Articles | Volume 24, issue 2
Hydrol. Earth Syst. Sci., 24, 501–513, 2020
https://doi.org/10.5194/hess-24-501-2020
Hydrol. Earth Syst. Sci., 24, 501–513, 2020
https://doi.org/10.5194/hess-24-501-2020

Research article 03 Feb 2020

Research article | 03 Feb 2020

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

Christian Lehr and Gunnar Lischeid

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Cited articles

Bartlein, P. J.: Streamflow anomaly patterns in the U.S.A. and southern Canada — 1951–1970, J. Hydrol., 57, 49–63, https://doi.org/10.1016/0022-1694(82)90102-0, 1982. 
Buell, C. E.: The topography of the empirical orthogonal functions, Preprints, Fourth Conference on Probability and Statistics in Atmospheric Sciences, 18–21 November 1975, Tallahassee, Florida, USA, American Meteorological Society, 188–193, 1975. 
Buell, C. E.: On the physical interpretation of empirical orthogonal functions, Preprints, Sixth Conference on Probability and Statistics in Atmospheric Sciences, 9–12 October 1979, Banff, Alberta, Canada, American Meteorological Society, 112–117, 1979. 
Coppola, E., Szidarovszky, F., Poulton, M., and Charles, E.: Artificial Neural Network Approach for Predicting Transient Water Levels in a Multilayered Groundwater System under Variable State, Pumping, and Climate Conditions, J. Hydrol. Eng., 8, 348–360, https://doi.org/10.1061/(ASCE)1084-0699(2003)8:6(348), 2003. 
Coulibaly, P., Anctil, F., Aravena, R., and Bobde, B.: Artificial neural network modeling of water table depth fluctuations, Water Resour. Res., 37, 885–896, https://doi.org/10.1029/2000WR900368, 2001. 
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Short summary
A screening method for the fast identification of well-specific peculiarities in hydrographs of groundwater head monitoring networks is suggested and tested. The only information required is a set of time series of groundwater head readings all measured at the same instants of time. The results were used to check the data for measurement errors and to identify wells with possible anthropogenic influence.