An Integrated, Fast and Easily Useable Software Toolbox Allowing Comparative and Complementary Application of Various Parameter Sensitivity Analysis Methods

Abstract: The analysis of parameter sensitivity in environmental models is an excellent technique to assess a model’s behavior, to determine its potential utility, to support its calibration, and to identify areas of improvement. Recent work on comparing sensitivity analysis methods shows that the methods available today are complementary, i.e. multi-ple methods should be used to assess a model. We present a software toolbox for global sensitivity analysis which supports the investigation of parameter sensitivity using differ-ent methods. The toolbox includes Regional Sensitivity Analysis, Morris Method, and a Sobols method. The majority of these methods require input data from a Monte-Carlo-Sampling which has to be carried out in advance, others demand for special properties of the sampling. Therefore, in most cases, huge computational effort has to be spent to generate several sampling data. To overcome this deficit the data from a single Monte-Carlo-Sampling is used to train an Artificial Neural Network (ANN) which imitates the original model. By using this approach, arbitrary samplings can be easily drawn from the ANN-based emulator. This approach also gives an objective measure of the quality of the sampling itself and provides criteria on how many samples are required to ge