Vector time series modelling of turbidity in Dublin Bay

Amin Shoari Nejad, Gerard D. McCarthy, Brian Kelleher, Anthony Grey and Andrew Parnell

Journal of Applied Statistics, 2024, vol. 51, issue 14, 2744-2759

Abstract: Turbidity is commonly monitored as an important water quality index. Human activities, such as dredging and dumping operations, can disrupt turbidity levels and should be monitored and analysed for possible effects. In this paper, we model the variations of turbidity in Dublin Bay over space and time to investigate the effects of dumping and dredging while controlling for the effect of wind speed as a common atmospheric effect. We develop a Vector Auto-Regressive Integrated Conditional Heteroskedasticity (VARICH) approach to modelling the dynamical behaviour of turbidity over different locations and at different water depths. We use daily values of turbidity during the years 2017–2018 to fit the model. We show that the results of our fitted model are in line with the observed data and that the uncertainties, measured through Bayesian credible intervals, are well calibrated. Furthermore, we show that the daily effects of dredging and dumping on turbidity are negligible in comparison to that of wind speed.

Date: 2024
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DOI: 10.1080/02664763.2024.2315470

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