Discrimination of water quality monitoring sites in River Vouga using a mixed-effect state space model

Abstract

The surface water quality monitoring is an important concern of public organizations due to its relevance to the public health. Statistical methods are taken as consistent and essential tools in the monitoring procedures in order to prevent and identify environmental problems. This work presents the study case of the hydrological basin of the river Vouga, in Portugal. The main goal is discriminate the water monitoring sites using the monthly dissolved oxygen concentration dataset between January 2002 and May 2013. This is achieved through the extraction of trend and seasonal components in a linear mixed-effect state space model. The parameters estimation is performed with both maximum likelihood method and distribution-free estimators in a two-step procedure. The application of the Kalman smoother algorithm allows to obtain predictions of the structural components as trend and seasonality. The water monitoring sites are discriminated through the structural components by a hierarchical agglomerative clustering procedure. This procedure identified different homogenous groups relatively to the trend and seasonality components and some characteristics of the hydrological basin are presented in order to support the results.