Sensitivity analysis of a sensitivity analysis: We are likely overlooking the impact of distributional assumptions

•We propose a method to quantify the impact of parameter distributions on SA results.•We generated parameter distributions by resampling literature data.•SA results were largely affected by parameter distributions (ranks overturned).•We demonstrated how uncertainty in distributions could be crucial for SA studies. Although uncertainty in input factor distributions is known to affect sensitivity analysis (SA) results, a standard procedure to quantify its impact is not available. We addressed this problem by performing a SA (generating sample of parameter distributions) of a SA (generating samples of parameter values for each generated distribution) of the WARM rice model using the Sobol’ method. The sample of distributions was generated using distributions of jackknife statistics calculated on literature values. This allowed mimicking the differences in distributions that could derive from different selection of literature sources. Despite the very low plasticity of WARM, the ranks of the two most relevant parameters was overturned in 22% of the cases and, in general, differed from what achieved in earlier SAs performed on the same model under similar conditions. SA results were mainly affected by uncertainty in distribution of parameters involved in non-linear effects or interacting with others. The procedure identified parameters whose uncertainty in distribution can alter SA results, i.e., parameters whose distributions could need to be refined.