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Balance between the Reliability of Classification and Sampling Effort: A Multi-Approach for the Water Framework Directive (WFD) Ecological Status Applied to the Venice Lagoon (Italy)
Copyright policy )Balance between the Reliability of Classification and Sampling Effort: A Multi-Approach for the Water Framework Directive (WFD) Ecological Status Applied to the Venice Lagoon (Italy)
The Water Framework Directive (WFD) requires Member States to assess the ecological status of water bodies and provide an estimation of the classification confidence and precision. This study tackles the issue of the uncertainty in the classification, due to the spatial variability within each water body, proposing an analysis of the reliability of classification, using the results of macrophyte WFD monitoring in the Venice Lagoon as case study. The level of classification confidence, assessed for each water body, was also used as reference to optimize the sampling effort for the subsequent monitorings. The ecological status of macrophytes was calculated by the Macrophyte Quality Index at 114 stations located in 11 water bodies. At water body scale, the level of classification confidence ranges from 54% to 100%. After application of the multi-approach (inferential statistics, spatial analyses, and expert judgment), the optimization of the sampling effort resulted in a reduction of the number of stations from 114 to 84. The decrease of sampling effort was validated by assessing the reliability of classification after the optimization process (54&ndash
99%) and by spatial interpolation of data (Kernel standard error of 22.75%). The multi-approach proposed in this study could be easily applied to any other water body and biological quality element.
Library of Congress Subject Headings: lcsh:Hydraulic engineering lcsh:Water supply for domestic and industrial purposes lcsh:TC1-978 lcsh:TD201-500
Microsoft Academic Graph classification: Multivariate interpolation Statistical inference Uncertainty analysis Reliability (statistics) Ecology Sampling (statistics) Macrophyte Water Framework Directive Environmental science Spatial variability
Settore BIO/07 - Ecologia, Transitional waters, Macrophyte Quality Index (MaQI), transitional waters, Geography, Planning and Development, Aquatic Science, Biochemistry, Macrophyte Quality Index (MaQI), Water Science and Technology, Confidence interval, Confidence interval; Kernel standard error; Macrophyte Quality Index (MaQI); Transitional waters; Uncertainty analysis, Uncertainty analysis, Kernel standard error
Settore BIO/07 - Ecologia, Transitional waters, Macrophyte Quality Index (MaQI), transitional waters, Geography, Planning and Development, Aquatic Science, Biochemistry, Macrophyte Quality Index (MaQI), Water Science and Technology, Confidence interval, Confidence interval; Kernel standard error; Macrophyte Quality Index (MaQI); Transitional waters; Uncertainty analysis, Uncertainty analysis, Kernel standard error
Library of Congress Subject Headings: lcsh:Hydraulic engineering lcsh:Water supply for domestic and industrial purposes lcsh:TC1-978 lcsh:TD201-500
Microsoft Academic Graph classification: Multivariate interpolation Statistical inference Uncertainty analysis Reliability (statistics) Ecology Sampling (statistics) Macrophyte Water Framework Directive Environmental science Spatial variability
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Water Framework Directive, European Union (WFD E.U.). Establishing a Framework for Community Action in the Field of Water Policy; Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000; EU: Brussels, Belgium, 2000.
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citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).3 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).3 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!The Water Framework Directive (WFD) requires Member States to assess the ecological status of water bodies and provide an estimation of the classification confidence and precision. This study tackles the issue of the uncertainty in the classification, due to the spatial variability within each water body, proposing an analysis of the reliability of classification, using the results of macrophyte WFD monitoring in the Venice Lagoon as case study. The level of classification confidence, assessed for each water body, was also used as reference to optimize the sampling effort for the subsequent monitorings. The ecological status of macrophytes was calculated by the Macrophyte Quality Index at 114 stations located in 11 water bodies. At water body scale, the level of classification confidence ranges from 54% to 100%. After application of the multi-approach (inferential statistics, spatial analyses, and expert judgment), the optimization of the sampling effort resulted in a reduction of the number of stations from 114 to 84. The decrease of sampling effort was validated by assessing the reliability of classification after the optimization process (54&ndash
99%) and by spatial interpolation of data (Kernel standard error of 22.75%). The multi-approach proposed in this study could be easily applied to any other water body and biological quality element.