Sunday, September 22, 2019

A little, non conclusive, reflection on the non-linear evolution of hydrology (and science)

It seems that some science (and our in particular, where isolating experiments is often impossible and we have to deal with complexity and just observations) proceeds upon tradition accepting it passively and without discussion of the mainstream ideas, and there is a large inertia to adopt new views. Legacy to old and even wrong ideas has its own reasons. First they were not completely unreasonable (but people apply also to unreasonable ideas with absolute dedication, which is so diffuse that, I guess is due to some evolutive selection). Then, after a first acceptance by the community, a lot of people adapted their work, calibrated their parameterizations, designed their experiments and push them to the limit before accepting the idea that new paradigms are necessary. I think some reflected on this before (i.e. Kuhn, The Structure of Scientific revolution, 1962)
However, this reflection came to me by a couple of readings: the paper on the 23th problems in hydrology (Bloeschl et al., 2019), which I coauthored with other 200 or so and observing the case of evaporation from capillaries. Let’s start from the latter case.

Some theory was known since 1918 and 1921 with the work of Lucas (1918) and Washburn (1921), e.g. in Ramon and Oron, 2008. For what it seems it has not been used for a century in studies about soil water flows or plants xylem motion despite it could have had some contents to promote. In particular to increase the knowledge in the cohesion-tension theory. Textbooks struggle to find the reasons for which plants can develop depressions so high as -30 MPa (Strook et al., 2014) and, using the statics physics contained in the Young-Laplace law, need to find nanometer interstices in leaves to get this results (even the notable Nobel, 2017, book). These papers failed or, a least, are not convincing me. I believe the cause is purely dynamical and driven by atmospheric demand as those old papers would suggest. Why almost nobody referred to those old papers and argued accordingly ?

The 23th open questions in hydrology, while letting me unsatisfied, brought to me thinking which are the achievements of Hydrology since Green-Ampt (Buckingham, Sherman, Richards), let say the first part of the XX century, to get a perspective. To a superficial reader it could appear that a lot was already there and therefore we did not assist to any “scientific revolution”. It is really clear to me that to really understand it, we should reread the old papers, with appropriate eyes, though. In our paper on IUH (Rigon et al., 2016), we claim that we tend to read old contributions with contemporary view and see in pioneering papers concepts that were not actually there. The list collected by various authors in benchmark papers selections can also be a place were to start. Appropriate analyses has to be done (and interest in History of Hydrology is growing) to fully understand ourselves as contemporary hydrologist.
I have to reflect and read a little more.


Tuesday, September 17, 2019

Advances in Richards 2D presentation at the Italian Hydrological Society meeting

The work of Niccolò Tubini is going who already developed a very solid Richards1D (with ponding),  code coupled with the energy budget is proceeding towards a 2D version on unstructured grids coupled, at present with a 1D de Saint-Venant equation. This is the summary of the work done so far given at the Italian Hydrological society meeting in Bologna.

Actually the de Saint-Venant coupling is not yet ready but it will be very soon. Stay tuned. Click on the figure for getting the presentation.

Friday, September 6, 2019

Quantum Computing will change the hydrologists life ?

I struggle for most of my scientific life with computing issues. The main focus was to choose the right language: C over FORTRAN first, then C++ and Java, and finally Java plus Python recently, passing trough various experiences with BASIC, Smalltalk, the Wolfram Language, R).
And with the language a programming paradigm, from old "go to"s to procedural programming, from procedural programming to functional programming and object oriented programming. It has been a long journey a little aside from the main stream of my colleagues, once FORTRANers, now mostly PYTHONers (and MATLABbers though) and it still continues.
However all of this was in the big stream of computer machines working with processors built in a certain way. Since many years from now the focus moved then in parallelizing the codes to serve vectorial chips, multicores processors, and factories of computers sharing tasks (and I confess I remained a little behind in this).

Now, in a decade or so (but I suspect in twenty years) the traditional way of writing algorithms will change: no more bits but Qbits. The era of quantum computing seems just behind the curtain.
To this scope documentation start to be present and remarkably IBM provides some tutorials if you want to start training yourself. You can start from Qiskit by

Wednesday, September 4, 2019

Stomatal resistance and Transpiration

There are several factor influencing water vapor availability in the leaves’ viscous layer and we can start from the water availability in soil. To get into the root, water of some capillaries must be close to roots. Experimental studies about soil tend to say that flux (to the atmosphere) is sustained at the maximum rate to a critical point of soil suction. Does roots cease to sip water when water is not anymore a connected phase ? Or can roots extract water from vapor ? Or what else ? I do not feel that these questions were answered properly in literature, but I also confess I missed some reading so far of the papers where the coupling soil-roots has been treated explicitly.

The other big topic is the physiological reaction to water scarcity. Plants in fact can close stoma: they are like a tap which is being closed with an effect in literature is known as “stomatal resistance”. It cuts the evaporative flux to oppose to the evaporation demand and the reduction is usually represented as a multiplicative factor, the stomatal conductance (actually the inverse of a resistance) which multiply the driving force, which is given as a different of water vapor concentration between the zone very close to the available liquid water and a zone in the viscous boundary layer (VBL) a little apart, such that:
$$Tr = g_l (c(z_0) - c(z))$$
where $T_r$ is transpiration, $g_l$ the stomatal conductance, $c(z_0)$ is the water vapor concentration close to the leaves surface and $c(z)$ is is the vapor concentration at distance $z$.
There is a variety of plants actions that regulate the stomatal resistance which are summarised in the isohydric and anisohydric behavior (Martinéz-Vilalta and Garcia-Forner, 2016). In the first case, the plant progressively closes the stoma as reaction to water stress to maintain as much as possible a balanced water content. In the other case the plant delays stoma closure in the measure it can resist to manifestation of cavitation and produces in its interior a very uneven water distribution. Actually the stomatal resistance $g_s$ is not the only one affecting plants. Plants have roots and a steam that convey water fluxes and also the flux there is traditionally treated as a viscous flow with some resistance. In that cases though, the driving force is the gradient of water potential or, if we prefer the Nobel (1999) view, of the chemical potential (of which the water potential is a particular expression).
Assuming an almost stationary situation along the root-stem-leaves system, the connection between plants compartments can be manipulated within the electric circuitry analogy (resistances sums to obtain a total resistance, as $g_v = 1/g_r+1/g_s+1/g_l$).
This model allows to obtain the suction in leaves, which, in turn, controls the quantity of water vapor in stomatal cavities.
The resistances are further unknown in the coupled water-energy-momentum system that determines evaporation, heat transfer and the water budget, however $g_l$ has been found to be connected to carbon cycle productivity trough the so called Ball-Berry formula (1987, BB). BB (see also Collatz et al, 1991) has been built out of empirical bases and it was subsequently modified (e.g Verhoef and Egea, 2014) to include physiological reactions and the production of abscisic acid, ABA (Buckley, 2017).
To obtain the final result of transpiration, (besides the determination of roots and stem resistances), there is the further problem of the coupling of stoma with the VBL. Again the tradition assume quasi-stationarity of the fluxes and therefore uses the resistance metaphor, assigning to the VBL a resistance according to an integrated Fick’s law. Also in this case, resistances are summed to obtain the comprehensive flux law that regulates the water ascending.
New questions arise: which is the dominant between the two resistances ? Is the resistance metaphor really applicable ?

A couple of papers, in particular, Manzoni et al., 2013 and Bonan et al. 2014 offer two remarkable points of view of the matter. Manzoni is more interested to processes, equations and general issues with plants hydraulics. Bonan et al. goal is the implementation of a model of the soil-plant-atmosphre continuum and therefore its appendixes can be useful to understand some of the details that can be perceived as ambiguous by the beginners in the field. Bonan's treatment is “traditional” being based on the set of assumptions all literature use which give you back an already well packaged simplification of the physics involved. Manzoni et al. put more emphasis on the biophysical aspects and their connections with plants physiology and use partial differential equations to illustrate the concepts. Both of them have a large list of references and, together with the recent work of Verohef and Egea (2016, VE) and the work of Dewar, 2002, can be a solid start for any study of the subject. VE in particular, compare various approaches to modelling the water stress and discuss their ability to reproduce experimental data. One of its main interest is to clarify if either water content or the water pressure explains better plant’s transpiration behavior. VE approach is very practical, since it does not discuss the rational behind the different approaches but just use and test them. The final verdict that pressure explain more properly: this is not so clear indeed until the end. Apparently the result is counter-intuitive with respect the organization of the paper that starts from empirical observation that transpiration follow a two-stage behavior (similar to the one seen in soils) when actual (daily) relative transpiration is plotted against the water available. Therefore there is no better that read it to get the vision clear.

  • Ball, J. T., Woodrow, J. B., & Berry, J. A. (1987). A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions. Progress in Photosynthesys Research, 4, 221–224.
  • Bonan, G. B., Williams, M., Fisher, R. A., & Oleson, K. W. (2014). Modeling stomatal conductance in the earth system: linking leaf water-use efficiency and water transport along the soil–plant–atmosphere continuum. Geoscientific Model Development, 7(5), 2193–2222.
  • Buckley, T. N. (2017). Modeling Stomatal Conductance. Plant Physiology, 174(2), 572–582.
  • Collatz, G. J., Ball, J. T., Grivet, C., & Berry, J. A. (1991). Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer,. Agricultural and Forest Meteorology, 54, 107–136.
  • Dewar, R. C. (2002). The Ball-Berry-Leunning and Trdieu-Davis stomata models: synthesis and extension with a spatially ggregated picture of guard cell function, 25, 1383–1398.
  • Martínez-Vilalta, J., & Garcia-Forner, N. (2016). Water potential regulation, stomatal behaviour and hydraulic transport under drought: deconstructing the iso/anisohydric concept. Plant, Cell and Environment, 40(6), 962–976.
  • Manzoni, S., Vico, G., Porporato, A., & Katul, G. (2013). Biological constraints on water transport in the soil-plant-atmosphere system. Advances in Water Resources, 51(C), 292–304.
  • Nobel, P. (1991). Pysicochemical and environmental plant physiology (pp. 1–637). S.Diego (CA): Academic Press, Inv.
  • Verhoef, A., & Egea, G. (2014). Modeling plant transpiration under limited soil water: Comparison of different plant and soil hydraulic parameterizations and preliminary implications for their use in land surface models. Agricultural and Forest Meteorology, 191, 22–32.

Tuesday, September 3, 2019

FOSS4G Bucharest 2019

In FOSS4G FOSS stands for Free and Open Source Software 4G for Geospatial. It is a great association that since fifteen years promotes open source spatial tools. It is the arena were great tools like GRASS, QGIS, GvSIG, Gdal, and our Horton Machine found the place to tell about their potential. It is a group of friends that meet every year with enthusiasm to compare their achievements and their perspective.  This here FOSS4G was in Bucharest, and the great news is that many of the talks were recorded and are now available for browsing. You can find them by clicking on the Figure below.
I includes the talk by Andrea Antonello about the new version of GEOpaparazzi working on Android and IOS !

Friday, August 30, 2019

Using colors in science and color blindness

Recently we send to reviewers a paper dealing with graphs. After the first revision we realised that we should have paid some attention to the colors we use, especially because they are meant to convey information (a lot of) to any reader. One over eight people is known to suffer of some color-blind limitation and therefore it is worth to made efforts to get color-blind friendly palettes of colors (yes, it is not just a question of percentages).

This topic has been addressed in various papers Wong [2011]; Johnson and Hertig [2014]; Keene [2015]; Stauffer et al. [2015]; Nuez et al. [2018] and we refer to those papers for the main issues in making a good choice of colors. There are various colorblind types, the three more diffuse ones ;being: protanopia, deuteranopia, or tritanopia Wong [2011] and we have tried to to understand how these people perceive our graphics.
As Rudis et al. [2018] says graphs and drawing must be ”spanning as wide a palette as possible so as to make differences easy to see, perceptually uniform, meaning that values close to each other have similar-appearing colors and values far away ;from each other have more different-appearing colors, consistently across the range of values; robust to colorblindness, so that the above properties hold true for people with common forms of colorblindness, as well as in grey scale printing ..”,
To understand how colors appear to color-blind people, or to our dog, we can use the information in other website, for instance the one by Martin Krizywinsky.
But I suppose you want to use some desktop based software to do your representations. We have a little choice here. I used the web-based software by David Nichols, which can be found here. R-software users can use the VIRIDIS package but also observe that the popular ggplot2 has its own dedicated palettes. I also know for experience that Python matplotlib already does concerned default choices in this field, as apparent from the central figure of this post. Java programmers can browse Contrast-Finder. Finally if you wants just to do-it-yourself, you can read this stack-overflow thread.
If you are interested to maps, you can give a look here.

Now you cannot escape the necessity to do colorblind friendly drawings.


Friday, August 23, 2019

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 pretended 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 guy 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 pretending me to be your butler. A good professor can  recognize to be wrong but you will be his/her PhD student and s/he is pretending to suggest the way to go along with you on top of her/his 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 him/her politely that a new perspective is better  before s/he changes her/his.  Besides, keep in mind that a good professor can estimate the effort, pain and costs to pursue the  goal you suggest and pay attention to what s/he says about.

A checklist. Going to the matter, we pretend you:
  • to show solid backgrounds (not necessarily erudition) and attitude to learn
  • 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 delineated 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. 

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