Abstract. 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.
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