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Estimating aggregated nutrient fluxes in four Finnish rivers via Gaussian state space models

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Helske, J., Nyblom, J., Ekholm, P., & Meissner, K. (2013). Estimating aggregated nutrient fluxes in four Finnish rivers via Gaussian state space models. Environmetrics, 24(4), 237-247. https://doi.org/10.1002/env.2204
Published in
Environmetrics
Authors
Helske, Jouni |
Nyblom, Jukka |
Ekholm, Petri |
Meissner, Kristian
Date
2013
Discipline
TilastotiedeStatistics
Copyright
© 2013 John Wiley & Sons, Ltd. This is a final draft version of an article whose final and definitive version has been published by John Wiley & Sons.

 
Reliable estimates of the nutrient fluxes carried by rivers from land-based sources to the sea are needed for efficient abatement of marine eutrophication. Although nutrient concentrations in rivers generally display large temporal variation, sampling and analysis for nutrients, unlike flow measurements, are rarely performed on a daily basis. The infrequent data calls for ways to reliably estimate the nutrient concentrations of the missing days. Here, we use the Gaussian state space models with daily water flow as a predictor variable to predict missing nutrient concentrations for four agriculturally impacted Finnish rivers. Via simulation of Gaussian state space models, we are able to estimate aggregated yearly phosphorus and nitrogen fluxes, and their confidence intervals. The effect of model uncertainty is evaluated through a Monte Carlo experiment, where randomly selected sets of nutrient measurements are removed and then predicted by the remaining values together with re-estimated parameters. Results show that our model performs well for rivers with long-term records of flow. Finally, despite the drastic decreases in nutrient loads on the agricultural catchments of the rivers over the last 25 years, we observe no corresponding trends in riverine nutrient fluxes. ...
Publisher
Wiley-Blackwell
ISSN Search the Publication Forum
1099-095X
Keywords
harva aineisto Kalmanin suodin Kalmanin tasoitin sparse data Kalman filter Kalman smoother simulointi interpolointi
DOI
https://doi.org/10.1002/env.2204
URI

http://urn.fi/URN:NBN:fi:jyu-201603141838

Publication in research information system

https://converis.jyu.fi/converis/portal/detail/Publication/22471883

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