A discrete Bayesian network to investigate suspended sediment concentrations in an Alpine proglacial zone

In this study, a Bayesian Network (BN) is used to model the suspended sediment concentrations (SSC) in the catchments of the glaciers Noir and Blanc in the Ecrins National Park, France, and at the distal end of the proglacial zone into which both torrents drain. Relationships between air temperature, glacier discharge and SSC are represented as random variables; thereby taking the natural next step from proposed modified rating curve methods which increasingly approximate random variable approaches. Hydrological relationships are propagated through the network via conditional probability distributions computed from 980 field records obtained at three monitoring sites during July 2005. Rainfall affected data are removed from the modelling process. A two-sample Kolmogorov–Smirnov goodness-of-fit (two-sample KS) test (n = 5) shows good agreement between the probability distributions of SSC predicted by the BN, and those recorded in the field at the outflow of the proglacial zone over an air temperature range of 5–25 °C. The BN performs poorly for air temperatures between 25 and 30 °C and this is attributed to limited field records covering this temperature range. Discussion of the significant limitations surrounding the widespread application of BNs in hydrological modelling are offered with a focus on data volume and temporal limitations. Copyright © 2008 John Wiley & Sons, Ltd.