Abstract
Abstract. 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.
Highlights
Sustainable management of groundwater resources aims to ensure that the abstraction of groundwater does not exceed groundwater recharge over the long term (e.g. Gleeson et al, 2012)
For comparison we considered the nearby observation well NB-Hotel Vier Tore approximately 600 m further south which was formerly excluded from the principal component analysis (PCA) due to known anthropogenic influence (Sect. 2.2)
To select only those principal components (PCs) which are representative for the monitored region in the analysed period, a series of PCAs were performed based on randomly selected subsets of the complete dataset to identify the stable PCs
Summary
Sustainable management of groundwater resources aims to ensure that the abstraction of groundwater does not exceed groundwater recharge over the long term (e.g. Gleeson et al, 2012). Declining trends in hydrological systems do not necessarily imply anthropogenic influence, but they might rather be indications of naturally occurring long-term persistence (Hurst, 1951; Mandelbrot and Van Ness, 1968; Mandelbrot and Wallis, 1969) induced for example by the natural fluctuation of climatic drivers Continuity of hydrologic records for more than “just” a few decades is mandatory to incorporate this issue in water management (Hirsch, 2011). This holds in particular for groundwater monitoring due to the much more pronounced filtering of short-term fluctuations in the subsur-
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