Articles | Volume 22, issue 8
https://doi.org/10.5194/hess-22-4401-2018
© Author(s) 2018. 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-22-4401-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Detecting dominant changes in irregularly sampled multivariate water quality data sets
Christian Lehr
CORRESPONDING AUTHOR
Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
University of Potsdam, Institute for Earth and Environmental Sciences, Potsdam, Germany
Ralf Dannowski
Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
Thomas Kalettka
Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
Christoph Merz
Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
Institute of Geological Sciences, Workgroup Hydrogeology, Freie Universität Berlin, Berlin, Germany
Boris Schröder
Landscape Ecology and Environmental Systems Analysis, Institute of Geoecology, Technische Universität Braunschweig, Langer
Kamp 19c, 38106 Braunschweig, Germany
Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Altensteinstraße 6, 14195 Berlin, Germany
Jörg Steidl
Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
Gunnar Lischeid
Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
University of Potsdam, Institute for Earth and Environmental Sciences, Potsdam, Germany
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In hydrology, domain dependence (DD) of spatial Principal Component patterns is a rather unknown feature of the widely applied Principal Component Analysis. It easily leads to wrong hydrological interpretations. DD reference patterns enable to differentiate from the effect. Here, we (1) explore the DD effect, (2) present two methods to calculate DD reference patterns and (3) discuss considering DD. Scripts with an introduction to the DD effect and an implementation of both methods are provided.
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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.
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Short summary
We suggested and tested an exploratory approach for the detection of dominant changes in multivariate water quality data sets with irregular sampling in space and time. The approach is especially recommended for the exploratory assessment of existing long-term low-frequency multivariate water quality monitoring data.
We suggested and tested an exploratory approach for the detection of dominant changes in...