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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 6, issue 1 | Copyright
Geosci. Model Dev., 6, 117-125, 2013
https://doi.org/10.5194/gmd-6-117-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 29 Jan 2013

Development and technical paper | 29 Jan 2013

Using multi-model averaging to improve the reliability of catchment scale nitrogen predictions

J.-F. Exbrayat1,2, N. R. Viney3, H.-G. Frede2, and L. Breuer2 J.-F. Exbrayat et al.
  • 1Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia
  • 2Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (IFZ), Justus-Liebig-Universität Gießen, Germany
  • 3CSIRO Land and Water, Canberra, ACT, Australia

Abstract. Hydro-biogeochemical models are used to foresee the impact of mitigation measures on water quality. Usually, scenario-based studies rely on single model applications. This is done in spite of the widely acknowledged advantage of ensemble approaches to cope with structural model uncertainty issues. As an attempt to demonstrate the reliability of such multi-model efforts in the hydro-biogeochemical context, this methodological contribution proposes an adaptation of the reliability ensemble averaging (REA) philosophy to nitrogen losses predictions. A total of 4 models are used to predict the total nitrogen (TN) losses from the well-monitored Ellen Brook catchment in Western Australia. Simulations include re-predictions of current conditions and a set of straightforward management changes targeting fertilisation scenarios. Results show that, in spite of good calibration metrics, one of the models provides a very different response to management changes. This behaviour leads the simple average of the ensemble members to also predict reductions in TN export that are not in agreement with the other models. However, considering the convergence of model predictions in the more sophisticated REA approach assigns more weight to previously less well-calibrated models that are more in agreement with each other. This method also avoids having to disqualify any of the ensemble members.

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