A copula-based sensitivity analysis method and its application to a North Sea sediment transport model

TL;DR

This paper introduces a copula-based sensitivity analysis method that extends the Morris algorithm to handle dependent parameters, demonstrated on a sediment transport model with correlated inputs, showing improved accuracy over the classic approach.

Contribution

The paper presents a novel extension of the Morris sensitivity analysis method that incorporates dependency information via copulas, addressing a key limitation of the original algorithm.

Findings

The new method aligns well with expert judgment on parameter importance.

It outperforms the classic Morris method when parameters are dependent.

The approach is applicable to complex models with correlated inputs.

Abstract

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.