Articles | Volume 12, issue 7
https://doi.org/10.5194/tc-12-2175-2018
https://doi.org/10.5194/tc-12-2175-2018
Research article
 | 
06 Jul 2018
Research article |  | 06 Jul 2018

Glacio-hydrological melt and run-off modelling: application of a limits of acceptability framework for model comparison and selection

Jonathan D. Mackay, Nicholas E. Barrand, David M. Hannah, Stefan Krause, Christopher R. Jackson, Jez Everest, and Guðfinna Aðalgeirsdóttir

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Cited articles

Aðalgeirsdóttir, G., Guðmundsson, S., Björnsson, H., Pálsson, F., Jóhannesson, T., Hannesdóttir, H., Sigurðsson, S. Þ, and Berthier, E.: Modelling the 20th and 21st century evolution of Hoffellsjökull glacier, SE-Vatnajökull, Iceland, The Cryosphere, 5, 961–975, https://doi.org/10.5194/tc-5-961-2011, 2011. a
Allen, R., Pereira, L., Raes, D., and Smith, M.: Crop evapotranspiration – Guidelines for computing crop water requirements – FAO Irrigation and drainage paper 56, Tech. rep., Food and Agriculture Organization of the United Nations, Rome, Italy, 1998. a, b, c
Arnold, N. S., Rees, W. G., Hodson, A. J., and Kohler, J.: Topographic controls on the surface energy balance of a high Arctic valley glacier, J. Geophys. Res.-Earth Surf., 111, F02011, https://doi.org/10.1029/2005JF000426, 2006. a
Barrand, N. E., Murray, T., James, T. D., Barr, S. L., and Mills, J. P.: Optimizing photogrammetric DEMs for glacier volume change assessment using laser-scanning derived ground-control points, J. Glaciol., 55, 106–116, https://doi.org/10.3189/002214309788609001, 2009. a
Beven, K.: A manifesto for the equifinality thesis, J. Hydrol., 320, 18–36, https://doi.org/10.1016/j.jhydrol.2005.07.007, 2006. a
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Short summary
We apply a framework to compare and objectively accept or reject competing melt and run-off process models. We found no acceptable models. Furthermore, increasing model complexity does not guarantee better predictions. The results highlight model selection uncertainty and the need for rigorous frameworks to identify deficiencies in competing models. The application of this approach in the future will help to better quantify model prediction uncertainty and develop improved process models.