Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: Ensemble combinations and predictions |
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| Authors: | Neil R Viney H Bormann L Breuer A Bronstert BFW Croke H Frede T Gräff L Hubrechts JA Huisman AJ Jakeman GW Kite J Lanini G Leavesley DP Lettenmaier G Lindström J Seibert M Sivapalan P Willems |
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| Institution: | 1. CSIRO Land and Water, GPO Box 1666, Canberra, ACT 2600, Australia;2. Institute for Biology and Environmental Sciences, Carl von Ossietzsky University, Oldenburg, Germany;3. Institute for Landscape Ecology and Resources Management, Justus-Liebig University, Giessen, Germany;4. Institute for Geoecology, University of Potsdam, Germany;5. Integrated Catchment Assessment and Management Centre, The Australian National University, Canberra, Australia;6. Afdeling Ecologie en Water, Lisec NV, Genk, Belgium;g ICG-4 Agrosphere, Forschungszentrum Jülich, Germany;h Hydrologic Solutions, Pantymwyn, United Kingdom;i Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA;j United States Geological Survey, Denver, CO, USA;k Swedish Meteorological and Hydrological Institute, Norrköping, Sweden;l Department of Physical Geography and Quaternary Geology, Stockholm University, Stockholm, Sweden;m Centre for Water Research, University of Western Australia, Perth, Australia;n Hydraulics Laboratory, Katholieke Universiteit Leuven, Belgium |
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| Abstract: | This paper reports on a project to compare predictions from a range of catchment models applied to a mesoscale river basin in central Germany and to assess various ensemble predictions of catchment streamflow. The models encompass a large range in inherent complexity and input requirements. In approximate order of decreasing complexity, they are DHSVM, MIKE-SHE, TOPLATS, WASIM-ETH, SWAT, PRMS, SLURP, HBV, LASCAM and IHACRES. The models are calibrated twice using different sets of input data. The two predictions from each model are then combined by simple averaging to produce a single-model ensemble. The 10 resulting single-model ensembles are combined in various ways to produce multi-model ensemble predictions. Both the single-model ensembles and the multi-model ensembles are shown to give predictions that are generally superior to those of their respective constituent models, both during a 7-year calibration period and a 9-year validation period. This occurs despite a considerable disparity in performance of the individual models. Even the weakest of models is shown to contribute useful information to the ensembles they are part of. The best model combination methods are a trimmed mean (constructed using the central four or six predictions each day) and a weighted mean ensemble (with weights calculated from calibration performance) that places relatively large weights on the better performing models. Conditional ensembles, in which separate model weights are used in different system states (e.g. summer and winter, high and low flows) generally yield little improvement over the weighted mean ensemble. However a conditional ensemble that discriminates between rising and receding flows shows moderate improvement. An analysis of ensemble predictions shows that the best ensembles are not necessarily those containing the best individual models. Conversely, it appears that some models that predict well individually do not necessarily combine well with other models in multi-model ensembles. The reasons behind these observations may relate to the effects of the weighting schemes, non-stationarity of the climate series and possible cross-correlations between models. |
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| Keywords: | Multi-model ensembles Single-model ensembles Catchment modelling Ensemble combination Uncertainty Land use change |
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