Statistical methods for adaptive river basin management and monitoring
Decision-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.
...


Publisher
University of JyväskyläISBN
978-951-39-7378-0ISSN Search the Publication Forum
1456-9701Contains publications
- Artikkeli I: 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. DOI: 10.3390/s90402862.
- Artikkeli II: 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. Manuscript.
- Artikkeli III: 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. DOI: 10.1007/s00267-015-0514-0.
- Artikkeli IV: 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. DOI: 10.5268/IW-5.3.736.
- Artikkeli V: Pätynen A., Kotamäki N. & Malve O. 2013. Alternative approaches to modelling lake ecosystems. Freshwater Reviews 6: 63–74. DOI: 110.1608/FRJ-6.2.704.
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