Tracing biogeochemical processes in stream water and groundwater using non-linear statistics

Lischeid, Gunnar; Bittersohl, J.

doi:10.1016/j.jhydrol.2008.03.013

Cited by 31 publications

(24 citation statements)

References 54 publications

(68 reference statements)

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“…Like in preceding studies (e.g., Lischeid and Bittersohl, 2008; performed in this study only slightly better with respect to the representation of interpoint distances than PCA ( Table 2), suggesting that mainly linear relationships were of importance for the overall dynamics in the data set. This is usually not known in advance.…”

Section: Exploratory Frameworksupporting

confidence: 71%

“…linear benchmark for Isomap (demonstrated by Lischeid and Bittersohl, 2008). In a straightforward way this allows for 1) assessing whether the dominant correlation structures in the data set are mainly linear or non-linear, and 2) identifying those components, samples, sites and periods deviating from the linear behaviour as captured by the PCA.…”

Section: Exploratory Frameworkmentioning

confidence: 99%

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Detecting dominant changes in irregularly sampled multivariate water quality data sets

Lehr¹,

Dannowski²,

Kalettka³

et al. 2018

Preprint

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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, 20 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 25 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 30 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 35 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-40 linearities are considered explicitly. The approach is especially recommended for the exploratory assessment of existing long term low frequency multivariate water quality monitoring data.

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Section: Exploratory Frameworksupporting

confidence: 71%

Section: Exploratory Frameworkmentioning

confidence: 99%

Detecting dominant changes in irregularly sampled multivariate water quality data sets

Lehr¹,

Dannowski²,

Kalettka³

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recent reports produced by the Environment Agency of England and Wales (EA), which is responsible for implementing the WFD, have recognised that many UK water bodies are at risk of failing to meet the target of "good ecological status" by 2015, with only 26% of rivers in England and Wales doing so in 2010 [7,[18][19][20]. To improve the ecological status of river systems, it is necessary to understand the sources and processes directly controlling water quality and to quantify the impacts of anthropogenic pressures and climatic variability [21,22]. One of the recognised challenges is the ability to link catchment and within-river processes to specific in-stream water quality and ecological responses.…”

Section: Introductionmentioning

confidence: 99%

The Water Quality of the River Enborne, UK: Observations from High-Frequency Monitoring in a Rural, Lowland River System

Halliday

Skeffington

Bowes

et al. 2014

Water

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This paper reports the results of a 2-year study of water quality in the River Enborne, a rural river in lowland England. Concentrations of nitrogen and phosphorus species and other chemical determinands were monitored both at high-frequency (hourly), using automated in situ instrumentation, and by manual weekly sampling and laboratory analysis. The catchment land use is largely agricultural, with a population density of 123 persons km −2. The river water is largely derived from calcareous groundwater, and there are high nitrogen and phosphorus concentrations. Agricultural fertiliser is the dominant source of annual loads of both nitrogen and phosphorus. However, the data show that sewage effluent discharges have a disproportionate effect on the river nitrogen and phosphorus dynamics. At least 38% of the catchment population use septic tank systems, but the effects are hard to quantify as only 6% are officially registered, and the characteristics of the others are unknown. Only 4% of the phosphorus input and 9% of the OPEN ACCESSWater 2014, 6 151 nitrogen input is exported from the catchment by the river, highlighting the importance of catchment process understanding in predicting nutrient concentrations. High-frequency monitoring will be a key to developing this vital process understanding.

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“…Correspondingly, high concentrations of cations were found in these samples as well. In contrast, high pH values and high Si concentrations were usually found in the deep groundwater samples or where old groundwater discharged and NO 3 and SO 4 concentrations were low due to denitrification and desulfurication (Lischeid and Bittersohl 2008).…”

Section: Principal Component Analysismentioning

confidence: 91%

Non-linear visualization and analysis of large water quality data sets: a model-free basis for efficient monitoring and risk assessment

Lischeid

2008

Stoch Environ Res Risk Assess

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Environmental monitoring programs provide large multivariate data sets that usually cover considerable spatial and temporal variabilities. The apparent complexity of these data sets requires sophisticated tools for their processing. Usually, fixed schemes are followed, including the application of numerical models, which are increasingly implemented in decision support systems. However, these schemes are too rigid with respect to detecting unexpected features, like the onset of subtle trends, nonlinear relationships or patterns that are restricted to limited sub-samples of the total data set. In this study, an alternative approach is followed. It is based on an efficient nonlinear visualization of the data. Visualization is the most powerful interface between computer and human brain.

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Tracing biogeochemical processes in stream water and groundwater using non-linear statistics

Cited by 31 publications

References 54 publications

Detecting dominant changes in irregularly sampled multivariate water quality data sets

Detecting dominant changes in irregularly sampled multivariate water quality data sets

The Water Quality of the River Enborne, UK: Observations from High-Frequency Monitoring in a Rural, Lowland River System

Non-linear visualization and analysis of large water quality data sets: a model-free basis for efficient monitoring and risk assessment

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