Monday, December 30, 2019

Using GEOframe-NewAGE for operational modelling

Finally we did it ! Thanks to ARPA Basilicata and the work of my co-authors, Marialaura Bancheri  (GS) and Salvatore Manfreda (GS)  this long term objective of mine had a first realisation. We think GEOframe is a brilliant platform for operational hydrology, due to its flexibility and expandability.
we did not produce much new science with this paper. However, I think the introduction is brilliant, the explanation of how the models work of unsurpassed clarity, thanks to the use of EPNs, and, obviously, the system informatics really outstanding. You can find below the Figure representing the core modelling structure with this graphic system.
 Below you also find the representation of  how routing was implemented.  The latter figure shows how a dam was inserted in modelling. Because the EPN is actually reflected into the internal informatics, based of Net3, the dam can be inserted or excluded, according to what we want to simulate, without breaking the model, just on the basis of scripting.
It will be interesting from now on to monitor  how the system actually work in the operational context, and we will give proper information of it in the future. The paper is open access on Water, and you can see it here.

What do I have to do with polyacrylate ?

It happens that a researcher deviates a little from his mainstream research. This is one of the cases. Dott. Pier Giuseppe Marcon came to us suggesting that polyacrylate could have peculiar thermodynamics characteristics and decreasing its and surrounding temperature when wet and around to 20-30 Centigrades. He arrived to me because of this blog. I suggested that I was certainly able to figure out a way to simulate the dynamics of the substance but, at the state of art of our knowledge, I was not the right person to talk about because the polyacrylate needed some charcgerisation before I could envision its thermodynamics modelling. Therefore I involved a  colleague, Rosa di Maggio, for getting the work done.  The result is this paper, in which I learned a few things. First of all that phases of matter are not that well defined as I believed initially. Secondly that water can form compounds when it is not free. That cannot be considered exceptional findings but made me to reflect that neglecting water chemical bounding can bring to mistakes.
The paper can be found by clicking on the Figure above (from Britannica) you get the paper. Its abstract reads: "Super-adsorbent polymers have the capacity to immobilize huge quantities of water in the form of hydrogel, thanks to their confguration. A commercial sodium polyacrylate (PA) was analysed as such and at diferent water uptakes, indicated through the weight ratios PA:H2O. The hydrogels were prepared using diferent type of water (tap, distilled and deuterated) and characterized by Infrared and Raman spectroscopic analyses, nuclear magnetic resonance experiments, CHN elemental analysis, measurements of thermal conductivity and difusivity. All the measurements were done in order to assess applications of PA:H2O gels as Thermal Energy Storage systems for improving thermal performances of building envelope through passive solar walls. It has been observed that the behaviour of the hydrogels depends both on temperature and water content. In certain conditions such as low weight ratios, a spontaneous and quick cooling of the hydrogel could be observed. The curves of heat fow and average specifc heat (cp) were determined as a function of temperature in order to investigate the states of water in PA hydrogels. When a few water molecules are present, they are mainly and strongly bonded with carboxylate groups. Increasing the amount of water, greater shells of solvation around ionic groups form and water molecules can even interact with neighbouring non-polar hydrocarbon groups. At very high amount of water molecules, they are much more involved into H-bonds among themselves, rather than with PA, so that water pools form into the links of polymeric network. Bulk-like water can freeze and melt. Whatever the amount of water in the hydrogel, its thermal capacity is higher than dry polymer, because the heat can be absorbed by the continuous desorption of water from polymer to bulk-like water (watergel→waterliquid), which can evaporate as temperature approaches 100 °C (watergel→waterliquid→watervapour)."

Next will be to envision a theory for the behavior shown. A glimpse of it can be found on this note.

Wednesday, December 11, 2019

Green Water and Blue Water, the Alps and the Climate Change

Thanks mostly to the work of Theodoros Mastrotheodoros and Simone Fatichi (GS), we submitted a paper to Nature Climate Change that was entitled "More green and less blue water in the Alps during warmer summers" which was eventually accepted. It  investigates under the climate change pressure the partition on water between runoff and evapotranspiration on the whole Alps with simulation on a grid of 250 m.

It is not certainly the first effort on the Alps. However,  for its resolution and quantity of data used the paper marks a benchmark for present research. Here it is its abstract:

Climate change can reduce surface-water supply by enhancing evapotranspiration in forested mountains, especially during heatwaves. Here, we investigate this “drought paradox” for the European Alps combining a new database of more than 1200 stations and hyper-resolution ecohydrological simulations to quantify the blue (runoff) and green (evapotranspiration) water fluxes. We show that during the historical 2003 heatwave, evapotranspiration in large areas over the Alps was above average, despite the exceptionally low precipitation, amplifying the runoff deficit by 32% in the most runoff- productive areas (1300 to 3000 m above the sea level). An increase in air temperature by 3 °C could enhance annual evapotranspiration by up to 100 mm (45 mm on average), which would reduce annual runoff at a rate similar to a 3% precipitation decrease. This suggests that green water feedbacks, which are often poorly represented in large-scale model simulations, pose an additional threat to water resources, especially in dry summers. We conclude that integrating hyper-resolution ecohydrological modelling into climate change impact assessment studies can support more realistic predictions of water availability in mountain regions.

By clicking on the figure you can access a preprint.

Wednesday, December 4, 2019

How to read and decently plot a DEM with Python

I worked for long time to build  GISes (e.g. JGrass, uUdig and so on), an adventure that was supported and eventually taken in charge by Hydrologis. After accepting the reality the QGIS won the battle, I am not still satisfied with it and always looking to alternatives. One that I practice a little is to use as vehicle for mapping Jupyter and Jupyterlab. Therefore,  copying  around, I was able to produce decent maps. You can find one of the result of my search in the figure below.

Nothing really original from me: I copied from the bests I found and adapted to my desiderata. Clicking on the Figure above you access the Jupyter notebook where all the procedure is explained. Aa I wrote,  it is very much a work in progress. Therefore any suggestion is welcomed.

Monday, December 2, 2019

On a flooding vignette that is often posted on the web

This picture whose I do not know the authorship is going around the web to support the idea (Which I also support) that streams should be left as natural as possible to avoid flooding. But is this image really hydrologically sound? 

Let say that the top row is highly simplified and objects size incorrect. In the first top figure, it is unconceivable that vegetation stays at tens or hundreds of meters from the stream. Usually vegetation goes very close to the stream, at least the vegetation that can have its roots inside the water table. This also means that, in figure 2, top row, when the flood comes, it will inundate part of the forest. Finally figure 3 top row should have more evident traces of the flood, for instances some sediment here and there. Correctly, streams are  not channels, as the first figure on bottom row wants to convey, where humans make what it should not be done. The human in the picture, however, is not represented at a size compatible with the trees dimensions on top row. From passing for figure on top to the bottom ones, there is a zooming in or the digger should be something like 40 m high if proportion were right. The same for the houses in figure 2, bottom row: they are certainly not proportionate with respect to the trees dimensions. Let’s say that the picture is made to convey sense of disproportion and emphasize that human intervention is wrong. Art freedom, maybe, where the concept is more realistic than the real. 
Certainly the rectification of the river is a bad thing and the houses were built too close to the river. Streams are source of ecosystem services and boost the economy so, in the past,  it was taken the risk  to build close, or even over the river. This was done many times, in a trade-off that was deemed reasonable decades ago, but  that it does not seems justifiable anymore now.  We bear the weight of history here. 

As many knows, human activities alter infiltration in soils and rainfall, consequently, rainfall is more efficiently transformed in runoff. However, unless we claim an extraordinary intervention of climate, the flood in the last figure is grossly exaggerated, if the cause is the city itself. If we think the houses are no more than 15 m (not 40 m as  could be suggested by the the proportionality between the top and the bottom rows), the last picture show a ten or more meters of water over the usual level. By far, this cannot be generated (all over and upstream the city until the horizon) by the changes happened in urban soil use. The flood, in fact, is instead reasonably caused by a  massive runoff generated in the upstream basin, not by the local urban runoff. 

To sum up, the message is shareable "Please give room to river to expand" and "Do not build too close to river" (this increases exposition and vulnerability). However the cause for the flood in the last picture is not the city itself. Certainly, the inappropriate urban planning increased the risk and the losses but it cannot have enhanced the flood that much.

There are obviously cases in which the floods are caused by the city impermeability: this is when the catchment is smaller or of a size comparable to city itself. Just in this case, the soil modifications cause the runoff which in turn causes the flood. The city of Genova, in Italy, is one of these cases in which "urban rivers" exist: but there are many others in our contemporary world where we have many huge megacities (but this it is not the case of this figure).

Concluding, the message is right but the hydrology is wrongly represented.  Yes, I know: I have been too picky.

P.S. - Besides water in floods is rarely blue though. Usually, it is full of sediment and of the color of the soil it transports.

Friday, November 29, 2019

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  [...]"

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:

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.


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.

Thursday, November 21, 2019

Future Hydrology and Leonardo da Vinci

I was asked to talk about Leonardo da Vinci (see here about his resumé) and project in the future its intuitions about doing science and hydrology. Not an easy task tough. Fortunately there were large analogies with some recent reflections I made on this blog, especially regarding patterns and patterns description.
What I produced, indeed,  would need some further thinking and deepening, but, at the talk I had some time constraints and I could not expand further (I took a lot more time than planned, when the Convener told us, we have more time). Anyway, the result is here below
and you can click on the Figure to see the presentation (some Figure may not be visualised in the on-line preview).  A little of bibliography here:
  • Bancheri, M., Serafin, F., & Rigon, R. (2019). The Representation of Hydrological Dynamical Systems Using Extended Petri Nets (EPN). Water Resources Research, 8(01), 159–27.
  • Capra, F.,2007, The Science of Leonardo, The Doubleday Broadway Publishing Group, a division of Random House Inc., New York. 
  • Corsini, A. (2018). Modeling (understanding and controlling) turbulent flows: the heritage of Leonardo da Vinci in modern fluid dynamics (pp. 1–15). Budapest.
  • Dietrich, W. E., Wilson, C., Montgomery, D. R., McKean, J., & Bauer, R. (1992). Erosion thresholds and land surface morphology. Geology, 20, 675–679.
  • Geymonat, L. (1970). La tecnica nel quattrocento - Lenardo da Vinci. In Storia del Pensiero Filosofico e Scientifico, Vol II (pp. 48–59).
  • Giometto, M., Christen, A., Egli, P. E., Schmid, M. F., Tooke, R. T., Coops, N. C., & B, P. M. (2017). Effects of trees on mean wind, turbulence and momentum exchange within and above a real urban environment. Advances in Water Resources, 106, 154–168.
  • Macagno, E. (1991). Some remarkable experiments of Leonardo da Vinci. La Houille Blanche, (6), 463–471.
  • Monaghan, J. J., & Kajtar, J. B. (2014).  Leonardo da Vinci’s turbulent tank in two dimensions. European Journal of Mechanics B/Fluids, 44, 1–9.
  • Montgomery, D. R. (1999). Process domains and the river continuum. Journal of the Water Resources Association, 397–410.
  • Pacioli, L., De Divina Proportione, Milan, 1509
  • Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., Prabhat. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 1–10.
  • Rigon, R. (A. Rinaldo, supervisor), Principle of self-organization in the evolutive dynamics of river networks, Universities of Padua, Florence and Trento, Ph.D. dissertation, 1994) 
  • Stroock, A. D., Pagay, V. V., Zwieniecki, M. A., & Michele Holbrook, N. (2014). The Physicochemical Hydrodynamics of Vascular Plants. Annu. Rev. Fluid Mech., 46(1), 615–642.
  • Weinan, E. (2019). Machine Learning: Mathematical Theory and Scientific Applications. Notices of the American Mathematical Society, 66(11), 1813–1820.

Monday, November 11, 2019

Some about the legacy of model topology in estimating travel times

This talk is connected to the paper in preparation about the equivalence of hydrological dynamical systems (HDSys), and on the Extended Petri Nets. We discuss how to pass from the water budget to the related system of age-ranked functions and  show some simple solutions of the master equations of the related travel times.

Our thesis is that once you have the structure of the water budget model, you can get the equations for the age-ranked functions automatically. Under the hypothesis of uniform mixing, you also know the solutions of your system ....

Monday, October 21, 2019

A little of summary of what we did in the LifeFRANCA project

This is the review of the CUDAM work done during the Life FRANCA project. The main material can be found at  the OSF LifeFRANCA/CUDAM site.
Please click on the figure above to access the presentation.

Saturday, October 12, 2019

Understanding and Modelling the Earth System with Machine Learning (USMILE) - Synergy

The bad thing about ERC grants (and ERC Synergy grants) is that usually very low information is available about them. Which is quite contradictory, since being those (supposed to be) the best and visionary projects in a certain field, they should be open to the masses who should learn a lot from them. Among the Synergy grants approved just yesterday, there is this one, which is certainly of interest for hydrologists, which aims to focus of new models of the global Earth. Actually somewhere in the press, was mentioned the word hybrid (physics-ML) modelling of observation of Earth system, which means that it is not all about machine learning (this is the ML above) but also the integration of these models with traditional equations solvers.  The colleague who proposed the project are, as you can verify, all brilliant top scientists: Veronika Eyring (a modeller, climatologits, GS ), Markus Rechstein (a biogeo-chemist with modelling abilities, @Reichstein_BGC, GS), Gustau Camps-Vall (head of a signal processing and visualisation group@isp_uv_es, GS) and Pierre Gentine ( Hydrologic Cycle, Land-Atmosphere-interactions, turbulence, convection, soil moisture, looking at the global scale, GS). 

Browsing (click on the GS above), their impressive paper production, one can have an idea of what their project can be, but some particular papers, as this one, recent, in Nature, can be considered as a “proof of concept” of the project, I guess. 

More divulgative material can be found, instead here. Mark Reichstein explain also some of the concepts of using deep learning in the Global Cycle Cycles in Stockholm here.  

Thursday, October 10, 2019

Virtual Reality, Augmented Reality and Mixed Reality in Hydrology and Earth System Science

I did some searches on the web to see if there are application of virtual reality (VR), augmented reality (AR) or mixed reality (MR) (see also here for a tech-magazine type of introduction) around that could be used in hydrology.
On the general issues there are many information on the web, but if you think that reading a book can save a lot of time, this book is Augmented Reality by Dieter Schmalstieg and Tobias Höllerer.
These technologies are largely advertised by the big IT companies (e.g. Google). Incredible user experiences and interactions with data can be seen in the scifi movies we watch daily. Application in Earth Sciences are scarce though. 

To be short for what regards hydrology: I found application in Iowa, in Germany and a group of people that moves around the Virtual Geosciences Conferences (from which I robbed the image) and in general, geologists found simple and direct applications of it. In hydrology just relatively few papers: by Su et al (2008), by Billen et al. , (2018), and those cited below of the Julich Forschungszentrum (JF).
In Iowa there is an active group of hydroinformatics. They have a VR/AR/MR page here and because their involvement in real time flood forecasting, their work is mainly in that direction. In the US there is NOOA that has done some activity, but especially looking to the global planet and the space. NOOA, not surprisingly (?), is more interested in dissemination of results through virtual reality than using AR, MR in supporting Earth Science Research.
In Germany I found a few experiences. One is the group working at "Data Assimilation for Improved Characterization of Fluxes across Compartmental Interfaces” that includes ESA, a few good Universities and research centers. Their site is, however cryptic about results. Their focus is to support data assimilation. The JF plays in it a central role with its models, and they, in fact, have something to show. In fact, browsing Lars Bike website, one can also find some publications (e.g., Yan et al, 2019Rink et al., 2018Rink et al, 2017Helbig et al., 2015, Bilke et al., 2014) that can be useful to read for understanding some directions to go.
To have the very best experiences of the VGS, the best thing is to browse their 2018 proceedings

There is not very much around beyond that, and therefore, I think there is a lot of room to use them in Earth sciences.
From my model builder and user I see exciting the possibility to interact in a more immersive way with input and output of models.
Notwithstanding we are usually looking for the Holy Grail of simplified models, we have to get used to manage and have a supervision of more large data sets, maybe also as a necessary step to get simpler models.
Input data are many and complicate to grasp if the model has to describe complex reality and patterns are not that visible at the first sight since we have to educate our eyes, as any guy using a microscope, or a telescope knows, since the Galileo times.
As a modeler, I would like the data must be visualized and modified promptly for changing simulations behavior. I also dream that the models could be driven by human interaction in real time, changing parameters on the fly copying with the changes the flow of measures impose. Like we were driving a starship.
Obviously this model/data navigation needs to be recorded and re-analyzed afterwards to learn better what has happened (an this further requires tools)
In our field, visual data are usually spatially distributed datasets or graphs and distributed datasets could cover static quantities like the terrain topography, the landscape, or dynamical quantities as soil moisture, temperature, water velocity and, possibly, also some other less trivial quantities like entropy fluxes, water celerity, nutrients concentration.
Brought to the fields those data can be useful to setup measures, tp drive field inspection, “learn by seeing" data and the situation together in an immersive environment, where discrepancy between what expected and what seen can become evident than in traditional situations.
There are non secondary application to education which seems trivial to VR/MR/AR experts, because virtual reality classes seem actually be already available. However if we look carefully, these cover usually very elementary topics and seldom support high education (with exceptions, see Aubert et al, 2015). What I could find is here, and here for example, with incursions ob the psychology of learning here.
Clearly VR/AR could interact also with crowd science initiatives, as has been already envisioned, but could be certainly enhanced.

For the interested there is also a Journal, Virtual Reality, where something interesting about water can be actually found (but I confess I am not able to evaluate how good the journal is).


Aubert, A. H., Schnepel, O., Kraft, P., Houska, T., Plesca, I., Orlowski, N., and Breuer, L.: Studienlandschaft Schwingbachtal: an out-door full-scale learning tool newly equipped with augmented reality, Hydrol. Earth Syst. Sci. Discuss., 12, 11591–11611,, 2015.
Bilke, L., Fischer, T., Helbig, C. et al. Environ Earth Sci (2014) 72: 3881.
Billen, M. I., Kreylos, O., Hamann, B., Jadamec, M. A., Kellogg, L. H., Staadt, O., & Sumner, D. Y. (2008). A geoscience perspective on immersive 3D gridded data visualization. Computers & Geosciences, 34(9), 1056–1072.
Kromer, R. (2018). VGC2018, 1–101.
Helbig C, Bilke L, Bauer H-S, Böttinger M, Kolditz O (2015) MEVA - An Interactive Visualization Application for Validation of Multifaceted Meteorological Data with Multiple 3D Devices. PLoS ONE 10(4): e0123811.
Rink, K., Bilke, L., Kolditz, O., (2017) Setting up Virtual Geographic Environments in Unity, in Workshop on Visualisation in Environmental Sciences (EnvirVis), Rink K., Middel A. , Zeckzer D. and Bujack R. Eds., 978-3-03868-040-6
Karsten Rink, Cui Chen, Lars Bilke, Zhenliang Liao, Karsten Rinke, Marieke Frassl, Tianxiang Yue & Olaf Kolditz (2018) Virtual geographic environments for water pollution control, International Journal of Digital Earth, 11:4, 397-407,
Su, S., Cruz-Neira, C., Habib, E., & Gerndt, A. (2009). Virtual hydrology observatory: an immersive visualization of hydrology modeling. In I. E. McDowall & M. Dolinsky (Eds.), (Vol. 7238, pp. 72380H–9). Presented at the IS&T/SPIE Electronic Imaging, SPIE.
Yan C., Rink K., Bilke L., Nixdorf E., Yue T., Kolditz O. (2019) Virtual Geographical Environment-Based Environmental Information System for Poyang Lake Basin. In: Yue T. et al. (eds) Chinese Water Systems. Terrestrial Environmental Sciences. Springer, Cham

Wednesday, October 9, 2019

Comments on: Thinking on Optimal Theories in Hydrology

I think the comments made by Christian Massari on yesterday post require some deepening.

He wrote:

C - .. I have some comments from your text….

C - I went trough it and I enjoyed a lot the reading. The patterns like perspective is something that is there, closer to reality than the classical Eulerian approach where objects are divided in elements and physical quantities are attempted to be described for a number of discretized points (with point-based physical laws ) of what you called “shapes”. Is this approach really valid?
R - The question is well posed. To really answer to this, we should be able to build a “statistical mechanics” out of the finer scales to obtain the behavior at the larger scales.
This is, for instance, the case of Richards equation which is, in its essence, mass conservation plus a hypothesis about pore filling and emptying, plus an intrinsic assumption about the randomness of the medium (pore dimensions are connected randomly). These hypotheses leaves behind macropores and preferential flow (I know how to include them, however) but produces a working equation at the Darcy scale. It is less clear for other compartments of the hydrological cycle.
If we have a catchment, the traditional lumped approach is to consider it composed by hydrologic response units (HRUs) that then interacts to get the whole catchment rules (the big picture, often called semi-distributed). To my knowledge, the model Topkapi (Liu and Todini, 2002) are obtained by integration of the smaller scale hydrology, Topmodel (Beven and Kirkby, 1979, Beven and Freer, 2001) is another type of aggregation (related to the production of the runoff by saturation excess), the Geomorphic Instantaneous Unit Hydrograph (GIUH, Rodriguez Iturbe and Valdez, 1979; Rigon et al, 2016) a way to aggregate HRUs using travel time concepts. (I wrote about this here). All of them are types of aggregation of fluxes driven by a scope (getting the saturated area, getting the discharge, a.k.a the hydrologic response) and it is not clear if they can be aggregated when the goal is wider (for instance getting all the fluxes and the energy budget).
Gray (1982) and coworkers, (references later) envisioned a method of integration over space and time to make emerge laws bottom up that were subsequently popularised by the work by Reggiani et al. (1999, 2003). However, their work is based on the naive assumption that the topology of the interactions is irrelevant. Topology of interactions is instead fundamental to get the right fluxes at the large scale and it is explicit (and simplified) in both Richards and GIUH (but, as we said, at the price to let something out). So we should envision a way to built HRUs and make them to interact properly. This is also when known in physics, where the process of aggregation implies the “renormalization” of interactions. Kenneth Wilson got the Nobel prize for getting a clue of it. For renormalization working, however, the system must have certain properties of scale invariance, in which the form of the equation remain invariant but the coefficients change of magnitude. Notably Richards equation is almost scale invariant (e.g Sposito, 1997) and we are now able to verify it numerically. Most of the systems we deal with are, however, not self-similar and equations must change when changing scale.
In this context a guiding principle could be to search for emerging conservation laws (e.g. Baez et al., 2018), from which extract budgets' equations.
At present we can only make hypotheses, and, classically, think HRUs as reservoirs connected by empirical laws of fluxes, whose behavior can be tested in various ways, including the use of tracers. It is the practical trick that many of us use with some satisfaction (maybe the more clear statements about this approach are in the work by Fabrizio Fenicia: see Fenicia et al. (2016) for an example). On this type of models we wrote a paper, yesterday accepted in WRR. In this paper we deal with the representation of the models, but in reality representation methods reveal the assembly of compartmental models and a give clear suggestions on how to obtain travel times equations (the topic of an incoming paper). However the answer to your question is: at present we do not really know.

C - Is the whole system the simple sum of the the parts?
R - I said before, talking about Reggiani et al. that no, this is not the case. The topology of interactions counts.
C - Maybe this is true as we move to the microscopic scale but even at this scale things are organized in shapes (i.e., molecules). So it seems a scale dependent problem. So we have the chance for every system or compartment to use the “right" scale to describe processes and properties as patterns.
R -True
C -At that scale there is an undoubtedly existing organizing/optimality principle that is able to make that shape recognizable and distinguished from the rest, a complex system behaving as whole and having certain relation with the rest.
R -Right

C - You talked about three main processes/systems like turbulence, water flow in vegetation and river network organization but of course we could identify others which are not only related to water movement but also for instance to soil properties, meteo forcing organization (e.g. rainfall and temperature and humidity patterns induced by landscape) and so on…
R -Right

C - This could a be direction we could take for example by looking at pattern like modelling (Grimm et al. 2005).
R - Pattern based dynamics is a successful story that works with individuals interacting by a rule. This is not very much different to our lumped model, except for the fact that these interactions can be at discrete times. However, they are not essentially different. The issue remains to get the law of interaction right (or approximately right). These systems, as we did with systems of ordinary differential equations are representable by Petri nets, on graphs, and topological methods are available to get some clue of their collective behavior.
C - I am honest I am not in the topic so I need to study it but it seems something interesting. How we could join our forces?
Because we do not have the machinery (yet, but probably forever) to perform renormalisations, we need to deduce empirically both the patterns and the fluxes among patterns at the aggregated scale. To this scope smart use of remote sensing is essential. Eventually we could be able to do an operation of reverse engineering and understand how to deduce them from basic (finer scale laws).
We, observers, have the task of identifying these patterns or shapes based on observations (ground and remote sensing) also by unconventional ways (i.e. Clemens), you modelers, will have the task of translating in mathematical ways the the existence of these shapes and patterns as well as the relation between them (which are likely again the results of optimality principles).
R - Right.


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Tuesday, October 8, 2019

Thinking on Optimal Theories in Hydrology

In Nature we have to deal with forms that we somewhat recognize and distinguish from the rest (Thom, R., 1975). These forms (shapes), as we know since D'Arcy Thomson (1917, see also Ball, 2013), have functionalities that are shaped by some dynamics that we struggle since then (and maybe before) to find and understand. Because forms and functional forms are so ubiquitous we are brought to think that there is some design to produce them (Zanella,  G. Sopra una conchiglia fossile nel mio studio, About a Fossil shell in my room) but this is from the science point of view an error of perspective (Monod, J., 1970) and an undecidable question. K. Lorenz, on the other side warns often that evolution does not produce always functional forms (or behaviours) but let grow also unnecessary “neutral” stuff which is not useful nor a handicap. The discussion can grow very general and philosophical, and pursuing it could be the topic of another post.
We are interested here to grasp the grow of forms and patterns by means of methods that are proper of mathematics, physics and chemistry (on the illuminating example of E. Schroedinger, What is life ?, 1944). It is to be remarked here that to appeal to hard sciences does not mean a priori a reductionist approach, in which the systems are pruned apart and loose their quality. This is especially true for living systems but also for complex Earth science cycles as the hydrological one, and we aim to keep the systems and their dynamics together, emphasizing the interactions that makes forms to emerge at various scales. 

The general but qualitative understanding of the physics and mathematics related to these problems, is that, deprived of its teleology to be investigated elsewhere and eventually, behind forms and patterns there is some “optimality principle” or, stated in other words, that Newtonian mechanics, physics and chemistry of complex systems evolve solutions that require spatio-temporal structures and patterns. The creation of “forms” is extended not only to the immediately appearance that we perceive with our (highly biased by evolution, though) senses, but are minimal or maximal solutions when observed from the point of view of a certain variable. Unfortunately we are not really able to move, based on the present knowledge, from the basic principles to the appropriate laws of structures interactions, just through derivation of statistical law or integration over degrees of freedom.

The theories of optimality assume among the driving forces moving the dynamics of a system in Nature there are some extensive quantities like, for instance, entropy that are pushed to increase. For instance entropy, because,  entropy of a closed thermodynamic system is shown to grow. In fact this is a principle of Equilibrium Thermodynamics. However,  in Non Equilibrium Thermodynamics  it can be derived, at least  for some simple systems, i.e. is not anymore a principle,  (e.g. see the Thermodynamics derivation of evaporation in Monsoon and Baldocchi, 2014).
Moreover, in open systems, entropy of a subsystem can decrease, so in such context it cannot be used to understand the asymptotic state of the system. Fortunately,  the rate of production of entropy could. As a matter of fact, in non-equilibrium, non-autonomous, open systems, asymptotic states could not be so relevant, and instead what matters are “intermediate asymptotics” as pointed out for fluid dynamics by Barenblatt, 1996 and, before Baranblatt, by Ilya Prigogine work on dissipative structures, i.e. structures that are identified because, they represents steady (or at least somewhat persisting) states of a (thermo)dynamic system out of equilibrium.

It can happen, for instance, that intermediate asymptotics are obtained by minimizing energy dissipation, i.e. the quantity of energy that is transformed into heat (or non-usable energy). This is the case of river networks (Rodriguez-Iturbe et al., 1992) and implies that the maximum entropy of the comprehensive system is obtained as slowly as possible, meaning that the available energy is used at its best for producing work.

This behavior seems logically correct for living systems, and seems justified by evolution and selection: the most efficient survives and reproduces (Virgo, 2001) but is intriguing the fact that river networks are not living systems and anyway they obey such a type law.  Therefore it seems implied that some general dynamic law is the origin of all (e.g. Prigogine, 1945). The mathematical problem is all but trivial, and such minimization (or maximization) problems has received recently the attention of the Field prize (e.g. see the work of Alessio Figalli), but, forgive me if I dare to say that is simpler than the physical problem to understand why certain equations that bring to optimisation problems are valid. Anyway, optimisation problems are mathematically obtained by expressing the large scale dynamics of the (river) networks as a functional to be optimised (actually expressed in Rodriguez-Iturbe et al work in discrete form). In optimal channel network (by Rodriguez-Iturbe et al, 1992), this functional is, obtained by using the basic Newton law (in appropriate form) and some additional hypothesis, derived from observations or educated guesses. One would like to obtain it without heuristics but this seems out of our present possibilities notwithstanding the large literature on optimal transportation networks (e.g Barabasi, Network science, 2018). It should be noted, however, that the case of networks is produced by some twofold optimality: a tradeoff between maintaining an optimal transport and optimally maintaining the the structure that conveys optimally the transported.

An epitome of structure analysis is also the Navier-Stokes equation and turbulence. Here we have the formation of structures that dissipate kinetic energy, the vortexes, actually of all dimension from the scale of observation to the dissipation scale.
We recognize these patterns as preferential flow of energy, or, in the case of turbulence as a form of quasi-random disorganization whose structure is particularly evident in the 4/3 Kolmogorov law. In this case, the equation is directly the Newton law (plus Newton’s hypothesis on stresses), so, despite the complexity of the outcomes, the physics is very directly reduced to mathematics which, BTW, in this case is unable to completely solve the problem.

A third piece of the elephant is the water flow in vegetation. It happens to maintain the temperature of the plant system in a range acceptable for plants to comfortably survive subject to weather and climate forcings and at the same time, not secondarily or maybe primarily, fix $CO_2$ to build their structure and carbohydrates trough photosynthesis. So plants need to optimise their fit to varying weathers for maximizing their production. A plant can be decomposed in its main structural parts: root, steams, leaves and their functional counterparts, xylem and phloem (e.g. Stroock et al., 2014), each part can be disassembled back to the singles cells and chloroplasts. But after the reductionist operation, plants overall functioning remain partially elusive and resistant to quantitative treatment if we do not treat plant’s part as a system (with a lot of osmoregulatory subsystems, e.g. Perri et al., 2019) and ecosystems where plants of various species interacts in competition, cooperation and coopetition for light, water, nitrogen, or phosphorus. The structure of plant and ecosystems and their interactions, evidently does not violate basic physical laws, their functioning respect mass and energy conservation, and momentum (of water, for instance) is peculiarly dissipated to obtain the scope of water supply to very high height and very negative pressures (up to -30 MPa). Optimization here involves various aspects, including the scaling of xylem dimensions (e.g. Olson et al., 2014), to obtain optimal sapping performances. Besides, recent work by Hildebrandt et al. (2016) shows evidence of optimal use of water when the energy budget is properly accounted for.
Soils are not a passive medium, first because themselves contain a lot of aggregated microbic biota (so far mainly neglected in hydrological analyses) and secondly because it is the environment where soil-roots interact. Also in soil, even according to more traditional views, there are optimisation processes when, during evaporation, the rate of water uptake is maintained constant up to critical soil water content (stage I evaporation) after which, evaporation strongly decreases (stage II evaporation). This is provided by a series of feedbacks among small and large soil pores, viscous forces and cohesion processes still not well understood (but kind of well described in Lehman et al., 2008).

Hydrology in the critical zone (CZ, the elephant) is therefore overwhelming difficult because it is the compendium of optimisation processes regulated by networks, vegetation, NS equations and water flowing in soil. According to what we focus on, we can isolate various non trivial dynamics. However the challenge is to model their comprehensive interplay for which still we do not have appropriate mathematics, observations and tools.

Comments following this link

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  • Virgo, N. (2011, March 23). Thermodynamics and structure of Living Systems, Ph.D. dissertation, University of Sussex

Tuesday, October 1, 2019

Discharge predictions on the Netravati River Basins using GEOframe-NewAGE

Giuseppe Formetta (GS) started to collaborate with some Indian colleagues for predicting discharges of Netravati River Basins. He used a modelling solution out of those from GEOframe-NewAGE to get his results and and presented the results at the last meeting of the Italian Hydrological Society held in Bologna. You can see the results of this work in the slides below.

He used CHIRPS data for precipitation and substantially a version of Hymod for any HRU to get runoff. Results are quite interesting.

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.