Articles | Volume 22, issue 8
https://doi.org/10.5194/hess-22-4401-2018
https://doi.org/10.5194/hess-22-4401-2018
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

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Cited articles

Aubert, A. H., Gascuel-Odoux, C., Gruau, G., Akkal, N., Faucheux, M., Fauvel, Y., Grimaldi, C., Hamon, Y., Jaffrézic, A., Lecoz-Boutnik, M., Molénat, J., Petitjean, P., Ruiz, L., and Merot, P.: Solute transport dynamics in small, shallow groundwater-dominated agricultural catchments: insights from a high-frequency, multisolute 10 yr-long monitoring study, Hydrol. Earth Syst. Sci., 17, 1379–1391, https://doi.org/10.5194/hess-17-1379-2013, 2013. 
Basu, N. B., Destouni, G., Jawitz, J. W., Thompson, S. E., Loukinova, N. V., Darracq, A., Zanardo, S., Yaeger, M., Sivapalan, M., Rinaldo, A., and Rao, P. S. C.: Nutrient loads exported from managed catchments reveal emergent biogeochemical stationarity, Geophys. Res. Lett., 37, L23404, https://doi.org/10.1029/2010GL045168, 2010. 
Basu, N. B., Thompson, S. E., and Rao, P. S. C.: Hydrologic and biogeochemical functioning of intensively managed catchments: A synthesis of top-down analyses, Water Resour. Res., 47, W00J15, https://doi.org/10.1029/2011WR010800, 2011. 
Beudert, B., Bässler, C., Thorn, S., Noss, R., Schröder, B., Dieffenbach-Fries, H., Foullois, N., and Müller, J.: Bark Beetles Increase Biodiversity While Maintaining Drinking Water Quality, Conserv. Lett., 8, 272–281, https://doi.org/10.1111/conl.12153, 2015. 
Bieroza, M. Z., Heathwaite, A. L., Mullinger, N. J., and Keenan, P. O.: Understanding nutrient biogeochemistry in agricultural catchments: the challenge of appropriate monitoring frequencies, Environ. Sci.-Proc. Imp., 16, 1676–1691, https://doi.org/10.1039/C4EM00100A, 2014. 
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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.