Freshwater reservoirs play a significant role in the global carbon cycle by processing and storing large quantities of dissolved organic matter (DOM). Quantifying the magnitude of DOM fluctuations across multiple temporal scales can advance our understanding of how the controls on reservoir carbon cycling may vary. We monitored fluorescent DOM (fDOM) using an in situ epilimnetic sensor at a ten-minute resolution over one year in a eutrophic reservoir in southwestern Virginia, USA with low dissolved organic carbon concentrations (2–6 mg L⁻¹). We determined the dominant time scales of variability and key environmental predictors of fDOM concentrations using continuous wavelet transforms and autoregressive time series modeling. Throughout the year, fDOM concentrations varied considerably, with maximum concentrations in the autumn (30.0 quinine sulfate units) and minimum concentrations in the spring (4.7 quinine sulfate units). The monthly time scale was the dominant time scale of variability, but the daily time scale was significant during the summer. Based on the autoregressive time series analysis, precipitation, water temperature, and shortwave radiation were important environmental predictors of fDOM on daily time scales, while water temperature alone best predicted monthly variability. Our study is one of the first to reveal substantial variability in fDOM concentrations during a full year, emphasizing the need for long-term, high-frequency in situ DOM monitoring to capture changes occurring on multiple time scales. By quantifying the variability and environmental predictors of fDOM on different time scales, we are able to better understand how and why DOM concentrations change throughout the year.

淡水水库通过处理和存储大量溶解的有机物（DOM）在全球碳循环中发挥重要作用。量化跨多个时间尺度的DOM波动幅度可增进我们对储层碳循环控制方式可能如何变化的理解。我们在美国西南弗吉尼亚州的富营养化油藏中使用原位表皮电磁传感器以十分钟的分辨率监测荧光DOM（fDOM），该溶液在一年中的溶解有机碳浓度较低（2–6 mg L ^-1）。我们使用连续小波变换和自回归时间序列建模确定了可变性的主要时间尺度和fDOM浓度的关键环境预测因子。全年中，fDOM浓度变化很大，秋季最大浓度（30.0硫酸奎宁单位），最小浓度春季（4.7硫酸奎宁单位）。每月时间标度是可变性的主要时间标度，但夏季夏季的日度标度很重要。根据自回归时间序列分析，在每日时间尺度上，降水，水温和短波辐射是fDOM的重要环境预测指标，而水温仅能最好地预测每月变化。我们的研究是第一个揭示整年fDOM浓度变化较大的研究，其中强调需要长期，高频原位DOM监测来捕获在多个时间尺度上发生的变化。通过量化不同时间尺度上fDOM的变异性和环境预测因素，我们可以更好地了解DOM浓度全年变化的方式和原因。