This month, we are pleased to interview Dr. George Burba. Dr. Burba is a LI-COR Science & Strategy Fellow, Global Fellow at Daugherty Water for Food Institute, Senior Member of the National Academy of Inventors, and a Graduate Adjunct Professor at the University of Nebraska-Lincoln. In this interview, we will know more about Dr. Burba’s career, his recommendations for early careers considering working in the instrumentation field, and discuss automated flux measurements, which is the future of eddy covariance networks.
Please tell us briefly about your career and your interest in science.
My father was a scientist, planetologist. My mom was a mechanical engineer. My grandfather was also a scientist, chemist. So, I guess the fundamental interest in science came from being raised in such an environment.
On the personal level, I always loved nature: being outdoor always felt right. So, I became a certified Professional Mountaineering Instructor, and naively expected this to be my life-long career.
Of course, in Soviet Union at that time, this profession would not pay at all, and it was nearly always a volunteer engagement. So, my first degree was in International Economics. It was an objectively interesting subject initially, but it started to get a bit boring soon after: I did not like the perspective of spending the rest of my life limited to just this area. People and professional culture in that community at that time were also not very exciting to me.
So, then I got into Atmospheric and Ecosystem Sciences, and it got really exciting right away, in terms of both topics and people. How nature actually works was a fascinating subject. People and professional culture were just what I loved. This was fun from the start in 1988, and it is still as much fun now!
How do you see the future of ground instrumentation in the flux community and its interaction with remote sensing measurements? And, what would be the role automated flux measurements?
Particularly in FluxNet/AmeriFlux community, the methodology for flux measurements have largely matured by now. This means that, in addition to process-level studies (most of what we did with flux data up to very recently), the flux measurements could become an automated flux monitoring tool. Very early growth of this approach could be seen in some aspects of the design and scope of NEON and ICOS flux monitoring infrastructures.
The future automated flux monitoring could be very similar to a weather monitoring done via Automated Weather Station (AWS) networks, where data from numerous AWS flow, in near-real-time, into weather models combining ground measurements, remote sensing measurements and modelling, to provide the society with practical weather inventory and weather prediction tools.
However, adding the monitoring paradigm to the process-level paradigm is not an easy process. As a flux community, we are still and often locked into the process-level-only mentality. Of course, it should be noted that the process-level studies are very important, as this app
Fig. 1 Presentation at Nanjing University about automated flux measurements, October 2018
roach studies important bio-physical processes in great detail in order to understand how exactly a specific process works and why exactly it works. Flux community has been doing these studies starting in the late 70’s, and have made many very significant, even fundamental, discoveries.
The process-level approach requires flux towers to be very accurate and well-resolved, looking into a small spot in great detail, much like a microscope. For example, you may want to see reduction in canopy CO2 uptake in the afternoon because of stomatal closure in response to a large vapor pressure deficit. While extremely interesting scientifically, aside from the flux community and some parts of ecological community, very few people could appreciate something like that.
Process-level studies are critical for fundamental and applied sciences, climate and agricultural research, but in order for flux measurements to jump into the mainstream and become recognized as important for everyone, they need to happen at the automated flux monitoring level.
Monitoring is different from process-level research. It is no longer a microscope but more like a wide-angle lens, looking everywhere. This what most of weather stations do: they provide us with approximate near-real-time data that could be used for many practical everyday things (charting routes for airplane and ships, warning of thunderstorms and hurricanes, etc.).
To achieve this, the AWS near-real-time data, which could be considered point measurements, are overlaid with remote sensing data, which are broad-scale measurements, and run through the models that eventually end up as near-real-time weather monitoring on our phones.
Nearly all logistics and movements of goods across the globe, most of agricultural planning, individual family travel planning, and many tiny little daily personal decisions are ultimately connected to the near-real-time automated weather station networks.
Similar paradigm could, in the near-future, be applied to the flux station networks. Weather stations could be coupled with flux station, and vice-versa, and as a result, what we could see on our phones would essentially be the near-real-time ecosystem services provided by our local ecosystems to our local society, and even to individual people.
Much like weather stations, automated flux networks would tune remote sensing and modelling products to report daily evapotranspiration at 30 m scale, carbon sequestered, CH4 emitted, and so on…
In other words, at the automated monitoring level, flux stations could be used as verification points and tuning tools for remote sensing and modeling products. The advantage of the automated flux stations is that the data are in near-real-time, while the disadvantage is that the data are less accurate, but these data are still quite sufficient for most of regular decision-making.
The societal benefits, as well as appreciation of ecosystem services, would then increase exponentially:
- For economy and environment, decision-making benefits can range widely, from computing carbon credits in near-real-time, to telling how much water to apply to avoid expensive overirrigation and nitrogen leaching, and inform important land management decisions (to cut the forest or to keep the forest, to drain the wetland or to keep the wetland, etc.)
Fig. 2 AGU with LI-COR crew, December 2017
- For an individual, anyone could easily see on their phone how much carbon was sequestered by their nearby forest, or how much arsenic was cleaned from the nearby stream by their nearby wetland.
- This economic-environmental-personal nexus could seamlessly revolutionize how the broad society views ecosystems, agricultural food production, climate change, etc.
In addition to a much more efficient decision-making, monitoring automated flux networks could help conclude the never-ending fighting about climate change. If I can casually monitor day-in and day-out how much an ecosystem around me is doing for my own good and for the good of my kids, the abstract discussion about climate change might just go away: there is no discussion in modern society about how important the weather is. We need to get the ecosystem flux monitoring to that same level of broad acceptance.
In addition, that is where I think the flux community has opportunity to make a huge impact: we are very close to be able to form a global monitoring network of automated near-real-time flux measurements, 5 years optimistically and 15 years pessimistically. However, someone has to champion this.
Do you think innovation and leadership are acquired or innate? What are your advice for early careers who are considering/interested in a career related to instrumentation and new device invention?
It is probably a mixture of the acquired and innate, and also, of the will and chance, like everything else in life.
If you have nothing in a certain area of life (no interest and no talent) then there is nothing really there to develop. It is still possible but extremely hard to develop something from zero. But, if you have even just a little bit of either desire or talent, or both, then you can certainly develop it more and more. My suggestion would be to look into yourself, see what you have more and try to develop it, instead of struggling with what you do not want to do, or have very little ability to do. Because that is going to be a very big struggle.
Specifically, for early career researchers who are considering working in the instrumentation field, the following career skillset would be quite important:
- Understanding the natural processes quantified by a future instrument is essential.
Fig. 3 LI-COR Experimental Research Station where we test new instrumentation and new methods
- Strong math and physics are both extremely helpful.
- Some familiarity with business side (e.g., how R&D and manufacturing works) will save you years of life in the stress equivalent!
- Long-term outlook (how this device will improve science and people’s lives) would help you get through many “writer’s” blocks.
- Bouncing ideas off friends and relatives regardless of their background can help get through these periodic blocks as well.
- In order to make the invention actually useful, it needs to be invented, developed, optimized, manufactured, supported and then used by many. The invention itself is perhaps 10-15% of this overall effort. Many people from different areas would need to be involved to make it successful. This situation has to be understood and well appreciated on a personal level.
- Don not stress out: yes, it is very important to work smart all the time and to work very hard at times, but health, family and friends are more important.
Who have been your mentors and how have they helped you arrive at where you are today?
Family was an important factor by teaching me fundamentals of analytical thinking and interest in science, highlighting the importance of education and doing good work, and building my confidence and professional ethics.
I was also fortunate to have encountered many Mentors, with capital M, who were able to create a true mentor-student synergy and to make their students better in many ways, and in their own personal ways. Here are three great examples:
- Professor Vladimir Solntsev from Lomonosov Moscow State University. He championed a true open-mindedness in science and the-art-of-science approach. I have learned from him quantitative methods in ecosystem science, but also the importance of considering scientific arguments from all people regardless of their area of expertise, background, or personal philosophy, as well as the closely related importance of a chill factor.
- Professor Shashi Verma from the University of Nebraska-Lincoln. He championed scientific professionalism, integrity and responsibility, as well as critical importance of being very precise in asking scientific questions and giving scientific answers. I have learned from him most of what I know about micrometeorology, as wells as critically important practical skills: design, planning and executions of scientific experiments, clear scientific writing, deep mathematical understanding of natural processes and measurement methods, and guidance on how to be a professional scientist.
- Professor Dayle McDermitt, University of Nebraska-Lincoln, and VP-emeritus at LI-COR. He championed open-mindedness, scientific curiosity, and continuous life-long learning in broad areas of science and technology. Dayle’s mentorship generated, encouraged, supported and guided so many things: inventiveness, innovation, unconventional thinking, scientific elegance, professional and personal integrity, attention, strategy, diplomacy. He helped me tremendously in every aspect of my professional development.
How did you find remote working experience during COVID pandemic? Any fun story that happen with you in the field/at work that you would like to share with us?
The 2020 was actually very productive: I wrote a chapter for an Elsevier book, and wrote a full new eddy covariance book, which was a “covid project”. I had more time working from home, because I did not have to drive back and forth to work. So, was able to work more hours, and still had more time to do fun things. I was also able to go to our experimental site, but not to the various university sites where we have collaborators.
Probably many fun stories happened, but one I can easily remember right now.
Fig. 4 University of Nebraska Agricultural Carbon Sequestration Site in irrigated corn
Professor Dario Papale came to Lincoln for a flux data processing symposium and a meeting at the university long time ago. I knew him of course, but did not know him too well at that time. So, I thought: what would be an exotic and interesting activity for a person from Europe travelling to the center of the Great Plains? — Texas Roadhouse (a very-cowboy much-steakhouse) seemed like a perfect idea, and I tried to make it a surprise.
We sat at the table, and it turned out Dario was a vegetarian! Everything was basically made of meat there: so, he ended up having to eat a Blooming Onion, a huge deep-fried onion chopped at the top, and deep-fried 😊
That was a big miss on my part, but Dario was very gracious, and I think, he even ate most of the onion too.