Bayesian Analysis of a Water Quality Turbidity High Frequency Time Series Through Markov Switching Autoregressive Models

In order to provide simulation inputs for investigations on diffuse water pollution and support rural land management policy on soil and water management, a turbidity time series recorded in a Scottish stream for more than a year, along with two covariates, is considered. Turbidity time series have complex dynamics because they are non-linear, non-Normal, non-stationary, with a long memory, and present missing values. Given these issues the turbidity process is analysed by Markov switching autoregressive models under the Bayesian paradigm using novel evolutionary Monte Carlo algorithms. Hence, it is possible to efficiently fit the actual data, reconstruct the sequence of hidden states, restore the missing values, and classify the observations into a few regimes, providing new insight on turbidity dynamics.