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Statistical summer mass-balance forecast model with application to Brúarjökull glacier, South East Iceland

Statistical summer mass-balance forecast model with application to Brúarjökull glacier, South East Iceland


Titill: Statistical summer mass-balance forecast model with application to Brúarjökull glacier, South East Iceland
Höfundur: Eythorsson, Darri   orcid.org/0000-0003-3653-3936
Gardarsson, Sigurdur   orcid.org/0000-0002-4705-1572
Gunnarsson, Andri   orcid.org/0000-0002-9041-8763
Hrafnkelsson, Birgir   orcid.org/0000-0003-1864-9652
Útgáfa: 2018-03-19
Tungumál: Enska
Umfang: 311-320
Háskóli/Stofnun: Háskóli Íslands
University of Iceland
Svið: Verkfræði- og náttúruvísindasvið (HÍ)
School of Engineering and Natural Sciences (UI)
Deild: Umhverfis- og byggingarverkfræðideild (HÍ)
Faculty of Civil and Environmental Engineering (UI)
Raunvísindadeild (HÍ)
Faculty of Physical Sciences (UI)
Birtist í: Journal of Glaciology;64(244)
ISSN: 0022-1430
1727-5652 (e-ISSN)
DOI: 10.1017/jog.2018.22
Efnisorð: Glacier mass balance; Glacier modelling; Ice and climate; Melt-surface; Jöklafræði; Bráðnun (jöklafræði); Líkindafræði; Spálíkön
URI: https://hdl.handle.net/20.500.11815/917

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Tilvitnun:

Eythorsson, D., Gardarsson, S. M., Gunnarsson, A., & Hrafnkelsson, B. (2018). Statistical summer mass-balance forecast model with application to Brúarjökull glacier, South East Iceland. Journal of Glaciology, 64(244), 311-320. doi:10.1017/jog.2018.22

Útdráttur:

Forecasting of glacier mass balance is important for optimal management of hydrological resources, especially where glacial meltwater constitutes a significant portion of stream flow, as is the case for many rivers in Iceland. In this study, a method was developed and applied to forecast the summer mass balance of Brúarjökull glacier in southeast Iceland. In the present study, many variables measured in the basin were evaluated, including glaciological snow accumulation data, various climate indices and meteorological measurements including temperature, humidity and radiation. The most relevant single predictor variables were selected using correlation analysis. The selected variables were used to define a set of potential multivariate linear regression models that were optimized by selecting an ensemble of plausible models showing good fit to calibration data. A mass-balance estimate was calculated as a uniform average across ensemble predictions. The method was evaluated using fivefold cross-validation and the statistical metrics Nash–Sutcliffe efficiency, the ratio of the root mean square error to the std dev. and percent bias. The results showed that the model produces satisfactory predictions when forced with initial condition data available at the beginning of the summer melt season, between 15 June and 1 July, whereas less reliable predictions are produced for longer lead times.

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This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

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