Detecting dominant changes in irregularly sampled multivariate water quality data sets
Authors: Lehr Christian , Dannowski Ralf , Kalettka Thomas , Merz Christoph , Schröder Boris , Steidl Jörg , Lischeid Gunnar 
Published: February 2018

Abstract. Time series of catchment water quality often exhibit substantial temporal and spatial variability which can rarely be traced back to single causal factors. Numerous anthropogenic and natural drivers influence groundwater and stream water quality, especially in regions with high land use intensity. In addition, 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 of single drivers. Detecting such changes is of fundamental interest for water management purposes as well as for scientific analyses. We suggest the new term dominant changes for changes in multivariate water quality data that concern (1) more than a single variable, (2) more than one single site and (3) more than short-term fluctuations or single events and present an exploratory framework for the detection of such dominant changes in multivariate water quality data sets with irregular sampling in space and time. Firstly, we used a non-linear dimension reduction technique to derive multivariate water quality components. The components provide a sparse description of the dominant spatiotemporal dynamics in the multivariate water quality data set. In addition, they can be 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 seasonal patterns and linear and non-linear trends. Spatial and temporal heterogeneities are efficiently used as a source of information rather than being considered as noise. Besides, non-linearities are considered explicitly. The approach is especially recommended for the exploratory assessment of existing long term low frequency multivariate water quality monitoring data. We tested the approach with a large data set of stream water and groundwater quality consisting of sixteen hydrochemical variables sampled with a spatially and temporally irregular sampling scheme at 29 sites in the Uckermark region in northeast Germany from 1998 to 2009. Four components were derived and interpreted as (1) the agriculturally induced enhancement of the natural background level of solute concentration, (2) the redox sequence from reducing conditions in deep groundwater to post oxic conditions in shallow groundwater and oxic conditions in stream water, (3) the 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 1st component during the course of the observation period was interpreted as response to the temporal variability of the thickness of the unsaturated zone. A steady increase of the 2nd component throughout the monitoring period at most stream water sites pointed towards progressing depletion of the denitrification capacity of the deep aquifer.