Title: LUISA (Library User Interface for Sensitivity Analysis): a generic software component for sensitivity analysis of bio-physical models
Authors: DONATELLI MarcelloCONFALONIERI RobertoCERRANI IacopoFANCHINI DavideACUTIS MarcoTARANTOLA StefanoBARUTH Bettina
Citation: 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation - ISBN: 978-0-9758400-7-8 p. 859-865
Publisher: Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation
Publication Year: 2009
JRC N°: JRC52258
URI: http://www.mssanz.org.au/modsim09/C3/donatelli_C3b.pdf
http://publications.jrc.ec.europa.eu/repository/handle/JRC52258
Type: Contributions to Conferences
Abstract: Abstract: Sensitivity analysis is crucial to better understand the behavior of models, both for developers and users. Developers can be supported in avoiding overparameterizations and in focusing their attention only in the processes with a significant impact on the output(s) of interest. Model users can benefit of sensitivity analysis by identifying the most relevant parameters in a particular biophysical context and, therefore, in optimizing the available resources for determining their value, through direct measurements or via calibration. When biophysical, deterministic models are run in a stochastic fashion using weather series, and when other inputs of a model vary substantially, the results of sensitivity analysis may differ, suggesting different, site specific strategies, for operational use. The availability of a generic software component able to be integrated in modeling and simulation environments would hence allow the estimate of differences in the behavior of models in different soil-plant-climate-management scenarios. It is possible to classify the methods for sensitivity analysis developed in the last decades in three groups: the one-factor-at-a-time method, the methods based on regression and the variance-based Monte-Carlo methods. The first group is represented by Morris¿ method, which calculates two metrics: the average (µ) and the standard deviation (s) of the population of the incremental ratios according to an opportune generation of a sample of the possible combination of parameters. The most famous methods belonging to the second group are the Latin Hypercube, the Random and the Quasi-random Lp-Tau. They differ in the method used for generating the sample, while are all based on a linear regression between the differences in the output and those in the values of parameters to calculate sensitivity indices. The third group is based on the decomposition of the total variance in summands of increasing dimensionality and it is able to quantify the effect of the interactions among parameters. The methods based on this principle are Fourier Amplitude Sensitivity Test (FAST), Extended FAST, and Sobol¿s. The last group, and in particular the Sobol¿s method, is considered the most powerful and precise in identifying the output sensitivity to the model parameters. Their drawback is the computational cost since they involve the estimation of k-dimensional integrals. On the other hand, the Morris¿ method is the one requiring the smaller sample for ranking the parameters according to their relevance and it is considered particularly suitable for preliminary screenings of models with several parameters. This paper describes the LUISA (Library User Interface for Sensitivity Analysis) component, based on the SimLab (http://simlab.jrc.ec.europa.eu/) C++ DLL. LUISA has been developed in C# under the .NET platform, with the goal of facilitating implementing sensitivity analysis capabilities on bio-physical model frameworks. As illustrative case studies, spatially distributed sensitivity analysis of two different biophysical models were carried out using the MARS database (http://mars.jrc.ec.europa.eu/), in order to cover the pedo-climatic conditions of Europe. The two models used are the WARM model for rice simulations and the generic crop simulator CropSyst. Results are presented and discussed according to the spatial variability of their relevance and to the identification of clusters based on the parameters ranks.
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