{"id":922,"date":"2019-07-20T18:52:16","date_gmt":"2019-07-20T22:52:16","guid":{"rendered":"http:\/\/hydrouncertainty.org\/?p=922"},"modified":"2021-01-13T07:33:31","modified_gmt":"2021-01-13T12:33:31","slug":"hallway-conversations-grey-nearing","status":"publish","type":"post","link":"http:\/\/hydrouncertainty.org\/2019\/07\/20\/hallway-conversations-grey-nearing\/","title":{"rendered":"Hallway Conversations \u2013 Grey Nearing"},"content":{"rendered":"\n

A \u2013Streams of Thought\u2013 contribution by Sina Khatami.<\/strong><\/em><\/p>\n\n\n\n

\"20180403_headshots_17\"<\/figure><\/div>\n\n\n\n

Asst\/Prof. Grey Nearing is a hydrologist at the Department of Geological Sciences at The University of Alabama (UA). Prior to joining UA, he has worked as Project Scientist at the National Center for Atmospheric Researchand, and Research Scientist at the NASA Hydrologic Sciences Lab. I\u2019ve enjoyed an ongoing dialectical debate with Grey, intense yet delightful, on the philosophy of science particularly hydrological uncertainty. It\u2019s been a pleasure to interview Grey.<\/p>\n\n\n\n

Can you tell us a little about your background and education?
\n<\/strong>I studied Math in undergraduate because I felt that this would \nkeep my options open in terms of future career paths. I went into the \nEnvironmental Sciences mostly because this is where I found a graduate \nassistantship (through the US Department of Agriculture). I chose my PhD\n adviser because I enjoyed reading his papers.<\/p>\n\n\n\n

Was becoming a scientist your career plan when you were a student? Tell us about the journey how you got here?
\n<\/strong>I started college to be a software engineer but decided that I \ndidn\u2019t want to spend all my time writing code (I currently spend about a\n third to half of my time programming). Several of my friends from high \nschool and college that did become software engineers ended up being \nfairly bored with their careers after a few years. Then again, some of \nthe young software engineers that I work with (e.g., at consulting and \ntech companies) really love their work and have skills that allow them \nto accomplish more in the field than any trained Hydrologist that I \nknow.<\/p>\n\n\n\n

Your research has a particular focus on Information Theory \n(IT) and its application in hydrological sciences. How did you become \ninterested in IT, and do you think it\u2019s a worthwhile research focus to \npursue in hydrological sciences?
\n<\/strong>I\u2019m not sure that information theory is a worthwhile research topic. I got into it because I was unsatisfied with the philosophical<\/em>\n discussion about uncertainty in Hydrology journals. It didn\u2019t make \nsense to me to try to quantify what we don\u2019t know (i.e., to quantify \nuncertainty), and information theory seemed to provide a way to reframe \nquestions about inference under partial information. There are certain \nboundedness theorems in information theory that don\u2019t exist in \nprobability theory that I think might help provide coherency to \nquestions about imperfect inference; if we learn how to ask scientific \nquestions in the context of information theory rather than probability \ntheory. I\u2019m not certain about this, but I am certain that I don\u2019t like \nthe existing literature on uncertainty quantification.<\/p>\n\n\n\n

You\u2019ve moved from academia (University of Arizona) to the \npublic sector (NCAR and NASA) and then moved back to academia again \n(University of Alabama). How did you find these transitions?
\n<\/strong>I\u2019m not sure it\u2019s that big of a jump from academia to \ngovernment research, but there are differences. Projects at government \nlabs tend to be high-profile, and you work on them with large(r) teams. \nIf you want to be part of a big project and don\u2019t mind having to meet \ndeadlines related to code production, then this is a good option. My \nwork as a project scientist at NCAR and NASA was definitely less \nstressful than academia. But I had less creative freedom.<\/p>\n\n\n\n

And what skills do you think you need to develop now for an academic career that you didn\u2019t need to previously?
\n<\/strong>Time management, perpetual self-motivation, and the ability to \nhandle rejection are the skills that matter most in academia. It\u2019s hard \nto work so much, and have your grant proposals rejected so often, \nwithout losing the sense of excitement that keeps the work interesting. \nAcademia is hyper-competitive, which was exciting at first, but starts \nto be a little tedious after a few years.<\/p>\n\n\n\n

What are your current research projects?
\n<\/strong>I\u2019m working on the intersection of machine learning and \nprocess-modeling. How can we take the best parts of both and combine \nthem?<\/p>\n\n\n\n

You\u2019ve been recognized as an AGU\u2019s Outstanding Reviewers of 2017 by the Water Resources Research <\/em>Editorial.\n What do you think led to this great recognition, and what is your \nadvice on paper review to young and early career hydrologists?
\n<\/strong>Some of the advice I sometimes get from senior scientists about\n paper and proposal reviews is that this should not be a priority for \nyoung scientists. But if you don\u2019t do your fair share then you are a \nburden on the community. My rule is to review at least 3x the number of \npapers that I submit, otherwise I\u2019m asking others for more review effort\n than I am willing to give. I spend a lot of time on (most) of my \nreviews, and I think I am often the third reviewer because I recommend \nto reject a lot.<\/p>\n\n\n\n

What do you account as your major breakthrough so far in your research career as an early career scientist?
\n<\/strong>I don\u2019t have a major breakthrough. There aren\u2019t many major \nbreakthroughs in Hydrology. I\u2019ve not seen one in the field in several \nyears. My opinion is that Hydrology isn\u2019t a field defined by \nbreakthroughs \u2013 it is a field of incremental progress toward relatively \nwell-defined objectives.<\/p>\n\n\n\n

What do you account as your major challenges ahead of you in your research career as an early career academic?
\n<\/strong>Success boils down to funding. That\u2019s really all there is to \nit. If I\u2019m successful in getting proposals funded, I will have the \nresources (people) to pursue bigger projects. The other path to success \nin science is fundamentally new theory development, but I don\u2019t see this\n as likely in a field as saturated as Hydrology.<\/p>\n\n\n\n

How do you describe your research style? Is novelty<\/em> your main criterion for research?
\n<\/strong>I am a contrarian<\/em>, so not only is novelty the \nmain criteria for me in choosing a research direction, but I often \nchoose to work on projects just because they are contrary to what others\n are working on.<\/p>\n\n\n\n

How do you approach creativity in your research? In other \nwords, what people and activities helped you develop the mindset for \n\u201coutside the box\u201d thinking.
\n<\/strong>When I\u2019m being creative, I just think a lot. I think about \nprojects when I\u2019m at the gym, walking to class, eating dinner, going to \nbed, etc. If I think about something long enough, understand it well \nenough, and keep myself active, I\u2019ll often have an interesting thought. \nIn the ~10 years that I\u2019ve been in the field I\u2019ve gone in and out of \nphases where I do this vs. where I just work on routine stuff that needs\n to get done.<\/p>\n\n\n\n

Do you think that creativity and success are correlated?
\n<\/strong>Probably not. The big names in Hydrology got there because they\n led a big project with societal relevance. My personal assessment is \nthat creativity and success in Hydrology are anti-correlated, if \nanything. Creativity is rewarded in science when there are new things to\n discover, but in an incremental, applied field like Hydrology, the \nqualities that lead to success are the ability to clearly articulate and\n implement the next incremental step.<\/p>\n\n\n\n

What are your main hobbies besides work?
\n<\/strong>I don\u2019t do anything other than work. Honestly, I don\u2019t think \nit\u2019s realistic for most people to have a work-life balance as a \nnon-tenured academic. Maybe an exceptional person can pull this off.<\/p>\n\n\n\n

How are you balancing your work and life? Any regrets or advice for early career and aspiring hydrologists?
\n<\/strong>I don\u2019t have any regrets. My job is what I\u2019m interested in and \nwhat I love doing. My only advice is that you really have to love doing \nthis work if you go into academia. There are plenty of jobs in industry \nthat pay more and require less time commitment and stress.<\/p>\n\n\n\n

What are the grand gaps\/questions of hydrological sciences \nthat in your opinion the community, particularly the early career \nhydrologists, should tackle?
\n<\/strong>Right now, I think the big challenge that Hydrology might \nparticipate in is about understanding the interface between physical \nscience and data-driven discovery. In its current form, this is about \nhow we can merge physics with machine learning<\/em>, but it\u2019s \nfundamentally the same problem as understanding the roles of \nhypotheses\/theory vs. statistics in traditional scientific inference. At\n a philosophical level, merging physics with machine learning is the \nsame problem as uncertainty quantification, which is just the problem of\n merging probability with physics.<\/p>\n\n\n\n

I also still see some potential for finding scale-relevant \nhydrological laws. I know that this is generally considered to be a \nfailed project, but I think there may have been some opportunities \nmissed in previous efforts to constrain interactions between \nmulti-timescale models using maximum entropy and maximum entropy \nproduction. This has been tried for decades (going back to the 1950s), \nbut I\u2019m not sure that it was done well. An example of this is the study \nby Wang and Bras (2011) on maximum entropy production models of \nevapotranspiration, which are fantastic.<\/p>\n\n\n\n

I really only see two fundamental challenges in the field \u2013 \nscale-relevant theories and uncertainty quantification, in whatever form\n the latter might take (I would argue that it\u2019s really a problem of \ninference under partial information). Both of these projects have been \naround for a long time with some practical advances but no real \ntheoretical advances. If someone could make a real dent against one of \nthese problems, it would be a big deal. Of course, there are a lot of \npractical challenges in Hydrology \u2013 predicting effects of climate change\n on local hydrological systems, predictive modeling anthropogenic \ninfluences on watersheds, closing the water cycle with remote sensing, \npartitioning evaporation and transpiration over large scales, etc. \u2013 but\n these aren\u2019t what I would call Grand Challenges<\/em>. The Grand Challenges in the field have not changed since Beven outlined the two I mentioned above in his speech at the Water for the Future<\/em> conference published in 1987.<\/p>\n\n\n\n

What are the biggest challenges and opportunities for \nhydrologists in the next 10 years? In the next 50 years? Especially the \nones that interest you.
\n<\/strong>To be honest, I think our discipline is at an existential tipping point. We are firmly in the era of Big Data<\/em>,\n and machine learning is better, almost across the board, at simulating \nhydrological systems than our best process-based models that have \nresulted from the previous decades of incremental development. Soon, I \nexpect that most water managers will buy their water-related information\n products from companies like IBM and Google; the latter is apparently \nworking on a global streamflow modeling system.<\/p>\n\n\n\n

I think the major question facing our community right now is to \nunderstand where, when, and under what conditions our traditional \nphysical science adds value in the emerging Age of Artificial Intelligence<\/em>.\n Maybe calling this an existential crisis is a little dramatic, but I do\n think that understanding this cohesion (science + machine learning) \nwill be the challenge that ends up having the largest overall impact on \nour field over the next 50 years.<\/p>\n\n\n\n

About the author
\n<\/strong>Sina Khatami, a PhD Candidate in hydrology and uncertainty at \nthe University of Melbourne, is currently the Secretary of Young \nHydrologic Society (YHS) and an Editor of YHS Blogs. He is also a \ncommittee member of AGU\u2019s Hydrology Section Hydrological Uncertainty \nTechnical Committee since 2018, and Student Subcommittee (H3S) since \n2017. Correspondence to sina.khatami@unimelb.edu.au<\/a>.<\/p>\n\n\n\n

References
\n<\/strong>Beven, K. J. (1987) Towards a new paradigm in hydrology. In Water for the future: hydrology in perspective<\/em>, International Association of Hydrological Sciences, Rome Symposium<\/em> (eds JC Rodda, NC Matalas), IAHS Publ. No. 164, pp. 393\u2013403. London, UK: IAHS.<\/p>\n\n\n\n

Wang, J., and Bras, R. L. ( 2011), A model of evapotranspiration based on the theory of maximum entropy production, Water Resour. Res.<\/em>, 47, W03521, doi:10.1029\/2010WR009392<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

A \u2013Streams of Thought\u2013 contribution by Sina Khatami. Asst\/Prof. Grey Nearing is a hydrologist at the Department of Geological Sciences at The University of Alabama (UA). Prior to joining UA, he has worked as Project Scientist at the National Center for<\/p>\n","protected":false},"author":3,"featured_media":924,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0},"categories":[1,14],"tags":[],"jetpack_featured_media_url":"http:\/\/hydrouncertainty.org\/wp-content\/uploads\/2019\/07\/20180403_headshots_17-e1563663253840.jpg","_links":{"self":[{"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/posts\/922"}],"collection":[{"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/comments?post=922"}],"version-history":[{"count":4,"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/posts\/922\/revisions"}],"predecessor-version":[{"id":1102,"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/posts\/922\/revisions\/1102"}],"wp:featuredmedia":[{"embeddable":true,"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/media\/924"}],"wp:attachment":[{"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/media?parent=922"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/categories?post=922"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/hydrouncertainty.org\/wp-json\/wp\/v2\/tags?post=922"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}