Keywords
monitoring, optimal experimental design, river water quality modelling, uncertainty analysis
Start Date
1-7-2004 12:00 AM
Abstract
To evaluate the future state of river water in view of actual 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. It is of high importance to modellers to minimise the uncertainty of the model predictions. Therefore different research is needed according to the origin of the uncertainty. If the uncertainty stems from input data uncertainty or from parameter uncertainty, more reliable results can be obtained by performing specific measurement campaigns. To guide these measurement campaigns, an uncertainty analysis can give important information. In this article an overview of different techniques that give valuable information for the reduction of input and parameter uncertainty is given. The practical case study is the river Dender in Flanders, Belgium. First a global sensitivity analysis shows the importance of the different uncertainty sources. Here it is seen that the parameters influence the model results more than the input data. Further an analysis in time and space of the uncertainty bands is performed to find differences in uncertainty between certain periods or places. More measurements are needed during periods or on places with high uncertainty. This research also shows that finding a link between periods with high uncertainty and specific circumstances (climatological, eco-regional, etc…) can help in gathering data for the calibration of submodels (eg. diffuse pollution vs. point pollution). The methods can be used for every variable under study and for all kind of rivers but the conclusions made for the practical case study are only applicable for the Dender.
The Evaluation of Uncertainty Propagation into River Water Quality Predictions to Guide Future Monitoring Campaigns
To evaluate the future state of river water in view of actual 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. It is of high importance to modellers to minimise the uncertainty of the model predictions. Therefore different research is needed according to the origin of the uncertainty. If the uncertainty stems from input data uncertainty or from parameter uncertainty, more reliable results can be obtained by performing specific measurement campaigns. To guide these measurement campaigns, an uncertainty analysis can give important information. In this article an overview of different techniques that give valuable information for the reduction of input and parameter uncertainty is given. The practical case study is the river Dender in Flanders, Belgium. First a global sensitivity analysis shows the importance of the different uncertainty sources. Here it is seen that the parameters influence the model results more than the input data. Further an analysis in time and space of the uncertainty bands is performed to find differences in uncertainty between certain periods or places. More measurements are needed during periods or on places with high uncertainty. This research also shows that finding a link between periods with high uncertainty and specific circumstances (climatological, eco-regional, etc…) can help in gathering data for the calibration of submodels (eg. diffuse pollution vs. point pollution). The methods can be used for every variable under study and for all kind of rivers but the conclusions made for the practical case study are only applicable for the Dender.
