Tuesday, October 17, 2017


This post contains the presentation given by Michele Bottazzi. His presentation look forward to dig into the forecasting of transpiration from plants (and evaporation from soils) through concentrated parameters modelling. His findings will have a counterpart in our JGrass-NewAGE system.
The figure illustrate his willing to find a new, modern, way to scale up leaf theories to canopy and landscape. The starting point is one recent work by Schymanski and Or but it will go, hopefully, far beyond it. Click on the Figure to access his presentation.

An ML based meta modelling infrastructure for environmental meodels

This is the presentation Francesco gave for his admission to the third year of Ph.D. studies. He summarizes his work done so far and foresees his work during the next year.
Francesco's work is a keystone of the work in our group, since he sustains most of informatics and pur commitment to OMS3. Besides of this two are his major achievements: the building of the Ne3 infrastructure (an infrastructure inside an infrastructure!)  which allows an enormous flexibility to our modelling, and the new road opened towards modeling discharges through machine learning techniques. But there are other connections he opens that are visible through his talk. Please clisk on the figure to access the presentation.

Sunday, October 15, 2017

A few topics for a Master thesis in Hydrology

After the series about Meledrio I thought that each one of the post actually identifies at least one Thesis topic:

Actually, each one of them could be material for more than one Thesis, depending the direction we want to take. All the Theses topics assume that JGrass-NewAGE is the tool used for investigations.
Actually there are some spinoff of those topics:
  • Using machine learning to set part of model inputs and/or 
  • Doing hydrological modeling with machine learning
  • Preprocessing and treating (via Python or Java) satellite data as input of JGrass-NewAGE (a systematisation of some work made by Wuletawu Abera on Posina cacthment and/or Blue Nile)
  • Implementation of the new version of JGrass-NewAGE on val di Sole
  • Using satellite data, besides geometric features, to extract river networks
  • Snow models intercomparison (GEOtop and those in JGrass-NewAGE, with reference to work done by Stefano Tasin and Gabriele Massera) 
Other to other Hydrological topics:
  • Mars (also here) and planetary Hydrology (with GEOtop or some of its evolutions which account for different temperature ranges and other fluid fluxes)
  • Copying with Evapotranspiration and irrigation at various scales
  • Copying the carbon cycle to the hydrological cycle (either in GEOtop or in JGrass-NewAGE)
Other possible topics regarding water management:
  • Hypothesis on the management of reservoir for optimal water management in river Adige.
  • Managing Urban Waters Complexity
Other possible topics regards, on a more theoretical (mathematical-physical) side:
On the side of informatics:
For who wants to work with us on the Master thesis, the rules to follow are those for Ph.D. students, even if to a minor extent. See here:

Saturday, October 14, 2017

Meledrio, or a simple reflection on hydrological modelling - Part V

Another question related to discharges is, obviously their measure. Is discharge measure correct ? Is the stage-discharge relation reliable ? Why do not give intervals of confidence for the measures ? Yesterday, a colleague of mine, told me. A measure without an error band is not a measure. That is, obviously an issue. But today reflection is on a different question.  We have a record of discharges. It could look like this (forgive me the twisted lines):
Actually, what we imagine is the following:
I.e. we think it is all water. However, a little of reflection should make us think that, a more realistic picture is:
Meaning that part of the discharge volume is actually sediment transported around. This open the issue on how to quantify it. Figure enlighten than during some floods, actually the sediment could be a consistent part of the volume, and, if we are talking of small mountain catchments like Meledrio, it could be the major part of the discharge. Hydraulics and sediment transport, so far, was used separately from hydrology and hydrology separated from sediment transport, but what people see is both of them (water and sediment).
This actually could be not enough. The real picture could be, actually like this:
Where we have some darker water. The mass transport phenomena, in fact, could affect part of the basin during intense storms, but the liquid water could not be able to sustain all this transport. Aronne Armanini suggested to me that, in that case, debris flow can start and be stopped somewhere inside of the basin. Te water content they have, instead, could be equally likely released to the streams and boosting furthermore the flood.  Isn't it interesting ? Who said that modeling discharges is an assessed problem ?

Friday, October 13, 2017

Meledrio, or a simple reflection on hydrological modelling - Part IV

An issue that often is risen is about the complexity of models. Assuming the same Meledrio basin, which is the model we can think to be the simpler for getting quantitatively the water budget ?
The null-null hypothesis model is obviously using the past averages to get the future. Operatively:
  • Get precipitation and discharge 
  • Precipitation is  separated by temperature (T) in rainfall (T>0) and snowfall. Satellite data can be used for the separation. 
  • Take their average (maybe monthly average)
  • Take their difference. 
  • Assume that the difference is  50% recharge and 50% ET

My null hypothesis is the following. I kept it simple but not too simple:
  • Precipitation, discharge and temperature are the measured data
  • Their time series are split into 3 parts (one for calibration, one for selection, see below, and one for validation)
  • Precipitation is measured and separated by temperature (T) in rainfall (T>0) and snowfall (T<0). Satellite data can be used alternatively for the separation. These variable can be made spatial by using a Kriging (or
  • Infiltration is estimated by SCS-CN method. SCS parameters  interval are set according to soil cover, by distinguishing it in qualitatively 4 classes of CN (high infiltrability, medium high, medium low, low). In each subregion, identified by soil cover, CN is let vary in the range allowed by its classification. Soil needs to have a maximum storage capacity (see also ET below). Once this has been exceeded water goes to runoff. 
  • Discharge is modeled as a set of parallel linear reservoirs. One for HRU (Hydrologic Response Unit). 
  • Total discharge is simply the summation of all the discharges of the HRUs.
  • CN and mean residence time (the parameter in linear reservoirs) are calibrated to reproduce total discharge (so a calibrator must be available)
  • A set of optimal parameters is selected, let say the most 1% best performing
  • Among those the best performing, the one 1%  performing against selection phase data  are kept (at least 10^4 values then, but possibly at least 10^5).
  • Precipitation that does not infiltrates is separated into evapotranspiration, ET, and recharge.
  •  ET is estimated with Priestly-Taylor (so you need an estimator for radiation) corrected by a stress factor, linearly proportional to the water storage content. PT alpha coefficient is taken at its standard value, i.e 1.28
  • What is not ET is recharge.  Please notice that there is a feedback between recharge and ET because of the stress factor. 
  • If present, snow is modeled through Regina Hock model (paper here), in case, calibrated trough MODIS.
The Petri Net representation of the model (no snow) can be figured out to be as follows:

The setup this model, therefore is not so simple, indeed, but not overwhelmingly complicate.

Any other model has to do better than this. If successful, it become hp 1. 
A related question is how we measure goodness of fitting and if we can distinguish the performances of one model from another one. That is, obviously, another issue.

Thursday, October 12, 2017

Meledrio, or a simple reflection on hydrological modelling - Part III

Well, this is not exactly Meledrio.  It starts a little downstream of it. In fact, we do not have discharge data in Meledrio (so far) and we want to anchor our analysis to something measured. So we have a gauge station in Malè. A gauge station for who does not know it, measure just water levels (stages) and them convert to water discharge through a stage-discharge relation (see USGS here). Anyway, a sample signal is here:
The orange lines represent discharge simulated with one of our models (uncalibrated at this stage). The blue line is the measured discharge (meaning the measured stage after having applied an unknown stage-discharge relationship, because the guys who should did not gave us it). But look at little more closer:
We could have provided a better zooming, however, the argument of discussion is: what the hell is all that noise in the measured signal ? It is natural ? It is error of measurements ? Is due to some human action ? 
Having a better zoom, one could see that that signal is almost a square wave going up and in few hours, and therefore the suspected cause are humans. 
Next question: how can we calibrate the model that does not have this unknown action inside to reproduced the measured signal ?
Clearly the question is ill-posed and we should work the other way around. Can we filter out in the measured signal the effect of humans ?
Hints: we could try to analyze the measured signal first. Analyzing actually could mean, in this case, to decompose it, for instance in Fourier series or Wavelets and wipe away the square signal (a hint in hints), reproducing an "undisturbed signal" to cope with. 
Then we could probably calibrate the the model to the cleaned data. Ah! You do not know what calibration means ? This is another story.

P.S. - This is actually part of a more general problem, which is measurement treatments. Often we, naively, treat them as true values. Instead they are not and should pre-analyzed for consistency and validate before. MeteoIO is a tool that answers to part of the requests. But, for instance, it does not treat the specific question above.

Wednesday, October 11, 2017

Meledrio, or a simple reflection on hydrological modelling - Part II

In the previous studies made on the hydrology of Meledrio some ancillary data are produced. For instance:

Soil Use

Usually also other maps are produced, for instance soil cover (which, in principle, could be different from soil use).  The problem I have is that, usually, I do not know what to do with these data.  There are actually two questions related to maps of such kind.
  • The first is,  are these characteristics are part of the model (see, for instance, the previous post)?. 
  • The second is, if the models somewhat contains a quantity, or a parameter,  that can be affected by the mapped characteristics, but the is not directly the characteristic,  how the parameter can be deduced ? In other words there is a (statistical) method to relate soil use to models parameters ?  
I confess that the only systematic trial to obtain this type of inference that I know are the pedotransfer functions. Whilst the concept could be exported to more general models' attributes, however they refer to very specific models that contains hydraulic conductivity or porosity as a parameter and not to other models, for instance those based on reservoirs, where hydraulic conductivity usually is not explicitly present.
Another typology of sub-models where something similar exists is the SCS-CN model.  Specific models, sometimes can contain specific conversion tables produced either by Authors than practictioners (SWAT, for instance).  In SCS-CN, the tables of soil categories are associated with values of the Curve Number parameters, and people pretend to believe that the association is reliable. But it is fiction not science.
In a time when reviewers say that modelling discharges is not enough to assess the validity of a hydrological model, at the same time they allows holes in the peer review process where papers make an unscrupulous use of the same concept.  
There is actually a whole new science branch, hydropedology, that seems devoted to the task to transform maps of soil properties into significant hydrological numbers (mine is the brutal interpretation of it, obviously hydropedology has the scope to understand, not only to predict), and I add below some relevant reference.  However, the analysis are fine and interesting food to thoughts, but the practical matter is still scanty. Probably for two facts: because normal statistical inference is not enough sophisticated to obtain important results (beyond pedotransfer functions) and because (reservoir type of) models have parameters that are too much involved to be interpreted as a simple function of a mapped characteristics. An opportunity for machine learning techniques ?


Lin, H., Bouma, J., Pachepsky, Y., Western, A., Thompson, J., van Genuchten, R., et al. (2006). Hydropedology: Synergistic integration of pedology and hydrology. Water Resources Research, 42(5), 2509–13. http://doi.org/10.1029/2005WR004085

Pacechepsky, Y. A., Smettem, K. R. J., Vanderborght, J., Herbst, M., Vereecken, H., & Wösten, J. (2004). Reality and fiction of models and data in soil hydrology (pp. 1–30).

Vereecken, H., Schenpf, A., Hoopmans, J. V., Javaux, M., Or, D., Roose, J., et al. (2016, May 13). Modeling Soil Processes: Review, Key Challenges, and New Perspectives. http://doi.org/10.2136/vzj2015.09.0131

Vereecken, H., Weynants, M., Javaux, M., Pachepsky, Y., Schaap, M. G., & Genuchten, M. T. V. (2010). Using Pedotransfer Functions to Estimate the van Genuchten–Mualem Soil Hydraulic Properties: A Review. Vadose Zone Journal, 9(4), 795–27. http://doi.org/10.2136/vzj2010.0045

Terribile, F., Coppola, A., Langella, G., Martina, M., & Basile, A. (2011). Potential and limitations of using soil mapping information to understand landscape hydrology. Hydrology and Earth System Sciences, 15(12), 3895–3933. http://doi.org/10.5194/hess-15-3895-2011