Subscribe
Share
Share
Embed
Stanford Professor and Researcher Margot Gerritsen discusses a new era of data-driven decision-making, powerful advances in data science, and its applications in climate change and sustainability.
You already know the facts about climate change. Now, we need cultural evolution. In this podcast, we apply cutting-edge insights from diverse fields to tackle climate change and environmental issues more effectively.
Climate Clear is powered by AreaHub, a climate and environmental hazards platform.
Note: Some expert guests on Climate Clear may be AreaHub advisors.
Climate Clear Episode 2
===
Alison: Hi, I'm Alison Gregory and you're listening to Climate Clear, powered by AreaHub this is the show where we help you discover climate and environmental issues and a clear, digestible way by talking to experts on these topics - all in less than 15 minutes.
Today, we'll be hearing from Margot Gerritsen. She's a professor of energy resources engineering at Stanford University. She's also associate director of Stanford Data Science and co-founder and co-director of Women In Data Science Worldwide Initiative.
Margot thank you so much for joining us. Do you mind starting us off by sharing a bit about your background in particular, how your engineering and data science work became so focused on environmental sustainability?
Margot: Hey, Alison, it's really great to be here today. I really started my career as a mathematician and, over time, I discovered engineering processes, particularly fluid flow processes, which led me to studying coastal ocean dynamics, aerodynamics, and later, also groundwater and reservoir dynamics related to gas and oil.
And I got involved in not just looking at negative environmental impacts of gas and oil production and how to mitigate them, but also in techniques like carbon sequestration. So, after a few wanderings around the world, I did my Ph.D. in the United States, but I got back to Stanford, 20 years ago and joined energy resources engineering.
That department is doing a lot also in renewable energy and got very, very interested in large-scale solar in wind energy. And now, also in decommissioning of internal combustion engine view. Now in everything that we do, data comes up all the time. Regenerating data, doing a lot of observations, we're studying data through processes of remote sensing, using satellite images, for example.
And we're ultimately making decisions based on models that are very much data-driven. So we're sitting now in a time where there is a really nice interplay between a lot of the research that we've done traditionally and a new kind of research that we're doing in this area of data.
Alison: That's fascinating. And I love hearing about your trajectory and how fluid flow processes brought you towards data. In particular, the interplay with data science in terms of these climate-related and sustainability issues. You said that we're going towards a new kind of approach. We'd love to hear more about that.
Margot: What has happened over time, in research and also in decision-making, is that we're moving from a thought process that is data supported, where data comes in and supports your decision-making process or your research.
We're now entering an era where data seems to come first. There's so much data out there right now because serving data and also the generation of data through, for example, big simulation models is so, so much cheaper than it ever was before.
It's really mind-boggling that in my career, which spans around 30- 35 years, we've seen this unbelievable increase in computer power and decrease in costs associated with computing power, as well as decreasing costs related to data gathering. Now we have so much observational power. Tools are available that can be run fast on very large data sets and the data comes in fast and furiously as well.
Now in a lot of what we do, based on what we discover in the data, the patterns we see, or the anomalies that we see -equally important- we try to then create a picture of how things work. We try to also predict out what may happen later. So this is a new approach.
Now it also has its pitfalls because you have to be very careful that you still really understand the underlying physics, that you're very careful about any biases that may come in, but it does sort of ring in this new era in decision-making.
Alison: Margot, that's really interesting in terms of this new data-driven era. I wonder if you could share with us what you think might be the results of us being able to observe more, observed faster, and predict better, when it comes to climate-related issues and sustainability issues.
Margot: We can, I hope, predict better. And I put that in there because there are certainly cases when we only focus on the data without really understanding physics, or human behavior, or engineering processes underlying the data that would make bad predictions. But in many cases, having this unbelievable volume of data is really making a difference.
Just think about how clearly we can now observe water content in soils. That is something that, in the past, we would do by going to an agricultural area, doing measurements, taking soil samples, and looking at moisture content. Now, with remote sensing, we could do this via satellite. We can create pictures quite fast now, and almost daily, of soil moisture, which of course is incredibly important when you're trying to assess the effectiveness of crops, the crop progress, the potential for an area to be used to grow crops or support cattle, and things like that.
It also allows us to monitor the drought developments much better. We can see the impact of sustained droughts better. We can see, and observe very clearly, the growth and propagation of desert-like areas. We can see the advance of deserts. In some areas, we see desert areas really expanding, and now we can observe that really well.
And there are so many other examples where using remote sensing, we have a much better idea, much faster, of what's happening. We can look at ocean currents, we can observe weather patterns so much better, and hope that we have gotten a little bit better because of all that data about weather and precipitation that we can all observe from space and actual weather prediction.
Alison: And as you think about this data-driven first approach and this tremendous power that we now have to utilize data in the ways you're describing, where do you think we might be able to see some of the best applications in terms of solutions?
Margot: Absolutely. There are people working in many, many, many different areas now in data science and climate or data science and sustainability.
Some of them we've seen advances that really useful work that has come out. And then others, the jury is still out a little bit. Just to give some examples from my classroom, I've had students look at satellite imagery to find the areas in Africa, for example, where poverty was highest, where there's very little evidence of irrigation and where people really suffer from increased drought, for example, or from a flood. And it helps the development agencies to find areas in greatest needs most rapidly. So that's a very interesting example.
Another example that some of my students have worked on has to do with wildland fires. One of the biggest tasks after a wildland fire is for insurance companies, and also local governments, to assess the damage that fire has done to the community. For example, which buildings were destroyed, to what extent have they been destroyed? And with satellite imagery, before and after fires, this is also possible.
There are people that are trying in the wildland fire area also to use data-driven approaches to predict the behavior of fires. That is one of those areas where we're quite far from being able to do that. We do have data on fires now. But these new fires that we've been seeing in the last five years, are so different than the fires that we've been used to because of decreased moisture content, fuel increased lay down a fuel, so there's a lot more fuel in the forest that leads to much hotter fires than we've seen before, and hotter fires tend to create, at some point, their own kind of winds and weather. And that really impacts the way that they're moving. Temperatures are also higher, wind patterns may have shifted. So there's a lot of newness to the fires that we're seeing. And we probably don't have enough data right now to really, from the data, predict out what another fire may do, because almost every fire has unique behavior at this time. We're still trying to really understand these changes. So those are a couple of other examples.
Now we, of course, have for a long time understood insulation. So that means, what kind of energy the sun gives to us at particular locations around the world. So we've done that. We've looked at that, and of course, maps like that can give you an idea of where solar energy systems, like solar PV, or concentrated solar, may be very useful.
Increasingly, we've mapped out, using data methods and also very large-scale simulation models, we've mapped out the wind resource around the world. So when you're thinking about putting in distributed local energy solutions in areas of interest or wherever you would like to help the local population by increasing energy supply, you can now do this in a much better way.
So, those are interesting examples.
There's a lot going on also around natural hazards, tracking tsunamis, understanding tsunami damage, understanding what may or may not be possible, storm surges, earthquakes too, so many examples!
Alison: Those are a great set of examples, thank you so much, Margot.
You were talking about things that were in other parts of the globe and things that could affect Americans. And it makes me wonder, notwithstanding the fact that prediction can be particularly challenging, such as an example you gave of wildfires where they're changing so much right now, but from what you can see currently, what do you think Americans can expect in the next few years?
Margot: In terms of sustainability, and the environment, and climate, I think we're really going to see a much better understanding of the local impacts, or regional impacts, of climate change. We are understanding climate change a lot better now than we did 10 years ago. And at the same time, what actually happens in individual smaller regions is still a bit hard to predict.
So we understand average behavior may be for the West or for the Southeast. But if, as a citizen you say, what do you think will happen with the snowpack -just to give one example because I happen to live nearby this now- or in the cascades in Oregon, particularly around Mount Bachelor ski area, or the snowpack thickness that we may expect in the next couple of years in the Sierra because that's so incredibly important for the water resource in California.
We're still a bit far from that, but I think, in the next couple of years, the models and the prediction capabilities will get more advanced and you will really be able to see some reasonable predictions that are much more local.
So that is incredibly important for everyone. And also very interesting for people, right?
There's also a lot of work going on right now in understanding the impact of air pollution, which is a huge, huge problem in many areas of the States, and it has a lot of impact on people's individual health, certainly through respiratory diseases, asthma, you name it, but also in other ways, because of heat exhaustion, because of dry air, because of the AQI being very, very poor from wildland fires.
And we're also getting much better at predicting AQI measurements this -air quality index- from day to day. So that people can maybe take action. Now, not everyone has the agency to do so. And I understand that. But wouldn't it be great if you could say to people: two days from now we're expecting a very high AQI, if you can move somewhere else temporarily, or think about how in the coming days you can limit your time outdoors, try to change your plans now, in advance, so that you don't have to be exposed to this higher AQI?
So there are lots of little things and also big things that we will be seeing in the next five to ten years.
Alison: It seems that data science is just advancing so rapidly that I hold a lot of hope it can provide some tremendously beneficial insights and direction for people.
One of the questions I have, is are there things that you think that we should be doing in connection with trying to understand our local areas and preparing for an increasingly intense climate-affected world?
Margot: I always feel that it's really important for people to gather as much information as they can about their local environment, that they know what natural hazards or man-made hazards they may close by. As you know, because AreaHub works in that area. It's not always easy for the average person to actually do this and find this.
The web has brought a lot of that much closer to all of us, but finding the data relevant to your own neighborhood or to your own city is not always easy. So staying informed and getting informed through aggregators, like AreaHub, is a really good idea.
Staying informed of what your local government puts out, in terms of warnings and predictions. For example, here in Bend, Oregon, where I live right now, the local irrigation district and the water boards are putting out a lot of information about local water usage, about drought indicators, and they're predicting out what may happen in years to come. So as an individual, looking at this and saying, okay, I need to really make sure that my water consumption will go down because it will get harder and harder to sustain this level of water use in the community.
But what I hear from a lot of neighbors is that they're not aware of this. So it's good to do that. Really, really good to get involved at a local level, too, to make sure that you have a say in decisions that are made around new developments, for example, that may have impacts on air quality, through increased traffic, or may have an impact on water consumption locally.
Many many different examples, but I can't resist and promote AreaHub here because I think it's one of the very few websites that are available for people to understand what's cooking in their own neighborhood faster.
Thank you,
Alison: Margot. That's a very nice comment. We certainly are passionate about helping people to understand their local areas and to stay informed.
Margot, thank you so much. It's been really interesting to hear about your journey towards these issues, what you're observing lately, and some of the applications that are of tremendous interest to us and to many listening,
You're listening to climate clear and thank you all for joining us today.