Calibration/Validation in Mohid Land

Before you start

Model lack of accurancy usually is simpton of three things:

- or model does not describe fully the system physics/relations

- or model data provided (initial or boundary) is not good enough to describe its state or variability (spatially or temporally)

- or both,

If we agree that Mohid Land model has the main physical processes that describe watershed hydrodynamics than one should have in mind that model results accurancy will suffer from the given data difference to reality. And those data consist of:

- Digital Terrain model that will affect land and river slopes, drainage direction and accumulation, etc

- Land Use map that affects vegetation growing, runoff resistance to flow, impermeabilization etc

- Soil Map and van Genuchten parameters that affect porous media flow, infiltration, etc

- Rain data that is the main driver of the water cycle and can have high spatial and temporal variation and usually prone to errors (collection errors, publication errors, data manipulation errors) that may impact severely the results.

In the next chapters it will be addressed the calibration/validation method that should be used, assuming that in terms of DTM, soil and land use map the user is using the best available data.

Step 1 - Validate your rain

Since rain is the main driver of the water cycle, data gaps or data errors will compromise your results. For example if you are looking to simulate floods there can not be a flood in the model if it does not rain in that period, or model will generate really high floods if abnormally high rain is inputed.

It is very important that the rain used for feeding the model is the best one. And the best is the rain that represents the rain regime and events occured in the watershed. With that said it is important that if the watershed has areas with higher rain intensity or lower that they are represented by rain stations so that the difference will be taken in account by the model.

So What stations to use and how to trust in your data?

The first things to do is to look at rain stations. Collect all the data in the watershed but also in neighbour watersheds and do annual and monthly accumulations of data with the number of sampling days per month and per year. When graphing the accumulated annual and monthly precipitations, the stations (or periods) with bad data will pop up trough low sampling days or curves in graphs that are not correlated to other stations.

In annual accumulation data, stations should be correlated and the highest or lowest rain stations should maintain the ranking. In annual accumulated rain graphs also is possible to see the areas with higher rain intensities and lower and the number of stations that need to use in order to capture the spectrum of precipitation regimes. It is also a good help to use maps of annual precipitations to check the consistency of the data or the lack of a precipitation station for a high or low precipitation regime; in the case of lacking stations inside the watershed to represent the precipitation regimes it should be taken from a station outside the watershed.

So removing stations with uncorrelated or inconsistent data and using the stations that are consistent between each other and represent the precipitation regimes is the method right to select precipitation data.

In the case that looking for fast floods than additionaly the precipitation stations should have sub-daily data and rain events should occur at the same period of the recorded flow or level, if not the model obviously will not be able to represent the floods.

Step 2 - Validate evapotranspiration or river flow in long term simulation

After the rain is consistent and representative of the precipitation regime(s) in the watershed it is needed to validate the long term water fluxes and the weight of evapotanspiration.

If actual evapotranspiration data is available (e.g. EO data) than the comparison should be done directly to that data.

Things to check:

Reference Evapotranspiration is computed from atmosphere properties and should be checked with other sources of data.

Calibration parameters:

Kc - relates reference evapotranspiration (from alfafa) to crop transpiration (specific of crop and plant density)

Feddes head limits - represent the crop stress to water contents