Conditioning temperature‐index model parameters on synoptic weather types for glacier melt simulations |
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| Authors: | T Matthews R Hodgkins R L Wilby S Guðmundsson F Pálsson H Björnsson S Carr |
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| Institution: | 1. Department of Geography, National University of Ireland, Maynooth, Kildare, Ireland;2. Department of Geography, Loughborough University, Loughborough, UK;3. Institute of Earth Science, University of Iceland, Reykjavík, Iceland;4. School of Geography, Queen Mary University of London, London, UK |
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| Abstract: | Temperature‐index models are widely favoured as a pragmatic means of simulating glacier melt because of their generally good performance, computational simplicity and limited demands for in situ data. However, their coefficients are normally treated as temporally stationary, unrealistically assuming a constancy of the prevailing weather. We address this simplification by prescribing model coefficients as a function of synoptic weather type, in a procedure that utilizes reanalysis data and preserves the minimal data requirements of temperature‐index models. Using a cross‐validation procedure at Vestari Hagafellsjökull, Iceland, and Storglaciären, Sweden, we demonstrate that applying transient model coefficients, for three temperature‐index models, results in statistically significant increases in the skill with which melt is modelled: Median simulation improvements in the Nash–Sutcliffe efficiency coefficient of 7.3 and 23.6% are achieved when hourly and daily melt totals are evaluated respectively. Our weather‐type modelling approach also yields insight to processes driving parameter variability, revealing dependence that is consistent with a priori considerations of the surface energy balance. We conclude that incorporating weather types into temperature‐index models holds promise for improving their performance, as well as enhancing understanding variability in coefficient values. Copyright © 2014 John Wiley & Sons, Ltd. |
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| Keywords: | temperature index glacier melt modelling weather types glacier– climate interactions nearest‐neighbour resampling |
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