Exploring the influence of lake water chemistry on chlorophyll a: A multivariate statistical model analysis

Liu, Yong; Guo, Hao; Yang, Pingjian

doi:10.1016/j.ecolmodel.2009.03.010

Cited by 63 publications

(26 citation statements)

References 27 publications

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“…DO was the main factor influencing the nutrients cycle under certain environmental conditions, which has been proved in many previous studies (Boström and Pettersson, 1982;Kim et al, 2004;Zhang et al, 2014). Nitrogen (N) and phosphorus (P) commonly determine the growth of algae and excessive nutrients often result in nuisance algal blooms (Busse et al, 2006;Liu et al, 2010). Recent studies indicated that Chl-a can be used to predict TN and TP in various water environments (Meeuwig et al, 2000;Hoyer et al, 2002).…”

Section: Correlation Analysesmentioning

confidence: 96%

Real-time eutrophication status evaluation of coastal waters using support vector machine with grid search algorithm

Kong

Sun

et al. 2017

Marine Pollution Bulletin

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Section: Correlation Analysesmentioning

confidence: 96%

Real-time eutrophication status evaluation of coastal waters using support vector machine with grid search algorithm

Kong

Sun

et al. 2017

Marine Pollution Bulletin

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“…Most studies on controls of trophic state focused on the relationships among total phosphorus, total nitrogen, and chlorophyll a, primarily in temperate lakes and reservoirs, although there were some efforts to model trophic state in tropical reservoirs (e.g., Salas andMartino 1991, Huszar et al 1998). Models usually have nutrients driving standing stock of chlorophyll (e.g., An and Park 2002, Freeman et al 2009, Liu et al 2010 to characterize the mechanisms involved in biomass limitation (Håkanson et al 2007.…”

mentioning

confidence: 99%

Limiting factors for phytoplankton growth in subtropical reservoirs: the effect of light and nutrient availability in different longitudinal compartments

Cunha

Calijuri

2011

Lake and Reservoir Management

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“…TP and CHL-a concentrations along with SD are also used as indicators of trophic states in the lakes by the Alberta Environment [42]. Liu et al [44] found that TP had the greatest positive influence on CHL-a, whereas SD had the negative influence. Figure 4 shows the relation between number of clusters and the cost function (À2log-likelihood) of the K-fold cross-validation with EM clustering algorithm for the period 1994-1997.…”

Section: Dominant Water Quality Parametersmentioning

confidence: 99%

“…5 over the entire dataset, the temporal change in cluster number for each lake is investigated (see Tab. 2). The temporal variation was analyzed in surface water quality studies using multivariate statistical techniques [6,24,44].…”

Section: Dominant Water Quality Parametersmentioning

confidence: 99%

A Methodology for Clustering Lakes in Alberta on the basis of Water Quality Parameters

Akbar

Hassan

Achari

2011

CLEAN Soil Air Water

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In this study, a methodology for clustering 18 lakes in Alberta, Canada using the data of 19 water quality parameters for a period of 11 years (1988-2002) is presented. The methods consist of (i) principal component analysis (PCA) to determine the dominant water quality parameters, (ii) cluster analysis techniques to develop the characteristics of the clusters, and (iii) pattern-match lakes to determine the appropriate cluster for each of the lakes. The PCA revealed that three principal components (PCs) were able to explain $88% of the variability and the dominant water quality parameters were total dissolved solids, total phosphorus, and chlorophyll-a. We obtained five clusters for the period 1994-1997 by using the dominant parameters with water quality deteriorating as the cluster number increased from 1 to 5. Upon matching cluster patterns with the entire dataset, it was observed that some of the lakes belonged to the same cluster all the time (e.g., cluster 1 for lakes Elkwater, Gregg, and Jarvis; cluster 3 for Sturgeon; cluster 4 for Moonshine; and cluster 5 for Saskatoon), while others changed with time. This methodology could be applied in other regions of the world to identify the most suitable source waters and prioritize their management. It could be helpful to analyze the natural controlling processes, pollution types, impact of seasonal changes and overall quality of source waters. This methodology could be used for monitoring water bodies in a cost effective and efficient way by sampling only less number of dominant parameters instead of using a large set of parameters.

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Exploring the influence of lake water chemistry on chlorophyll a: A multivariate statistical model analysis

Cited by 63 publications

References 27 publications

Real-time eutrophication status evaluation of coastal waters using support vector machine with grid search algorithm

Real-time eutrophication status evaluation of coastal waters using support vector machine with grid search algorithm

Limiting factors for phytoplankton growth in subtropical reservoirs: the effect of light and nutrient availability in different longitudinal compartments

A Methodology for Clustering Lakes in Alberta on the basis of Water Quality Parameters

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