An uncertainty analysis allow propagating uncertainties in parameters, derived variable initial conditions and data variables through the model in order to obtain a model output uncertainty.

Why you will like this module:

• Easy: Almost no work to set it up, just define the source uncertainties and you are good to go.
• Standard: The well-known Monte Carlo technique is used to propagate the uncertainties.
• LHS: Latin Hypercube Sampling is used to make sure the uncertainty distributions are sampled evenly.

Source Components

Source components are model components (parameters, data variables and/or derived variable initial conditions) which are uncertain and for which the uncertainty will be propagated through the model. Currently two types of uncertainty distributions can be assigned to them: uniform and triangular.

More details in the Uncertainty Analysis User Guide.

Target Components

For each of the samples taken from the source component uncertainty distributions, a model evaluation is performed. After all evaluations are performed, the output uncertainty distribution can be assessed. The PhytoSim Uncertainty Analysis module summarizes the distribution based on the minimum, mean and maximum values and the 5th, 50th (median) and 95th percentiles. The 95th percentile represents the target component value for which 95% of the model evaluations were below and 5% above.

More details in the Uncertainty Analysis User Guide.

Dependencies (modules required by this module):

• PhytoSim Data I/O
• PhytoSim Modelling
• PhytoSim Simulation