globalchange  > 气候变化与战略
DOI: 10.5194/hess-22-4401-2018
论文题名:
Detecting dominant changes in irregularly sampled multivariate water quality data sets
作者: Lehr C.; Dannowski R.; Kalettka T.; Merz C.; Schröder B.; Steidl J.; Lischeid G.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2018
卷: 22, 期:8
起始页码: 4401
结束页码: 4424
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
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.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/163222
Appears in Collections:气候变化与战略

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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), Müncheberg, Germany, University of Potsdam, Institute for Earth and Environmental Sciences, Potsdam, Germany

Recommended Citation:
Lehr C.,Dannowski R.,Kalettka T.,et al. Detecting dominant changes in irregularly sampled multivariate water quality data sets[J]. Hydrology and Earth System Sciences,2018-01-01,22(8)
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