HR: 08:45h
AN: H41G-04    [Abstracts]
TI: Parameter identification and uncertainty estimation for a spatially distributed rainfall-runoff model using global optimization
AU: * Feyen, L
EM: luc.feyen@jrc.it
AF: Land Management Unit, Institute for Environment and Sustainability, Joint Research Centre, TP261, Via E. Fermi, 1, Ispra, 21020 Italy
AU: Vrugt, J A
EM: vrugt@lanl.gov
AF: Earth and Environmental Sciences Division, Los Alamos National Laboratory, Mail Stop T003, Los Alamos, NM 87545 United States
AU: van der Knijff, J
EM: johan.van-der-knijff@jrc.it
AF: Land Management Unit, Institute for Environment and Sustainability, Joint Research Centre, TP261, Via E. Fermi, 1, Ispra, 21020 Italy
AU: De Roo, A
EM: ad.de-roo@jrc.it
AF: Land Management Unit, Institute for Environment and Sustainability, Joint Research Centre, TP261, Via E. Fermi, 1, Ispra, 21020 Italy
AB: In this paper we address the problem of parameter identification and parameter uncertainty estimation for the spatially distributed rainfall-runoff model LISFLOOD. This model forms the core of the European Flood Alert System (EFAS), an activity that forecasts floods at the European scale with a lead-time of 3-10 days. LISFLOOD is driven by meteorological input data and simulates river discharge in large drainage basins as a function of spatial information on topography, soils and land cover. Even though LISFLOOD is physically-based to a certain extent, some processes are only represented in a lumped conceptual way. As a result, some parameters lack physical basis and cannot be directly inferred from quantities that can be measured. In the current LISFLOOD version five parameters need to be determined by calibration. We employ the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm to automatically calibrate the model against discharge observations. The resulting posterior parameter distribution reflects the residual uncertainty about the model parameters and forms the basis for making probabilistic flow predictions. We investigate what is the minimum level of spatial detail of the 5 unknown parameters for large-scale river flow forecasting. As an illustrative example, we demonstrate the methodology for the Danube catchment.
DE: 1846 Model calibration (3333)
DE: 1869 Stochastic hydrology
DE: 1873 Uncertainty assessment (3275)
DE: 1879 Watershed
SC: Hydrology [H]
MN: Fall Meeting 2005