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

  • 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 hydrographGroundwater 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.zeige mehrzeige weniger

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Verfasserangaben:Christian Lehr, Gunnar LischeidORCiDGND
URN:urn:nbn:de:kobv:517-opus4-511992
DOI:https://doi.org/10.25932/publishup-51199
ISSN:1866-8372
Titel des übergeordneten Werks (Deutsch):Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
Schriftenreihe (Bandnummer):Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe (1424)
Publikationstyp:Postprint
Sprache:Englisch
Datum der Erstveröffentlichung:03.02.2020
Erscheinungsjahr:2020
Veröffentlichende Institution:Universität Potsdam
Datum der Freischaltung:13.03.2024
Freies Schlagwort / Tag:fluctuations; network; nonstationarity; principal components; rotation; streamflow variability; time-series; united states; water
Ausgabe:2
Seitenanzahl:15
Quelle:Hydrol. Earth Syst. Sci., 24, 501–513, 2020 https://doi.org/10.5194/hess-24-501-2020
Organisationseinheiten:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Geowissenschaften
DDC-Klassifikation:5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften
Peer Review:Referiert
Publikationsweg:Open Access / Green Open-Access
Lizenz (Deutsch):License LogoCC-BY - Namensnennung 4.0 International
Externe Anmerkung:Bibliographieeintrag der Originalveröffentlichung/Quelle
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