Variance-based Sobol' sensitivity is one of the most well known measures in global sensitivity analysis (GSA). However, uncertainties with certain distributions, such as highly skewed distributions or those with a heavy tail, cannot be adequately characterised using the second central moment only. Entropy-based GSA can consider the entire probability density function, but its application has been limited because it is difficult to estimate. Here we present a novel derivative-based upper bound for conditional entropies, to efficiently rank uncertain variables and to work as a proxy for entropy-based total effect indices. To overcome the non-desirable issue of negativity for differential entropies as sensitivity indices, we discuss an exponentiation of the total effect entropy and its proxy. We found that the proposed new entropy proxy is equivalent to the proxy for variance-based GSA for linear functions with Gaussian inputs, but outperforms the latter for a river flood physics model with 8 inputs of different distributions. We expect the new entropy proxy to increase the variable screening power of derivative-based GSA and to complement Sobol'-index proxy for a more diverse type of distributions.

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