Accelerating Bayesian inference in hydrological modeling with a mechanistic emulator. (November 2018)

• Record Type: Journal Article
• Authors: Machac, David; Reichert, Peter; Rieckermann, Jörg; Del Giudice, Dario; Albert, Carlo
• Is Part Of: Environmental modelling & software. Volume 109(2018)
• Page Start: 66
• Page End: 79
• Publication Date: 2018-11
• Subjects: 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.2018.07.016
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: As in many fields of dynamic modeling, the long runtime of hydrological models hinders Bayesian inference of model parameters from data. By replacing a model with an approximation of its output as a function of input and/or parameters, emulation allows us to complete this task by trading-off accuracy for speed. We combine (i) the use of a mechanistic emulator, (ii) low-discrepancy sampling of the parameter space, and (iii) iterative refinement of the design data set, to perform Bayesian inference with a very small design data set constructed with 128 model runs in a parameter space of up to eight dimensions. In our didactic example we use a model implemented with the hydrological simulator SWMM that allows us to compare our inference results against those derived with the full model. This comparison demonstrates that iterative improvements lead to reasonable results with a very small design data set. Highlights: Mechanistic emulation. Design data points selection. Calibration of hydrological models.