HR: 13:55h
AN: H13I-02    [Abstracts]
TI: A new approach to the calibration of complex watershed models in ungauged basins using regionalized hydrologic indices
AU: * Wagener, T
EM: thorsten@engr.psu.edu
AF: Pennsylvania State University, Department of Civil and Environmental Engineering, Sackett Building, University Park, PA 16801, United States
AU: Bhushan, R
EM: rxb938@psu.edu
AF: Pennsylvania State University, Department of Civil and Environmental Engineering, Sackett Building, University Park, PA 16801, United States
AU: Zhang, Z
EM: zzx509@yahoo.com
AF: Pennsylvania State University, Department of Civil and Environmental Engineering, Sackett Building, University Park, PA 16801, United States
AU: Reed, P
EM: preed@engr.psu.edu
AF: Pennsylvania State University, Department of Civil and Environmental Engineering, Sackett Building, University Park, PA 16801, United States
AB: Hydrologic models are increasingly using more complex representations of watershed processes, which is reflected by the recent focus on spatially distributed models in the literature. Although these models seek to more realistically represent watersheds, many of their parameters cannot be estimated reliably from physical characteristics only, leading to uncertain priors and unreliable predictions if observations of the watershed response are not available for model conditioning. Large parts of the world, including most of the US river network, remain ungauged and alternative approaches to reduce a priori parameter uncertainty have to be found. Here we present a new approach based on the regionalization of streamflow characteristics in an uncertainty framework. A novel multi-objective formulation of the calibration problem allows for the use of powerful evolutionary algorithms to condition any watershed scale model on expected regionalized ranges of streamflow characteristics at ungauged locations. Our initial study using 30 UK watersheds shows that this approach results in reliable and sharp ensemble predictions, while the computational efficiency of the approach show its potential for application to complex spatially-distributed models. Global sensitivity analysis is used to analyze differences in the use of local and regional observations.
DE: 1800 HYDROLOGY
DE: 1804 Catchment
DE: 1847 Modeling
DE: 1860 Streamflow
DE: 1873 Uncertainty assessment (3275)
SC: Hydrology [H]
MN: 2007 Fall Meeting