HR: 11:40h
AN: H12C-06    [Abstracts]
TI: Sensitivity Analysis to Identify `Soft Data' for the Evaluation of a River Water Quality Model
AU: * Vandenberghe, V
EM: veronique.vandenberghe@biomath.ugent.be
AF: Ghent University, Department of Applied Mathematics, Biometrics and Process Control, BIOMATH, Coupure Links, 653, Ghent, 9000 Belgium
AU: Bauwens, W
EM: willy.bauwens@vub.ac.be
AF: Free University of Brussels, Laboratory of Hydrology and Hydraulic Engineering, Pleinlaan, 2, 1050, Brussels Belgium
AU: Vanrolleghem, P A
EM: peter.vanrolleghem@biomath.ugent.be
AF: Ghent University, Department of Applied Mathematics, Biometrics and Process Control, BIOMATH, Coupure Links, 653, Ghent, 9000 Belgium
AB: A sensitivity analysis is performed to identify the parameters of a river water quality model that have the most influence on the model outputs. Results of a sensitivity analysis provide guidelines about how parameter uncertainty will affect the model output, but always relate to the specific circumstances under which the model was build and calibrated. If the model has to be applied on a river with different characteristics, again an extended dataset is needed to identify the important parameters of the model and the associated uncertainty levels. If uncertainty and characteristics of the river basin can be linked in advance, this could open perspectives for model applications in ungauged basins. The aim of this research is to examine this link by testing the sensitivity of a river water quality model to the a priori assumption of parameter values. In non-linear models, the propagation of uncertainty in a particular parameter depends on several factors, such as the values of the other model parameters and the specific conditions. The values of parameters refer in most cases to specific circumstances. For example, a river with high algae blooms during summer periods will have its parameters of the algae growth model adapted to the growing species when calibrated. The presented analysis can reveal important information about the uncertainty propagation for situations were no or poor measurements are available. Indeed, if general clusters can be found of cases in which some parameters are more sensitive than others, then this information can be used as 'soft data' to identify when certain parameters become more important than others. Once the important parameters are known, optimal experimental design techniques can be used to determine the optimal measurement strategy that allows a better identification of these parameters before calibrating the model. Here, a water quality model of the river Dender implemented in the ESWAT simulator, is used as an application of the described methodology on a real case study. Latin Hypercube Sampling around nominal values is applied, with ranking of the parameter uncertainty after a multi-linear regression. This sampling is repeated with different nominal values for the parameters within realistic ranges. By grouping the model simulations on the basis of water quality variables exceeding certain levels during some periods of the year, clusters are formed. Finally, the parameter values of those clusters are evaluated in relation to external circumstances.
DE: 1860 Runoff and streamflow
DE: 1871 Surface water quality
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting