  Spatio-temporal models are widely used in many research areas including
ecology. The recent proliferation of the use of in-situ sensors in streams and
rivers supports space-time water quality modelling and monitoring in near
real-time. In this paper, we introduce a new family of dynamic spatio-temporal
models, in which spatial dependence is established based on stream distance and
temporal autocorrelation is incorporated using vector autoregression
approaches. We propose several variations of these novel models using a
Bayesian framework. Our results show that our proposed models perform well
using spatio-temporal data collected from real stream networks, particularly in
terms of out-of-sample RMSPE. This is illustrated considering a case study of
water temperature data in the northwestern United States.
