOT provides data from hundreds of thousands of lakes, as well as the discharge volumes of medium-to-large rivers. These measurements support a variety of research and help scientists study the dynamics of floodplains and wetlands; assemble a global inventory of water resources, including lakes, transboundary rivers (i.e., those that cross international borders), and reservoirs; and better understand the global water cycle on land.

That might sound like a tall order for one satellite, but Dr. Tamlin Pavelsky, freshwater science lead for NASA's SWOT mission and professor of hydrology at the University of North Carolina at Chapel Hill, reveals how SWOT's unique capabilities provide observations previous hydrology-related missions could not. He also discusses some of the practical applications for SWOT data, shares his suggestions for getting acquainted with SWOT data products, and explains why he finds the SWOT mission so exciting.

How does SWOT differ from and complement previous hydrology-related satellite missions?

There are two answers to your question. The first is that there have been a number of missions that observe different aspects of the water cycle. For example, data from the Gravity Recovery and Climate Experiment (GRACE) mission can be used to measure changes in groundwater storage and the Global Precipitation Measurement (GPM) mission is for precipitation, so in that sense, SWOT is adding to our body of knowledge on the water cycle. It tells us about surface water, about rivers and lakes and some wetlands, how much water is getting stored, how that storage is changing, and how much water is flowing through those water bodies. That's really important for quantifying the water cycle as a whole and SWOT is providing that new component.

The second is that, for the people who study rivers and lakes in particular, we have a long history of repurposing satellites that were really built to answer other questions about the planet's systems. We've used Landsat, which as its name implies, was really mostly built for people who were studying land, but we can use it to see where water is and how it's changing over time. We've repurposed altimeters that were designed for oceanography to get water levels of rivers and lakes, and so on. SWOT is really the first mission that is for surface water. Hydrology is part of the core of the mission, and so that's one of the things that makes SWOT really exciting for me.

So, in regard to studying Earth's water cycle, are you suggesting that SWOT, in conjunction with these other missions, enables scientists to track water as it moves through the entire water cycle?

In many ways, I think the Holy Grail of NASA Earth science is to be able to monitor the water cycle from space to understand how it's changing. For that to happen, we need to be able to track many of the different components of the water cycle and to be able to say, "Here’s what is happening with precipitation." We can see that with GPM. And then we bring in SWOT data and we can see what happens to, say, a flood wave that's moving down a river and maybe makes it out into the ocean. Then we can use SWOT to look at what's happening with topography and maybe we could use one of the sensors on another mission to tell us about its impact on ocean salinity, and then we can use another satellite to estimate to help us estimate evaporation. So, that is definitely a goal.

What can you tell me about SWOT's Ka-band Radar Interferometer (KaRIn) instrument, the observations it provides, and how they benefit the hydrological science community?

KaRIn is really new. There has never been anything like it on an orbital spacecraft. We've had other radars in space, of course, and we've had other radars that could do interferometry in space, but SWOT has a Ka-band radar, which has a wavelength of about a centimeter. Also, most radars look way off to the side whereas SWOT, while it's not quite looking straight down, is just barely off to the side.

What’s the difference between, for example, a one-centimeter wavelength and a six- or seven-centimeter wavelength, or a radar that looks way out here and not here? Well, it turns out these make a big difference. For example, with most other radars that we have in space, water looks dark and land looks bright. With SWOT, water looks bright. We get a lot of returns off of water surfaces and it's the land that looks dark. So, if you want to study what's happening with water, you want those returns off of the water surface. SWOT is just so unique and there were many questions about what we were going to be able to see with it that we simply could not answer before launch. So, it's really been a process of discovery for us.

What are some applications of SWOT data?

I like to say that freshwater is both the source of our superpowers and our kryptonite. On the one hand, it's our most important resource. We use it for agriculture. We use it for industry. We use it for drinking water, transportation, recreation; it's involved in almost everything we do. We have to have it and we need to know how much we have—and that amount changes over time.

At the same time, floods are the most expensive and most dangerous natural disasters worldwide. Droughts, the lack of water, can be incredibly damaging too. So, [freshwater is] both incredibly important and a hazard, and SWOT's going to help us with both of these aspects. We're going to be able to observe how much freshwater we have—the amount of water stored in pretty much every reservoir in the world—and how that amount is changing over time.

This image shows SWOT water surface elevation over rice fields in the Central Valley of California on December 3, 2023. During this time of year, the rice fields are inundated with water as part of the California Winter Rice Habitat Incentive Program, which encourages farmers to flood fields for birds. SWOT data show the extent of the inundation and the elevation of the water surfaces in the fields. Lighter colors (greens, yellows) indicate greater water surface elevation; darker colors (purples, blues) indicate lower water surface elevation. Image courtesy of Tamlin Pavelsky.

We're also going to be able to observe flood waves—the structure of how water moves down a river during a flood—and that's going to be directly useful because we have computer models that we can use to simulate and predict what a flood might look like. Sometimes these models work well and sometimes they don't. Right now, we really don't have good enough data against which to test the models. SWOT is going to really help with that because what it's observing is exactly what these models try to predict—where the water goes, how deep it is, etc., and that's exactly what we get from SWOT. So, SWOT is going to be able to help us both augment our superpower and protect us against our kryptonite.

Can you elaborate on how SWOT's observations of reservoirs benefit water resource management?

Yes. I'll give you two examples. One of our early adopters is the Texas Water Development Board. Water is really important in Texas, and you’d assume the board regularly monitors all of the state's reservoirs, right? They've got on-the-ground monitoring, but it turns out they have on-the-ground monitoring for only a few hundred of their 7,000 reservoirs. For the rest, they're using extrapolation to figure out how much water these reservoirs contain and how the water levels in them are changing because it's really expensive to monitor things on the ground. If you think about most of the rest of the world, either a lot of the reservoirs aren't monitored at all or they're monitored, but the data aren't shared. So, for the vast majority of reservoirs in the world, if you asked me, "Can you go and find on the ground data for these reservoirs?" The answer would be, "No." SWOT addresses that directly.

Another application is that there are a lot of small reservoirs that might be owned by a town or a small municipality somewhere, and they're used for drinking water, hydropower generation, or for flood control. In a lot of cases, the people managing those have other jobs and they're not actively thinking about the reservoir on a daily basis. Now, imagine that we have a forecast of a big rainfall event. [Using SWOT observations] we can contact these managers beforehand and say, "All the reservoirs that are going to be affected by this storm have all got pretty high water levels right now. It would be really beneficial if we could lower that storage and release some water now so all the new water coming in can be stored and not lead to flooding downstream." In fact, one of the things that we've thought about doing is basically creating an early warning system for such managers that would send them a text message or an email warning them of such a scenario.

What should data users know or be aware of prior to using SWOT data?

There are a number of things that are important. The first is that we have several data products. When I get started on some kind of a project, the first thing I think is, "OK, which data product do I want to use?"—as some of our data products are rather unusual.

We've got a standard raster data product that shows things like inundation, extent, and water surface elevation, and you can absolutely go use that. But unusually, we also have these vector data products, where we've taken all the SWOT data and turned them into one dimensional river center lines or polygons that represent lake areas. Those have heights attached to them and they're distributed as shapefiles, so users don't have to figure out how to use a netCDF or HDF5 file. It's not that there aren't a lot of people who can work with these formats, but there are a lot more people who, while very competent in performing GIS analysis, may not be familiar with some of NASA's more esoteric data formats. The barrier to using SWOT data in your environment is pretty low because a lot of the stuff that we're providing is drag and drop into QGIS or ArcGIS. That said, the danger of this approach is that it's easy for people to just start looking at the data and to not do their homework.

So, my recommendation is that when users start working with these data, go and download the product description document and look through it. In our vector products, for example, or in the raster product, we have a bunch of different variables, fields, attributes, etc., and some of them are really important. Users are going to want to look at some of the quality flags too. When creating SWOT data products, we took the approach of including everything, but then flagging it rather than removing it if we thought it was bad or suspect. What that means is, if you open up any SWOT data product or granule, there's a reasonable chance you're going to see something that doesn't make sense. However, if you go and look at the quality flags, you're probably going to see that we flagged it and why.

Do you recommend people work with SWOT data in a cloud computing environment?

That's complicated because some SWOT files are large while others are not. For example, if you want to work with the vector data product, those files are maybe tens of megabytes and often cover a whole pass over an entire continent. So, I don't think there's any problem with users downloading them to their computers. However, there's another data product called the Pixel Cloud data product. This is a product from which all the other hydrology-focused data products are derived, so it's a lot larger and a lot more data-intensive, and users might want to think about working in the cloud when working with that.

This image shows SWOT Water Surface Elevation data (in meters) for the Mississippi River on February 12, 2024. Brighter colors (yellows, greens) indicate higher elevations; darker colors (blues, purples) indicate lower elevations. Image courtesy of Tamlin Pavelsky.

You mentioned that several SWOT products are derived from the Pixel Cloud product. What might users need to know about the hierarchy of SWOT datasets?

SWOT definitely has a hierarchy of data products—that's the right word to use. The first ones are really the relatively raw radar data products, and most hydrologists are never going to want to go and look at them because they're not georeferenced; they're in radar system coordinates. The Pixel Cloud product is the most basic product for hydrology that is actually georeferenced. When users load it up, they’ll see all these points that are classified water or land and each one represents a certain area and has an elevation associated with it, along with a bunch of supporting information. So, it can be really powerful to work with because, for example, not all of the lakes that SWOT observes are in the vector product and not all of the rivers that SWOT can see are in the river vector product, and if you want to work in complicated areas like wetlands, it might be that the Pixel Cloud product is the best way to go.

But again, I would reiterate that users should read the documentation because the later products, like the lake vector, river vector, and raster products, all have water surface elevations that are relative to the geoid. But in the Pixel Cloud product, the elevations are relative to the ellipsoid, and you have to apply a geoid correction and some other corrections if you want to get something comparable. So, there's a lot of complexity; people who read the documentation will have an easier time.

Finally, in addition to the product documentation, what resources are available to help users learn about SWOT data?

The first thing users should do is go to NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC). PO.DAAC staff have put together and are distributing a lot of resources to assist users in working with SWOT data. In addition to the product description document for each data product, PO.DAAC provides links to a variety of resources pertaining to SWOT data products and how they work. That's where I would start.

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