A –Streams of Thought– contribution by Kevin Roche.
Sally Thompson is an Assistant Professor of Environmental Engineering at the University of California, Berkeley. Professor Thompson completed her undergraduate studies at the University of Western Australia and worked for several years in environmental consulting. She then moved on to the Nicholas School of the Environment at Duke University as a General Sir John Monash Fellow. Her current research spans an array of fields, including ecohydrology, nonlinear dynamics, and water sustainability. Professor Thompson kindly answered our questions to Kevin Roche (KR) at the 2015 American Geophysical Union Fall Meeting in San Francisco.
KR: Could you describe the main goals of your lab?
SET: We have three main thrusts. We do a lot of work in ecohydrology, where we link biological processes to the functioning of hydrosystems. Most particularly, we think about terrestrial ecology. Vegetation is at the heart of a two-way feedback where water availability controls which plants grow where and when, and the plants themselves control various aspects of the water cycle. That’s been really important in arid ecosystems, where water availability is what’s limiting plant growth.
In a separate part of our work, we’re interested in trying to improve predictability and gain understanding in systems that are sparsely monitored. We think this is critically important because sparsely-monitored areas often represent the developing world, and places that are predicted to be under significant water stress or facing water security risks. To that end, we’ve been working with minimally parameterized models: models that perhaps you could feed with remote sensing data or that are amenable to the predictions in ungauged basins approach (Blöschl et al., 2013), but incorporate as much physics as possible.
We’ve also been working on observational strategies and recently have been collaborating with folks in robotics who have been developing observational platforms capable of being deployed and redeployed.
KR: You and collaborators have also developed co-evolutionary models, where systems are subject to some external forcing that changes both in time and with human response (Thompson et al., 2013). Are these models is robust enough to accurately predict real-world hydrological scenarios?
SET: I think co-evolutionary models are interesting from an intellectual point of view. I don’t think they are, at this point, ready to solve real-world problems. What I’m really talking about right now is data analysis. For instance, we’ve been working lately in a catchment outside of Bangalore, where we have one set of data for incoming flows to the local reservoir. We have about four rain gages whose qualities we trust. We have survey data from going out to a number of local farmers, telling us about how they’ve experienced groundwater changes throughout their lives, and we have two temperature sensors. So that’s the data we have, and this is in a place that has gone from a river that flows very reliably to becoming entirely intermittent and supplying only about 20% of its historical flow. Our challenge is to work out why this change happened, as well as to convince people what the problem is. When you talk to the folks in charge of this basin you hear, “This is a land-use change problem, driven by too many people putting structures into the river channel.” You’ll hear, “This is a problem created by the Western world, because it’s due to climate change.” And what you won’t hear from anyone is what we think the real problem is, which is massive extraction from groundwater pumping. So in order to advance that dialogue, we can’t come in as experts and say, “Well you’re all wrong.” We have to come in and say, “OK, we hear there’s a wide range of potential hypotheses. People are already acting on the basis of these perceptions they have without a lot of data, and we have to at least start ruling out some of these slightly less credible theories.” So that’s what I mean by a data analysis framework. It’s something that’s very minimalistic, but it recognizes that we actually have to address water problems in the context of how stakeholders perceive them.
KR: Have you utilized tools from other fields as you develop these ideas?
SET: We’re seeing opportunities to take statistical frameworks from other disciplines and apply them to hydrology, particularly with the causal reasoning frameworks that get used in fields like epidemiology and applied economics. This is really coming out of the push to think about large-n hydrology—hydrology over many, many basins, where there are multiplicities of confounding factors—and trying to infer cause and effect. It’s a problem that you’ve had to confront if, for instance, you were working on trialing a drug in the community, and you couldn’t get it wrong. So epidemiology has had a lot of success in thinking about treating these problems statistically, and we’ve had the capacity to start refining the statistics we’re using in hydrologic analyses, but I’ve been led by the nose here by students who are working in an interdisciplinary setting between applied economics and hydrology. It’s not very sexy. It’s not very process oriented. But I think it’s really important as we start to ask the questions about the detection and attribution of climate and anthropogenic change.
KR: How has your own research portfolio evolved?
SET: What I’m trying to recognize more and more is that there’s an infinity of exciting scientific problems that are intellectually tantalizing and would be personally rewarding to work on, but I don’t honestly think as a planet we have the luxury to let our intellectuals gratify their intellect at the expense of doing work that has an end use. I think this is particularly true in the water sciences, so increasingly I’m trying to find problems that are use-inspired: ones where there’s a real world problem that, in order to be solved, requires a new sort of scientific understanding (Thompson et al., 2013). I think those are the ones I want to work on because there’s an ethical dimension to the choices we make about what we choose to put our time and resources into. I’m still very susceptible to intellectual flights of fancy; but more and more, I’m trying to be use inspired.
KR: Do you have any advice for graduate students who are approaching the end of their degree?
SET: Get it done! There are two kinds of dissertations: a perfect dissertation and a finished one. Perfectionism and insecurity about the quality of work that’s getting done can really be an obstacle to very good scientists. I think that’s a sign of insecurity, and we all feel insecure. We all have imposter syndrome, but ultimately you just have to make yourself write. I would advise to just knuckle down and get it done.
You can learn more about Sally Thompson and her research on her faculty home page.
Blöschl, G., Sivapalan, M., Wagener, T., Viglione, A., and Savenije, H. H. G. (Eds.): Runoff Prediction in Ungauged Basins—Synthesis Across Processes, Places and Scales, 465 pp., Cambridge Univ. Press, Cambridge, U.K. ISBN: 978-1-107-06983-1, (2013).
Thompson, S. E., Sivapalan, M., Harman, C. J., Srinivasan, V., Hipsey, M. R., Reed, P., Montanari, A., and Blöschl, G.: Developing predictive insight into changing water systems: Use-inspired hydrologic science for the anthropocene. Hydrol. Earth Syst. Sci., 17 (12), 5013-5039. DOI:10.5194/hess-17-5013-2013, (2013).
About the author
Kevin Roche is a PhD researcher at the University of Northwestern, Department of Civil and Environmental Engineering and active member of the YHS-AGU branch.