Efficient uncertainty quantification for impact analysis of human interventions in rivers

Human interventions to optimise river functions are often contentious, disruptive, and expensive. To analyse the expected impact of an intervention before implementation, decision makers rely on computations with complex physics-based hydraulic models. The outcome of these models is known to be sensitive to uncertain input parameters, but long model runtimes render full probabilistic assessment infeasible with standard computer resources. In this paper we propose an alternative, efficient method for uncertainty quantification for impact analysis that significantly reduces the required number of model runs by using a subsample of a full Monte Carlo ensemble to establish a probabilistic relationship between pre- and post-intervention model outcome. The efficiency of the method depends on the number of interventions, the initial Monte Carlo ensemble size and the desired level of accuracy. For the cases presented here, the computational cost was decreased by 65%.