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

Abstract

Abstract. Groundwater level data is monitored by environmental agencies to support sustainable use of groundwater resources. For this purpose a high spatial coverage of the monitoring networks and continuous monitoring in high temporal resolution is desired. This leads to large data sets that have to be quality checked 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 identification of well-specific peculiarities in hydrographs of groundwater head monitoring networks. The only information required is a set of time series of groundwater head 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 it 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 analysed observation wells, respectively 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 to quality check the data for measurement errors and identify wells with possible anthropogenic influence. The approach was tested with 141 groundwater head series of the state authority groundwater monitoring network in northeast Germany covering the period from 1993 to 2013 in approximately weekly resolution.

Disentangling coastal groundwater level dynamics on a global data set

“…A1). In line with recent publications, a systematic and additional visual inspection of groundwater time series was found to be beneficial (Barthel et al, 2022;Berendrecht et al, 2022;Lehr and Lischeid, 2020;Retike et al, 2022). Subsequently, temporal availability criteria were checked once again, with remaining data gaps being linearly interpolated and time series were transformed to the 0-1 scale for calculating indices.…”