Optimisation and sensitivity analysis

A majority of responses (73%, Figure 14) show an interest in having a powerful calibration (inverse modelling) tool to:

optimise parameters;
quantify the uncertainties in the predictions;
determine the key parameters that may vary during time (e.g. during dry or wet periods).

Automatic calibration tool boxes, which find the optimal parameter sets, are also important for users (Figure 15) and therefore should be implemented in the hydrological modelling platform. There is also an interest in future use of the Bayesian and the PEST tool boxes, while a few participants suggested alternative inverse modelling tools.

A participant responded: "There is no answer to ALL modelling problems. This is very much dependent on the model aims and the nature of the model and the optimisation problem (non-linearity, objectives, calibration data, dimensionality, run times etc.). There is no plug-and-play model calibration/ uncertainty estimation software. PEST works well for some (linear) groundwater problems, but a higher degree of expertise is required for more complex calibration problems, where it might fail altogether."