I would like to characterize and quantify hydrological partitioning. I would like to detect the impacts of landuse changes on water partitioning but take into considerations small scales of landuse changes (approximately 100 ha; I expect to make this my HRU). I know i could use a semi-distribute model but is there a possibility of applying conceptual model instead??
Dear Pauline,
I am not able to answer your question, because I don't have knowledge about partitioning or semi-distribute model. I hope you can have answers from Prof. Sayama.
Best regards,
Ushiyama
Dear Pauline,
Bear in mind that in a conceptual model, the mass balance in accounted for while the momentum is assumed. In your type of project, I would have gone for a lumped model inside but since you are looking for the effect of small scale landuse changes, you could go for semi-distributed. But at that very smal scale, you need some more thinking.
Before I go into my answer, may I ask, are you planning on doing field measurements or is it purely a numerical experiment?
In any case, you can use a conceptual mode. Loot at the following work: https://elib.uni-stuttgart.de/handle/11682/290
and also
http://www.seibert-space.com/hydro/pdf/Seibert_phd_thesis.pdf
In my opinion though it is not the best approach. I will try to explain:
The overall nature of the model, where a significant number of the parameters do not have a direct physical meaning. Take for example the HBV modell in the work I am refering from Götzinger. In practical terms the effect of landuse changes is reflected through the "alpha" parameter, which is the "exponent of non-linear reservoir". In the reference above "alpha" is basically optimitzed for each land use type in such a way that the sum (in broad terms) of "alpha" for all land uses results in the best match to the observed runoff data. This is basically mathematics at work, not physics. Of course you could interpret the results for "alpha" (see page 33 on Götzinger) in view of hydrological common sense, but at the end you cannot give a definite physical meaning to it.
Given the above, it cannot account for interaction among explanatory variables including spatial interactions, wich to my opinion, could be meaningful for your question at hand and the level of detail you are lookint at.
One more point to consider, at least from the results from Götzinger, is that this conceptual models tend to describe "average" conditions more better than extremes. This is not surprising as such conceptual models imply some sort of lumping. Furthermore, you will have difficulties "guessing" the proper parameter values.
So, if you are trying to carry out some numerical experiment, conceptual models are not the way.
If on the other hand you are trying to do something along the lines of Götzinger, then you may follow a similar approach, but with a lower explanatory power from the model.
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