Department of Soil and Environment, Swedish University of Agricultural Sciences, Uppsala, Box 7014, 750 07, Sweden.
Lancaster Environment Centre, Library Avenue, Lancaster University, Lancaster LA1 4YQ, United Kingdom.
Sci Total Environ. 2018 Jul 15;630:738-749. doi: 10.1016/j.scitotenv.2018.02.256. Epub 2018 Feb 27.
Recent technological breakthroughs of optical sensors and analysers have enabled matching the water quality measurement interval to the time scales of stream flow changes and led to an improved understanding of spatially and temporally heterogeneous sources and delivery pathways for many solutes and particulates. This new ability to match the chemograph with the hydrograph has promoted renewed interest in the concentration-discharge (c-q) relationship and its value in characterizing catchment storage, time lags and legacy effects for both weathering products and anthropogenic pollutants. In this paper we evaluated the stream c-q relationships for a number of water quality determinands (phosphorus, suspended sediments, nitrogen) in intensively managed agricultural catchments based on both high-frequency (sub-hourly) and long-term low-frequency (fortnightly-monthly) routine monitoring data. We used resampled high-frequency data to test the uncertainty in water quality parameters (e.g. mean, 95th percentile and load) derived from low-frequency sub-datasets. We showed that the uncertainty in water quality parameters increases with reduced sampling frequency as a function of the c-q slope. We also showed that different sources and delivery pathways control c-q relationship for different solutes and particulates. Secondly, we evaluated the variation in c-q slopes derived from the long-term low-frequency data for different determinands and catchments and showed strong chemostatic behaviour for phosphorus and nitrogen due to saturation and agricultural legacy effects. The c-q slope analysis can provide an effective tool to evaluate the current monitoring networks and the effectiveness of water management interventions. This research highlights how improved understanding of solute and particulate dynamics obtained with optical sensors and analysers can be used to understand patterns in long-term water quality time series, reduce the uncertainty in the monitoring data and to manage eutrophication in agricultural catchments.
近年来，光学传感器和分析器的技术突破使得水质测量间隔能够与水流变化的时间尺度相匹配，从而更好地了解许多溶质和颗粒物质的时空不均匀来源和输送途径。这种将化学图谱与水流图谱相匹配的新能力，重新激发了人们对浓度-流量（c-q）关系的兴趣，以及其在描述流域储存、时滞和风化产物及人为污染物的遗留效应方面的价值。在本文中，我们根据高强度管理农业流域的高频（亚小时）和长周期低频（两周/月）常规监测数据，评估了多个水质指标（磷、悬浮泥沙、氮）的河流 c-q 关系。我们使用重采样的高频数据来检验从低频子数据集得出的水质参数（如均值、95%分位数和负荷）的不确定性。结果表明，水质参数的不确定性随着采样频率的降低而增加，这是 c-q 斜率的函数。我们还表明，不同的来源和输送途径控制着不同溶质和颗粒物质的 c-q 关系。其次，我们评估了不同溶质和流域的长周期低频数据得出的 c-q 斜率的变化，并发现由于饱和和农业遗留效应，磷和氮表现出强烈的化学稳定行为。c-q 斜率分析可以为评估当前监测网络和水管理干预措施的有效性提供有效工具。这项研究强调了光学传感器和分析器在获得的溶质和颗粒动力学方面的改进理解如何用于理解长期水质时间序列中的模式，降低监测数据的不确定性，并管理农业流域的富营养化。
