The good, the bad and the outliers: automated detection of errors and outliers from groundwater hydrographs

The good, the bad and the outliers: automated detection of errors and outliers from groundwater... Suspicious groundwater-level observations are common and can arise for many reasons ranging from an unforeseen biophysical process to bore failure and data management errors. Unforeseen observations may provide valuable insights that challenge existing expectations and can be deemed outliers, while monitoring and data handling failures can be deemed errors, and, if ignored, may compromise trend analysis and groundwater model calibration. Ideally, outliers and errors should be identified but to date this has been a subjective process that is not reproducible and is inefficient. This paper presents an approach to objectively and efficiently identify multiple types of errors and outliers. The approach requires only the observed groundwater hydrograph, requires no particular consideration of the hydrogeology, the drivers (e.g. pumping) or the monitoring frequency, and is freely available in the HydroSight toolbox. Herein, the algorithms and time-series model are detailed and applied to four observation bores with varying dynamics. The detection of outliers was most reliable when the observation data were acquired quarterly or more frequently. Outlier detection where the groundwater-level variance is nonstationary or the absolute trend increases rapidly was more challenging, with the former likely to result in an under-estimation of the number of outliers and the latter an overestimation in the number of outliers. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Hydrogeology Journal Springer Journals

The good, the bad and the outliers: automated detection of errors and outliers from groundwater hydrographs

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by Springer-Verlag GmbH Germany
Subject
Earth Sciences; Hydrogeology; Hydrology/Water Resources; Geology; Water Quality/Water Pollution; Geophysics/Geodesy; Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution
ISSN
1431-2174
eISSN
1435-0157
D.O.I.
10.1007/s10040-017-1660-7
Publisher site
See Article on Publisher Site

Abstract

Suspicious groundwater-level observations are common and can arise for many reasons ranging from an unforeseen biophysical process to bore failure and data management errors. Unforeseen observations may provide valuable insights that challenge existing expectations and can be deemed outliers, while monitoring and data handling failures can be deemed errors, and, if ignored, may compromise trend analysis and groundwater model calibration. Ideally, outliers and errors should be identified but to date this has been a subjective process that is not reproducible and is inefficient. This paper presents an approach to objectively and efficiently identify multiple types of errors and outliers. The approach requires only the observed groundwater hydrograph, requires no particular consideration of the hydrogeology, the drivers (e.g. pumping) or the monitoring frequency, and is freely available in the HydroSight toolbox. Herein, the algorithms and time-series model are detailed and applied to four observation bores with varying dynamics. The detection of outliers was most reliable when the observation data were acquired quarterly or more frequently. Outlier detection where the groundwater-level variance is nonstationary or the absolute trend increases rapidly was more challenging, with the former likely to result in an under-estimation of the number of outliers and the latter an overestimation in the number of outliers.

Journal

Hydrogeology JournalSpringer Journals

Published: Sep 26, 2017

References

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