Articles | Volume 23, issue 9
https://doi.org/10.5194/hess-23-3603-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-23-3603-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
04 Sep 2019
Review article |
| 04 Sep 2019
| 04 Sep 2019
Error in hydraulic head and gradient time-series measurements: a quantitative appraisal
Karlsruhe Institute of Technology (KIT), Institute of Applied Geosciences (AGW), Karlsruhe, Germany
Connected Waters Initiative Research Centre (CWI), the University of New South Wales (UNSW), Sydney, Australia
Vincent E. A. Post
Federal Institute for Geosciences and Natural Resources (BGR), Hanover, Germany
Margaret Shanafield
National Centre for Groundwater Research and Training (NCGRT), College of Science and Engineering, Flinders University, Adelaide, Australia
Torsten Krekeler
Federal Institute for Geosciences and Natural Resources (BGR), Hanover, Germany
Eddie W. Banks
National Centre for Groundwater Research and Training (NCGRT), College of Science and Engineering, Flinders University, Adelaide, Australia
Philipp Blum
Karlsruhe Institute of Technology (KIT), Institute of Applied Geosciences (AGW), Karlsruhe, Germany
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- Efficient uncertainty quantification for seawater intrusion prediction using Optimized sampling and Null Space Monte Carlo method S. Saad et al. 10.1016/j.jhydrol.2023.129496
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Latest update: 21 Feb 2025
Short summary
The flow of water is often inferred from water levels and gradients whose measurements are considered trivial despite the many steps and complexity of the instruments involved. We systematically review the four measurement steps required and summarise the systematic errors. To determine the accuracy with which flow can be resolved, we quantify and propagate the random errors. Our results illustrate the limitations of current practice and provide concise recommendations to improve data quality.
The flow of water is often inferred from water levels and gradients whose measurements are...
