Predicting seasonal and hydro-meteorological impact in environmental variables ...

• Author(s): Gonçalves, A. ; Costa, Marco
• Date: 2013
• Persistent ID: http://hdl.handle.net/10773/9191
• Origin: RIA - Repositório Institucional da Universidade de Aveiro
• Subject(s): Hydrological basin; Water quality; State-space modelling; Kalman filter; Distribution-free estimation
• Document Type: Article
• Language: English

Description

This study focuses on the potential improvement of environmental variables modelling by using linear state-space models, as an improvement of the linear regression model, and by incorporating a constructed hydro-meteorological covariate. The Kalman filter predic- tors allow to obtain accurate predictions of calibration factors for both seasonal and hydro-meteorological components. This methodology can be used to analyze the water quality behaviour by minimizing the effect of the hydrological conditions. This idea is illustrated based on a rather extended data set relative to the River Ave basin (Portugal) that consists mainly of monthly measurements of dissolved oxygen concentration in a network of water quality monitoring sites. The hydro-meteorological factor is constructed for each monitoring site based on monthly precipitation estimates obtained by means of a rain gauge network associated with stochastic interpolation (kriging). A linear state-space model is fitted for each homogeneous group (obtained by clustering techniques) of water monitoring sites. The adjustment of linear state-space models is performed by using distribution-free estimators developed in a separate section.