Performs and prints results of parameter identifiability analysis [Doherty and Hunt, J. Hydrology, 366, 119-127, 2009]. Parameter identifiability is a measure that can be used to rank input parameters in terms of their relative identifiability based on a calibration dataset. Identifiability is defined as the capability of model calibration to constrain parameters used by a model. It requires that the sensitivity of each model parameter be calculated for each observation. Singular value decomposition (SVD) of the weighted sensitivity matrix is then undertaken to quantify the relation between the parameters and observations that, in turn, allows selection of calibration solution and null spaces spanned by unit orthogonal vectors. Parameter identifiability is quantitatively defined as the direction cosine between a parameter and its projection onto the calibration solution space. This varies between zero and one, with zero indicating complete non-identifiability and one indicating complete identifiability.
>>> print IDENTIFIABILITY matrices