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

与冰川径流相关的有机碳(OC)输出的预测非常有限，现有关于OC输出的研究主要基于一种综合方法，使用单个冰样点和质量平衡来计算冰川OC的年平均出口量。这种物质平衡方法没有考虑有机碳潜在的日变化和季节变化，因此可能不能准确反映冰川有机碳出口率。因此，重要的是要在高时间分辨率(季节、事件、日)下考虑冰川水文学的时间变化，并考虑不同的相关径流成分，以便进一步阐明冰川在河流有机碳输出中的作用。该项目的目的是在高时间分辨率上系统地研究冰川衍生有机碳(浓度、组成和生物有效性)的输出，并考虑冰川径流产生过程中生物化学的时间变异性。只有详细了解冰川水文对有机碳出口的影响，才能对未来由于冰川退缩而释放的有机碳做出可靠的预测。调查将在冰岛温带冰川Falljökull进行，Falljökull是Öraefajökull和Vatnajökull冰盖的一部分，自1932年以来每年测量一次(冰锋)。使用创新的方法，如机器学习方法，结合流量过程线分离，将有助于了解不同冰川流量源区的时间相互作用及其日变化和季节变化。将涉及有机碳和排放动力学的过程理解联系起来，将能够对冰川有机碳的输出进行建模，同时考虑有机碳的组成。总共将采集972个冰、雪和水样，包括季节性、日间和事件采样，并使用直接安装在冰川末端的校准便携式水质计，每隔60分钟自动连续测量水温、电导率、浊度、水位和荧光DOM。使用一系列最先进的实验室设备和方法(C/N-和TOC-Analyzer，Picarro)，我们将分析BDOC、DOC、POC、光学性质(荧光、吸光度)、营养物质(PO4、NO3、NO2、NH4)和稳定同位素(18O、2H)。使用多变量统计技术(例如，主成分分析、主成分分析)将有助于确定时间模式、过程和驱动因素。将对冰川有机碳的通量和出口进行模拟(PARAFAC、SIMMR、LOADEST)。这种结合排放分离和机器学习技术的有机碳输出的系统调查将促进当前关于冰川来源有机碳的浓度、组成和生物有效性的日变化和季节变化的过程知识。此外，它将有助于可靠地预测由于气候变化引起的冰川融化过程中的变化而导致的冰川有机碳出口的(未来)动态。

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.