BaHSYM: Parsimonious Bayesian hierarchical model to predict river sediment yield. (September 2020)

• Record Type: Journal Article
• Authors: Zoboli, Ottavia; Hepp, Gerold; Krampe, Jörg; Zessner, Matthias
• Is Part Of: Environmental modelling & software. Volume 131(2020)
• Publication Date: 2020-09
• Subjects: Bayesian hierarchical modelling -- Erosion -- Sediment transport -- River sediment yield -- Spatial prediction -- Temporal prediction -- Cluster analysis; 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.2020.104738
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The prediction and control of river sediment yield (SY) are critical but challenging tasks. Erosion and sediment transfer in river catchments are controlled by different processes, whose relative importance varies in space and time. We thus put forward that SY can be estimated more efficiently by using explicitly the information contained in the similarity within groups. To test this hypothesis, we developed a novel Bayesian hierarchical model, applied it to a sample of heterogeneous river catchments and compared its fixed-effects and mixed-effects performance incorporating different group levels, namely gauges, rivers, basins and clusters of catchments. With a parsimonious linear model consisting of four variables (specific and extreme discharge, elevation and retention coefficient), we achieved good performance criteria in the calibration (NSE: 0.79–0.85) and in the cross-validation for temporal and spatial prediction (NSE: 0.71 and 0.72, respectively). These results support the promising potential of this technique. Highlights: We develop a Bayesian hierarchical framework to model river sediment yield (BaHSYM). For spatial predictions, we combine BaHSYM with clustering of catchments. Robust predictions can be achieved with a parsimonious linear model. The mixed-effects model performs largely better than ordinary linear regression.