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

• Published in: Journal of Glaciology
• Main Authors: Eythorsson, Darri, Gardarsson, Sigurdur, Gunnarsson, Andri, Hrafnkelsson, Birgir
• Other Authors: Umhverfis- og byggingarverkfræðideild (HÍ), Faculty of Civil and Environmental Engineering (UI), Raunvísindadeild (HÍ), Faculty of Physical Sciences (UI), Verkfræði- og náttúruvísindasvið (HÍ), School of Engineering and Natural Sciences (UI), Háskóli Íslands, University of Iceland
• Format: Article in Journal/Newspaper
• Language: English
• Published: Cambridge University Press (CUP) 2018
• Subjects: Glacier mass balance; Glacier modelling; Ice and climate; Melt-surface; Jöklafræði; Bráðnun (jöklafræði); Líkindafræði; Spálíkön; Brúarjökull; Nash; Sutcliffe; glacier; Iceland; Journal of Glaciology
• Online Access: https://hdl.handle.net/20.500.11815/917; https://doi.org/10.1017/jog.2018.22

Publisher's version (útgefin grein) 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. We thank the University of Iceland Research Fund which is supporting the first author. The project was also supported by the Energy Research Fund of the National Energy Company, Landsvirkjun by grants no. MEI-03-2015 and DOK-02-2017 Peer Reviewed