Elucidating the temporal variability of glacial organic carbon concentration and composition toward determining carbon export via discharge separation and machine learning techniques (Falljökull, Iceland)

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Predictions of organic carbon (OC) export related to glacier runoff is very limited and existing studies about OC export are primarily based on an integrated approach, using single ice sampling points and mass balances to calculate an average annual export of glacier derived OC. This mass balance approach does not account for potential diurnal and seasonal changes in OC, and may therefore not accurately reflect glacial OC export rates. Therefore, it is important to consider temporal shifts in glacial hydrology at a high temporal resolution (seasonal, event, diurnal), and to account for different relevant runoff components, in order to further elucidate the role of glaciers in riverine OC export. This project aims to systematically investigate the export of glacier derived OC (concentration, composition and bioavailability) at a high temporal resolution, and consider the biochemical temporal variability with variations in glacier runoff generation. Only by understanding the effects of glacier hydrology on OC export in detail reliable predictions for future release of OC due to glacier retreat can be made. The investigations will be carried out at the temperate Icelandic glacier Falljökull, part of Öraefajökull and Vatnajökull ice cap and measured annually since 1932 (ice front). The use of innovative methods such as machine learning methods, in combination with a discharge hydrograph separation, will help to understand the temporal interplay of different source areas of glacier discharge and its diurnal and seasonal variability. Connecting process understanding involving OC and discharge dynamics will enable the modelling of the export of glacial OC, while taking OC composition into consideration. Altogether 972 ice, snow and water samples will be taken including seasonal, diurnal and event sampling in connection with continuous measurements of water temperature, conductivity, turbidity, water level and fluorescent DOM automatically at 60 min intervals using a calibrated portable water quality meter installed directly at the glacier terminus. Using an array of state-of-the-art laboratory equipment and methods (C/N- and TOC-Analyzer, Picarro), we will analyze BDOC, DOC, POC, optical properties (fluorescence, absorbance), nutrients (PO4, NO3, NO2, NH4) and stable isotopes (18O, 2H). The use of multivariate statistical techniques (e.g., PCA, CCA) will help to identify temporal patterns, processes and drivers. Fluxes and export of glacial OC will be modelled (PARAFAC, SIMMR, LOADest). This systematically investigation of OC export combined with discharge separation and machine learning techniques will advance current process-knowledge about diurnal and seasonal changes in the concentration, composition and bioavailability of glacier derived organic carbon. Moreover, it will contribute to the ability to reliably predict the (future) dynamics of glacier derived OC export due to climate-change induced variations in glacier melting processes.