Environmental Modelling & Software 22 (2007) 725e732
www.elsevier.com/locate/envsoft
Evaluation of uncertainty propagation into river water quality
predictions to guide future monitoring campaigns
V. Vandenberghe a,*, W. Bauwens b, P.A. Vanrolleghem a
a Ghent University, Department of Applied Mathematics, Biometrics and Process Control, BIOMATH Coupure Links 653, B-9000 Ghent, Belgium
b Free University of Brussels, Laboratory of Hydrology and Hydraulic Engineering, Pleinlaan 2, B-1050 Brussels, Belgium
Received 24 October 2005; received in revised form 15 December 2005; accepted 15 December 2005
Available online 3 April 2006
Abstract
To evaluate the future state of river water in view of actual pollution loading or different management options, water quality models are
a useful tool. However, the uncertainty on the model predictions is sometimes too high to draw proper conclusions. Because of the complexity
of process based river water quality models, it is best to investigate this problem according to the origin of the uncertainty. If the uncertainty
stems from input data or parameter uncertainty, more reliable results are obtained by performing specific measurement campaigns. The aim of
the research reported in this paper is to guide these measurement campaigns based on an uncertainty analysis. The practical case study is the
river Dender in Flanders, Belgium.
First an overview of different techniques that give valuable information for the reduction of input and parameter uncertainty is given. A global
sensitivity analysis shows the importance of the different uncertainty sources. Further an analysis of the uncertainty bands is performed to find
differences in uncertainty between certain periods or locations. This shows that the link between periods with high uncertainty and specific
circumstances (climatological, eco-regional, etc.) can help in gathering data for the calibration of submodels (e.g. diffuse pollution vs. point
pollution).
2006 Elsevier Ltd. All rights reserved.
Keywords: Monitoring; Optimal ex