HR: 17:15h
AN: H14A-06    [Abstracts]
TI: Parameter Estimation for a Physically-Based Model Using Multi-Objective Approach Constrained With Additional Internal States
AU: * Zhang, G
EM: gzhang@tudelft.nl
AF: Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, Delft, 2628 CN, Netherlands
AU: * Zhang, G
EM: gzhang@tudelft.nl
AF: Unit Land and Water, DHV B.V., Laan 1914 nr.35, Amersfoort, 3818 EX, Netherlands
AU: Fenicia, F
EM: fenicia@lippmann.lu
AF: Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, Delft, 2628 CN, Netherlands
AU: Fenicia, F
EM: fenicia@lippmann.lu
AF: Public Research Center–Gabriel Lippmann, 41 Rue du Brill, Belvaux, L-4422, Luxembourg
AU: Savenije, H H
EM: h.h.g.savenije@tudelft.nl
AF: Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, Delft, 2628 CN, Netherlands
AB: Parameter estimation (i.e. model calibration) is a critical procedure for not only determining a successful model application, but also assessing model uncertainties, thus helping improve model development. Physically-based distributed hydrological models are increasingly used as required in water management because of the complexity of the processes to be represented. Given the fact that the distributed catchment characteristics, represented by parameters, could not be directly measured in most cases, model calibration is therefore inevitable. Calibration and uncertainty assessment for such complex models are more challenging than for those simpler ones due to the large number of parameters associated with integrated multiple processes description and large computing resources demands. There is ample literature on the approaches to model parameter estimation and applications of such approaches. Multi-objective Pareto-optimality approaches, such as MOSCEM-UA, are amongst the state-the-art approaches in modeling practices. The multi-objective optimization approaches, however, have not yet widely applied to physically-based distributed models, due to the aforementioned challenging issues. This work presents an application of MOSCEM-UA algorithm to a newly developed physically-based model REWASH. REWASH is a model based on the Representative Elementary Watershed (REW) concept that describes hydrological processes at the watershed scale, using the basic physical conservation laws. The elementary watersheds, i.e. the sub-watersheds are the hydrological response units of a catchment when using REWASH model. In this study, REWASH model was applied to simulate rainfall-runoff relation for the Hesperange catchment in Luxembourg. Due to its physically-based and semi-distributed nature, the applied hydrological model reproduces not only stream flows at the catchment outlet and the sub-watersheds' outlets, but subsurface flows and groundwater table variations as well. Therefore, in addition to the use of stream flow measurements for model calibration and uncertainty analysis, groundwater table gauging data were also used to help constrain parameter space. In model parameter identification, objective functions in favor of both high flows and low flows were employed to optimize the model performance. Results of this study show that parameters for subsurface processes are better identifiable than those for surface processes. This work also demonstrates that MOSCEM-UA is an efficient tool in parameter identification giving more insight to the structural behavior of the model.
DE: 1804 Catchment
DE: 1816 Estimation and forecasting
DE: 1846 Model calibration (3333)
DE: 1847 Modeling
DE: 1879 Watershed
SC: Hydrology [H]
MN: 2007 Fall Meeting