Showing posts with label Rainfall-Runoff. Show all posts
Showing posts with label Rainfall-Runoff. Show all posts

Monday, June 8, 2020

Concentration time, if existent, is a statistical concept


Among the various times we use in describing the catchment, concentration time is one of them. It is referred, in the old textbooks, as the largest travel time of water parcels (i.e. statistically significant amount of water molecules that are though to move together) in a catchment. Travel time, in turn is the time a parcel of water employs to across the catchment from its injection (as rainfall) to its exit (as part of discharge). The Figure 1 below illustrate two parcels with different travel times, with parcel 1 arriving faster to the outlet, for being close to it.
The concept of concentration time gained its importance since the Mulvaney theory of "the rational method" reported, for instance,  in K. Beven book (2012). For giving a meaning to it, we can assume that,  if parcels are though to move with constant velocity in a catchment, then, once their distance from the outlet along the drainage directions (see the width function concept) is known,  travel times is obtained by dividing  that distance by the parcels’ velocity.
Rigon et al., 2016 gives a review of this concept in the framework of the geomorphological unit hydrograph based on the width function (or WFIUH). The oldest hydrologists would also remind a simplified version of the story, where, essentially the catchment is seen as a rectangular planar hillslope and the flow is though to be parallel as in Figure 2 below.
Parcels move in essentially rectilinear paths, with constant velocity. Parcels like the no  2 are on the divide and parcel like the no 1 very close to the outlet, that is in Figure 1 a sort of trench. In this case, varying the duration of precipitations, we obtain a hydrograph  which is a triangle or a trapeze. It can be demonstrated that when a rain of constant fixed intensity falls on this catchment, we obtain the maximum discharge possibile when its duration equals the parcels no2 travel time, the largest one. Continuing to argue about models, not about what happens in reality, it can be seen also that, from the point of view of the instantaneous unit hydrograph theory (IUH),  concentration time is the extension of the domain of definition of the IUH distribution function ($t_c$  in Figure 3). 
Unfortunately, most of IUHs do not have a finite domain but an infinite one, the simplest being probably the exponential IUH $$IUH(t;\lambda) = \frac{1}{\lambda} e^{-t/\lambda}$$ (see also Rigon et al., 2011). This implies that for most IUHs, the concentration time does not exist as a rigorous concept.   Besides, the dynamics of water parcels as depicted in simplified theories was completely screwed up by tracers experiments that have determined that the age of water in floods is very much larger than believed, and usually what we see in rivers and torrents is old water not the one just fallen during the last precipitation (though undoubtedly was the rainfall to trigger it). 
The concept of concentration time,  resists in operational hydrology because there is a certain evidence that floods are generated by precipitations of increasing duration with increasing basins area,  and this correlates with the idea of concentration time exposed above for the planar hillslope.  However,  in complex catchments, it cannot be something different from a statistical concept. We already mentioned briefly that a catchment is not a huge planar hillslope and that water parcels move in complicate ways through it.  Moreover, the expansion of the river networks during storms (e.g. Durighetto et al.,  2020)  implies the necessity to add a further dynamic to concentration times perceptual model.

After all the above considerations, if something like the concentration time exists, it is a characteristic statistical time which identified the duration of the rainfalls that generate the  largest peak discharges. It should depend on catchment size and topology (besides on the rainfall). We believe that it increases with catchment size, but being any catchment different, it remains a slippery concept. A solid statistical study would be required to clarify, once for all, the issue.

References



Monday, January 20, 2020

Video Lectures on Hydrology

I am collecting here my video lectures on Hydrology (in my broken English). These are mostly part of the two GEOframe Winter School held in 2019 and 2020 and from a Summer School on Landslides made a few years ago. Since video lectures on these topics are uncommon, I think it is useful to index them.  I also invite anyone who has similar contributions to share them. I will be happy to add them to my list here.

Here they are below subdivided by arguments with their companion slides:

Catchments Delineation and Geomorphometry

Data Interpolation with Kriging 

Richards equation
Radiation for Hydrologists
Evaporation and Transpiration
Hydrological Dynamical Systems  (a.k.a. lumped hydrological rainfall-runoff models) 
Other resources

  • Other Videos, that I am providing for my Hydrological Modelling Class are here.
  • Video collected by Kevin McGuire (GS) are here.


If you do not want to be just a tourist, you can go deeper and exercise with  Jupyter lab and GEOframe. For the latter, please see the material of the GWS2020. To anyone requesting, I can provide the original slides.

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 2 parts (one for calibration 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.
  • 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
Geo-lithology-Lithology

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 ?

References

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

Tuesday, October 10, 2017

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

The problem is well explained by the following figure, which represents the statistics of slopes in Meledrio basin.
The overall distribution is bimodal, that make us to suspect that something was going on. In fact, this below is the Google view of the basin.
It clearly show that the hydrographical right side of the basin (on the left in figure) is the one that has steeper slopes, and the left side the one that has the lower ones. This is definitely shown by the slope map
(Please observe that the map is reversed with respect the Google view, since there we were looking to the basin from North). Different slopes, would be associated in our mind with different runoff and subsurface water velocities. This would clearly be accounted for in a model like GEOtop but not (at least explicitly) by a system of reservoirs, especially when we calibrate all the reservoirs all together. A possible partition of the basin in the Jgrass-NewAGE system is represented below
Because the single Hydrologic Response Units are mostly on one side of the catchment, they could be said to be in a area which is homogeneous from the point of view of slope statistics. Therefore, when we treat it as a collection of reservoirs, in principle we could parameterise them differently, according to their slope. In practice, however, we do not have enough measurements to be able to do this separate calibration and we look at the basin homogeneously.  Are we not missing something ?
Well, we are. The first thinking would be to try to add to our reservoir the knowledge gained from geomorphology, and assume that the mean travel time, or some relevant parameter connected to it, depends proportionally to (mean) slope (or some of its power) and inversely to the distance water has to across to get out of the HRU. This is obviously possible, and maybe we could easily try it.
In general, however,  hydrologists who are not stupid, do not care of it. Why ? The reasons can be that our assumption that slopes count is blurred by the heterogeneity of the other factors that concur to form the hydrologic response. However, the magnitude of the heterogeneity can be different at different scales and could be really nice to do some investigations in this direction.

Thursday, March 30, 2017

Modelling discharge in an Alpine basin with JGrass-NewAGE

This and a related post reports about the Master thesis by Niccolò Tubini and Stefano Tasin. It was a couple years ago that I graduate my last Master guy, and I am happy with these two graduations.
Stefano thesis is in Italian. So I am summarising it a little bit below.
JGrass-NewAGE has a a snow module that was developed by Giuseppe Formetta (GS). Giuseppe developed also a component called  Adige-Hymod for runoff estimation. The two were not tested conjointly (well, they were), and we would like to have a new case to understand more about the behaviour of the model and sharpen the methods we use with it.
 
Stefano did it, making leverage on the NewAGE database of river Adige and using, side by side with NewAGE, GEOtop as the true to reproduce in matter of snow. Other directions could have taken, but Stefano chose this one with excellent results. He had in mind a relatively small basin in the Norther part of Italy that was known to be dominated by snow (and glacier melt) and he wanted to investigate how much of discharge depends upon snow melting. The figure above is one of his results, which shows an excellent discharge fitting and quite impressive demonstration of how snowmelt counts in this case. Thinking that snow on the Alps is going to almost disappear cause the climate change, the basin will go to a quite large change in the discharge regime. It is foreseeable that winter discharge will grow in place of the summer ones, with possible modifications of the discharges distributions.
The thesis and the simulations files used are here.