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.

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.

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.

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.

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