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Lookup NU author(s): Professor Hayley Fowler, Dr Stephen Blenkinsop
Global climate change may have large impacts on water supplies, drought or flood frequencies and magnitudes in local and regional hydrologic systems. Water authorities therefore rely on computer models for quantitative impact prediction. In this study we present kernel-based learning machine river flow models for the Upper Gallego catchment of the Ebro basin. Different learning machines were calibrated using daily gauge data. The models posed two major challenges: (1) estimation of the rainfall-runoff transfer function from the available time series is complicated by anthropogenic regulation and mountainous terrain and (2) the river flow model is weak when only climate data are used, but additional antecedent flow data seemed to lead to delayed peak How estimation. These types of models, together with the presented downscaled climate scenarios, can be used for climate change impact assessment in the Gallego, which is important for the future management of the system. (c) 2007 Elsevier Ltd. All rights reserved.
Author(s): Burger CM, Kolditz O, Fowler HJ, Blenkinsop S
Publication type: Article
Publication status: Published
Journal: Environmental Pollution
Year: 2007
Volume: 148
Issue: 3
Pages: 842-854
Date deposited: 12/05/2010
ISSN (print): 0269-7491
ISSN (electronic): 1873-6424
Publisher: Pergamon
URL: http://dx.doi.org/10.1016/j.envpol.2007.02.002
DOI: 10.1016/j.envpol.2007.02.002
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