Abstract

The modelling of multivariate riverine water quality time series poses some challenging problems including: weak dependency between observations; nonlinearity; non-Normality; seasonality and missing data. We demonstrate that periodic multivariate Normal hidden Markov models (MNHMMs) are appropriate tools to analyse riverine water quality time series. We introduce a fully Bayesian inference procedure for this class of models, where the number of hidden states of the Markov process is unknown and reversible jump Markov chain Monte Carlo (RJMCMC) methods are developed. We present a case study using long-term dissolved inorganic nitrogen time series measured in three Scottish rivers. Our results show the strength of the hidden Markov multistate approach for analysing long-term multivariate riverine water quality time series. Copyright (C) 2010 John Wiley & Sons, Ltd.

Keywords

dissolved inorganic nitrogen; missing data; monthly periodicity; reversible jump MCMC; Scottish rivers

Published in

Environmetrics
2010, Volume: 22, number: 3, pages: 304-317

Associated SLU-program

SLU Future Forests


UKÄ Subject classification

Fish and Aquacultural Science


Publication identifier

DOI: https://doi.org/10.1002/env.1051i

Permanent link to this page (URI)

https://res.slu.se/id/publ/33819