There's a model for everything, including Yellowstone's colorful thermal springs

This is a photograph of Morning Glory Pool from Aug. 23, 2012. Credit Joseph Shaw, Montana State University

This is a photograph of Morning Glory Pool from Aug. 23, 2012. Credit Joseph Shaw, Montana State University

Scientists from Montana and Germany develop simple new model that explains the brilliant deep hues of Yellowstone National Park’s most beautiful thermal springs

WASHINGTON D.C. – Researchers at Montana State University and Brandenburg University of Applied Sciences in Germany have created a simple mathematical model based on optical measurements that explains the stunning colors of Yellowstone National Park’s hot springs and can visually recreate how they appeared years ago, before decades of tourists contaminated the pools with make-a-wish coins and other detritus.

The model, and stunning pictures of the springs, appear today in the journal Applied Optics, which is published by The Optical Society (OSA).

If Yellowstone National Park is a geothermal wonderland, Grand Prismatic Spring and its neighbors are the ebullient envoys, steaming in front of the camera and gracing the Internet with their ethereal beauty. While the basic physical phenomena that render these colorful delights have long been scientifically understood — they arise because of a complicated interplay of underwater vents and lawns of bacteria — no mathematical model existed that showed empirically how the physical and chemical variables of a pool relate to their optical factors and coalesce in the unique, stunning fashion that they do.

“What we were able to show is that you really don’t have to get terribly complex – you can explain some very beautiful things with relatively simple models,” said Joseph Shaw, a professor at Montana State University and director of the university’s Optical Technology Center. Shaw, along with his Ph.D. student Paul Nugent and German colleague Michael Vollmer, co-authored the new paper.

Using a relatively simple one-dimensional model for light propagation, the group was able to reproduce the brilliant colors and optical characteristics of Yellowstone National Park’s hot springs by accounting for each pool’s spectral reflection due to microbial mats, their optical absorption and scattering of water and the incident solar and diffuse skylight conditions present when measurements were taken.

“When we started the study, it was clear we were just doing it for fun,” Vollmer said. But they quickly discovered there was very little in the scientific literature on the subject. That’s when things got interesting.

Montana State University, in Bozeman, Mont., is a short drive away from Yellowstone National Park. In the summer of 2012, Vollmer, on sabbatical from the Brandenburg University of Applied Sciences, travelled with Shaw and Nugent to the park. Using handheld spectrometers, digital SLR cameras for visible images and long wave infrared thermal imaging cameras for non-contact measurement of the water temperatures, the group took measurements at a number of pools in Yellowstone, including Morning Glory Pool, Sapphire Pool and Grand Prismatic Spring. Using these data, along with previously available information about the physical dimensions of the pools, they were able to create a simple model whose renderings of the pools were strikingly similar to actual photographs.

In the case of Morning Glory Pool, they were even able to simulate what the pool once looked like between the 1880s and 1940s, when its temperatures were significantly higher. During this time, its waters appeared a uniform deep blue. An accumulation of coins, trash and rocks over the intervening decades has partially obscured the underwater vent, lowering the pool’s overall temperature and shifting its appearance to a terrace of orange-yellow-green. This change from blue was demonstrated to result from the change in composition of the microbial mats, as a result of the lower water temperature.

A general relationship between shallow water temperature (hence microbial mat composition) and observed colors was confirmed in this study. However, color patterns observed in deeper segments of the pool are caused more by absorption and scattering of light in the water. These characteristics – mats having greater effect on color in shallow water, and absorption and scattering winning out in the deeper areas – are consistent across all the measured pools.

“Our paper describes a very simple, 1-dimensional model, that gives the first clue if you really want to do more,” Vollmer said.

“We didn’t start this project as experts on thermal pools,” Shaw said. “We started this project as experts on optical phenomena and imaging, and so we had a lot to learn.”

“There are people at my university who are world experts in the biological side of what’s going on in the pools,” Shaw said. “They’re looking for ways to monitor changes in the biology – when the biology changes, that causes color changes – so we’re actually looking at possibilities of collaborating in the future.”

Future work for Nugent, Vollmer and Shaw includes delving further into infrared imaging at Yellowstone National Park.


Paper: P.W. Nugent, J.A. Shaw and M. Vollmer, “Colors of Thermal Pools at Yellowstone National Park,” (, Applied Optics, Vol. 54, Issue 4, pp. B128-B139 (2015)


49 thoughts on “There's a model for everything, including Yellowstone's colorful thermal springs

  1. I don’t understand why a model is required.
    Why can’t they explain the thing in a scientific (perhaps even peer reviewed) paper.

    • This whole AGW hysteria has given model an undeservedly bad name . The issue is whether the model is built upon well understood and experimentally verified physics , or “cloud” computing with no “audit trail” back to the essential physics . The entire engineering world runs on models . It’s the only way to get quantitative values for any even slightly complicated phenomena .

      • @ Bob Armstong. Here here. My research was in Thin Film optics. And yes I used models. But these were essentially large matrix calculations based on the very sound Maxwell’s equations. They were essentially design calculations, and where the current model did not replicate observations, futher experimental study was made, such as for scatter ( again based on sound physics) and interlayer absorbtion – my experimental area.
        I had to discard a number of model approaches that simply did not fit my observations – now that is where the climate modellers are going wrong. Their models do not fit observations, but they still carry on. Just plain nuts.

      • PiperPaul: The fact that a system is chaotic does not mean it is completely unpredictable. It only means that aspects that depend sensitively on the initial conditions are unpredictable. For example, I think you’d agree that a climate model could predict that here in Rochester the average temperature in July is a lot warmer than the average temperature in January, despite the fact that it is a chaotic system.

      • Joel.
        It is unlike you to be grasping at straws:
        “””””….. For example, I think you’d agree that a climate model could predict that here in Rochester the average temperature in July is a lot warmer than the average temperature in January, …..””””
        Come on now Joel. Do you have examples of when the average Rochester July Temperature has actually been colder than the average January Temperature, or has that not been observed.
        Can you tell us what percentage of the time in Rochester, the sun doesn’t rise in the East; but in the west instead ??
        Keeping a diary of the eternal annual Temperature cycling, to drag out every year does not exactly rise to the level of actual prediction of an actual number for a “lot warmer” Temperature in any given month.
        That is weather forecasting (or hindcasting), and NOT climate prediction.

      • George – You miss the point, which is simply that “chaotic” does not mean you can’t make any predictions. It means that you can’t predict things that are very sensitive to the initial conditions. Things like the season cycle and the response of the Earth to changes in radiative balance are not very sensitive to the initial conditions.

  2. Very small concentrations of elements can change the color of solutions and crystals and the temperature and “lawns of bacteria” (not so good on prosthetic heart valves and cardiac pacemakers) are worth an on-site visit to the Park.
    Please leave your “make-a-wish coins and other detritus” at home.

    • I agree, coins are basically copper based and would dissolve in the hot chemical rich waters. Copper minerals can be green or blue. Reduced temperature could be due to reduced heat below since guysers act as hot spring water would continually be renewed from below.

  3. A model can never be more than a mere reproduction. Perhaps some models can hope to capture a measure the beauty of the real thing. But they will never be as beautiful (or savage) or mysterious as the real thing. People don’t create nature, they merely copy it; or harness a small measure of its forces. That’s all.

  4. Because the climate models abuse science, “model” has become a knee-jerk word. But this model sounds OK. It’s “very simple” “one-dimensional”, so it doesn’t have vast arrays of parameters that can be tweaked to achieve any desired outcome. All the researchers are saying is that they have done the maths behind the science, and it’s quite simple. There is a very real contrast between this and the endless fiddling, obfuscation and spin which accompanies the climate models.

      • If you’re looking for the most objectionable word in the current “science” environment, my nomination would be the word “unprecedented.”

  5. “There are people at my university who are world experts in the biological side of what’s going on in the pools,” Shaw said. “They’re looking for ways to monitor changes in the biology – when the biology changes, that causes color changes – so we’re actually looking at possibilities of collaborating in the future.”
    Who ain’t looking for an endless stream of funding ?

  6. No, Everything in physical science NEEDS a model. That is how science works!
    (Excluding purely descriptive catalogs of properties of things.)
    For those not trained as scientists, I can understand the confusion. E=mc2, F=ma, everything that scientist dream up as an explanation, is a ‘model’. We compare the “model” output with reality. Scientists deal with aspects of reality by putting formulas to them. The formulas and theories are NEVER reality, no matter how good they are. Most ‘models’ like Newtonian mechanics fail for the very small and the very fast (for example). When scientists confuse their “models” (simple or requiring super computers) with reality, then they are no longer true scientists.
    The term has become corrupted by its use for large weather or climate models, and these are often NOT true scientific models, as much of the maths isn’t based on solid physics and chemistry, but rather guesswork as to what is really going on.
    When a model truly predicts what is happening, (Such as force=mass times acceleration) we have confidence that our understanding closely approximates reality.
    But never, never should the model be confused with what really is.

      • It’s all pure fiction. We made it up in our heads out of whole cloth, as a short hand way of guessing what the likely outcome of an experiment, never performed might be, without having to do the experiment.
        Otherwise the earth would be devoid of trees, which are all stored in compendia of all knowledge for the results of every possible imaginable experiment.
        And we would never be able to retried any particular result in any finite time.
        So if our guesstimathematics hints at something peculiar that might happen. Well then we could try and do the experiment, to see if that really does.
        If not, we get out the pen, and reshuffle the wording in our models, to be more like what we observe when we do the experiment.
        To follow any other path, we would never get anything done, because we would all be trying to do every possible experiment just to say “I was the first”.
        Both our science models, and the math to manipulate them are all pure fiction that we made up.
        That doesn’t matter a jot, so long as it reasonably explains what we observe. It doesn’t have to seem rational (EG QM). Nor does it even have to be unique.
        All a model has to do, is explain the experiments we do do, and suggest what will happen in the case of the ones we don’t do.
        In that sense our models are quite real.

    • Well said kiwi..
      Science/maths/physics are nothing but our attempt to explain the physical world around us.
      Sometimes the explanation is “good” and passes the test of time and reality.
      Sometimes the explanation is not so good, and is falsified by tests of time and reality.
      Climate science currently resided mostly in the latter category.

    • Some models are good but climate models are bad because they do not show or predict what is going on.

  7. Not sure I can stomach this article while sober. I have had a bellyful of computer models. Maybe some Ardbeg Uigeadail might help. Would my Scottish skeptic friends concur?

    • Nice. Was this your vacation?
      I am once again giving serious consideration to a traveling/working/wandering trip west of the Mississippi beginning this spring. Looking to see the sights and find a place to land. Not unlike Willlis’ adventures.

    • Camped, wandered, engaged a little fishing “research”, and took fair amount of photos over 6 week stay in and around Yellowstone this past summer. Would post a photo, but I haven’t figured out how and my cyber connection is awful –
      I was greatly amused around the thermals where “Danger – Keep Out” signs beside many pools were accompanied by piles of Bison patties.
      I was much less amused to find many “No Dogs” signs beside which people were carrying what might be dogs in shoulder bags.
      Aside from the tourist sites, I too was more comfortable away from the roads and 4.5 million visitors – and there are many, many special things to experience and see away from the crowds and roads – but carry the bear spray and take along a friend.

  8. The temperatures changes of Yellowstone’s thermal features do not require coins or trash. They do that all on their own due to natural variability. Not everything on earth is the fault of man and NOTHING stays the same for all time. This human = bad crap is getting old and mostly wrong in any case.

    • I’ve seen displays of stuff excavated from hot springs. There’s a lot of human trash. And branches, sometimes bones. All coated with minerals. People speed the process along. Natural processes are as much involved. Hope for an earthquake to make things novel.
      BTW, I consider natural variability to mean “Oh, perhaps CO2 isn’t responsible for all that climate change. Natural variability, you know, the stuff we’ve never bothered to study so we can’t describe it in quantitative terms, might be involved too. But just a little.”

      • Heat engines are chaotic; a candle flame flickers. Volcanoes are not on-off processes and heat transfer from the earth’s core to the surface varies naturally, sometimes hot, sometimes not. If one vent gets plugged another will form because of the laws of thermodynamics.
        Ec1.9 What has been is what will be, and what has been done is what will be done, and there is nothing new under the sun.
        It’s the same kind of silliness about species extinction, bad in the short term, I suppose, but evolution will result in new ones.

  9. As I have watched climatology deteriorate over the decades into superstition and magical thinking; I have discovered that any wild assed guess is now “science” as long as it 1) can be somewhat simulated by a computer game, 2) makes the need for “government protection” stronger, and 3) fits in with the world view of the crony-capitalist or the socialist.
    On a related note: I was told by a middle school lad that being good at “Angry Birds” should make one an “A” student in science since it was “applied science”. Hmmmmm.
    On another related note: I sometimes make models too. Well, I did when I was younger but now my wife tells me that I can’t. 🙁

  10. Modelling of optical scatter is already done with modern optical analysis tools. Here is a “tech note” on how to model the “blue sky”;
    Zemax is a registered trademark. This is one of the more advanced optical modelling tools available. They offer a free demo version you can download and experience the tools available. It will not let you save an actual design of your own, you need to purchase the software to do that (~ $3,000 to $6,000)
    There is also an article on their website about how to model the “sparkle” from a diamond, it works quite well. Simply define a material by it’s optical properties and mechanical shape (with lots of facets) then illuminate with a white light source, slowly rotate it and viola…. sparkles.
    Creating a model is easy, making it match reality is more challenging….
    Or, in climate science you can just make reality match the model, piece of cake, easy peasy….
    Cheers, KevinK

  11. “…relatively simple models”. Isn’t this the trap they fell into with climate science? I will be surprised if there are not many variables which they have failed to include in their models.
    Never mind, we know it all!

    • There is nothing especially wrong about using models (even simple ones) to describe empirical observation. Using them to predict the future is where you get into trouble.

  12. “They’re looking for ways to monitor……..”
    Of course they are. Who isn’t?
    Monitoring is the ultimate make work and much of it is paid glorified hobby-like play time.
    The opportunities for monitoring are also limitless.
    It’s doesn’t require much imagination to invent something to monitor.
    Many of these monitors trip over it then go on and on how interesting and important it is.

  13. Unlike some here, I find this paper both beautiful science and highly relevant to climate change.
    They took two previously well established scientific principles–bacterial mats change with temp, and optics change with depth, combined them in a simple model, and reproduced observational nature. Meaning their simple combined model reproduces/explains observed nature reasonably well. Meaning, to quote Feynman, “we understand it” because we can simply explain it.
    Compare and contrast to climate models. They are not simple 1D simple mixtures of two known things. They are complicated 3D mashups of many not so well known things (starting with Navier-Stokes and the ‘religious’ proclamation of Feynman thereon, “Book 2, chapter 41, verse 6” for the cognoscenti). I quote from RF’s Lectures on Physics 2-41-6:
    “We have just seen that the complexities of things can so easily and dramatically escape the simplicity of the equations which describe them. Unaware of the scope of simple equations, man has concluded that nothing short of God, not mere equations, is required to explain the complexities of the world….We cannot say whether something beyond [ equations, Schroedinger’s] is needed or not. And so we can all hold strong opinions either way.”
    Fundamental inherent defects in CMIP5 models are enumerated in essay Models all the Way Down in ebook Blowing Smoke. But that in no way criticizes the beautifully accurate results here in simple models of Yellowstone’s beautiful pools.
    ‘All models are wrong. Some are useful.’….George Box, 1987. Skeptics should recognize when they should not longer be skeptical, but rather admit the beauty of congruence between observation, theory, and the bridging model constructs. As here.
    TY for this post.

  14. “When we started the study, it was clear we were just doing it for fun,” Vollmer said. But they quickly discovered there was very little in the scientific literature on the subject. That’s when things got interesting.
    Yeah…they found grant money lurking about.
    When we get back to the model being a tool and not the answer, maybe we can take them seriously, again.

  15. Difference between writing a model and writing a novel, is that models describe important facts of the reality quantitatively. A model that does not match the measured observations of the reality is a fiction novel.

  16. That photo reminds me of a giant eyeball from a monster movie, which looks like it has teared up. LOL. Hard to take this seriously.

Comments are closed.