HR: 11:25h
AN: H12C-05    [Abstracts]
TI: AN EVALUATION OF MODEL STRUCTURE UNCERTAINTY EFFECTS FOR HYDROLOGICAL SIMULATION
AU: Payne, J
EM: jtpayne@n-h-i-org
AF: Natural Heritage Institute, 926 J Street #501, Sacramento, CA 95816 United States
AU: * Butts, M
EM: mib@dhi.dk
AF: DHI Water & Environment, Agern Alle 5, Hoersholm, DK 2970 Denmark
AU: Overgaard, J
EM: jov@dhi.dk
AF: DHI Water & Environment, Agern Alle 5, Hoersholm, DK 2970 Denmark
AU: Kristensen, M
EM: mik@dhi.dk
AF: DHI Water & Environment, Agern Alle 5, Hoersholm, DK 2970 Denmark
AU: Madsen, H
EM: hem@dhi.dk
AF: DHI Water & Environment, Agern Alle 5, Hoersholm, DK 2970 Denmark
AB: The complexities of sustainable water management have led to increasing use of integrated hydrological models for hydrological prediction and forecasting. While the inherent uncertainty in hydrological simulation is widely recognised, finding methods that address all the sources of uncertainty remains a complex and challenging problem. One of the most challenging aspects of this problem is how to consider model structure uncertainties. Relatively few studies have directly addressed the effect of model structure on model performance and uncertainty predictions. In many cases the model structure error is used to account for residual errors once the other sources of uncertainty have been quantified. To address the issue of model structure uncertainty a general integrated modelling framework is described for considering the effect of different model structures on model predictions. A methodology is proposed for evaluating alternative model structures. This methodology is then applied to a US NWS study catchment, the Blue river basin as part of the NWS Distributed Model Intercomparison Project. The relative performance of different acceptable model structures is evaluated as a representation of structural uncertainty and compared to the uncertainty estimates arising from measurement uncertainty, parametric uncertainty and the rainfall input. The results show that model performance is strongly dependent on model structure and the uncertainty associated with model structure is similar in magnitude to the other sources. The same methodology was applied to evaluate multimodel ensembles. It was found that the ensemble average of 10 acceptable models performs better than any single model in a split sample test. Regression methods were then used to identify which model structures provide significant contributions to accurate hydrological simulation.
UR: http://www.nws.noaa.gov/oh/hrl/dmip/
DE: 1854 Precipitation (3354)
DE: 1860 Runoff and streamflow
DE: 1866 Soil moisture
DE: 1869 Stochastic processes
DE: 1894 Instruments and techniques
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting