气候变化领域集成服务门户(CCPortal): Detecting dominant changes in irregularly sampled multivariate water quality data sets

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DOI	10.5194/hess-22-4401-2018
	Detecting dominant changes in irregularly sampled multivariate water quality data sets
	Lehr C.; Dannowski R.; Kalettka T.; Merz C.; Schröder B.; Steidl J.; Lischeid G.
发表日期	2018
ISSN	1027-5606
起始页码	4401
结束页码	4424
卷号	22 期号:8
英文摘要	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. © Author(s) 2018.
语种	英语
scopus关键词	Aquifers; Groundwater resources; Time series; Water quality; Agricultural practices; Anthropogenic influence; Denitrification capacity; Natural background levels; Solute concentrations; Spatio-temporal dynamics; Temporal and spatial variability; Temporal variability; Rivers; anthropogenic source; data acquisition; data assimilation; groundwater; groundwater pollution; mixing ratio; multivariate analysis; redox conditions; sampling bias; stream channel; vadose zone; water quality; Germany
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159939
作者单位	Lehr, C., Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany, University of Potsdam, Institute for Earth and Environmental Sciences, Potsdam, Germany; Dannowski, R., Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany; Kalettka, T., Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany; Merz, C., Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany, Institute of Geological Sciences, Workgroup Hydrogeology, Freie Universität Berlin, Berlin, Germany; Schröder, B., Landscape Ecology and Environmental Systems Analysis, Institute of Geoecology, Technische Universität Braunschweig, Langer Kamp 19c, Braunschweig, 38106, Germany, Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Altensteinstraße 6, Berlin, 14195, Germany; Steidl, J., Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany; Lischeid, G., Leibniz Centre for Agricultural Landscape Research (ZALF), Münc...
推荐引用方式 GB/T 7714	Lehr C.,Dannowski R.,Kalettka T.,et al. Detecting dominant changes in irregularly sampled multivariate water quality data sets[J],2018,22(8).
APA	Lehr C..,Dannowski R..,Kalettka T..,Merz C..,Schröder B..,...&Lischeid G..(2018).Detecting dominant changes in irregularly sampled multivariate water quality data sets.Hydrology and Earth System Sciences,22(8).
MLA	Lehr C.,et al."Detecting dominant changes in irregularly sampled multivariate water quality data sets".Hydrology and Earth System Sciences 22.8(2018).