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dc.contributor.authorKotamäki, Niina
dc.contributor.authorJärvinen, Marko
dc.contributor.authorKauppila, Pirkko
dc.contributor.authorKorpinen, Samuli
dc.contributor.authorLensu, Anssi
dc.contributor.authorMalve, Olli
dc.contributor.authorMitikka, Sari
dc.contributor.authorSilander, Jari
dc.contributor.authorKettunen, Juhani
dc.date.accessioned2019-05-31T08:42:03Z
dc.date.available2019-05-31T08:42:03Z
dc.date.issued2019fi
dc.identifier.citationKotamäki, N., Järvinen, M., Kauppila, P., Korpinen, S., Lensu, A., Malve, O., . . . Kettunen, J. (2019). A practical approach to improve the statistical performance of surface water monitoring networks. <em>Environmental Monitoring and Assessment</em>, 191 (6), 318. <a href="https://doi.org/10.1007/s10661-019-7475-3">doi:10.1007/s10661-019-7475-3</a>fi
dc.identifier.otherTUTKAID_81346
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/64281
dc.description.abstractThe representativeness of aquatic ecosystem monitoring and the precision of the assessment results are of high importance when implementing the EU’s Water Framework Directive that aims to secure a good status of waterbodies in Europe. However, adapting monitoring designs to answer the objectives and allocating the sampling resources effectively are seldom practiced. Here, we present a practical solution how the sampling effort could be re-allocated without decreasing the precision and confidence of status class assignment. For demonstrating this, we used a large data set of 272 intensively monitored Finnish lake, coastal, and river waterbodies utilizing an existing framework for quantifying the uncertainties in the status class estimation. We estimated the temporal and spatial variance components, as well as the effect of sampling allocation to the precision and confidence of chlorophyll-a and total phosphorus. Our results suggest that almost 70% of the lake and coastal waterbodies, and 27% of the river waterbodies, were classified without sufficient confidence in these variables. On the other hand, many of the waterbodies produced unnecessary precise metric means. Thus, reallocation of sampling effort is needed. Our results show that, even though the studied variables are among the most monitored status metrics, the unexplained variation is still high. Combining multiple data sets and using fixed covariates would improve the modeling performance. Our study highlights that ongoing monitoring programs should be evaluated more systematically, and the information from the statistical uncertainty analysis should be brought concretely to the decision-making process.fi
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer Netherlands
dc.relation.ispartofseriesEnvironmental Monitoring and Assessment
dc.rightsCC BY 4.0
dc.subject.otherpintavesifi
dc.subject.otherseurantafi
dc.subject.othermonitoringfi
dc.subject.otherwater framework directivefi
dc.subject.otherclassificationfi
dc.subject.otherconfidencefi
dc.subject.otherchlorophyllfi
dc.subject.otherphosphorusfi
dc.titleA practical approach to improve the statistical performance of surface water monitoring networksfi
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201905062411
dc.contributor.laitosBio- ja ympäristötieteiden laitosfi
dc.contributor.laitosThe Department of Biological and Environmental Scienceen
dc.contributor.oppiaineYmpäristötiede ja -teknologia
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2019-05-06T09:15:43Z
dc.description.reviewstatuspeerReviewed
dc.relation.issn0167-6369
dc.relation.numberinseries6
dc.relation.volume191
dc.type.versionpublishedVersion
dc.rights.copyright© The Author(s) 2019
dc.rights.accesslevelopenAccessfi
dc.format.contentfulltext
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1007/s10661-019-7475-3


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Except where otherwise noted, this item's license is described as CC BY 4.0