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dc.contributor.authorKotamäki, Niina
dc.date.accessioned2018-03-09T11:01:19Z
dc.date.available2018-03-09T11:01:19Z
dc.date.issued2018
dc.identifier.isbn978-951-39-7378-0
dc.identifier.otheroai:jykdok.linneanet.fi:1860703
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/57287
dc.description.abstractDecision-making at different phases of adaptive river basin management planning rely largely on the information that is gained through environmental monitoring. The aim of this thesis was to develop and test statistical assessment tools presumed to be particularly useful for evaluating existing monitoring designs, converting monitoring data into management information and quantifying uncertainties. River basin scale monitoring was performed using a wireless sensor network and a data quality control system and maintenance effort was assessed. National-scale, traditional monitoring data and linear mixed effect modelling were used to estimate the uncertainty in two status class metrics (total phosphorus, and chlorophyll-a) by quantifying temporal and spatial variance components. The relative sizes of the variance components were then used to determine how to efficiently allocate the monitoring resources. Nutrient and chlorophyll-a statuses were linked to external loading utilizing a large amount of monitoring data and a hierarchical Bayesian approach. This linkage was the basis for developing a practical assessment tool for lake management. To evaluate the network of relationships affecting phytoplankton development between water quality variables, structural equation modelling was used. Model residual and parameter uncertainty, and thus uncertainty in the assessment result, were estimated using probabilistic Bayesian modelling. In general, the results of this study suggest that the used statistical methods appear to be particularly useful for decision-making under an adaptive management framework, as they enabled predictions to be made based on existing monitoring data and have measures of uncertainty associated with the outcomes. The results suggest that the uncertainties often stem from the lack of input data or insufficiently allocated monitoring. Therefore, it should be ensured that information gaps in the nutrient loading values, as well as in other, especially biological variables, are sufficiently covered.
dc.format.extent1 verkkoaineisto (48 sivua, 59 sivua useina numerointijaksoina) : kuvitettu, karttoja
dc.language.isoeng
dc.publisherUniversity of Jyväskylä
dc.relation.ispartofseriesJyväskylä studies in biological and environmental science
dc.relation.haspart<b>Artikkeli I:</b> Kotamäki N., Thessler S., Koskiaho J., Hannukkala A.O., Huitu H., Huttula T., Havento J. & Järvenpää M. 2009. Wireless in-situ sensor network for agriculture and water monitoring on a river basin scale in Southern Finland: evaluation from a data user’s perspective. Sensors 9(4): 2862–2883. </i><a href=" http://dx.doi.org/10.3390/s90402862"target="_blank"> DOI: 10.3390/s90402862.</a>
dc.relation.haspart<b>Artikkeli II:</b> Kotamäki, N., Järvinen, M., Kauppila, P., Korpinen, S., Lensu, A., Malve, O., Mitikka, S. and Kettunen, J. A practical approach to improve statistical performance of WFD monitoring networks. <i>Manuscript</i>.
dc.relation.haspart<b>Artikkeli III:</b> Kotamäki N., Pätynen A., Taskinen A., Huttula T. & Malve O. 2015. Statistical dimensioning of nutrient loading reduction - LLR assessment tool for lake managers. Environmental Management 56: 480–491. </i><a href=" http://dx.doi.org/10.1007/s00267-015-0514-0"target="_blank"> DOI: 10.1007/s00267-015-0514-0.</a>
dc.relation.haspart<b>Artikkeli IV:</b> Pätynen A., Kotamäki N., Arvola L., Tulonen T. & Malve O. 2015. Causal analysis of phytoplankton development in a small humic lake using structural equation modelling. Inland Waters 5: 231–239. </i><a href=" http://dx.doi.org/10.5268/IW-5.3.736"target="_blank"> DOI: 10.5268/IW-5.3.736.</a>
dc.relation.haspart<b>Artikkeli V:</b> Pätynen A., Kotamäki N. & Malve O. 2013. Alternative approaches to modelling lake ecosystems. Freshwater Reviews 6: 63–74. </i><a href=" http://dx.doi.org/10.1608/FRJ-6.2.704"target="_blank"> DOI: 110.1608/FRJ-6.2.704.</a>
dc.relation.isversionofYhteenveto-osa ja 5 eripainosta julkaistu myös painettuna.
dc.subject.otheradaptive management
dc.subject.otherBayesian inference
dc.subject.othereutrophication
dc.subject.othermonitoring
dc.subject.otherstatistical methods
dc.subject.otheruncertainty
dc.subject.otherWater Framework Directive
dc.titleStatistical methods for adaptive river basin management and monitoring
dc.identifier.urnURN:ISBN:978-951-39-7378-0
dc.type.dcmitypeTexten
dc.type.ontasotVäitöskirjafi
dc.type.ontasotDoctoral dissertationen
dc.contributor.tiedekuntaMatemaattis-luonnontieteellinen tiedekuntafi
dc.contributor.yliopistoUniversity of Jyväskyläen
dc.contributor.yliopistoJyväskylän yliopistofi
dc.contributor.oppiaineYmpäristötiedefi
dc.relation.issn1456-9701
dc.relation.numberinseries344
dc.rights.accesslevelopenAccessfi
dc.subject.ysoympäristönhoito
dc.subject.ysovesienhoito
dc.subject.ysovesipolitiikka
dc.subject.ysopäätöksenteko
dc.subject.ysovaluma-alueet
dc.subject.ysovedenlaatu
dc.subject.ysorehevöityminen
dc.subject.ysomonitorointi
dc.subject.ysosensoriverkot
dc.subject.ysotilastomenetelmät
dc.subject.ysobayesilainen menetelmä


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