  This paper describes a novel sensitivity analysis method, able to handle
dependency relationships between model parameters. The starting point is the
popular Morris (1991) algorithm, which was initially devised under the
assumption of parameter independence. This important limitation is tackled by
allowing the user to incorporate dependency information through a copula. The
set of model runs obtained using latin hypercube sampling, are then used for
deriving appropriate sensitivity measures.
  Delft3D-WAQ (Deltares, 2010) is a sediment transport model with strong
correlations between input parameters. Despite this, the parameter ranking
obtained with the newly proposed method is in accordance with the knowledge
obtained from expert judgment. However, under the same conditions, the classic
Morris method elicits its results from model runs which break the assumptions
of the underlying physical processes. This leads to the conclusion that the
proposed extension is superior to the classic Morris algorithm and can
accommodate a wide range of use cases.
