Detecting dominant changes in irregularly sampled multivariate water quality data sets

Lehr, Christian; Dannowski, Ralf; Kalettka, Thomas; Merz, Christoph; Schröder, Boris; Steidl, Jörg; Lischeid, Gunnar

doi:https://doi.org/10.5194/hess-22-4401-2018

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.

https://doi.org/10.5194/hess-22-4401-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 8

Research article

|

21 Aug 2018

Research article |

| 21 Aug 2018

Detecting dominant changes in irregularly sampled multivariate water quality data sets

Christian Lehr, Ralf Dannowski, Thomas Kalettka, Christoph Merz, Boris Schröder, Jörg Steidl, and Gunnar Lischeid

Supplement

https://doi.org/10.5194/hess-22-4401-2018-supplement

Data sets

Measurement of groundwater heads, Quillow catchment, Germany C. Merz and J. Steidl https://doi.org/10.4228/ZALF.2000.272

Model code and software

R scripts for the detection of dominant changes in irregularly sampled multivariate water quality data sets C. Lehr, T. Kalettka, C. Merz, J. Steidl, and G. Lischeid https://doi.org/10.4228/ZALF.2017.340

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.