Lost and (not yet) found

Recently I posted the history of JGrass-NewAGE, and I was wondering where the old material of JGrass-NewAGE version 0 was. All was more than prototypical and quite operative (with maybe some flaws in the science) and  worth to remain  alive. Wasn't the use of OMS etc. looking to some reproducibility and maintainability ?
Finally I recovered in my HD the part of the documentation of that version which can be downloaded from here (unfortunately is in Italian).

The database developed in PostgreSQL/PostGIS referred in the "manual" was found stored in a computer accessible trough the password "idrologia", and we are going to see if it is still usable and extendable. Actually, as apparent from my previous post, most of the components of the project can be considered obsolete, enhanced by those available with JGrass-NewAGE version 1. However, at least one component is still missing, and I hope to be able to get it back on-line: the one integrating the de Saint-Venant Equation in 1-D. It was actually saved in a public repository (so, it should be), but the repository was changed, and not maintained anymore. So people have to look at old backups to get it back.

Lessons learned: 1 - there should be an explicit will to maintain old stuff (quite an obvious conclusion) and not to make it vanish as vapour in summer. 2 - Even if the project was produced as Open Source, if it is not maintained, or at least left in a public site which is maintained unaltered, the effort is meaningless.   Useful stuff can be easily lost. 3 - Data can be lost even easier than software. A repository - Github-like for data should be mandatory. We'll see what we can do.

MeteoIO

As known, we use MeteoIO in our GEOtop 2.0. Despite the fact that we implemented most of the same capabilities inside GEOtop directly. And despite we also reimplemented the same (and in some case more articulate possibilities inside JGrass-NewAGE). Many the reasons: having alternatives to compare is good; it is not possible to keep pace in every subject necessary to built a modeling system, and having someone doing things for you is the essence of the success of a open source project; the JGrass-NewAGE system is not yet at the stage to be interoperable with GEOtop tools. In any case Mathias Bavey and Thomas Egger did an excellent work in documenting MeteoIO with this paper appeared in GMD, one of our journals of election.

The project is open source, well designed, constantly maintained and evolved, in C++. Have a nice reading.

Pfafstetter Numbering and the organisation of river networks

We just have accepted a paper related to the topological ordering we use inside our model JGrass-NewAGE.  This ordering derives is a generalisation of the Pfafstetter algorithm.
Once understood, it can be observed that this Pfafstetter ordering can be used to drive, for instance parallel execution of operations. But this is another story. We actually use to orderly process the basin partition in JGrass-NewAGE modules. The paper, entitled "Digital watershed partition within the NewAGE-Jgrass system" by Formetta et al.  can be downloaded from here.

Related topics are covered in posts of the Horton Machine, and particularly in the book Chapter written early this year for the British Gemorphological society. Abera's blog also contains a related post that can clarify some other issues. 

Using geostatistics to integrate satellite information and modelling on soil moisture

This paper has a long history and explore the idea that geostatistics can be used to integrate satellite information when this is missing. At the same time the whole information is used for assimilated for better driving the Community Land Model. Thank you to Han Xujun for pursuing the publication, when I abandoned any hope, notwithstanding that the paper is a good one.

The paper is entitled: Soil Moisture Estimation by Assimilating L-Band Microwave Brightness Temperature with Geostatistics and Observation Localization, and my co-authors are (in order):
Han Xujun, Xin Li, Rui Jin, and Stefano Endrizzi.  The paper has been accepted by PLOSONE, and you can find the pre-print  here.

Other papers by Xujun are available from his Research Gate Profile.

A little of further bibliography:

Han, X. J., et al. (2014). "Soil moisture and soil properties estimation in the Community Land Model with synthetic brightness temperature observations." Water Resources Research 50(7): 6081-6105.

Han, X. J., et al. (2013). "Joint Assimilation of Surface Temperature and L-Band Microwave Brightness Temperature in Land Data Assimilation." Vadose Zone Journal 12(3).

Han, X., et al. (2012). "Spatial horizontal correlation characteristics in the land data assimilation of soil moisture." Hydrology and Earth System Sciences 16(5): 1349-1363.

Imagine to be a hydrologist and you want to learn Java

Dear * to learn Java: 

you can use my lectures (still being produced): http://abouthydrology.blogspot.it/2013/07/java-for-hydrologists-101.html

Read the books I list in my blog: http://abouthydrology.blogspot.it/2012/12/a-little-java-library-for-beginners.html. Many of them are specifically dedicated to Numerics and Scientific Computation.

Learning Java is very much programming, not just reading. So You have to choose a task and try to perform it. Good experiments should be, creating reusable classes for:

• Reading and writing data to a file
• For a generic function
• For solving an Ordinary differential equation (or a set of them: Lorenz' chaotic equations would be a good exercise indeed

If you do math, sooner or later you to use matrixes. You do not need to reinvent the wheel, even if a standard choice has not been yet emerged. See here.

In general all the Java resources I came across (including this one, are at):  http://abouthydrology.blogspot.it/search/label/Java

Francesco Serafin, master thesis, introduces to various tools and methods that can be used to integrate partial differential equations: http://abouthydrology.blogspot.it/2014/07/patterns-for-application-of-modern.html

The subsequent step is to learn OMS: maybe this can be a little more complicated. Anyway, I way to do this is to start with the 2013 summer school:

http://abouthydrology.blogspot.it/2013/10/the-summer-school-on-object-modelling.html

or you can go and work out the examples you can find at the original OMS site: http://abouthydrology.blogspot.it/search/label/OMS3

or my own information on the topic: http://abouthydrology.blogspot.it/2013/01/object-modelling-system-resources.html

I will be improving the resources all the time. So check them once in a while !

Evapotranspiration parameters in coarse grained modelling

To have a little rehearsal on Evapotranspiration look first at my post on Potential Evapotranspiration. where its estimation with Dalton equation and simplified model, like Penman-Monteith (PM) or Priestley-Taylor (PT) is covered.  We concentrate here on the simplest of the formulas, the PT's one.

Once your get PT alpha_p, you can estimate pET but still you have to introduce a further reduction to get the actual evapotranpiration (aET). The method popularized by the ecohydrology literature (e.g. read Amilcare Porporato here) is to introduce a linear decrease of pET with water storage in the root zone "reservoir".

Both the passage, the determination of pET in the framework of PT and the linear reduction with storage have, in my view, strong drawbacks from the quantitative point of view.
One can get the alphap, but literature show a huge variability. So literature is quite useless to obtain quantitative results, with a decent certainty.
The (linear) decrease of ET with soil moisture requires the determination of at least one additional coefficient. In fact, it is well known that ET has two stage: stage one, when ET is "at the potential rate", independently from the water content up to a critical soil moisture, well below saturation, when ET is depressed, not by increasing suction (the so called Kelvin effect, which is a second order effect) but by the fact that pores at the soil or leaf surface to which water is supplied are more and more far apart (see recent literature by Dani Or and co-workers). This critical soil moisture, at which the second stage ET starts is a further coefficient, and its identification with saturation implies a clear underestimation of ET. It is usually given for granted by my friends ecohydrologists and my master IRI's literature that it can be determined. But I do not have to remind to you all how much elusive it is the definition of the "root zone soil moisture" just to cite a practical aspect of it.
Even if field-fellow-scientists claim to have measured it, I know that who tried in lab of few square meters really struggled to close the water budget budget under very controlled conditions (let's say: personal communications). In nature, as my hero Pete Eagleson teaches, interaction among plants distribution, atmosphere, and rugged terrain makes any of the above coefficient heterogeneous, and the trials to find a rational to all of it, kind of frustrating to my eyes.

Said all of this, let's go back to PT, and you can give a look to the presentation below to know what happens when you coarse-grain your model resolution in time and space.

References

[1] Priestley, C.H.B. and Taylor R. J., On the assessment of surface heat flux and evaporation using large scale parameters, Monthly Weather Review, Vol. 100, No 2, 81-92,1972

[2] - Rigon, R.,  Evapotranspiration  Slides

[3] - Rigon, R.,  Solar Radiation Slides

[4] - Rodriguez-Iturbe, I., Porporato, A., Ridolfi, L., Isham, V., & Cox, D. (1999). Probabilistic modelling of water balance at a point: the role of climate, soil and vegetation. Prooceedings of the Royal Society, 455, 3879–3805

[5] - Rigon R., Bertoldi G e T. M. Over, GEOtop: A distributed hydrological model with coupled water and energy budgets, Vol. 7, No. 3, pages 371-388

[6] Bertoldi G. R. Rigon e T. M. Over, Impact of watershed geomorphic char- acteristics on the energy and water budgets, Vol. 7, No. 3, pages 389-394, 2006

Luca Brocca interview on Research Gate

Luca Brocca recently was very much interviewed for one of his achievements about the use of remote sensing in hydrology. He had this smart idea of obtaining rainfall from soil-moisture data. His SM2RAIN is a simple algorithm for estimating rainfall from soil moisture data that you can find in his web page together with  other interesting stuff:

• MISDc Rainfall-Runoff Model (and MILc)
• Soil Moisture Model
• Neyman-Scott Rectangular Pulse Model
• FARIMA
• Satellite vs in situ soil moisture observations
• Long-term (1989-2012) soil moisture data set

The paper that generate a big wave was:

Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data,  available here. He also had the honour of a Nature Research Highlight mention. All of this deserve mention by itself. However, he was so kind to mention me in this recent Research Gate Interview. Thank you Luca !