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

阐明冰川有机碳浓度和成分的时间变化，通过排放分离和机器学习技术确定碳输出（Falljökull，冰岛）

• 批准号: 504341843
• 负责人: Professor Dr. Peter Chifflard
• 金额: --
• 依托单位: Fachgebiet Boden- u Hydrogeographie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/504341843?language=en
• 关键词: Elucidating; temporal; variability; glacial; organic

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.

与冰川径流有关的有机碳（OC）输出的预测是非常有限的，现有的研究主要是基于一个综合的方法，使用单一的冰采样点和质量平衡来计算冰川派生的OC的平均年输出。这种质量平衡的方法不占潜在的昼夜和季节变化的OC，因此可能无法准确地反映冰川OC的输出率。因此，重要的是要以高时间分辨率（季节性、事件性、昼夜性）考虑冰川水文的时间变化，并考虑不同的相关径流成分，以进一步阐明冰川在河流有机碳输出中的作用。该项目旨在系统地调查冰川源OC的输出（浓度，成分和生物利用度）在一个高的时间分辨率，并考虑生物化学的时间变化与冰川径流的变化。只有详细了解冰川水文对OC输出的影响，才能对冰川退缩导致的OC未来释放做出可靠的预测。调查将在冰岛温带冰川Falljökull（Öraefajökull和Vatnajökull冰帽的一部分）进行，自1932年以来每年测量一次（冰锋）。使用机器学习方法等创新方法，结合流量过程线分离，将有助于了解冰川流量不同来源地区的时间相互作用及其日变化和季节变化。连接过程的理解，涉及OC和放电动力学将使模拟的出口冰川OC，同时考虑OC的组成。总共将采集972个冰、雪和水样本，包括季节性、昼夜和事件采样，并使用直接安装在冰川终点的校准便携式水质计，以60分钟的间隔自动连续测量水温、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分析仪，Picarro），我们将分析BDOC，DOC，POC，光学特性（荧光，吸光度），营养物质（PO 4，NO3，NO2，NH 4）和稳定同位素（18 O，2 H）。多变量统计技术的使用（例如，分析将有助于确定时间模式、进程和驱动因素。将模拟冰川OC的通量和输出（PARAFAC，SIMMR，LOADest）。结合排放分离和机器学习技术，对OC输出的系统调查将推进当前有关冰川有机碳浓度、组成和生物利用度的昼夜和季节变化的过程知识。此外，它将有助于可靠地预测（未来）动态的冰川派生OC输出由于气候变化引起的变化，冰川融化过程。