Efficient uncertainty quantification for impact analysis of human interventions in rivers. (September 2018)

• Record Type: Journal Article
• Authors: Berends, K.D.; Warmink, J.J.; Hulscher, S.J.M.H.
• Is Part Of: Environmental modelling & software. Volume 107(2018)
• Page Start: 50
• Page End: 58
• Publication Date: 2018-09
• Subjects: Impact analysis -- Uncertainty -- Rivers -- River works -- Intervention -- Hydraulic modelling -- Markov chain Monte Carlo; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.05.021
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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%. Highlights: Novel method to estimate uncertainty of the impact of human intervention in rivers. Good agreement with classical Monte Carlo simulation at lower computational cost. Efficiency increases if more interventions are evaluated. Accuracy of uncertainty estimation is explicitly quantified. Method applied to idealised human intervention designed to lower flood risk.