Monday, June 11, 2018

On complex networks computation of mountain catchments

From June 12 to June 14 2018 in Trento there is the 5th European IAHR conference. I am convening (and also presenting a contribution) which derives from our modelling within the GEOframe system.
The scope of the presentation is to inform about functionalities of GEOframe and something of what it is hidden under the hood. Clicking on the figure above, please access the pdf of the presentation.

Wednesday, June 6, 2018

Snow related Ph.D. position (the Stradivari snow project)

The Stradivari project research aims to build better tools for analysing the processes of the hydrological cycle. The project is more focused on the tools (building the violin) but it does not forget the music that has to be played. It is conceived to account for hydrological processes interactions and feedbacks, and develop new mathematics (equations) for their description. Use of appropriate contemporary numerics is also part of the project. The overall project builds on the foundations given by the GEOtop (http://abouthydrology.blogspot.com/2015/02/geotop-essentials.html) model and the GEOframe-NewAGE (http://geoframe.blogspot.com/) infrastructure. 

It is time to move on the generation of snow models. The computation abilities and the physics of snow are now much better known than thirty years ago. However, most of the snow model are based on parameterisations which should be updated. We start from the experience made in GEOtop (versions 0.*,1.* and 2.*) which is capable of reliable snow height, temperature and densities, at operational level over all the Alps, but we look also to the experiences made by CROCUS, SnowPack and Alpine 3D. In particular GEOtop use highlighted various issues that we plan to overcome with a new version of the model, which requires both deepening the thermodynamics of snow and its numerical implementation.

In synthesis we identify the following aspects to be improved:
  • Actual GEOtop snow model is 1-d. It can improved including the vapor phase movements explicitly. Besides, we can add modules to have a better account for: density, viscosity and, adding the vapor phase, type of grain. 
  • The physics. Analysing the thermodynamics to keep out of the future formulation empirical parameterisations of the processes which revealed obsolete; 
  • Adding Richards equation for water percolation; 
  • Analysing the separation rainfall, snowfall through a more physical modelling than actual; 
  • the description of deposition on canopies, and subsequent effects of vegetation on sublimation; the effect of slope and topography characteristics on snow deposition and sublimation 
  • blowing snow in complex terrain 
  • assimilation of hydro-meteorological data and calibration 
  • integration with remote sensing data 
This research will be implemented in strict collaboration with MobyGIS, EURAC Research (Giacomo Bertoldi, Ph.D), Stephan Gruber,  Professor at Carleton University and with Niccolò Tubini, the Ph.D. student who is actually developing new theory codes for Richards equation and freezing soil. Collaborative and unselfish spirit is required in this research group.
The project has also some practical outcomes that are related to:
  • the avalanche triggering 
  • the water availability due to snow and glaciers melting (both in the short and long terms) 
  • the hydroelectric production 
All the code developed will be done in Github (or similar platform), inside the GEOframe community and will be Open Source according to the GPL v3 license.

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 could be three or four years.

This project can enter either the curriculum C (Environmental Engineering) or the curriculum A (Modelling and Simulation) of our doctoral school.

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

Evaporation and Transpiration

This contains the video related to my interpretation of evapotranspiration.

The thermodynamics of evaporation


Definitions
Transport of vapor in the atmospheric boundary layer
Evaporation from soils

Transpiration



The energy and mass budget 

The Penman-Monteith approach

Tuesday, June 5, 2018

From where do waters arrive

You cannot have an aqueduct if you do not have water to resupply it. So water comes from springs, wells (i.e. from groundwaters) or intakes (surface waters).

Wells
Intakes

Tuesday, May 29, 2018

Do not do statistics if you do not have casual effects in mind

Statistics was believed after the master of the last century to be the science of correlation, not of causation. However it is clear to our contemporary researchers, at least some of them, that interpreting data without any guess about causation can bring to wrong conclusion. Here below, please find an example from: "The book of why: the new science of cause and effect" by Judea Pearl and Dana MacKenzie.
You should first look at the right figure. The scatterplot presents a roughly linear relation between Exercise and Cholesterol in blood. First observation, set this way, we probably have to reverse the axes. In a causal interpretation, it appears that exercise cannot cause cholesterol. On the contrary the cholesterol presence impose to the subjects to exercise more. Or there is something strange in data. More exercise cannot cause, by our normal belief, more cholesterol.
However, this is not actually even the main point. What the right figure suggests is that there is a positive correlation between the two variables: more cholesterol implies more exercise. However, as the left figure reveals, the real situation is not quite true. Because a cause of cholesterol is age, it appears that is reasonable to consider also this variable in the analysis. Then, when we separate the data among ages sets, we can see a further structure in the data and, in each class of age, in fact, the correlation between exercise and cholesterol is reversed. The less you exercise, the higher is your cholesterol. At the same time, the younger you are the less cholesterol you are expected to have in your blood. Now the picture is coherent with our causal expectations. I think there is something to learn.

Wednesday, May 23, 2018

Sunday, May 20, 2018

Graphs, Correlation and Causality

A week ago I started to read “The book of why: the science of cause and effects" by Judea Pearl. (see also his website). This is part of my search for new mathematics for describing entangled hydrological and ecological processes (see also this post and links therein). It is a dissemination book and not intended to grow too technical. However, it arrives the moment when understanding technicalities becomes part of the full understanding, if you do not want to remain a tourist of the new knowledge and become instead an active user of it. Pearl says he dedicated most of his research life to these problems and, therefore, pretending to fill the gap in few weeks is a nonsense.
I require more time to go deeper but presently I have no time. Therefore let me take some annotations here for making easier future efforts. 
 
To go to the details, one can go to the more technical book by Pearl himself, Causality. However, it happened I went to browse some chapters of the very good Shalizi's book on statistics. Chapters from the 20th are a reasonable starting point. Shalizi book itself is not fully explicative but a compromise where some theorems are not deminstrated but assumed and make explicit. Nice enough but requiring in any case the appropriate dedication. Shalizi seems to be a voracious reader, and in the bibliography of his chapter 21, he cites some fundamentals work to put in line for a full understanding the topic. His subsequent chapters also enlarge the vision to information theory, and is connections between the science of causal statistics. Cool. While postponing the study and trying to grasp concepts, I fully report the bibliography I came across here below (mostly from Shalizi's).
All of these are also a good reading for those who believe that data science is a practice which springs out from nowhere.


References


  • Chalak, K., & White, H. (2012). Causality, Conditional Independence, an Graphical Separation in Settable Systems. Neural Computation, 1–60.
  • Cover, Thomas M. and Joy A. Thomas (2006). Elements of Information Theory. New York: John Wiley, 2nd edn.
  • Dinno, A. (2017). An Introduction to the Loop Analysis of Qualitatively Specified Complex Causal Systems (pp. 1–23).
  • Guttorp, Peter (1995). Stochastic Modeling of Scientific Data. London: Chapman and Hall.
  • Jordan, Michael I. (ed.) (1998). Learning in Graphical Models, Dordrecht. Kluwer Academic.
  • Kindermann, Ross and J. Laurie Snell (1980). Markov Random Fields and their Ap- plications. Providence, Rhode Island: American Mathematical Society. URL http://www.ams.org/online_bks/conm1/
  • Lauritzen, S.L., Dawid, A.P., Larsen, B.N., Leimer, H.G. (1990), Independence properties of directed Markov fields, Networks, 20, 491-505
  • Lauritzen, S.L. (1996) Graphical Models. New York: Oxford University Press.
  • Loehlin, John C. (1992). Latent Variable Models: An Introduction to Factor, Path, and Structural Analysis. Hillsdale, New Jersey: Lawrence Erlbaum Associates, 2nd edn.
  • Moran, P. A. P. (1961). “Path Coefficients Reconsidered.” Australian Journal of Statis- tics, 3: 87–93. doi:10.1111/j.1467-842X.1961.tb00314.x.
  • Pearl, J. (2000). Causality- Models, Reasoning, and Inference (pp. 1–386). Cambridge University Press.
  • Wright,S., The Method of Path Coefficients. Annals of Mathematical Statistics 5:161-215.
  • Wysocki, W. (1992). “Mathematical Foundations of Multivariate Path Analysis.” In- ventiones Mathematicae, 21: 387–397. URL https://eudml.org/doc/263277