Rules of the (ECHO) house (or what the Ph.D.s and postdoc should do) by Andrea Rinaldo

 

A Ph.D. position on Po River, DARTHs, Earth Observations

I have an open Ph.D. position which closes at July 6: - Evolution of the system GEOframe/OMS3/CSIP for the building ofa Digital Twin of the Hydrology of river Po - E66E23000170001 

It looks like it is very dedicate to informatics (see also here) but let me say that the candidate should write their  project with a broader view, although it must remain within the scope of what we are doing in the context of the Po River basin project and  related to the exploitation of satellite data to support hydrological modeling. The project that funds it, besides PNRR,  is 4DHYdro, which collects some of the best hydrological modellers in Europe (and from the projects' goal you can find inspiration). 

The general focus of the study are droughts and can contains more computer science-related parts, more conceptual parts, or more applied parts. The themes related to the processes are: snow, plant transpiration, and crop needs. The enabling technology is precisely the systematic use of Earth observation, and the concept paper for the whole system is the one about DARTHs. Further information on DARTHs can be found here. 

If, at this point, you are a little convinced to apply also consider the philosophy of our group that you can find in a sequence of posts, here and and links therein.

Our group is a  crew of international fellows: 2 Indians, 1 Pakistani, 1French, 1 Iranian, 1 Algerian and 8 Italians, including two professors, one researcher (at Eurac), two postdocs, and nine Ph.D. students already. 

For inquiring students. - II (and what is ph.D education about)

"My name is John, and I am an M.Sc. student of water engineering at the Beautiful University of Technology. My M.Sc. thesis subject is a groundbreaking one - 'Application of WRF Regional Model for Rainfall Prediction (Case Study: Northwest of Antarctica).' Under the guidance of Prof. Best Hydrologist, I have delved into the impact of predicted precipitation by the WRF model on flood forecasting in the Hec-Hms (or SWAT) model. My expertise and keen interest in climate change impacts on hydroclimatic extremes, numerical weather prediction, hydrology and water resource management, hydraulic and flood forecasting make me a force to be reckoned with in the field. "

I already wrote on the topic of students inquiring about the possibility to do a Ph.D with me. You can find previous notes here. However, I would still add something.

It's heartening to see that many schools are taking the direction of training students to run WRF, Hec-HMS, SWAT, R, Python and other tools. However, mastering tools is just one aspect of research. To truly investigate nature, one must understand where problems are and be able to modify paradigms and tools to solve them appropriately. 

As Richard Feynman said, 'The problem is not people being uneducated. The problem is that people are educated just enough to believe what they have been taught, and not educated enough to question anything from what they have been taught.' 

Becoming a solid Ph.D applicant requires making this switch and adding a few phrases to your  CV that show that you have understood the Feynman's point.  Besides, you should be inquisitive and show some fire inside for discovering new things and achieve results beyond the state-of-art (and for what regards working with me, love computer programming). A PhD is not just a status symbol or a way to gain a good salary - it's a calling for those who are truly passionate about pushing the boundaries of knowledge (see this nice infographics from an old post)

P.S. -  I also wrote this "Essential for Hydrologists" that could further help. 

Tips and tricks for a good oral presentation by Daniele Penna

 It is self-explanatory, I guess. First minutes are a joke. 

Ph.D. Scholarship @ Unitrento: Assessment of social and economic impacts caused by natural hazards in mountain regions

Death tolls and economic losses from natural hazards continue to rise in many parts of the world. Only in 2018 they caused almost 12000 deaths across the world and over 130 US billion dollars of economic losses (CRED, 2018). European states are experiencing a continuous and significant burden from multiple natural disasters (Wolfgang et al., 2019): in 2016 Germany, Belgium, and Switzerland have been hit by a series of flash floods and storms causing over $2.2 billion in losses; in 2013 Storm Xaver caused to northern Europe at least 15 fatalities, dozens of injured, and more than €800 million total economic losses (e.g. Rucińska, D., 2019); the July-August 2003 European heat wave that caused a total of 70,000 deaths (e.g. Russo et al., 2019; Bouchama, 2004).  

A combination of several factors contribute to explain the increasing social and economic toll caused by natural hazards: increase in exposed assets, i.e. rising population and capital at risk (e.g. Visser et al., 2014), effects of anthropogenic climate change on climatic extremes (e.g. Donat et al., 2016; Bouwer, 2011), better impact reporting procedures (e.g. Doktycz and Abkowitz, 2019).

International agreements on disaster loss reduction (Sendai Framework for Disaster Risk Reduction 2015–2030) explicitly recognizes the benefits of multi-hazard early warning and forecasting systems (MHEW&F-S)”. In 2017 Member States of the United Nations stated the deemed need of MHEW&F-S and agreed on its the definition as integrated system that “address several hazards and/or impacts of similar or different type in contexts where hazardous events may occur alone, simultaneously, cascadingly or cumulatively over time, and taking into account the potential interrelated effects” (UNISDR, 2017). Here the term early warning (EW) is extend with the term forecasting (&F) to explicitly acknowledge that each hazard have a specific forecast lead time which can varies from minutes/hours for flash-floods, days for pluvial floods or heat/cold waves, to months for drought hazard. The scientific community also agree in the need of novel approaches and local scale models for assessing impacts caused by climate change (e.g. Schewe et al., 2019).  In order to answer to this call and to move towards a rigorous framework for multi-hazard risk assessment in this project I propose to implement a novel local scale multi-hazard impact-centered forecasting system. It aims to:

·       Quantify the three fundamental components of the risk, i.e. hazard, exposure, and vulnerability, and to combine them in a multi-hazard framework, exploiting the most recent dataset and the more appropriate models;

·        Provide timely effective warnings (not just of the hazards but also) of the most probable sectorial impacts that may be triggered by multiple hazard conditions.

The system will be unique and novel because it will be the first operative system for multi-risk quantification including:

·       a local high-resolution meteorological forecasting system with operationally runs at 1 km resolution capable to explicitly model convective phenomena;

·       a detailed and component-based open-source framework for multi-hazard quantification, locally and automatically calibrated for estimating the probability of occurrence of floods, droughts, shallow-landslides/debris-flow, heatwave/coldwaves and windstorms;

·       a new set of exposure and vulnerability layers variable in space and time to account for accounting of socio-economic changes in the risk analysis;

·       an innovative framework based on a probabilistic graphical model (Bayesian Network) dynamic in time and variable in space, which consider all the risk components (hazards, exposure, and vulnerability as in Formetta and Feyen, 2019) as stochastic variables and models all their possible interactions using probabilistic expressions. The latter will be inferred using a Bayesian learning process involving: 1) a database of reported impacts (fatalities and economic losses) occurred in the past (1980-2018) in the study area specifically organized in the project and 2) the corresponding hazard probabilities, exposure, and vulnerability at the time of the reported event (computed by using points ii and iii).

The project study area is the Trentino Alto-Adige region located in the eastern Italian Alps. The choice of the area is motivated by different reasons: i) there is no such a system currently running (this is also valid for all the others Italian regions); ii) in the near future mountain regions will be even more exposed to the occurrence of climatic extremes due to climate warning. The selected geographical domain is only a test-bed where the framework will be implemented, set up, tested, and verified against observed data for each single component, i.e. meteorological forecasting skills (against rainfall or air temperature measurements), hydrological calibration and validation (against historical measured river discharge), hydrological forecasting skills (against observed river-discharge using forecasted meteorological forcing data), historical and forecasted impacts (against reported fatalities and economic losses

Please write to giuseppe.formetta@unitn.it for information or see directly the Department call (here)

References

Bouchama, A. (2004). The 2003 European heat wave. Intensive care medicine, 30(1), 1-3.

Bouwer, L. M. (2011). Have disaster losses increased due to anthropogenic climate change?. Bulletin of the American Meteorological Society, 92(1), 39-46.

Donat, M. G., Alexander, L. V., Herold, N., & Dittus, A. J. (2016). Temperature and precipitation extremes in century‐long gridded observations, reanalyses, and atmospheric model simulations. Journal of Geophysical Research: Atmospheres, 121(19), 11-174.

Doktycz, C., & Abkowitz, M. (2019). Loss and Damage Estimation for Extreme Weather Events: State of the Practice. Sustainability, 11(15), 4243.

Formetta, G., & Feyen, L. (2019). Empirical evidence of declining global vulnerability to climate-related hazards. Global Environmental Change, 57, 101920.

Rucińska, D. (2019). Describing Storm Xaver in disaster terms. International journal of disaster risk reduction, 34, 147-153.

Russo, S., Sillmann, J., Sippel, S., Barcikowska, M. J., Ghisetti, C., Smid, M., & O’Neill, B. (2019). Half a degree and rapid socioeconomic development matter for heatwave risk. Nature communications, 10(1), 19.

Schewe, J., Gosling, S. N., Reyer, C., Zhao, F., Ciais, P., Elliott, J., ... & Van Vliet, M. T. (2019). State-ofthe-art global models underestimate impacts from climate extremes. Nature communications, 10(1), 1-14

Math3ma

Since quite a while, I read, enjoyed and indexed in AboutHydrology the Math3ma  ("Mathema") blog by Tai-Danae Bradley. I enjoyed for the choice of the topics that I found stimulating and connected to my own research on hydrological networks and the way the material was presented. She graduated recently and produced one of the best ever non-technical introduction to scientific work I have seen.
I guess that any Ph.D. student should be motivated to produce similar products because the habits to expose their own research in widely understood concepts helps them to focalize what is important in their research and it is simply good for science in any case.

By clicking on the figure, you will be redirected to the you Tube video. The original blog-post is instead here.  The dissertation can be found on arXiv. The topic is generically a reflection on how to connect subsystems and which mathematics ca be useful to this scope. But certainly watching the ten minutes video gives a much better information.

To inquiring students

Since a few years, I am receiving emails from students inquiring for the possibility of doing Ph.D studies with me. The letter is usually of this type :

"Dear Dr. Riccardo Rigon,

Hope my email finds you well.My name is Donald Duck and I would like to hereby ask about the possibility of working under your supervision as a PhD student. I have received my MSc degree in * and **.

Sometimes, the candidate also says something like:

"I possess 5 years of work experience as a researcher, instructor and Environmental Consultant and have a related research background which led me to present 18 conference papers, 2 published papers and 3 submitted papers which are detailed in my CV.”

They continues,

"I was reading about your recent works on the website and due to the alignment of my research interests with your expertise in Environmental Engineering and the academic position of your university, I believe the valuable experience that I would have under your supervision will provide me with the ground to achieve my academic goals.
I am deeply interested to begin a PhD program at your university  [...]"

CV: https://en.wikipedia.org/wiki/Donald_Duck

RI: https://www.youtube.com/watch?v=GhBf4ClJwoY

Best regards,

Donald Duck

Receiving many of this letters, I have prepared an answer below, which I hope is useful to clarify some points and my feelings. 

I do research in Hydrology and you can be enrolled to our doctoral school by participating to a call, usually in the first months of every year. The Applicants are examined by a committee that tries to  choose the bests. We are always looking for outstanding students and dedicated people (not only me but also my colleagues).

For producing an endorsement for a candidate I do not personally know,  and forwarding it to the selecting committee,  I require the student to study the material of the last Winter School on GEOframe (on catchments studies)  or of the Summer School (on soil-plants-atmosphere interactions, process-based modelling), or apply to one of the Schools. I can wave their school fees if  they specify they wants to try to be enroll as a doctoral student. 

If the applicant agrees,  I and the group of GEOframers will dedicate some of their time helping  in the installations of  our software and eventually perform some case study. This application can be in the field of catchments hydrology (on the example of Dr. Abera papers cited below) or applications using the tools more explicitly developed to work on the Critical Zone (Richards equations coupled to the energy budget and evapotranspiration) on which I can give material and direction.
After the completion of the above task, I will be able to weigh their skills, to know how they can work in  my group and to consider them as interesting candidates for a Ph.D. with us (this does not mean they is not an interesting and skilled candidate for other groups or colleagues).
The enrollment though is not guaranteed, since the selection is  a public competition and many apply. However, my endorsement can help. Besides, it can be used also elsewhere, since it will be provided with a certification of having completed the GEOframe studies, and the candidate certainly did not waste their time having learned something useful for their hydrological career.  

I cover various topics in hydrology, and, all of them are explained in my blog AboutHydrology. Therefore, the astute candidate has to consider to browse what I do. Besides advancing theoretical parts of Hydrology, usually a Ph.D. student in my group is intended to produce working and tested codes (i.e. doing programming). All the code developed will be regularly uploaded to Github (or similar platform), inside the GEOframe community space, and will be Open Source according to the GPL v3 license. I am not usually interested in  doing research with SWAT, HEC_HMS or other hydrological models, different from those I develop. 

Further information of the policies of the research group can be found:

• To my students
• Working with us
• The scaring version of “working with us"
• About doing a Ph.D.
• Why Open Source ?
• Going beyond the present state of Art Of Hydrological Modeling (since 2011 but still valid)
• Authorship policies in the group
• Software documentation and autorship policy (the latter included at the bottom of the notebook)

P.S. 0 - For getting a Ph.D. opportunity or a postdoc position, one valuable way  is to subscribe to the AboutHydrology google group where you can find appropriate announcements

P.S.  I- About coding - The candidate will take care of implementing, besides the code, the appropriate procedures for continuous integration of the evolving source code, and s/he will be also asked to maintain a regular rate of commits to the common open platform. Despite these conditions, and being free and open source, the code will be intellectual property by the coder.
This will be guaranteed also by the components-based infrastructure offered by OMS3, which allows to better define the contributions of anyone.The implementation part will be followed, accompanied by testing activities, either for mathematical consistency, than for physical consistency with experiments and field measurements.The Ph.D. student is intended to produce, besides working and tested codes, also at least three papers in major journals (VQR Class A), of which, at least one as first Author. Duration of the doctoral studies is three years.

P.S. II - I am also considering with favor:
Applicants who wants to apply to build the new GEOtop snow model but with attention to forest-snow interactions.
Who wants to work on the infrastructure of the OMS3, GEOframe systems.
Who wants to exploit the capabilities of the GEOframe system to pursue the modelling of the river Adige (and/or other rivers in the world), including human infrastructures.

References

Abera, Wuletawu, Giuseppe Formetta, Marco Borga, and Riccardo Rigon. 2017. “Estimating the Water Budget Components and Their Variability in a Pre-Alpine Basin with JGrass-NewAGE.” Advances in Water Resources 104 (June): 37–54.

Abera, Wuletawu, Giuseppe Formetta, Luca Brocca, and Riccardo Rigon. 2017. “Modeling the Water Budget of the Upper Blue Nile Basin Using the JGrass-NewAge Model System and Satellite Data.” Hydrology and Earth System Sciences 21 (6): 3145–65.

Tubini, Niccolò, and Riccardo Rigon. 2021. “Implementing the Water, HEat and Transport Model in GEOframe WHETGEO-1D v.1.0: Algorithms, Informatics, Design Patterns, Open Science Features, and 1D Deployment.” https://doi.org/10.5194/gmd-2021-163.

What to keep in mind for a Ph.D. interview

We finished yesterday our interviews for selecting the new cohort of PhD students. (In our system we have a call a year, where we select and enroll all the students). So, I am in the best condition to do some comments and give some advise to future applicants. 

The first observation is that we where there to select the “best students” and therefore we were trying to put the applicants in comfortable conditions. * What we did was to ask the applicants to describe themselves and their research proposal and made some questions about their topic. Nothing apparently special.

In front of us we had people of very different ages and maturity: from 24 to 46 years old. People with different motivations and from different countries and continents. And we did not pretend from them the same attitude and skills.  In the younger, we were requiring enthusiasm, an outstanding CV in the studies they had, but we were less demanding on  pretending from them a comprehensive knowledge of literature. In the older we were more tight on analyzing their specific skills and in try to understand if they could complete, and at which level, the tasks implicit in doctoral studies after years spent out of Academia.

To the older, besides having done something, even in the different fields, we were requiring to demonstrate flexibility and attitude to work in group. We were in fact looking for mature personalities (both in the young and the old actually) but, at the same time, trying to avoid those who could not interact positively with the environment they will eventually stick for the future three/four years. 

Concept One: we search for the best fit. In all of our candidate we were looking for appropriate competence and proactivity: but this was a prerequisite. Be sure: excluding some very rare cases which are apparent, for their evident unicity, "we are all equidistant from Nobel prizes" performances. Therefore there are “best” applicants but, among the best, "best fit” applicants. Above all if we think that science is more a team work than matter for lonely nerds.

Concept two: how do you fit and do you really want to fit ? As I told, the applicant who gets the position, eventually has to stick with the environment around and deal with a supervisor. Therefore a suggestion I feel to give is: it is not forbidden to contact your potential supervisor in advance. This does not imply any commitment by anybody but allows to understand better each other. This can also  be functional  to build a successful research proposal, or to understand if you can (or want) fit with that supervisor without be driven crazy later.  Ph.D. studies are demanding for themselves and especially in a competitive environment as our University can be. Maybe, at the end of your conversation, you do not exactly want to do it. However, there is a chance that you love it. Then, go ahed. Ph.D. can be one of the best periods of your life. 

Concept three: professors pretend to know where their sub-discipline is going and what is best looking for. So, please, do not try to challenge too much their beliefs (see also point 1 and 2). 

Instead you can try to understand if their scientific methods combine with yours, and, in case, how you could interpret their work with your skills. 

As I wrote elsewhere,  do not come to me expecting me to be your butler. A good professor can  recognize to be wrong but you will be their PhD student and they is pretending to suggest the way to go along with you on top of their experience and dictating, at least at the beginning, the methods to use. It can be matter of discussion though (that's what PhD work is made of).  However, you have to convince them politely that your (one among many) new perspective is better,  before they changes their.  Besides, keep in mind that a good professor can estimate the effort, pain and costs to pursue the  goal you suggest and you pay attention to what they says about.

A checklist. Going to the matter, we pretend you:

• to show solid backgrounds (not necessarily erudition) and attitude to learn
• proactivity and enthusiasm;
• having an appropriate knowledge of the literature of the subject on which you want to work on;
• know what your professor does in his research, which tools he uses and so on;
• show to be able to express "research questions”;
• show that you that you can be "focused" and can obtain what is needed (papers, softwares, procedures, patents etc) in limited amount of time (three/four years)

Keep in mind that true research is not an application of some tool but is to delineate new challenges first and solve them eventually. Who is not able to understand what a scientific problem is, cannot be a successful researcher and not even a good Ph.D student. 

Finally:

• be open to acquire and investigate new tools (during your research life, you will be required to evolve them several times) 

Bonus:

• A link where to find interesting suggestions for Ph.D. works

Life After Ph.D. (especially for Italians but of general interest)

This is part of the Ph.D. days of Department of Civil, Environmental and Mechanical Engineering of University of Trento. October 29, 30, 31 were used to check the advancement of our almost one hundred Ph.D. students. Who was moving to the first to the second year, who from the second to the third, who had to prove to be able to write the final Thesis after the third year.

I think professor Scardi offered a good overview, worth to be listened to.

Open Science

Nothing really original in this post. I just recollect what already said in the FosterOpenScience web pages. Their definition is:

"Open Science represents a new approach to the scientific process based on cooperative work and new ways of diffusing knowledge by using digital technologies and new collaborative tools (European Commission, 2016b:33). The OECD defines Open Science as: “to make the primary outputs of publicly funded research results – publications and the research data – publicly accessible in digital format with no or minimal restriction” (OECD, 2015:7), but it is more than that. Open Science is about extending the principles of openness to the whole research cycle (see figure 1), fostering sharing and collaboration as early as possible thus entailing a systemic change to the way science and research is done." The wikipedia page is also useful.

This approach get along with the one of doing reproducible research which I already talked about several times. I do not have very much to add to what they wrote, but I also want to make you note that "there are in fact multiple approaches to the term and definition of Open Science, that Fecher and Friesike (2014) have synthesized and structured by proposing five Open Science schools of thought" .

In our work a basic assumption is that openness require also the appropriate tools, and we are working hard to produce them and use those other that make a scientific workflow open.

The four hours rule

I link here an article from the Guardian by Oliver Bukerman, in Italy reproduced by Internazionale dedicated to the quantity of creative work that can be done every day. He, in turn, takes inspiration from the Alex Pang's book Rest: Why You Get More Done When You Work Less.
The original article can be found here. The Italian version here.

Maybe four hours is too low but, at the same time, pretending to be creative more than four ours a day is not a sin of pride ?
I would be clear, I am not suggesting to my Ph.D.  students to work lazily, but to be conscious that quality counts more than quantity and quality also depends on rational management of our own mental health which is the what we need to preserve. Then it is clear that some (short) periods of life require exceptional efforts. But this works only if these are the exception. Not the rule.

The tale of open source codes

Prologue

Why did I choose to produce with the people directly working with me (ph.D students, master students, postdocs) open source software ?

- because is good for science
- because I am paid by a public institution
- because it is a neutral conditions that can serve the rights of all the participants (in particular mine of freely use and modify the software at my will an defend myself from who, people or institution, would like to close the software, even against me). On the other side,  my intention is clearly that my projects serve as a seed for developments of my students (or others) who can freely use the products of my research and maintaining it alive beyond me and despite me. *

I use GPL (for its interpretation, see here) but many others licences could work.

A declaration

In this way, I think, I have the right to claim to be able to use or peruse the software outcomes from my group. I declare that I want to use “fair play” rules, but, it should be clear that these rules cannot extend to limit my research freedom. People who claim the participation to papers where they give no contribution, except having producing the code that we produced together, have wrong arguments. People who claim to be involved in projects or researchers, without any other reason that I want to use the software they contribute (under GPL), have wrong arguments.
Neither they can claim that I have to warn and tell them personally what I am going to do in my research with the common code, for having their consent.
They would be right to protest, only if I would not enlighten their contribution on previous work properly.
My research for my own belief is actually very public and its evolution too. It can be found at the abouthydrology blog. My core research is shared with my teamwork. This includes just the people of whom I have direct responsibility for age and rule (Master students, Ph.D. students and postdocs) and whom I sustain with funding, my own time and ideas.

With all others, including my masters, and my former students, colleagues, friends, women and men that like my research topics and achievements, and me, I can have collaborations. This means that we can share part of our views, beliefs, discussions, fightings, friendship, papers, parts of code. However our own agendas, in this imperfect world, do not coincide, and if they do, this happens for an incredibly short time. It seems it is a declaration of distance, but it is just consciousness of how life works, and the first step to start an effective and respectful collaboration.

Q&A

Can my students refuse to develop OS software ?
No, as soon as it is the product of common intellectual efforts in which they, maybe, write the code, but I will say what to write.

Do I start collaborations in which not OS code can be developed ?
Never say never. However there should be very strong reasons because, from my side, I to support this. Certainly in projects there could be partners that develop non open source software, but this falls in the responsibilities of who gives the financial support.

Is the requirement of open sourceness enough ?
No, it isn't. Open Sourceness is useless if not followed by good practices of using open repositories and collaborative modalities of action.

Can my students refuse to learn these practices ?
For the common work no. I am not responsible for the rest. I tend to fully book their time, though.

Do I start collaborations where these practices are not followed?
I would prefer not, but I do. Certainly collaborations can be at different levels and rarely they are about co-producing software. I would not participate to joint projects where I put ideas and expertise and others write closed codes, unless they pay me or my group a lot. Really a lot. I can participate to projects where other subjects put their ideas, or ideas from literature, in their own closed code, and I put mine in OS codes. However, the situation I prefer would be a common production, as a community, of open source codes.

My own use cases

Here below I summarised (with quite large simplifications) my software history in order to further justify what I wrote above.

Professor means who puts science, time, and money (funds derived by projects). Student means who puts time and science. Companies means they put time and money and business related efforts. Agents, Subjects are generic actors of the play (they can be either students, professors or someone else). Community is the informal group that happened to gather around the projects and, eventually,  evolve them.

Case 0

Professor Z writes the initial library. On top of that A builds radiation budget. Student B writes surface water flows. Student C implements soil-atmosfere interactions. Students D writes vadose zone components. Student E writes snow treatment. Student F rewrites snow components, then rewrites most of the codes interacting with student G and student H. Student I writes codes for landslides triggering treatment. Student G writes a small but important portion of the a little but successful part of the freezing soil hydrology. Professor L hires F. F continues to rewrite parts. G start a huge operation of cleaning the code, moving it to C++, uploading it to an open repository. C comes back and starts to use the code in his research and occasionally hires H to do some ancillary work for treating data. In meantime G  has founded a company where the common code is the basis of the business. M company, initially hired by L, works on the code to refactor and enhance it. M works collaboratively with G and F. M to setup continuos integration. Student N starts to produce executables for the main operating systems and eventually on Cooker (fictional name). M embraces immediately this philosophy.


Case 1

Professor Z writes the initial library. Z writes more than fifty tools for terrain analysis. Student A (not the same as above) ports them to a major Open Source GIS. Z and A start the construction of a new GIS, say JG. Initially JG contains just the the terrain analysis tools and some simple hydrological model. They start to do schools for financing their project. This works for some years. Student B, in the meanwhile, has joined the crew and A & B funded the company AB. They live with schools, supports from a main project of Z and other resources (a main research projects). A cleans the tools' suite and inaugurates the name JGT for them. Z uses JGT in his classes.
Z, A, and B decided to join the development of UGIS. Some research projects supports them together with resources raised by the company AB on its own. Students C and students D write some further modules. UGIS  funding disappears, and UGIS slowly becomes an almost inactive project. AB brings JGT to an intermediate product ST. Z continues to USE JGT in his classes. AB finally joins the development of a new GIS, say GS.
(In the middle,  A adds new tools, AB wrote an Android app, Aapp, and expands its business. Aapp is not  related to JGT, but worth to mention). During the years A and B get a Ph.D. whose topics are related to the GIS work. Student E with a small effort brings back JGT also to a platform, OI that Z uses with his students.

Case 2

Thanks to an unexpected financial support from project 00, Professor Z hires a five students to build from the scratch a new modelling platform. For this new software enterprise, he and company AB (funded by his former students A and B) chooses the open source framework OI. He hires former student C, to help software developments and former student D and E for the general management of the project and data gathering, respectively. C works more on improving and enriching JGT (see case 1 above) which serves as a basis for the terrain analysis functional to modeling. A and B develop a full suite of model components (the new paradigm) for: temperature and rainfall interpolation, rainfall-runoff, evapotranspiration and various tools to visualise components' inputs and outputs. AB also designs and populates an SQL database that contains all the data of the projects. The projects 00 ends. The Institution that supported the project close it in a drawer.

With other financial support, former 00 project's tools are maintained in life. Open source framework OI is changed for open source framework OM with a notable reduction of code lines (but it is a huge code effort, indeed, almost entirely on AB shoulders). With embracing OM, also starts a research collaboration with professor U and W.

Student E comes into play. He realises that rainfall-runoff does not work well. AB company has to survive on its own and cannot give very much support (https://vimeo.com/144089061). E implements a new rainfall-runoff model. AB, however, hires F for a small project where he works on radiation. Eventually, E refactors F's work and highly expands it. E adds a new snow modelling component and does/refactors evapotranspiration. In doing this (pouring sweat and blood) he, however, has the guidelines of the open sources codes already written. E spends some periods at U and W. E also refactors and enhances the Kriging code. Eventually E graduates and starts his career as post-doc elsewhere. In the meanwhile he finalises his research in a series of papers.
Student G comes. He does not have programming skills, but quietly learns to use the components of E and produces some interesting papers where E is co-author.
A new student, H, comes into the game. She works first on radiation on top of E code, then she starts to implement tools for travel time analysis and another rainfall-runoff component.
Student L comes. He  has a strong attitude for informatics. He brings-in new ways to manage projects. H and L implement the OpenOpenSoftware repository, and the site BeatifulGEO (names are fictional, but tools real). H refactors the old code,  and together with L (who, sort of, leads the learning process), introduces design patterns for increasing code reusability. L provides the trickery to have continuous integration on OpenOpenSoftware using GETIT and connects software deployment to ISTOREIT to store official versions of the components. Students M and N come in to stage and start to use the code. Professor Z (with the help of H) starts to use the components with his students for his classes. Student L evolves the original OM capabilities to allow for more flexibility and to increase the computational power of the models. H brings-in her models into the new infrastructure.

Discussion and Conclusions

The above is a summary (where, I say again, I simplified many passages) of my main software enterprises. Could have they been evolved all differently (and better) if I would not have applied an open source strategy ? Probably yes, but I should have constrained the students to a contract about the property of the software. In this way I would have deprived my students of parts of their own work.
At the same time, I could not have left the software simply to them. The histories themselves show that I built my own work and research on the software we develop, and being free to use it and modifying it was a necessity. If I have needed to ask permission to use it, to sign a contract or so with someone (for instance who gave financial support), all the development would have been much more difficult to pursue. The same apply for other Actors who invested time and resources in the software development just because it was open. They are usually singles or low budget companies that could not have afforded expenses related to other type of licenses and be subjected to limitation of the software use.
Other researchers used the model. Being it free and open source was a clearly an added value for them.

Keeping the software close and commercial, besides not having scientific reasons (which require the contrary), would have obliged me to change myself in a businessman and turned away from my science. There are several cases of scientists that turned to captains of companies. But, for instance Stephan Wolfram, a gifted scientist, did not give very much contributions to science after he devoted his energies to Mathematica. Mathematica (probably the best computing environment ever) itself is his main achievement (which is not depreciable), despite his own claims on "New kind of Science"s.
The overburden required for managing a commercial software is not for all and has its own dynamics, that personally I  could not bear.

The fact that my code is free and open source has allowed (not without difficulties) self-instruction of new incomers. Various Agents had the possibility to start experiments and investigate new directions of development. Nobody needed to ask for starting them. Asking is a process that would have decreased dramatically people or groups pro-activity.

The Community had benefits from this policy. In some cases, single Actors could have thought that their contribution was not recognised enough and did not give to them an advantage. Their argument is  flawed. All of them had advantages from the collaborative environment and nobody (me included) could have produced what s/he has achieved without building on the shoulder of others and other open source projects.

Forgetting the above, some feel that their work is not enough protected, and being all open source, newcomers can more easily jump in and take advantage of their work.
Uncertainty on future, a competitive society, the pure necessity to find something that pays you for a decent life incline even to bests to a moderate selfishness or a moderate parasitic behavior. They do not want to give back to the community, after having got a lot from it, and act defensively.

Well, this behavior is absolutely possible if their developments do not use the original code that was produced as GPL. In particular, the components strategy used in project 2 above allows for building on top of the open source material new, undisclosed material, that anyone can use for his/her own profit, with a non open license.
I have to warn, however, that if the moderate parasitism grows too much, enthusiasm that is always necessary decreases,  the projects die, the source of benefits disappears and the community falls.

I would say that a mild parasitism is functional to the community if it is necessary to sustain the collaborative Subjects, and if eventually the Subjects give something back to the community. Parasitic Subjects themselves act in favour of the community by spreading and advertising the products, and sooner or later this will be bring benefits back (so do not blame them, they are, in any case, part of the stream).

Some Subjects actually wants an opaque management of the GPL philosophy in which people maintain an informal (but they pretend recognized) property of the software that goes beyond the copyleft and the intellectual recognition of their contribution. This would imply, in their mind: preferential redirection of funds towards them; involvement in papers or conferences contributions that use their code; veto power towards actions of thirds.
These desiderata are based on misunderstandings. It is clear that they will be involved in papers, conference, and decision. Any (wo)man and community of good-will will apply this policy in their favour, if they do not grow too greedy. But these actions are not mandatory and not even necessary. GPL does not implies them.

To be more clear, especially in hydrology, the market out there treats our model and softwares as a fungible commodity, that is, the market tends to treat all the codes as equivalent or nearly so with no regard to who produced them. (I think this is wrong, highly wrong, when brought to an excess).
But also the internal market, inside the community, treats them as commodities, meaning that, it would be dysfunctional, it would cause a waste of precious time, but any contribution is perceived as a thing that can be replaced (this is part of the not said history of 0,1,and 2 projects). Everybody is important nobody is necessary.


The A. paradox

One common argument of reluctant open sourceres  is: “I did not have still tapped the results of my own work and I should share it (statement 1)”, or "if I share it, others will use it without me and I will have no personal gain(statement 2)".
The first danger can be overcome, by an appropriate delay of the disclosure of documentation and explanatory material (I would not argue that keeping industrial secrets is useless, in general, however). That is: it is matter of having strategies that prevent the negative cases. In our field, however, being everything perceived as a commodity (see above) nobody will care to use our model or achievement instead than another one that gives what is (wrongly perceived) as similar, especially if our code is not known. Being open source with proper support actions helps model spreading.
Besides, looking at my histories (see also here), software changes fast and is, by no means, immutable. Histories 0,1, and 2 are signed by change. So the advantage one has with a new code in hands is ephemeral. In my own estimates you have just a a year of advantage for small codes, and a few years of advantage with a large and complex code. This small advantage, if you are smart, can be appropriately managed and used to produce new and more innovative code and so on. (Open sourceness is against stagnation).
Often, however, it is not the the fear of far away threats that makes problems, but the fear of close by Agents. Guy A fear that B in the group who came in after her/him, will get positions or funding with his/her work. I would say that this could happen but it is difficult. In a fair (not fear) competition A always wins over B, if the quality of B can just be attributed to codes that A developed. The real problem is when B is much better that A. But in that case, having A work for B is not important. B will get rewards instead than A almost always. For A, the best thing, in the medium range, is to collaborate with B.
What, finally I really call the A. paradox is in statement (2). If it is so easy to grab your work, then it would be equally easy to anyone to replicate it. Therefore your work is not giving to you any competing advantage, even if you keep it secret for a while. If it is not easy to grab, then, who wants to use it proficiently needs you. So you are the winner, not because you keep your code top secret, but because all the issues it solves require a complex expertise that only you, the author can have. So ….

Epilogue

Professor eventually Z disappears. Not because he dies (please do exorcisms), but because his role, in the growing group of people around projects has become more and more marginal. Subjects also acquired maturity and as well as the will to maintain the advantages that the work has produced with respect to competitors.
This passage requires that the initially informal community establish as a formal Community (they wrote here for Academics) with its rules, etiquette, and wise management. This, in turn, requires Subjects coordinate and share alike their views, plan together new developments, plan events to make the common work to grow. Balkanisation of the code (which GPL could allow) and internal conflicts (never avoidable, having the Subjects different agendas) should be managed appropriately, and this requires clear agreements, smart actions, good will, and wise arguments.
If the community grows, everybody would be safer, because cooperating is better than competing (see also coopetition).
A partial adoption of the Open Source strategy is instead very useless. Open source codes that are practically not available (as those that are open source but not freely downloadable) cannot grow a healthy community and, sooner or later, die.

* A final note

Actually even if in my intention is a project also for my students, not a few of my students do not deeply endorse it. Reasons for this can be, maybe found in their personal history, the chemistry of their bodies and minds, or something else, which is hidden to me. So far, I  overreacts feeling myself betrayed, when they dismiss in what I believe it is right. So, probably my attitude is not is not correct. Sons do whatever they want, and probably they are right to try to find their way. So I have to conclude that the above is MY dream, and I will not be upset anymore, if my academic sons search their own in a different way.

Working with us

After the scaring version, the practical version.  To incoming Ph.D. students

Dear *,

working with us means using our models

•   GEOtop 
•  JGrass-NewAGE

Both of them have a consistent history that involves also quite a group of publications. Their main information can be found following the links.

• GEOtop essentials 
• NewAGE essentials 

GEOtop,  to say the complete thruth, has a group of video tutorial (in Italian) that you can find here. University of Calabria paid Mountain-eering for doing them, so please use them with confidentiality.

Looking in perspective, I am working to a new incarnation of GEOtop in components. As I already explained here.  There will be then, a convegence of tools towards OMS3 and its evolutions.

So what I suggest ? First start to study the models at the links above. Willing to learn a computer language, start with Java. To start, read here.  To continue, go here.

In this language I invested quite a lot during the years. Why I choose Java can be found here.
I wrote it four years ago, but the concepts are still valid.  Recently I become more moderate, and opened to other languages. Here my opinion.

Dear incoming students who want to work with me

In these days, there are many students that write and candidate themselves to a Ph.D. position. I thank these students for the attention they give to me. However, they should read  what I write below carefully.

I want to tell them that in Italy, the procedure is not like in other States where the professor choses directly his/her Ph.D at any time of the year. We have a selection (meaning a competition) to which they should apply. In my Department, in any case, the professor has to say that, for a certain year, he/she wants to support a student, and has to co-finance the grant. So the fact that you show up and start a discussion is positive.

Regarding the matter, these students send me their CV which is sometime notable but rarely coincident with my research directions.  I do not want to be brutal, however, they have to refocus on the idea that I work hard  to pursue my own research, and, if they want to work with me, they need to like what I like. So in their presentation to me a statement like, "I would really like to work with you on the topic [put here one topic on which I work]",  is relieving me from some pain and shows that you are a smart man or woman.  Occasionally I also organise summer schools that are a good way to get in touch with me, and a place were I can evaluate you directly. [We can often give up your tuition fees (but we do not have money to support your travelling).]

Usually, I am not interested in river hydraulics, nor in sediment transport, not even in computational fluid mechanics (except maybe to integrate Navier-Stokes equations). Neither I am a structural engineer or a civil engneer, in strict sense. Other people at my Department are very good in the above topics, and they should be searched if the student want to pursue  those researches instead than mine. 

I am a hydrologist, and my interest are more or less depicted here, in these posts. What I am really working in these days are the Jgrass-NewAGE system (see also Wuletawu Abera defense post) and the informatics to build the new GEOtop. In perspective, I am also very interested in the thermodynamics (theory and implementation) of hydrological processes.

I pretend that a candidate has programming skills in Java or C++ (and Python), or the willing to pursue them. All the code my group develops is intended to be free software, and must be produced with appropriate documentation. Do not bother me, if you do not agree with this or you do not want to write code.
The reflection about research reproducibility and replicability is part itself of  my research work and, in my view "open-sourceness" is part of the process to obtain them.

To have an idea of my research see the projects I recently presented (WATER-MIX, PRECISE, WATSUP, Adige-CARITRO) for a possible funding. 

To see what I mean for a Ph.D. you can read here. It can be exciting, but it deserves the right mental and general attitude.

On failing and getting sick

There is an ongoing discussion in the GSoC mentors of which I am part this years, which regards a student felt depresses, and the way we judge his/her performance. The issue has a broader perspective brought up by Robert C. Helling (GS), which is interesting to annotate. 

"To put the issue in a broader perspective: In my paid life, I am the scientific coordinator of a graduate program at a major German university. After a few years, I realised there was quite a number of students that have to take breaks from university due to mental health problems (roughly 2 / 100 students / year) and I was worried that somehow this was the fault of our program (for example by putting to much pressure on the students). So I looked into this a but and sought advice from experts in this area. 

What I learned was that this is totally normal, it’s a phenomenon that is just not visible as there is a shyness to talk about this as mental health problems are still too much regarded as a personal weakness: Roughly one in three people at some point during their life suffer from a mental health problem that is so severe that it affects their ability to work and/or participate in social life for an extended period of time. And these do not show up at random times but usually a turning points in their life, for example a loved one dies, relationship break-up, you move to a new social environment (e.g. city / country / university) etc etc.

In the cases I met, they were relieved to hear that it is not just their personal problem (often perceived as a failure) but that this is quite common as I just explained. So in conclusion, I don’t think it helps to cover up the existence of these problems and pretend they don’t exist but talk about them openly (of course anonymously)."

On my personal experience, I know too that failing is not so uncommon and can happen, for various reasons, to everybody.  Not only geniuses get sick (not to forget John Nash) even if the debate if  creative and mental disorders are connected support certain evidence that "mads" re also creative. Anyway, I better support the idea, that looked very close, all of us have insane little behaviors, which, paraphrasing  Marcel Proust, " are necessary to make reality more bearable" but the equilibrium is unstable.

Dear Ph.D.s and Post-docs: Beware

So far I brought to their destination ten Ph.D. students. Which was their path in real life ?
One has a reasonable research carrier. Another one is trying to get into one in Saudi Arabia. Two made their step into the real life of an Engineer. Two founded a company. Two are doing a post-doc, and we will se where they will go. Other two, passed through post-docs, and still have to find their own way.
If I have to judge by my statistics, I can draw some (Bayesian) conclusions:

• Just a few Ph.D.s can enter a research/academic carrier (between ten and twenty per cent).
• For someone, maybe, doing a Ph.D.  was a waste of time. (But their opinion would be important)
• Some rolled up their sleeves and are trying to make their best to get their dream (of a having an interesting job) to come true. (They can fail and need support by their former advisors, academic policies, nations' wide consciousness of the importance of their role).
• More than four post-docs years are useless (they serve the PI, but not the postdoc). Post-docs, after a couple of years, have to come-back to reality and make plans.
• Someone is probably lost.

I always ask myself how much responsibility I have in the worst cases, and, frankly, I do not have an answer.  I will try to do better in future.

However the problem, as highlighted by this Nature commentary,  seems to be universal, at least in many of the developed countries. Academic positions are shrinking, and, at the same time, Ph.D. students are increasing in number. And, with them, post-docs. Now, we start to see the development of these long term post-docs carriers, without hope, and I ask myself with opposite moods: is this the best we can offer to them, and, why the hell, they do not face reality (that they will not have, in most of the cases, one position) ?
On the other side, I constantly prey for the western governments increasing their investments in research and innovation, as the real, long term, solution for exiting societal and economical crises. So far they did not, and, instead, many do the contrary. Cause and effect of the decline.

Said that, I will still continue to (moderately) enrol Ph.D. students. Ph.D. time can be great. You can see the world, meet outstanding people. Be on the edge. Hope for a better future, and that it is in your hands (and certainly it is too).

To early stage hydrologists (ESH)

Dear ESH,

you ask me to advise a young hydrologists. I just think that  he/she needs a master, first. I had the fortune to work at the beginning with Alessandro Marani, and subsequently Andrea Rinaldo and Ignacio Rodriguez-Iturbe. They had strong ideas on what is “the gold medal” that we have to gain, and had their own methods to pursue it (get ideas, get methods!). So, at least for me, the matter was to follow their own inclination, challenging myself to follow them, and see how much I liked it. 

So my first hint is: look for (one) some masters. You soon realise that some guys (or some places) are far far beyond the present state-of-art of the topic you are caring about.  Try to work with them. If you cannot, try to follow them and see what they do,  and understand what they think it is important. 

Copying from “smart people, can help you to save a lot of time, and avoid to spend huge time resources following unimportant topics.   In my academic life I saw very many “brilliant” guys (gals) spending their youth and hundreds of hours on irrelevant problems. However, if you do not give up, you will get the hot topic (and the people) that copes with you. The research company you get along, is going to stick with you for all of your life, and change you, especially  if you start an Academic path.

Not very much, indeed (meet the right people!), as an advise, but in a previous post you can find my decalogue for a hydrologist that can complete the topic.

Essential for Hydrologists

Some anonymous asked what is for me the  "Essential for a hydrologist".  Actually I tried to delineate this all along the blog. However, here below I will try a decalogue (with eleven statements actually ... it seems common):

GET KNOWLEDGE

0 - know which kind of hydrologist are you 

Are you studying all hydrological processes ? Or do you need to forecast (operationally) a particular part of the hydrological cycle ? Are you a modeller or an experimenter ? Do you study surface waters or groundwater ? Hydrology is very specialised and each sub-disciplines tends to use its own tools. One of my masters, Ignacio Rodriguez-Iturbe used to say that no hydrologist knows the whole hydrology. The water cycle, in fact, is so pervasive on the earth that it is difficult to know equally likely any hydrological subject.

 1-  In any case, know the fundamental processes (Hydrology in the XXI century is a physical science); remember that all in hydrology is a budget (mass, energy and momentum), and they are conserved.  Use sound theories and avoid unessential empiricism (this is what inspired my Slides about Hydrology)

2 - Be aware of scales. At which scale do you work ? Hydrology covers phenomena at closely molecular scale to continental scale. Methods for studying (models and measures) could be differentiated at different scales. Processes dominant at one scale can be negligible at other scales. Scaling characteristics are present everywhere … so actually some equations could be re-parametrized when used at a certain scale. Other equations simply emerge … (even if a bottom up statistical mechanics of hydrology is missing: lot of work for researchers!).

3 - Be aware of heterogeneities. Most of hydrological characteristics wildly vary in space, and this affects parameterisation of the phenomena at the scale of interest. Parameters could be sometimes thought as random variables (As in the case of hydraulic conductivity).

GET TOOLS (Select your ones, but if you can live with a community is better)

4 - know a GIS (e.g. gvSIG, open source is possible and better^1). 

Since hydrology is space varying and scale varying, you need something to visualise it properly. Spatial representation of phenomena and heterogeneities is, IMHO, one important key for understanding (so blessed is who discovers ways to identify spatial patterns and quantify them).

5 - Get a tool for quick calculations and visualisation (e.g. R^2) 

  Since Hydrology is making budgets you need some tool for making calculations. (Refrain: if it is open-source, it is better).

6 - Get a programming language, possibly Object Oriented  (e.g. Java. Other choices are possible indeed ^3) and put your hands in models^3b.

Learning is a process in which repetition and  calculation are important. Tools for quick calculations are not always suitable for more complex tasks, in which many people interact, exchange data, and explore complex non-linear physical environments like the hydrosphere. OO programming highly helps in maintaining and evolve complex code. (Java has a multitude of tools for doing that. In fact they were able to build a GIS and entire modelling systems in Java)

7 -Learn statistics (for data analysis and models outcomes  inspection)

Someone sees statistics as a tool for a forecasting. I tend to see in it more a tool for understanding (and building null hypotheses).  Data handling and understanding is at a core of any physical science. Maybe you do not perform experiments or field works, nevertheless a physical science has to do with data (huge amount of noisy data indeed), and you have anyway to cope with it. To do statistics you need tools (e.g. R). 

BEHAVE

8- Read the best papers of the best  researchers (learn from  smart people, and copy them). Remind that there are "poets" (original thinkers) and "translators" (followers).  Read the poets, even if the translators can be good. (e.g. see Benchmark papers - Scientists -WRR best papers)

9 - Avoid models and theories that "just work"^4  (but use them for surviving).

Programs and models that just work are useful to engineers who have to give answers and numbers. But at a certain point they fail miserably to give the right answer. Work for the right answer for the right reasons (anyway getting puzzled by the reasons something works despite the known evidence is important).

10 - Do not give up to improve your knowledge, and search for switching paradigms before paradigms reveal obsolete.


P.S. - One thing I forgot to say is that all model are obviously imperfect, and quantifying the forecasting error should be an exercise that any hydrologist should do any time he/she uses a model. Uncertainty in models, is certainly an issue.

___________________________________________________________________________________

^1  - I have to say that for professional printing people, even many open source users, eventually use Arc* to create maps. Printing options are certainly a weak point in open source GIS

^2 - Many use Matlab. I use to be a proficient Mathematica user. Others use IDL. All very good commercial product. But I decided to go open source.

^3 - Now is the moment of Python, which is, however, used conjointly with FORTRAN and/or C for the real task. So I believe that using Java for (almost) everything, and paying a little bit in performances,  is an economical choice.

^3b - If you work for an administration or a company, at the end, you will probably not program by yourself. You will simply use programs made by others (HEC-RAS, SWAT, SHETRAN to name a few). So you will turn your expertise in using them. But, obviously you have also to know the core hypotheses on which these models are based, and you certainly experiment that they will fail in some of your critical task. So somewhere there must be someone that, for you and for others, eliminate these drawbacks and push modelling on. That is the reason I strongly support modelling by components.

^4 Here a link to the Klemes Paper: "Dilettantism in Hydrology, Transition or Destiny", Water Resources Research, Water Resources Research, 1986, that further expands this topic.