HR: 0800h
AN: H51B-0358    [Abstracts]
TI: Tapping the Information Content in Rainfall-Runoff Data by Partitioning With Regression Trees
AU: * Iorgulescu, I
EM: Ion.Iorgulescu@epfl.ch
AF: Institute of Environmental Science and Technology, Ecole Polytechnique Fédérale de Lausanne, EPFL, ENAC, ISTE, GECOS, Lausanne, CH-1015 Switzerland
AU: Beven, K J
EM: k.beven@lancaster.ac.uk
AF: Institute of Environmental and Natural Sciences, Lancaster University, Lancaster University, Lancaster, LA1 4YQ United Kingdom
AB: The current approach in rainfall-runoff modeling that assumes global hydrologic and error models is successful when the latter are not to far from the 'true' ones. As we argue elsewhere, this will likely not be the case for applications on real data. Our main assumption is that there is more information than previously thought in long, good quality, rainfall-runoff records. We suggest that there is a potential tradeoff between the quantity of data and the 'strength' of the adopted modeling hypotheses. We will present and discuss a new partitioning method that is based on the "nonparametric direct mapping of rainfall-runoff relationships" approach we recently introduced. This method is based on the recursive binary partitioning of input space using regression trees, a non-linear, non-parametric, identification algorithm. We will show that the latter improves the consistency of some otherwise subjective choices, such as the objective functions or 'behavioural' thresholds used for model evaluation. A first application of the proposed methodology on real data will be presented.
DE: 1846 Model calibration (3333)
DE: 1847 Modeling
DE: 1873 Uncertainty assessment (3275)
DE: 1879 Watershed
SC: Hydrology [H]
MN: Fall Meeting 2005