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.

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.

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