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Semi-Automated Characterization of Streamwater Specific Conductivity Response to Storms
Journal of the American Water Resources Association ( IF 2.4 ) Pub Date : 2021-07-15 , DOI: 10.1111/1752-1688.12941
Daniel J. Demers 1 , Mark B. Green 2 , Scott W. Bailey 3
Affiliation  

Specific electrical conductivity (SC) is a basic, effective indicator of water quality. The recent increase in SC data collected with high-frequency sensors has created a strong need for algorithms that can aid interpretation of these data. This study presents an algorithm that finds and quantifies SC temporal patterns and applies that algorithm to a dataset from a 7.5 km2 forested catchment in central New Hampshire. During and after rain events, we show three patterns that emerge in SC time series: A solute flush, resulting in an initial increase in SC, followed by a dilution, followed by the SC’s recovery toward pre-rain conditions. We compared these SC patterns to precipitation amount and intensity, antecedent wetness, and seasonality. Our results indicate that the magnitude of the flush was driven primarily by precipitation intensity and total rainfall during a storm, and secondarily by antecedent moisture conditions. The magnitude of the dilution was driven mainly by the precipitation amount. The rate of SC recovery was driven by precipitation amount and was correlated with dilution. Overall, the algorithm successfully extracted event-driven characteristics in the SC time series, allowing the development of functional relationships with hydrologic drivers. Applying similar methodologies to more catchments in the future will help identify functional relationships at more sites and use these relationships to identify catchments most sensitive to future precipitation changes.

中文翻译:

对风暴的溪流比电导率响应的半自动表征

比电导率 (SC) 是水质的基本有效指标。最近使用高频传感器收集的 SC 数据的增加产生了对可以帮助解释这些数据的算法的强烈需求。本研究提出了一种算法,可以发现和量化 SC 时间模式,并将该算法应用于来自 7.5 km 2的数据集新罕布什尔州中部的森林集水区。在降雨事件期间和之后,我们展示了 SC 时间序列中出现的三种模式:溶质冲洗,导致 SC 初始增加,然后是稀释,然后是 SC 恢复到雨前条件。我们将这些 SC 模式与降水量和强度、前期湿度和季节性进行了比较。我们的结果表明,冲刷的大小主要是由暴雨期间的降水强度和总降雨量决定的,其次是由先前的水分条件决定的。稀释的幅度主要由沉淀量驱动。SC 回收率由沉淀量驱动,并与稀释相关。总体而言,该算法成功提取了 SC 时间序列中的事件驱动特征,允许发展与水文驱动因素的功能关系。未来将类似的方法应用于更多集水区将有助于识别更多地点的功能关系,并使用这些关系来识别对未来降水变化最敏感的集水区。
更新日期:2021-07-15
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