The implementation of the WFD has been, and still is, a challenge for almost all EU Member States. Considerable time and resources have been spent on developing tools to obtain the required data for the assessment and to pre-pare management plans (Hering et al., 2010). A well-acknowledged problem in the use of water quality monitoring data in assessment has been the effect of spatial and temporal variation on the precision and confidence of the data (Cars-tensen, 2007). This variation has been diffi-cult to overcome with traditional monitoring techniques, and the uncertainty that this causes in the monitoring data is still in many cases unclear. In practice, water managers face the problem of deciding whether the error associ-ated with the predicted average value is small enough to detect changes in the actual condi-tions (Noges et al., 2009). Although statistical methods to assess the uncertainty sources exist, they have not been implemented in monitoring programmes. In order to be feasible in
large-scale monitoring programmes, statistical sam-pling design tools need to be relatively easy to apply and also linked to the information avail-able from the monitored system.
This thesis research aimed to facilitate water quality monitoring by applying feasible statis-tical tools to assess water quality variability and by characterizing ecosystem interactions in different states.
The work derived following conclusions:
1. The required sampling effort and design are dependent on the specific properties of individual monitoring areas;
2. Ignorance of spatial and temporal variation can lead to erroneous summary statistics, and monitoring methods vary in their abil-ity to detect variation;
3. Data-rich monitoring methods provide an essential tool to estimate the adequate sampling intervals and locations and the characteristics of variance;
4. General statistical representations of the variance within water bodies can be creat-ed with certain limitations;
5. Information derived from past transitions provides a powerful insight into ecosystem interactions and responses to pressures that can be used in interpretation of recent ob-servations;
6. Sampling design should be seen as a ra-tional procedure where sufficient informa-tion is derived with a suite of monitoring methods that minimize the uncertainty sources, costs and time and acknowledge the properties of the monitored ecosystem;
7. Fundamentally, as understanding of the variance and history of the observed sys-tem increases, the requirements for sam-pling can also be more accurately defined.
The value of water quality monitoring should be evaluated against the consistency of col-lected data and the ability to answer to ex-plicit scientific questions (Lovett et al, 2007;
Erkkilä & Kalliola, 2007). As anthropogenic disturbance of aquatic systems, the depletion of natural resources and climate change
ceed, the significance of sound monitoring pro-grammes and long-term records is expected to increase (Lovett et al., 2007; Andersen et al., 2009; Bestelmeyer et al., 2011). In this thesis research, I aimed to contribute to the develop-ment of adaptive monitoring programmes that are calibrated to the typical variance within monitoring sites and that aim at maintaining the natural resilience of ecosystems. In the fu-ture, assessment of uncertainty sources in wa-ter quality monitoring will probably be further emphasized as the rationalization of monitoring programmes continues. Research is still needed in order to develop a feasible tool set for water ecosystem managers to assess the uncertainty sources, to integrate information and to evalu-ate the risk of misjudgements in relation to the expected costs.
ACKNOWLEDGEMENTS
I express my warmest gratitude to my supervi-sors. Professor Timo Kairesalo provided excel-lent working conditions for the field sampling and laboratory analyses in the Department of Environmental and Ecological Sciences, later the Department of Environmental Sciences.
He supported and guided me during the years of this study. I thank Professor Petri Pellikka for guidance and work in providing lectures and funding opportunities. I warmly thank the pre-examiners, Jouko Sarvala and Juhani Ket-tunen, for reviewing the thesis.
This work would not have been possible without the contribution of several people. I have been privileged to work with Mirva Ke-tola, truly an excellent scientist. The planning of field sampling would have been impossible without Tuukka Ryynänen. In addition, long measurements days were fun with Tuukka. I would also like to thank Kirsi Kuoppamäki (formerly Vakkilainen) for collaboration and insightfulness in connection with the long-term monitoring. I would additionally like to express my gratitude to all colleagues at the Geoinfor-matics unit in SYKE. Yrjö Sucksdorff never said ‘no’ to requests relating to this study, and all those in the remote sensing group provided
an enthusiastic environment, ideas and tools for this work. Gratitude to friendly and helpful people working with environmental ecology in Lahti. I would also like to thank Kari Kallio for discussions and sharing his knowledge on water quality monitoring during these years. Special thanks to Mirva, Kari and Mikko Kervinen for commenting this thesis. I am also grateful to Evelyn Gaiser for giving me an opportunity to work at the Florida Coastal Everglades LTER site and advance this work. Henry Brizeno and Joe Boyer are thanked for having me at SERC and for their guidance in the analysis of long-term water quality records.
I would like to dedicate this thesis to my parents. Thank you for your love and care. My late uncle Lauri, who tried to internalize scien-tific thinking in me. Finally, Hanna, Leo and Tom – my family. You gave the strength and ambition to complete this work. For me, you are everything.
This thesis research was partly funded by the Maj and Tor Nessling Foundation and the Onni and Hilja Tuovinen Foundation. The research was also advanced in the Water Quality Service for Lakes project funded by Tekes.
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