Sensitivity analysis and uncertainty quantification for environmental models

Environmental models often involve complex dynamic and spatial inputs and outputs. This raises specific issues when performing uncertainty and sensitivity analyses (SA). Based on applications in flood risk assessment and agro-ecology, we present current research to adapt the methods of variance-based SA to such models. After recalling the basic principles, we propose a metamodelling approach of dynamic models based on a reduced-basis approximation of PDEs and we show how the error on the subsequent sensitivity indices can be quantified. We then present a mix of pragmatic and methodological solutions to perform the SA of a dynamic agro-climatic model with non standard input factors. SA is then applied to a flood risk model with spatially distributed inputs and outputs. Block sensitivity indices are defined and a precise relationship between these indices and their support size is established. Finally, we show how the whole support landscape and its key features can be incorporated in the SA of a spatial model