Abstract
Abstract. Time series of groundwater and stream water quality often exhibit substantial temporal and spatial variability, whereas typical existing monitoring data sets, e.g. from environmental agencies, are usually characterized by relatively low sampling frequency and irregular sampling in space and/or time. This complicates the differentiation between anthropogenic influence and natural variability as well as the detection of changes in water quality which indicate changes in single drivers. We suggest the new term “dominant changes” for changes in multivariate water quality data which concern (1) multiple variables, (2) multiple sites and (3) long-term patterns and present an exploratory framework for the detection of such dominant changes in data sets with irregular sampling in space and time. Firstly, a non-linear dimension-reduction technique was used to summarize the dominant spatiotemporal dynamics in the multivariate water quality data set in a few components. Those were used to derive hypotheses on the dominant drivers influencing water quality. Secondly, different sampling sites were compared with respect to median component values. Thirdly, time series of the components at single sites were analysed for long-term patterns. We tested the approach with a joint stream water and groundwater data set quality consisting of 1572 samples, each comprising sixteen variables, sampled with a spatially and temporally irregular sampling scheme at 29 sites in northeast Germany from 1998 to 2009. The first four components were interpreted as (1) an agriculturally induced enhancement of the natural background level of solute concentration, (2) a redox sequence from reducing conditions in deep groundwater to post-oxic conditions in shallow groundwater and oxic conditions in stream water, (3) a mixing ratio of deep and shallow groundwater to the streamflow and (4) sporadic events of slurry application in the agricultural practice. Dominant changes were observed for the first two components. The changing intensity of the first component was interpreted as response to the temporal variability of the thickness of the unsaturated zone. A steady increase in the second component at most stream water sites pointed towards progressing depletion of the denitrification capacity of the deep aquifer.
Highlights
For the multivariate analysis in this study, we considered from the joint groundwater and stream water quality data set only the 16 variables with less than 5 % missing values, i.e. NH+4, NO−3, NO−2, PO34−, Na+, K+, Mg2+, Ca2+, Cl−, O2, pH, water temperature, redox potential (Eh), electric conductivity (EC), SO24− and dissolved organic carbon (DOC) (Table S3)
We achieved the best performance of the isometric feature mapping (Isomap) dimension reduction with k = 1300 (Table 2)
Non-linear Isomap performed in this study only slightly better with respect to the representation of inter-point distances than principal component analysis (PCA) (Table 2), suggesting that mainly linear relationships were of importance for the overall dynamics in the data www.hydrol-earth-syst-sci.net/22/4401/2018/
Summary
Numerous high-frequency sampling studies unravelled the high temporal variability of stream water quality (e.g. Kirchner et al, 2004; Cassidy and Jordan, 2011; Halliday et al, 2012; Neal et al, 2012; Wade et al, 2012; Aubert et al, 2013; Kirchner and Neal, 2013; Tunaley et al 2016; Rode et al, 2016; Blaen et al, 2017). There is common agreement that for short periods with high-frequency data, longer periods of low-frequency monitoring provide invaluable context (Burt et al, 2011; Neal et al, 2012; Halliday et al, 2012; Bieroza et al, 2014) This is especially true for existing long-term records which are required as reference to distinguish between natural short-term and long-term variability of the observed variables and the assessment of the effects of anthropogenic influence on water quality such as changes in land use in the catchment (Burt et al, 2008; Howden et al, 2011)
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