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Efficient screening of groundwater head monitoring data for anthropogenic effects and measurement errors

机译:高效筛选地下水监测数据的人为效应和测量误差

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Groundwater levels are monitored by environmental agencies to support the sustainable use of groundwater resources. For this purpose continuous and spatially comprehensive monitoring in high spatial and temporal resolution is desired. This leads to large datasets that have to be checked for quality and analysed to distinguish local anthropogenic influences from natural variability of the groundwater level dynamics at each well. Both technical problems with the measurements as well as local anthropogenic influences can lead to local anomalies in the hydrographs. We suggest a fast and efficient screening method for the identification of well-specific peculiarities in hydrographs of groundwater head monitoring networks. The only information required is a set of time series of groundwater heads all measured at the same instants of time. For each well of the monitoring network a reference hydrograph is calculated, describing expected “normal” behaviour at the respective well as is typical for the monitored region. The reference hydrograph is calculated by multiple linear regression of the observed hydrograph with the “stable” principal components (PCs) of a principal component analysis of all groundwater head series of the network as predictor variables. The stable PCs are those PCs which were found in a random subsampling procedure to be rather insensitive to the specific selection of the analysed observation wells, i.e. complete series, and to the specific selection of measurement dates. Hence they can be considered to be representative for the monitored region in the respective period. The residuals of the reference hydrograph describe local deviations from the normal behaviour. Peculiarities in the residuals allow the data to be checked for measurement errors and the wells with a possible anthropogenic influence to be identified. The approach was tested with 141 groundwater head time series from the state authority groundwater monitoring network in northeastern Germany covering the period from 1993 to 2013 at an approximately weekly frequency of measurement.
机译:环境机构监测地下水位,以支持地下水资源的可持续利用。为此,需要在高空间和时间分辨率下连续和空间综合监测。这导致了必须检查质量并分析的大型数据集,以区分局部人类学影响从每个孔的地下水位动态的自然变化。测量的技术问题以及局部人为影响可能导致水文中的局部异常。我们提出了一种快速有效地筛选方法,用于识别地下水监测网络的水文中的特定特点。所需的唯一信息是一组时间序列的地下水头都在同一时间内测量。对于监控网络的每个孔,计算参考水文,描述各自的预期“正常”行为,因为所监测区域的典型值。参考水文通过观察到的水文的多个线性回归与网络的主要成分分析的“稳定”的主成分(PC)计算,作为预测变量的所有地下水头系列的主要成分分析。稳定的PC是在随机数据采样过程中发现的那些PC,以与分析的观察井的特定选择相当不敏感,即完成系列,以及测量日期的具体选择。因此,它们可以被认为是各期间受监测区域的代表性。参考水文的残差描述了与正常行为的局部偏差。残留中的特点允许识别可能识别可能的人为影响的测量误差和孔的数据。该方法是以来自德国东北部的国家权威地下水监测网络的141个地下水6时序序列进行了测试,涵盖了1993年至2013年的次数测量频率约为2013年。

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