37th Annual Killian Award Lecture—Rafael Bras
SANYAL: OK, good afternoon. Welcome to MIT'S 2009 James R. Killian Junior Lecture. I'm Bish Sanyal, Chair of the Faculty. And I'm delighted to welcome you all to this wonderful event to hear the lecture to be delivered by this year's recipient of the Killian Award.
The Killian Award represents our-- the MIT faculty's-- deep appreciation of and respect for a colleague whose academic contribution is an example of the very best MIT can offer the world. We are fortunate to have, at MIT, a number of colleagues who are worthy of this honor. But that is also the reason why the task of selection of any one faculty to deliver the annual lecture can be difficult.
I want to thank the Faculty Committee, chaired by Professor Wanda Orlikowski, who guided us through this difficult task and for selecting a faculty member who is truly a role model for our junior faculty. Let me also thank President Hockfield and Provost Reif, other members of the senior administration, for joining us this afternoon to honor the recipient of this year's Killian award. I also want to acknowledge Dana Mead, the chairman of the MIT Corporation, for joining us.
So jointly-- the faculty as well as that administration and the members of the MIT Corporation-- have gathered here this afternoon to honor and hear one of our esteemed colleagues, Professor Rafael L. Bras, who will deliver the 2009 James R. Killian Lecture. By granting the Killian award to Professor Bras, we affirm and celebrate a particular kind of scholarship, which not only provides deep insight about scientific and engineering principles but also, and perhaps even more importantly, subjects such insights to social and political judgments, striving always to orient the scientific inventions for the betterment of humankind. Rafael Bras is a socially responsible and politically progressive civil and environmental engineer whose academic and social contributions have been many. The selection committee cited some of the hallmarks of his contributions in the program flyer, so I do not need to repeat all of them here.
Let me simply say that what stands out in Rafael's contribution is the centrality of his concerns for human existence. Since he joined MIT as a young assistant professor of civil engineering in 1976, Rafael Bras has worked for the last 35 years with two concerns, both of which go to the heart of human existence. One is to better understand earth's water cycles, which led to the creation of the discipline of hydrologic science through Rafael's innovative integration of traditional land hydrology with earth and atmospheric sciences, ecology, geology, and geomorphology.
This scientific journey, captured well in two of Professor Bras' now-considered-classic books, not only explains earth's water cycles but helps us to better utilize water and, at times, even protect ourselves from it as in his research for the city of Venice, which sought to safeguard its lagoons against flooding. Rafael Bras has spent the last 35 years of his academic career to help us better understand this life force, water, to make it more life giving and less life threatening and, thereby, has provided leadership to a host of interconnected domains of scholarly inquiry ranging from hydrologic science to environmental engineering and, may I add, planning.
The second element of Rafael Bras' concern has been equally central to human existence. Even though there is nothing natural about this phenomena, it is socially produced. By ordering the Killian Award to Professor Rafael Bras, we acknowledge the second contribution as well. So many of us have benefited from his deep insights about how social institutions must counteract the problem of racial injustice. As the head of the civil engineering department for nine years and as the chair of MIT Faculty from 2003 to 2005, Rafael Bras served as a moral voice for racial equality at MIT. He continues to play this role now at UC Irvine where he's the dean of the Henry Samueli School of Engineering.
Personally, I have been fortunate to learn from Rafael while he was the chair of the faculty. And even now, each year when our provost is required to present a status report on minority faculty and students, we are reminded that it was Rafael Bras who, as the chair of the faculty, had institutionalized race awareness in MIT'S deliberations. As a result, we are better off as an institution. And MIT acknowledged Rafael's contribution in this regard by awarding him Martin Luther King Jr. Leadership Award in 2006.
The nation, too, acknowledged his progressive leadership quality by awarding him the NASA Public Service Medal in 2002. The Killian Award Rafael Bras is going to be given this afternoon is one among many other awards he has already received. He was awarded the Horton Medal from the American Geophysical Union, the Clark Prize for Outstanding Achievements in Water Science and Technology, and the Simon W. Freeze Award for the American Society of Civil Engineers.
Rafael has been a member of the National Academy of Engineers since 2001. He's a fellow of the American Association for the Advancement of Science and an honorary diplomat of the American Academy of Water Resources Engineers. So Rafael Bras is quite used to being recognized for his outstanding professional contributions. But from our perspective, that of the MIT faculty, by granting him the Killian Award we want to express our deep appreciation of and immense respect for his contribution to MIT and the world beyond.
We also want to acknowledge that the highest form of scholarship is socially aware and seeks to make the world a more just and humane place. Rafael Bras represents this rare quality of scholarship we cherish. And that is why we miss him at MIT. Rafael, in the spirit of your departing comments last year at the faculty meeting, let me end by asking you a question your closest family members always ask of you. [SPEAKING SPANISH]
Let me ask, now, President Hockfield to join me in giving this citation. Let me read it for you. "The president and faculty of the Massachusetts Institute of Technology have the honor to present the James R. Killian Junior Faculty Achievement Award for the academic year 2008 to 2009 to Rafael L. Bras in recognition for his transformative scholarship in science and engineering and for his exemplary leadership in education policy and science."
[APPLAUSE AND CHEERING]
BRAS: Well, hopefully we'll get something up there. There we go. Good afternoon. It is nice to be home. Today, the title of the talk that I'm hoping you will enjoy is "Wonders of Planet Water, Complexity and Ordering Earth Systems."
Planet water-- it is 70% covered by water. But more so I hope to convince you that the water in our environment, the hydrologic cycle, pretty much sets a lot of our environment. It makes our life on Earth feasible. And, whether we like it or not, we depend completely on it. It is the circulatory system of the earth. It is the lymphatic fuel of the earth in all senses of those words.
Complexity and order is the next theme. And what I want to try, hopefully, through examples-- and I had a hard time picking examples. I've used this title before but not the examples because I was struggling to find out of the many, many years of work with my students which ones to pick that would illustrate to a general audience what I was trying to say. But I think I have, hopefully, achieved that. And you'll get to see it.
Let me begin very quickly, why this hydrologic cycle has captured my attention for so long. First of all because it's big. The hydrologic cycle-- the numbers that you're seeing there are all in cubic kilometers, thousands of cubic kilometers. And I just want to-- I'm not going to spend too much time on this-- point out a few things.
Over the oceans, 70% of the world, there is more evaporation than there is precipitation Over the land, there is more precipitation than there is evaporation. The difference between these two-- precipitation over land, evaporation-- is the run off that goes back into the oceans, at least that that is left after replenishing groundwater, soil water, ice and snow, and rivers, et cetera.
A few things about the storage is the amount, the volumes involved. Over the oceans, the oceans are a great amount of moisture, of water. The numbers there are very high, billions of cubic kilometers of water. The ice caps, for example, and snow, about 43,000 cubic kilometers of water.
But look at this number, the atmosphere-- either marine atmosphere or terrestrial atmosphere. It sounds very small relative to the others. But, in fact, it is that very dynamic atmospheric moisture, that turns over about once a week, that controls the temperature in this earth, makes it not be planet ice, and makes it possible for all of us to live on it.
The other thing that I want to, before I leave this slide, focus on is that these are very large numbers moving throughout the earth. But a lot of what we depend on, and a lot of what I'm going to be talking to you about, really and truly is the result of the differences of very large numbers. And it is those differences of very large numbers that makes it so uncertain, so variable, and, in many ways, so sensitive to our intervention or changes of any of those numbers.
Let me begin by talking of a particular topic, non-linear behavior of land and atmospheric interactions. And, again, I'm going to try to illustrate this. I borrowed this from the work. That particular diagram, I call it the spaghetti diagram, which I think was first generated by a student of Darren [INAUDIBLE] and also used by [INAUDIBLE].
But let me follow, just to illustrate the point, just to give you a sense of the complexity. If you think of precipitation, a drop of water coming down. And if you follow that line, it goes to replenish soil moisture. As I replenish that soil moisture, that soil moisture changes the albedo of the Earth, the amount of energy or radiation reflected from the earth.
It also provides water for evaporation. This "latent heat flux," that it says there, is evaporation, which is the connection between the land mass balance-- the hydrologic cycle-- and the energy cycle. It's the energy of the latent heat that connects the two cycles. They not only connect through a mass transfer, but it's the amount of energy utilized in evaporation that connects the two cycles. So that's the key in there.
Now this soil moisture, as I said, changes the albedo of the earth, which changes and impacts the radiation received from the ground. It changes or affects the evaporation of the latent heat flux, which changes the convection potential, which changes the cloudiness, which changes the radiation again. That convection potential leads to precipitation, which changes the soil moisture again.
Again, this is a very non-linear, very interacting cycle and the elements of that cycle. And can follow any point in this diagram and get to the same conclusion. But, interestingly, many of these non-linearities end up in very simple results, results are incredibly elegant in many ways and actually quite pretty, in my opinion.
One of these results comes about. And this is a work that we did. And I'm not going to try to give credit to everybody, here, but this was with a colleague, Ignacio Rodriguez-Iturbe and Darren [INAUDIBLE] a lot of years ago, let's say. But he said, we did our little experiment, imagining an earth which is a large continent. And in that large continent, we're going to allow the land to influence the atmosphere in a very simple way. I'm not going to explain the details.
But imagine I have this large continent, moisture coming in and moisture coming out in the atmosphere. And the only thing I'm doing in here is connecting the land via the latent heat, the evaporation, mixing up in the atmosphere and producing precipitation on the same land. So this precipitation you could attribute, in part, to that vection-- the PA, the amount of moisture coming in and, in part, to the recycled moisture that comes from the latent heat-- from the evaporation from the same land.
Well, interestingly enough, when you do that exercise, what comes out if you allow for some randomness in the surface properties of the continent-- of a very large continent, what you end up with is a stochastic differential equation that you can solve. And it has different solutions depending on how much randomness you include on the surface properties.
For very low randomness, you find a distribution of soil moisture for-- you know, we are at MIT, after all. So this is a probably density function. There's a soil moisture in the x-axis. This is the density of probability. And this looks like a bell shape, a Gaussian distribution.
This is the way I learned hydrology. If I had been asked to pick, if my life depended on it, the most common guess or the best guess of the amount of moisture on the ground without taking a sample and I had the distribution, I would have picked the central tendency, the mean or the mode of the distribution-- the central tendency here. Now, that results when I have a little amount of uncertainty on the ground. But as I push that uncertainty, you end up-- and change that distribution from a single mode to a bimodal distribution.
The interesting point of that is that it tells me that, if I had picked-- my guess had been-- the central tendency, the mean, about the most probable condition of the moisture, I would have been that because, in fact, that's the least probable result. What you have is a statement that says that nature, then, preferred to be in one of two states, either on the wet or the dry state, which is what those two modes indicate. Now, interestingly, that jives very well. And, remember, all we did was, in a very simple toy model, connect the atmosphere to a land mass.
It jives very well with observations in nature where extremes-- call it biblical extremes. There was a famous paper by Benoit Mandelbrot called "The Noah and Joseph Effect," Mandelbrot and Matalas. Again, periods of flood, periods of drought-- but periods of flood and periods of drought last for a long time. We know that, in hydrology, those phenomena are not white noise. They don't go up and down all the time. We are periods that are very wet or very dry.
And this is our, essentially, excursion from the mean. And this is precipitation in the Sahel if I recall correctly. So what you have is periods of excursions up and down. Well, this simple model that I just showed you before-- that interaction between the land and the atmosphere actually produces those results. It creates those bimodal states. So it tells you that there's a large probability that I would be on a wet state or on a dry state.
Very simply, by doing what I was told as a student at MIT was not important. And Pete will smile at this. The land masses did not matter when I went to school here. The oceans were the only thing that were import them. So, again, the whole thing in here is that interaction indeed produces results that are very much like nature.
Let me move, again, with a topic of organization, complex systems that lead to organized systems in simple ways. This is actually much more recent work. The last paper on this was just published in the proceedings of the National Academy. And this is [INAUDIBLE] whether clouds get organized in any way because our intervention in forest lands, in particularly the Amazon.
This is the Amazon, picture of the Amazon. By now, about 20% of the Amazon has been deforested. There's still a lot of forest left. And the rate is about 15,000 to 20,000 square kilometers a year at the moment. There's debate whether it has slowed down or not. There's some hope that, in fact, it has.
So a typical deforested region will be this area, this area of Rodonia. You'll see that patch. Keep in mind that shape of that patch moving diagonally in the center of that square because that's what I'm going to be addressing. This is all deforestation moving up-- or not all, but a lot of it moving up.
This is a picture I took when I took the Terrascope students to the Amazon some years ago, some six years ago. And, in fact, interestingly enough, I had this picture for a long time in my files. And I was giving a lecture on this topic of clouds and the Amazon and deforestation in Illinois just about a month ago in the Department of Atmospheric Sciences. And I kept thinking of it, oh, yeah, I have something that illustrates. And you'll see how it illustrates in a second. I found this picture so I resurrected it. This is a typical deforested region.
I want you to pay attention to these clouds. They are shallow clouds. They are, in fact, what I would call a fair-weather cloud. Shallow clouds do not produce a lot of precipitation.
But then what we did here, we took nine years-- every three hours-- of surveillance from a satellite and reconstructed the cloudiness day by day, hour by hour, over the Amazon-- over this region-- and computed what we call the cloud density, which is the amount of cloudiness in different parts of the basin of the region. And I think it's pretty evident to the eye. I'll show you the mathematical results in a second, but it always is easier to see it visibly.
The darkness in here is that diagonal deforested region. That is the region where there's a tendency-- and it comes out that way-- of higher density of shallow clouds. These are shallow clouds that I'm talking about now at the moment. So it tells me that, over the deforested region, there's a congruence of shallow clouds and deforestation.
And people have suspected this. In fact, we had produced predictions theoretically and numerically that this would happen. But nobody had the data nor the patience-- until Frederick [INAUDIBLE] did it first, one of my students-- to achieve, to just look at that data set and find that result. More recently, Jim [INAUDIBLE]-- who is sitting somewhere in the audience-- mathematically then used the same data.
And again, let me explain what this is. These are conditional probability densities on observations using Bayesian techniques. And what do you have in here is the probability, theta, that a shallow cloud will occur over a deforested region, a computer probably that a shallow cloud will occur over a deforested region, conditional on the observations.
And what we find is that the red in here clearly-- well, let me say if there was no preference of where the cloud occurs, that distribution would be center at 0.5. It would say that the probability is the same that it would occur over a forested or a deforested region. But what you see is that shallow clouds, in fact, have a very strong preference-- we said a probability of 0.8 and the distribution around it quite narrow-- to occur over the deforested region, shallow clouds.
But deep clouds, the blue, do not like that deforested region. In fact, the deep clouds occur over the forested region. So there's a complete organization. Why?
For two main reasons-- over to the forested region, after doing some analysis and atmospheric profiling, we find that the energy existing for the formation of cloud and convection is relatively small. But there are these breezes-- it's almost like sea breezes, the ones we're all used to if you live on the coast-- sea breezes that form between the forested to the deforested region. So there's an ability to initiate cloudiness. So cloudiness is initiated, but there's not enough energy for it to develop deeply.
While in the forests there's a lot of energy, but it doesn't initiate as easily. So when it happens, it's rare. But when it happens, it happens deeply. But it prefers to do it because that's where the energy is over the forested region. And, again, it jives very well. Observations confirm it. And the observations of the energy states confirm it.
Moving along, let's talk about self organization, another area of interest to me and my students, the self organization in fluvial geomorphology. Back in the first half of the 20th century-- from 1930 to 1950-- there was a giant of hydrologists called Robert Horton. And Robert Horton did anything and everything you could possibly imagine. You heard the Horton Medal that was mentioned earlier. It's named after him.
Horton observed that basins are dendritic structures. But they are, curiously, so similar. So what you have in here is looking at the same basin at different resolutions-- very high resolution to lower resolution to a lower resolution. The blue lines refer-- this is the resolution that you would normally see in a hand-drawn map.
What I want you to visualize is that, if I pick this resolution and I take an arbitrary branch or tributary to this basin and expand it to this scale, I would have no way to distinguish them. Statistically, they will be exactly the same. This is classical scaling. This is what fractals is all about, the ability-- or the fact that, when you look at things at different scales and you do the right normalization, it will be indistinguishable.
Horton then, in his time, produced all sorts of data analysis and, in fact, produced something called the Horton Numbers and the Horton Laws that, for years and years, sort of dominated the story of geomorphology. Then, following ideas of fractals by Benoit Mandelbrot and others, we started looking at trees and river basins and analyzing them.
And we started things as expected to some extent. But we started looking at some different statistics. This is the probability of exceedance of the length of a stream. So imagine that I take that tree and dissect it. I take little chunks and group each length of every stream segment and then do a study of how are they distributed.
So this is a probability of exceedance. So if I go to here, it says that I have the probability, almost 1, of exceeding very small lens and very little probability of exceeding this very large lens. So that sort of described the potpourri of length that I find in the tree of the river basin.
The interesting thing is that, no matter where you do this in the world-- and you plug that. This same scale would have a geometric distribution on log paper. These are logarithmic scales that have a slope of around minus 1.8, 1.9. It's universal. It's like that. In fact, I can do the same thing for the distribution of drainage areas.
And let me define the drainage area in here in a second. This is the area that-- if I'm standing at a point, I measure the area that flows through me. So I collect the area that flows through me. And I, again, group all those, analyze how are they distributed in size and for probability of exceedance. So small areas, I have large probability-- well, a probability of 1, ultimately that I will exceed the small areas-- and very small probability in this scale of getting very large areas, as you would expect. This is the organization of the drainage.
Now in nature, this thing-- that slope-- is, again, incredibly constant no matter where you go. It's about minus 0.45, 0.4, maybe you get to 0.5. It fluctuates, maybe 0.35. But it doesn't move much from there, quite incredible how that is. So we've got curious about what causes this. What causes, ultimately, this fractal organization? But it's not our arbitrary fractal organization because we're interested in, what is a river network fractal organization? That's the important thing.
That yielded the idea. It was the first of many that followed. But I picked this one because it's very easy to understand. Optimal channel networks-- again, Ignacio Rodriguez-Iturbe, and Renaldo, and myself and a whole bunch of other people came up with this idea. And we said, well, are there some principles that, when imposed-- if we assume that nature is following these principles-- will lead to an organization that produces that fractal organization of rivers, not an arbitrary network.
And the principles, which I will not try to justify or discuss here-- but minimal energy expenditure in any link of the network. In other words, nature wants to be efficient. Equal energy expenditure per unit area in a channel anywhere-- so whether I'm upstream or downstream the energy expended per unit area tends to be the same. Again, it's nature saying, I want to be a socialist, in some sense. I want to be fair. How do I spend the energy and where I spend it?
And, ultimately, I want to minimize the total energy over the network. Translate that, I want to move water and sediment as efficiently as I can. OK. Now, those were the concepts.
And it's very interesting. And that's why I picked this example because it's very easy to see, which is, if we then take those three things that I mentioned-- those three principles-- and impose them on an arbitrary network, it's a draining network. But it was gone completely randomly. It is not a river, just a randomly generated network. And we do the analysis of the probability of exceedances of length that I showed you before and the probability of exceedances of the area that I showed you before. These are not the straight lines that we saw before. They are not straight lines with the right slopes that I indicated to you before.
But if we impose the principles-- and this is illustrated by minimizing this energy. We started at that energy. That's the energy of this network. And we started minimizing, iterating until it deforms this and produces that. This came from that via the application of the principles. And then we do the analysis of that. Look at it, the probability of exceedances of areas, straight line, coefficient of 0.45, probability of exceedances of length, straight line coefficient of 1.9.
Whole idea again, we start with an arbitrary network-- a draining network, but not a river basin. The message, nature, again, organizes something very complicated and almost universally collapses it to a certain response and certain patterns that have very distinguishable signatures. They are not arbitrary. Not every network is a river basin. And it has to satisfy certain conditions.
From there, we move to the more prescriptive gaming-- let's put it that way-- where say, can we play God and then produce networks? And in here we just physically started writing equations. And let me just quickly give you a very simple rundown. Imagine that I have a section anywhere on earth.
And I measure total elevation, age of soil-- in here, HS. And I write an equation that says that the rate of change of the H-- in other words whether I'm eroding or accreting-- depends on how much I erode and how much I've produced. So this term is producing soil. That's weathering. These two terms are producing erosion and the position. So it's the divergence of fluxes of sediment, erosion, and the position.
And this term is one very effective way of removing sediment, which is landslides. What I have as landslides are the time scales of thousands of years that we're talking about. You change the elevation instantaneously. So, obviously, a lot more goes into this than that equation fully illustrates. But that's the essence of it.
And when we do that, we get extraordinary things. And I'm sort of simplifying things because there have been a lot of years from the time Gary [INAUDIBLE] used the first model of this to now. By now many things have changed a lot.
But this is one of the versions of the model. And I just want to show you. This is a coastal range-- or imagine a mountain range, not a coastal-- a mountain range producing alluvial fans. And it started flat, completely flat. And I let it run for thousands of years. And it starts receding back. And I want to pay attention to this. The blue in here is the coarse material. The reddish yellowish is the finer material.
So as you move down on this, what you see is the progression of the reddish-- now we are at 40,000 years-- the progression of the reddish area, the small sediment being flushed out literally, the creation of the fans. You'll see this and the upstream areas still quite coarse. At 100,000 years, you have your fans quite well developed, the coarse upper part, the more sandy sediment, smaller sediment downstream-- very realistic.
But this is, again, an example of how sometimes models of this nature are tremendous benchtop lapse. We were playing with this. And we realized that we had a problem. And the problem is the following. If we did like all good theoreticians will say, the world is homogeneous. Everything proper is a constant. There's no variability. And we ran the model. We got the wrong answer.
What we found is that, for example, if I had ran the model presuming that my substrate-- my soil-- was sand, and if I ran the model saying that my substrate was gravel, I get two things that may be correct. But in fact, I know one of them is not correct because when I plot something called a slope area curve-- and I'll explain in a second-- I get the wrong answer.
Let me tell you what the slope area curve is. Imagine, again, standing at the point. And your water is draining through you. So you know the area draining through your point. That's the area that I'm plotting here. And then I measure the slope at that point. So I'm going to plot the slope at the point and the area draining through that point.
If you do that-- again, almost universally, most parts of the world that I know of, certainly, and all basins that I've ever done-- what you'll find is that always plot, with a log log straight line, with a slope of around minus 0.5-- again, minus 0.35, minus maybe 0.6. But it doesn't deviate a lot. We certainly know that a slope of 0.14, which is this version of the model with the homogeneous sand, is wrong. That's not what we see in nature, OK, not in the general basin. You may see it in an alluvial fan but not in a basin.
So we said, well, let's be a little bit more realistic. And what would we did is we added-- think of it as adding degrees of freedom to the model. So rather than telling the model, say, you are homogeneous soil, you have a mixture. Allow the model to work with a mixture. And when I say work with a mixture, I really mean that. The model will erode, will crunch, will move, will mix, will deposit, erode again, crunch, move, mix for thousands of years.
Interestingly, then we get this result. If I started with 10% sand or 90% sand-- completely different substrates. So I start to saying, you have 10% sand, 90% gravel or 90% sand 10% gravel. And let the model then do the mixing and matching and depositing and eroding and crunching. And doing this, it changes the surface layer of the model dramatically.
For example, I started with 10% sand, but not as the mixture in here-- the color code. In fact, the overall basin was of coarse on the surface in the process. And it fines on the way down. You know, the fines are further down in the drainage. But it coarsens the overall basin. But, more importantly, look at the slope. This was 10% sand. I get 0.37 in this slope area curve. This is what we observe in nature.
I do it with 90% sand and 10% gravel and arbitrary other extremes. And it crunches. And, in fact, what it did is fine the surface. The surface is finer. Again, I get a slope of 0.44. I can do this with any mixture you want. And as long as it's a mixture and I let it go through the process of creating that surface layer, it will adjust itself to give me the right answer, as we observe in nature, that we could not simply obtain when we had the case of the homogeneous case.
So, again, nature once again telling us, yeah, life is complicated. But if you give me enough degrees of freedom, I'm going to give you a simple answer and, for that matter, a fairly universal answer. That's, again, what distinguishes a river basin tree from a tree outside. There are different processes.
Let me then move to vegetation landscape interactions, a topic that has captured my imagination for the last 10 years and many of my students. What's the problem? What is the issue where interested in? Again, a cycle of interactions-- we have the topography, properties of the hydrology represented by the soil moisture; energy, radiation coming in; and vegetation. This vegetation is going to grow if it finds enough moisture and finds enough radiation. And it has to be the right mix.
The soil moisture then feeds the vegetation. And the vegetation changes the properties of the soil itself and changes the ability of the soil to infiltrate and capture water. So it, in fact, changes the soil moisture. Not only that, this vegetation changes the erosive capability of water. So if I have vegetation, it changes, ultimately, the landscape. That's the theory.
But if I change the landscape, then I change the way that things drain. And if I change the way things drain, I go back and change the moisture, which then again changes where my vegetation is going to grow. So you see this, then, becomes a really intertwined dance between the vegetation, the landscape, the hydrology, and the radiation.
This is a wonderful picture because this avoid me giving you any mathematical results. You'll see the answer obviously. It's very common in semi-arid regions, water leaving the semi-arid regions. All northern exposure slopes show dense vegetation and, largely, bushes. All southern exposure slopes show grasses, some bushes but not very many-- different vegetation.
We know why this happens. We have done it in numerically, theoretically, and observationally. And it's really the balance between radiation in semi-arid regions and the amount of water available. Over the southern exposures, the radiation wins the day. And it dries too much.
Over the southern exposure, the moisture fights hard enough to provide sufficient, let's say, environment for the plants to develop. And once it does that, those plants get established and, in fact, feedback positively to make it even better for plants to develop. So there's a positive feedback. In fact, it changes the soil profile-- makes it deeper. And you have soil for moisture better. So there's a positive feedback that leads to this.
But then, remember, I had a look that connected to the landscape. Well, look at this. You see these northern exposures. The hill slope is shorter than the southern exposure. The slope of this is higher than that. We have corroborated-- we predicted that. We also can corroborate that through data analysis that, in fact, that is the tendency in all these areas.
So the northern exposure leads not only to an organized-- the hill slope patterns, north-south, leads to a different organization of plants. And those plants change, over thousands and thousands of years, the landscapes themself, in terms of slope and length of hill slopes. To me, that was quite a fascinating exercise.
So let's continue talking about plants. This is the same model. The landscape evolution model we have running with the soil and climate of an Oregon coastal range. If anybody's from Oregon, that actually doesn't look like Oregon, right? You would say, you are wrong. And you're right.
This looks more like badlands. It's a fluvially dominated rough-face system. This was run on bare soil. It had no vegetation. But everything else corresponds to Oregon. The climate as well as the soils is Oregon. But we do not get Oregon. So the question is, why? What, again, is missing in what we're presenting?
Well, we said, well, let's add some vegetation. So we do what we all do, all the good theoreticians. Let's take the easy path first, see what happens. And we just put vegetation constant in there and say, all right, here's your vegetation. It's there. You can landslide and wipe it out once in a while, but it's constant. The vegetation is constant. It has no dynamics, low on change. It doesn't take time to grow, doesn't take time to die.
And that's what we get. You get this-- very different than the previous one, right? But still-- well, you see, these features of the hill slopes is a landslide occurring, very long hill slopes, very straight hill slopes. But, again, if you are from Oregon, that's not Oregon.
Then we said, OK, look. Vegetation is not constant. So let's put a dynamic vegetation in it, vegetation that responds, dies, takes time to grow, takes time to die. If it landslides, it wipes it out. And it takes some time to come back. What happens in that case? There we go. We get that. And that's Oregon.
Message of the story, again-- statistically, I can show you the that is Oregon. This is statistically Oregon. [LAUGHS] What the message is, once again, nature leads to a unique solution. And that unique solution requires all the relevant components. It is not just a function of the soil or of the climate. It required, in this particular case, essentially the biosphere.
But it did not-- it was not enough to say, I just have plants. And I'll make it constant. You're required to have the ability for the plants to evolve in time and die and come back and so on. If you do not have that feature, you simply do not get the right answer which is, again, to me the revealing concept and, in many ways, the message of this talk, which I hope by now you have captured.
And I have shown you that this beautiful trip through hydrology has been made incredibly exciting by all these things that I did not know [LAUGHS] that come out of the exercise of engaging in the research, of trying things, of finding things. It is all a result of chance and necessity. Things adjust themself. And sometimes you get the wrong answer by not providing enough degrees of freedom, not providing all the elements that are required for nature to do its work. And I think that's the challenge to all of us in our field.
Two things before concluding that I wanted to mention. First of all, I must thank many. You know, the proverbial science is done on the shoulders of giants. Well, I have many giants. I have been standing on the efforts of many. Certainly my students-- many of them who are here-- have been incredibly good at being patient with me and collaborating with me. So that's a list of students up to the last one so far-- not only the MIT students but the post-docs and visiting students that have spent time.
And this is not a complete list, but it's all I could fit in there-- and certainly not a complete list of collaborators but some very important collaborators that have participated throughout the years, one way or another, in what I do. And not the last and not the least is the best staff. They are my angels, in many ways, that over these many years have supported all of my activity.
Second thing I want to say is that March has been a fantastic month for me. It is hard to imagine a better March. Needless to say, thanks to you, colleagues and friends for this honor. I think that certainly made my day. That's a dream come true. I'm really humbled. I will never forget it. I really thank you.
But another dream came true on the way here, which I am really--
COMPUTER VOICE IN VIDEO: Warning. Warning.
PILOT IN VIDEO: Ready to go flying today?
BRAS IN VIDEO: I think so.
PILOT IN VIDEO: All right. Ready, hit it!
COMPUTER VOICE IN VIDEO: Warning.
PILOT IN VIDEO: There we go as we climb on out.
BRAS: I was at Blue Angels a few weeks ago.
PILOT IN VIDEO: Passing 2,000 feet.
BRAS: And, believe me, I would have done anything-- and in fact I almost did-- including lying about the state of my ears. I have two tubes stuck in my ears, and I didn't tell them about it. But it was a wonderful experience. I recommend it--
Very mightily to all of you. Life is good! Thank you very much.
Thank you very much. I am told that we have a little time for questions if you have any and if I can answer. If not, by the way, I have a good chunk of my former students here. I should add, I had some of the two most wonderful days of my life, really a lot of fun. We had students coming from, I believe, five if not six continents to be here in the last two days.
SANYAL: Please come up to the microphone. We have 15 minutes so we can take a few questions.
AUDIENCE: Rafael, it's lovely to see you back.
BRAS: Thank you.
AUDIENCE: So thank you very much for sharing a bit more of yourself. I have-- I want to talk about politics.
BRAS: I take the Fifth!
AUDIENCE: Let me start out with an organizing principle of politics, which is that probably most honest politicians want to do the right thing. And of course they also want to get reelected. But I think, first of all, they probably want to do the right thing. And the problem is that, whether it's the economy or human health or the environment, the systems are immensely complex, as you've pointed out. In fact, they're too complex for most people who elect politicians to get their hands around them at all.
So politicians would like to have organizing principles that are at least comprehensible to most people and on which they can make some kind of the right, hopefully, decisions and ones that seem to get the support of most people because they understand the basic organizing principles. So what would be the basic organizing principles here for our planet with respect to some of the ecological problems that we're obviously facing now?
BRAS: Some of the what problems, I'm sorry?
AUDIENCE: Ecological problems. So we have climate change. We have drought. We have changes in weather patterns. We have floods and so forth. Are there a few-- maybe less than four-- that, in fact, a politician could grab hold of and try to influence the electorate in a way that might lead to some of the right decisions?
BRAS: Yeah. Well, let me-- I don't know if I can-- that's a tough question. So let me try to give you a few things. They're probably not organizing principles. But first there has to be a realization-- and I hope that all of us here realize it-- that as humanity we are at a point, probably for the first time in billions of years-- and millions of years, certainly, since humans showed up and thousands of years and animals showed up-- where we can, in fact, alter-- we are enough of us and have the technology to alter the earth.
There's no doubt in my mind that the atmosphere has been altered. There's no doubt in my mind that large-scale agriculture has changed land surfaces. And there's no doubt in my mind that the combination of those two things have changed the boundary layer in the atmosphere and the earth, the energy balance and the water balance, the hydrologic cycle.
So that's the first realization that I think politicians I don't believe have fully internalized. Earth and its resources and the oceans are seemingly, to them, immense when, in fact, they're not, not any longer. They were. I would argue that, maybe even 200 years ago, they were. But they're not any longer. There's plenty of evidence that we are, in fact, able and have managed to alter that earth and change it in unnatural ways.
Now having said that, if you accept that then the question is, what will happen? And if you're looking for the scientific principles, I think we have to seek-- following, at least, my idea presented in here, we have to argue that, if I put into a system a disruption, a major disruption of some sort-- let it be putting CO2 or any greenhouse gas in the atmosphere or increasing our agriculture tremendously or deforesting the Amazon-- that there will be a disruption, that the system will feel it. And it will settle back if you let it at something.
But that something is not necessarily the something we are used to. You may like it or you may not like it. But it's not going to be the same, for the most part, if the disruption is big enough. These things are fairly stable. In fact, Fatih, Professor Eltahir, did some study with one of his students where they took the Sahel and asked the question, if I change the climates by significant amounts on the Sahel, will it settle back to present conditions or other equilibrium conditions?
And what they found is that, within certain range of disruptions of, let's say, precipitation, the system settled back to what it was. But if you alter it sufficient, you change the precipitation sufficiently, it would reach another different equilibrium where the vegetation distribution of the Sahel will be completely different. No judgment of whether good or bad, it's just different.
So we have to understand how nature, how the paths to those final states will come about. Is it principles of energy-- like Jim [INAUDIBLE] will it's entropy of information type, not general thermodynamic entropy. There are ways that, maybe, we can approach this.
Would I tell that to a politician? No. I'm not going to convince any politician with that argument, except the first one. The first one, I think, is something that we all have the duty to tell the politicians. Really, really and truly we are not sort of living in a way that we do not alter the earth any longer. The longer we go, the more we'll have increased our ability to change the systems.
And once we change those large numbers that I showed you in the hydrologic cycle and the energy cycle, then everything we know depends on differences of those large numbers. And God knows which way it's going to go. And I think that's a challenge to us and then a way to translate it to them in a way that they will accept and understand it. But don't talk to them about entropy. [LAUGHS]
All right, yeah?
AUDIENCE: [INAUDIBLE]. And it's fascinating, the extent to which you can model these essential features of the watershed with the substrate and hydrologic cycle and some radiation and some vegetation that depends on moisture and radiation. My question is, a lot of forest ecologists spend a lot of time looking at nutrients like nitrogen. How is it that that doesn't need to be one of those essential components that you give to nature, or perhaps it is on some time scales? Do you have any comments on that?
BRAS: Oh it is. It is, but being smart I did all these studies on dry land so water was a limited resource.
That is that I set semi-arid or arid. But in fact, with Paul [INAUDIBLE] sitting here at the front from Harvard-- it's OK to mention Harvard. [LAUGHS] Paul and I have a project where we have linked his forest model-- that is a full forest model that includes not only the water cycle but also the nutrient components-- with an atmospheric model. And the whole idea is to begin, then, to study, for example, the health of the Amazon as with the forests.
Because even if you leave islands of forests, the question is whether those islands of forest will be healthy islands. There's plenty of evidence through 20, 25, 30 years of studies in the Amazon-- fragmentation studies where they took chunks and left them-- that, in fact, the remaining forests is not as healthy. And there's a lot of those factors that are nutrient related.
In fact, we have another student sitting there-- my youngest student, Robert-- that is looking at building the nitrogen cycles into these things to study wet regions where those are the limiting components.
AUDIENCE: OK, thanks.
BRAS: Paul, yeah.
SANYAL: Last question.
AUDIENCE: So, [CLEARS THROAT] Rafael, you'll have to excuse my voice. But my question is regarding our system of education, now, in environmental science. And you alluded to the fact that the things you learned back in your day are no longer necessarily true. We have learned a lot of things. How do you think it is changing the way in which we should educate our students going forward?
BRAS: Thank you, Paul. That's a very good question. And I hope to answer it by example. I think that, in many ways under the inspiration of people like Pete Eagleson sitting here over the years. The one thing that we have tried, Paul, as you know, is to really try to break the boundaries that exist in different professions. I would confidently say that all of my former students and present students sitting here have as much background in hydrology as in ecology, to a large extent-- either self taught or going to your classes, either way.
And I think we have to do more than that. I believe that somehow the disciplinary boundaries are still getting in the way-- not, honestly, getting in the way in our ability of you and I to teach our students but in the way, when they go out and find jobs, they find themselves-- like I have found myself many times here at MIT and where I am now-- where people don't know where to place me.
In many ways, many colleagues here and at Irvine look at me and say, hmm, you're not an engineer. I'm a little suspicious about you. [LAUGHS] And then I go to the school of science like I did here. I have a joint appointment at the school of science there, also. And they look at me and say, yeah, you're not like us. You're an engineer. And it is a terrible dilemma. But it is not in the way we-- you and I-- can do it and many of us are doing it but in changing the frame of mind of our institutions to somehow open those boundaries that exist between the disciplines.
SANYAL: We are going to conclude here. And there is a reception outside. Please join us.
BRAS: Thank you very much, again.