Input Forcing with measured data for sensitivity informed particle filter

I was just wondering if is possible to force input measured data (canopy cover data) into AquaCrop (python version) model and then start from that point with new (measured) data. If yes, how? Sorry, I am a kind of newbie to python, just trying to figure it out.

The thing I want to accomplish is like the authors did in this publication, but with matlab:

Thank you for helping!

Hi I can take a look next week. From a quick 5 min look through the article though it looks like they used the measured CC data to calibrate the CGC, CDC and CCx parameters each day of the simulation. So the method of adjusting these (and other) params to minimise the mean squared error between ACs CC output to your measurements should still stand. Though you may want to do this every day of the simulation.

Although you can ‘override’ aquacrops CC with your measurements I don’t really see what the point would be if you don’t also adjust the crop parameters with it.

Automatic calibration is a use case we are developing (and also as a web app so the user won’t have to code it up) so it could be a good opportunity to use your data as a test case. Can chat about this more next week

Tom

Thank you for the answer
The point is to use real field measured data to “correct” the model prediction. Not everyday of simulation but just the days of real field data acquisition. Something like this chart:

How can I override aquacrop CC with my measurements?

If you really want to just overide the CC on specific days with a specific measured value it would have to be done after the canopy_cover function inside solution.py.

But this dosent seem to me to be the right way to assimilate field data to correct the model. Without also adjusting the crop paramaters to account for your measurements, the model will be ‘wrong’ again within one day of you overiding the value.

Tom